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Added docs; refactor test Trajectory

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Filippo Martini 4 years ago
parent 6be81aaec2
commit 1a2b889b64
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# Minimal makefile for Sphinx documentation
#
# You can set these variables from the command line, and also
# from the environment for the first two.
SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
SOURCEDIR = .
BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
config: d42d832abe4d6a9e23dc099c95623ecc
tags: 645f666f9bcd5a90fca523b33c5a78b7

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classes.estimators package
==========================
Submodules
----------
classes.estimators.fam\_score\_calculator module
------------------------------------------------
.. automodule:: classes.estimators.fam_score_calculator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.parameters\_estimator module
-----------------------------------------------
.. automodule:: classes.estimators.parameters_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_constraint\_based\_estimator module
-----------------------------------------------------------------
.. automodule:: classes.estimators.structure_constraint_based_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_estimator module
----------------------------------------------
.. automodule:: classes.estimators.structure_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_score\_based\_estimator module
------------------------------------------------------------
.. automodule:: classes.estimators.structure_score_based_estimator
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.estimators
:members:
:undoc-members:
:show-inheritance:

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classes.optimizers package
==========================
Submodules
----------
classes.optimizers.constraint\_based\_optimizer module
------------------------------------------------------
.. automodule:: classes.optimizers.constraint_based_optimizer
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.hill\_climbing\_search module
------------------------------------------------
.. automodule:: classes.optimizers.hill_climbing_search
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.optimizer module
-----------------------------------
.. automodule:: classes.optimizers.optimizer
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.tabu\_search module
--------------------------------------
.. automodule:: classes.optimizers.tabu_search
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.optimizers
:members:
:undoc-members:
:show-inheritance:

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classes package
===============
Subpackages
-----------
.. toctree::
:maxdepth: 4
classes.estimators
classes.optimizers
classes.structure_graph
classes.utility
Module contents
---------------
.. automodule:: classes
:members:
:undoc-members:
:show-inheritance:

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classes.structure\_graph package
================================
Submodules
----------
classes.structure\_graph.abstract\_sample\_path module
------------------------------------------------------
.. automodule:: classes.structure_graph.abstract_sample_path
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.conditional\_intensity\_matrix module
--------------------------------------------------------------
.. automodule:: classes.structure_graph.conditional_intensity_matrix
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.network\_graph module
----------------------------------------------
.. automodule:: classes.structure_graph.network_graph
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.sample\_path module
--------------------------------------------
.. automodule:: classes.structure_graph.sample_path
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.set\_of\_cims module
---------------------------------------------
.. automodule:: classes.structure_graph.set_of_cims
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.sets\_of\_cims\_container module
---------------------------------------------------------
.. automodule:: classes.structure_graph.sets_of_cims_container
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.structure module
-----------------------------------------
.. automodule:: classes.structure_graph.structure
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.trajectory module
------------------------------------------
.. automodule:: classes.structure_graph.trajectory
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.structure_graph
:members:
:undoc-members:
:show-inheritance:

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classes.utility package
=======================
Submodules
----------
classes.utility.abstract\_importer module
-----------------------------------------
.. automodule:: classes.utility.abstract_importer
:members:
:undoc-members:
:show-inheritance:
classes.utility.cache module
----------------------------
.. automodule:: classes.utility.cache
:members:
:undoc-members:
:show-inheritance:
classes.utility.decorators module
---------------------------------
.. automodule:: classes.utility.decorators
:members:
:undoc-members:
:show-inheritance:
classes.utility.json\_importer module
-------------------------------------
.. automodule:: classes.utility.json_importer
:members:
:undoc-members:
:show-inheritance:
classes.utility.sample\_importer module
---------------------------------------
.. automodule:: classes.utility.sample_importer
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.utility
:members:
:undoc-members:
:show-inheritance:

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Examples
========
Installation/Usage
******************
Download the release in .tar.gz or .whl format and simply use pip install to install it::
$pip install PyCTBN-1.0.tar.gz
Implementing your own data importer
***********************************
.. code-block:: python
"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
The net in exam has three ternary nodes and no prior net structure.
"""
from PyCTBN import AbstractImporter
class CSVImporter(AbstractImporter):
def __init__(self, file_path):
self._df_samples_list = None
super(CSVImporter, self).__init__(file_path)
def import_data(self):
self.read_csv_file()
self._sorter = self.build_sorter(self._df_samples_list[0])
self.import_variables()
self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
def read_csv_file(self):
df = pd.read_csv(self._file_path)
df.drop(df.columns[[0]], axis=1, inplace=True)
self._df_samples_list = [df]
def import_variables(self):
values_list = [3 for var in self._sorter]
# initialize dict of lists
data = {'Name':self._sorter, 'Value':values_list}
# Create the pandas DataFrame
self._df_variables = pd.DataFrame(data)
def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
return list(sample_frame.columns)[1:]
def dataset_id(self) -> object:
pass
Parameters Estimation Example
*****************************
.. code-block:: python
from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import NetworkGraph
from PyCTBN import ParametersEstimator
def main():
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
#import data
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
importer.import_data(0)
#Create a SamplePath Obj passing an already filled AbstractImporter object
s1 = SamplePath(importer)
#Build The trajectries and the structural infos
s1.build_trajectories()
s1.build_structure()
print(s1.structure.edges)
print(s1.structure.nodes_values)
#From The Structure Object build the Graph
g = NetworkGraph(s1.structure)
#Select a node you want to estimate the parameters
node = g.nodes[2]
print("Node", node)
#Init the _graph specifically for THIS node
g.fast_init(node)
#Use SamplePath and Grpah to create a ParametersEstimator Object
p1 = ParametersEstimator(s1.trajectories, g)
#Init the peEst specifically for THIS node
p1.fast_init(node)
#Compute the parameters
sofc1 = p1.compute_parameters_for_node(node)
#The est CIMS are inside the resultant SetOfCIms Obj
print(sofc1.actual_cims)
Structure Estimation Example
****************************
.. code-block:: python
from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import StructureEstimator
def structure_estimation_example():
# read the json files in ./data path
read_files = glob.glob(os.path.join('./data', "*.json"))
# initialize a JsonImporter object for the first file
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
# import the data at index 0 of the outer json array
importer.import_data(0)
# construct a SamplePath Object passing a filled AbstractImporter
s1 = SamplePath(importer)
# build the trajectories
s1.build_trajectories()
# build the real structure
s1.build_structure()
# construct a StructureEstimator object
se1 = StructureEstimator(s1, 0.1, 0.1)
# call the ctpc algorithm
se1.ctpc_algorithm()
# the adjacency matrix of the estimated structure
print(se1.adjacency_matrix())
# save results to a json file
se1.save_results()

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.. PyCTBN documentation master file, created by
sphinx-quickstart on Wed Feb 24 18:06:35 2021.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to PyCTBN's documentation!
==================================
.. toctree::
:maxdepth: 3
:caption: Contents:
examples
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

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classes
=======
.. toctree::
:maxdepth: 4
classes

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vertical-align: top;
}
/* -- other body styles ----------------------------------------------------- */
ol.arabic {
list-style: decimal;
}
ol.loweralpha {
list-style: lower-alpha;
}
ol.upperalpha {
list-style: upper-alpha;
}
ol.lowerroman {
list-style: lower-roman;
}
ol.upperroman {
list-style: upper-roman;
}
:not(li) > ol > li:first-child > :first-child,
:not(li) > ul > li:first-child > :first-child {
margin-top: 0px;
}
:not(li) > ol > li:last-child > :last-child,
:not(li) > ul > li:last-child > :last-child {
margin-bottom: 0px;
}
ol.simple ol p,
ol.simple ul p,
ul.simple ol p,
ul.simple ul p {
margin-top: 0;
}
ol.simple > li:not(:first-child) > p,
ul.simple > li:not(:first-child) > p {
margin-top: 0;
}
ol.simple p,
ul.simple p {
margin-bottom: 0;
}
dl.footnote > dt,
dl.citation > dt {
float: left;
margin-right: 0.5em;
}
dl.footnote > dd,
dl.citation > dd {
margin-bottom: 0em;
}
dl.footnote > dd:after,
dl.citation > dd:after {
content: "";
clear: both;
}
dl.field-list {
display: grid;
grid-template-columns: fit-content(30%) auto;
}
dl.field-list > dt {
font-weight: bold;
word-break: break-word;
padding-left: 0.5em;
padding-right: 5px;
}
dl.field-list > dt:after {
content: ":";
}
dl.field-list > dd {
padding-left: 0.5em;
margin-top: 0em;
margin-left: 0em;
margin-bottom: 0em;
}
dl {
margin-bottom: 15px;
}
dd > :first-child {
margin-top: 0px;
}
dd ul, dd table {
margin-bottom: 10px;
}
dd {
margin-top: 3px;
margin-bottom: 10px;
margin-left: 30px;
}
dl > dd:last-child,
dl > dd:last-child > :last-child {
margin-bottom: 0;
}
dt:target, span.highlighted {
background-color: #fbe54e;
}
rect.highlighted {
fill: #fbe54e;
}
dl.glossary dt {
font-weight: bold;
font-size: 1.1em;
}
.optional {
font-size: 1.3em;
}
.sig-paren {
font-size: larger;
}
.versionmodified {
font-style: italic;
}
.system-message {
background-color: #fda;
padding: 5px;
border: 3px solid red;
}
.footnote:target {
background-color: #ffa;
}
.line-block {
display: block;
margin-top: 1em;
margin-bottom: 1em;
}
.line-block .line-block {
margin-top: 0;
margin-bottom: 0;
margin-left: 1.5em;
}
.guilabel, .menuselection {
font-family: sans-serif;
}
.accelerator {
text-decoration: underline;
}
.classifier {
font-style: oblique;
}
.classifier:before {
font-style: normal;
margin: 0.5em;
content: ":";
}
abbr, acronym {
border-bottom: dotted 1px;
cursor: help;
}
/* -- code displays --------------------------------------------------------- */
pre {
overflow: auto;
overflow-y: hidden; /* fixes display issues on Chrome browsers */
}
pre, div[class*="highlight-"] {
clear: both;
}
span.pre {
-moz-hyphens: none;
-ms-hyphens: none;
-webkit-hyphens: none;
hyphens: none;
}
div[class*="highlight-"] {
margin: 1em 0;
}
td.linenos pre {
border: 0;
background-color: transparent;
color: #aaa;
}
table.highlighttable {
display: block;
}
table.highlighttable tbody {
display: block;
}
table.highlighttable tr {
display: flex;
}
table.highlighttable td {
margin: 0;
padding: 0;
}
table.highlighttable td.linenos {
padding-right: 0.5em;
}
table.highlighttable td.code {
flex: 1;
overflow: hidden;
}
.highlight .hll {
display: block;
}
div.highlight pre,
table.highlighttable pre {
margin: 0;
}
div.code-block-caption + div {
margin-top: 0;
}
div.code-block-caption {
margin-top: 1em;
padding: 2px 5px;
font-size: small;
}
div.code-block-caption code {
background-color: transparent;
}
table.highlighttable td.linenos,
span.linenos,
div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
user-select: none;
}
div.code-block-caption span.caption-number {
padding: 0.1em 0.3em;
font-style: italic;
}
div.code-block-caption span.caption-text {
}
div.literal-block-wrapper {
margin: 1em 0;
}
code.descname {
background-color: transparent;
font-weight: bold;
font-size: 1.2em;
}
code.descclassname {
background-color: transparent;
}
code.xref, a code {
background-color: transparent;
font-weight: bold;
}
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
background-color: transparent;
}
.viewcode-link {
float: right;
}
.viewcode-back {
float: right;
font-family: sans-serif;
}
div.viewcode-block:target {
margin: -1px -10px;
padding: 0 10px;
}
/* -- math display ---------------------------------------------------------- */
img.math {
vertical-align: middle;
}
div.body div.math p {
text-align: center;
}
span.eqno {
float: right;
}
span.eqno a.headerlink {
position: absolute;
z-index: 1;
}
div.math:hover a.headerlink {
visibility: visible;
}
/* -- printout stylesheet --------------------------------------------------- */
@media print {
div.document,
div.documentwrapper,
div.bodywrapper {
margin: 0 !important;
width: 100%;
}
div.sphinxsidebar,
div.related,
div.footer,
#top-link {
display: none;
}
}

@ -0,0 +1,2 @@
.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../font/fontawesome_webfont.eot");src:url("../font/fontawesome_webfont.eot?#iefix") format("embedded-opentype"),url("../font/fontawesome_webfont.woff") format("woff"),url("../font/fontawesome_webfont.ttf") format("truetype"),url("../font/fontawesome_webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:0.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;border-top:solid 10px #343131;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60;*zoom:1}.rst-versions .rst-current-version:before,.rst-versions .rst-current-version:after{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px}.rst-versions.rst-badge .icon-book{float:none}.rst-versions.rst-badge .fa-book{float:none}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge .rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}img{width:100%;height:auto}}
/*# sourceMappingURL=badge_only.css.map */

@ -0,0 +1,93 @@
body, .entry-container,
.wy-nav-side,
.wy-side-nav-search,
.fundo-claro,
.wy-menu-vertical li.current,
.rst-content dl:not(.docutils) dt,
code, .rst-content tt,
.wy-side-nav-search > a:hover, .wy-side-nav-search .wy-dropdown > a:hover,
.wy-nav-content{
background-color: rgb(24, 26, 27) !important;
}
h2 a, h2 a:visited, h2 a:hover {
color: rgb(209, 206, 199);
}
body {
color: rgb(209, 206, 199);
}
a, a:hover, a:visited {
color: rgb(113, 178, 234);
}
.wy-menu-vertical a {
color: #b3b3b3;
}
code, .rst-content tt {
color: #fff8f8;
border: 0;
}
codeblock, pre.literal-block, .rst-content .literal-block, .rst-content pre.literal-block, div[class^="highlight"] {
border: 1px solid #000;
}
.rst-content dl:not(.docutils) dl dt {
background: rgb(24, 26, 27) !important;
}
.wy-side-nav-search > a, .wy-side-nav-search .wy-dropdown > a {
color: #fcfcfc;
}
.wy-alert.wy-alert-info, .rst-content .note, .rst-content .wy-alert-info.attention, .rst-content .wy-alert-info.caution, .rst-content .wy-alert-info.danger, .rst-content .wy-alert-info.error, .rst-content .wy-alert-info.hint, .rst-content .wy-alert-info.important, .rst-content .wy-alert-info.tip, .rst-content .wy-alert-info.warning, .rst-content .seealso, .rst-content .wy-alert-info.admonition-todo,
.admonition.note code{
background: #535050 !important;
}
@media screen and (min-width: 768px) {
.wy-nav-side{
width: 224px;
}
.wy-nav-content-wrap{
margin-left: 200px;
background:rgb(24, 26, 27);
}
}
.wy-nav-content-wrap{
margin-left: 200px;
background: #343131;
}
.hentry {
border-bottom: 2px solid rgb(24, 26, 27);
}
.wy-alert.wy-alert-danger .wy-alert-title, .rst-content .wy-alert-danger.note .wy-alert-title, .rst-content .wy-alert-danger.attention .wy-alert-title, .rst-content .wy-alert-danger.caution .wy-alert-title, .rst-content .danger .wy-alert-title, .rst-content .error .wy-alert-title, .rst-content .wy-alert-danger.hint .wy-alert-title, .rst-content .wy-alert-danger.important .wy-alert-title, .rst-content .wy-alert-danger.tip .wy-alert-title, .rst-content .wy-alert-danger.warning .wy-alert-title, .rst-content .wy-alert-danger.seealso .wy-alert-title, .rst-content .wy-alert-danger.admonition-todo .wy-alert-title, .wy-alert.wy-alert-danger .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-danger .admonition-title, .rst-content .wy-alert-danger.note .admonition-title, .rst-content .wy-alert-danger.attention .admonition-title, .rst-content .wy-alert-danger.caution .admonition-title, .rst-content .danger .admonition-title, .rst-content .error .admonition-title, .rst-content .wy-alert-danger.hint .admonition-title, .rst-content .wy-alert-danger.important .admonition-title, .rst-content .wy-alert-danger.tip .admonition-title, .rst-content .wy-alert-danger.warning .admonition-title, .rst-content .wy-alert-danger.seealso .admonition-title, .rst-content .wy-alert-danger.admonition-todo .admonition-title {
background: #db655a;
}
.wy-alert.wy-alert-danger, .rst-content .wy-alert-danger.note, .rst-content .wy-alert-danger.attention, .rst-content .wy-alert-danger.caution, .rst-content .danger, .rst-content .error, .rst-content .wy-alert-danger.hint, .rst-content .wy-alert-danger.important, .rst-content .wy-alert-danger.tip, .rst-content .wy-alert-danger.warning, .rst-content .wy-alert-danger.seealso, .rst-content .wy-alert-danger.admonition-todo {
background: #e18279;
color: #fff;
}
.wy-alert.wy-alert-warning, .rst-content .wy-alert-warning.note, .rst-content .attention, .rst-content .caution, .rst-content .wy-alert-warning.danger, .rst-content .wy-alert-warning.error, .rst-content .wy-alert-warning.hint, .rst-content .wy-alert-warning.important, .rst-content .wy-alert-warning.tip, .rst-content .warning, .rst-content .wy-alert-warning.seealso, .rst-content .admonition-todo {
background: #ca9f52;
color: #fff;
}
.wy-alert.wy-alert-warning .wy-alert-title, .rst-content .wy-alert-warning.note .wy-alert-title, .rst-content .attention .wy-alert-title, .rst-content .caution .wy-alert-title, .rst-content .wy-alert-warning.danger .wy-alert-title, .rst-content .wy-alert-warning.error .wy-alert-title, .rst-content .wy-alert-warning.hint .wy-alert-title, .rst-content .wy-alert-warning.important .wy-alert-title, .rst-content .wy-alert-warning.tip .wy-alert-title, .rst-content .warning .wy-alert-title, .rst-content .wy-alert-warning.seealso .wy-alert-title, .rst-content .admonition-todo .wy-alert-title, .wy-alert.wy-alert-warning .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-warning .admonition-title, .rst-content .wy-alert-warning.note .admonition-title, .rst-content .attention .admonition-title, .rst-content .caution .admonition-title, .rst-content .wy-alert-warning.danger .admonition-title, .rst-content .wy-alert-warning.error .admonition-title, .rst-content .wy-alert-warning.hint .admonition-title, .rst-content .wy-alert-warning.important .admonition-title, .rst-content .wy-alert-warning.tip .admonition-title, .rst-content .warning .admonition-title, .rst-content .wy-alert-warning.seealso .admonition-title, .rst-content .admonition-todo .admonition-title {
background: #ca7a35;
}
.wy-body-for-nav {
background-image: none !important;
}

@ -0,0 +1,494 @@
@import url("theme.css");
.wy-side-nav-search{
background-color: #595C5E;
}
.wy-side-nav-search input[type=text] {
border-color: #595C5E;
}
@media screen and (min-width: 768px) {
.wy-nav-side{
width: 224px;
}
.wy-nav-content-wrap{
margin-left: 200px;
background: #343131;
}
}
@media screen and (max-width: 768px) {
.wy-nav-content{
background-color: #343131;
}
}
.wy-nav-top{
background-color: #595C5E;
}
.wy-nav-content{
padding-top: 1%;
max-width: 100%;
background-color: #343131;
}
.rst-content{
margin-left: -3%;
}
pre{
font-size: 0.9em;
padding: 1%;
}
pre > span,
pre > p{
margin: 0;
font-size: 1em;
}
pre > span{
margin: 0;
}
.hentry{
padding-top: 2%;
padding-bottom: 5%;
padding-left: 3%;
padding-right: 3%;
border-bottom: 2px solid #343131;
}
.entry-container{
background-color: #fff; /*#EAEAEA; */
border-radius: 3px;
/* -webkit-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */
/* -moz-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */
/* box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */
-webkit-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36);
-moz-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36);
box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36);
position: relative;
z-index: 210;
}
.entry-content{
margin-right: 3%;
}
.wy-side-nav-search{
padding:0.639em
}
.wy-menu-vertical a:hover{
-webkit-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30);
-moz-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30);
box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30);
}
.wy-menu-vertical a{
width: 90%
}
#comments,
#comment-form{
padding: 20px;
}
@media screen and (max-width: 1330px) {
.fancybox img{
width: 310px; /* 88px / 633px */
height: 206px;
}
}
@media screen and (max-width: 1241px) {
.fancybox img{
width: 280px;
height: 186px;
}
}
@media screen and (max-width: 1154px) {
.fancybox img{
width: 260px;
height: 173px;
}
}
.expander {
position: absolute;
top: 5px;
left: 5px;
width: 16px;
height: 16px;
padding: 4px;
background: white url(/static/blog/img/fsbtn.png) center center no-repeat;
z-index: 99999;
cursor: pointer;
}
/* 340 */
/* .rst-content img { */
/* margin-top: 8px; */
/* margin-left: 10px; */
/* margin-bottom: 5px; */
/* } */
/* 260 */
.rst-content .row img {
margin-top: 8px;
margin-left: 5px;
border-radius: 3px;
-webkit-box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36);
-moz-box-shadow: 0 0 10px 5px /*rgba(50, 50, 50, 0.75)*/ rgba(2, 2, 2, 0.36);
box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36);
}
.rst-content{
z-index: 210;
margin-top: -60px;
}
h2 a,
h2 a:visited,
h2 a:hover{
color: #404040;
}
.wy-menu-vertical li.on a, .wy-menu-vertical li.current>a {
color: #b3b3b3;
background: #4e4a4a;
box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30);
border: none;
}
.wy-menu-vertical li.current {
background: #343131;
border: none;
}
.wy-menu-vertical li.current a{
border:none
}
.wy-alert.wy-alert-info .wy-alert-title, .rst-content .note .wy-alert-title, .rst-content .wy-alert-info.attention .wy-alert-title, .rst-content .wy-alert-info.caution .wy-alert-title, .rst-content .wy-alert-info.danger .wy-alert-title, .rst-content .wy-alert-info.error .wy-alert-title, .rst-content .wy-alert-info.hint .wy-alert-title, .rst-content .wy-alert-info.important .wy-alert-title, .rst-content .wy-alert-info.tip .wy-alert-title, .rst-content .wy-alert-info.warning .wy-alert-title, .rst-content .seealso .wy-alert-title, .rst-content .wy-alert-info.admonition-todo .wy-alert-title, .wy-alert.wy-alert-info .rst-content .admonition-title, .rst-content .wy-alert.wy-alert-info .admonition-title, .rst-content .note .admonition-title, .rst-content .wy-alert-info.attention .admonition-title, .rst-content .wy-alert-info.caution .admonition-title, .rst-content .wy-alert-info.danger .admonition-title, .rst-content .wy-alert-info.error .admonition-title, .rst-content .wy-alert-info.hint .admonition-title, .rst-content .wy-alert-info.important .admonition-title, .rst-content .wy-alert-info.tip .admonition-title, .rst-content .wy-alert-info.warning .admonition-title, .rst-content .seealso .admonition-title, .rst-content .wy-alert-info.admonition-todo .admonition-title {
background: #47494a;
}
.wy-alert.wy-alert-info, .rst-content .note, .rst-content .wy-alert-info.attention, .rst-content .wy-alert-info.caution, .rst-content .wy-alert-info.danger, .rst-content .wy-alert-info.error, .rst-content .wy-alert-info.hint, .rst-content .wy-alert-info.important, .rst-content .wy-alert-info.tip, .rst-content .wy-alert-info.warning, .rst-content .seealso, .rst-content .wy-alert-info.admonition-todo {
background: #ececec;
}
.wy-alert.wy-alert-danger, .rst-content .wy-alert-danger.note, .rst-content .wy-alert-danger.attention, .rst-content .wy-alert-danger.caution, .rst-content .danger, .rst-content .error, .rst-content .wy-alert-danger.hint, .rst-content .wy-alert-danger.important, .rst-content .wy-alert-danger.tip, .rst-content .wy-alert-danger.warning, .rst-content .wy-alert-danger.seealso, .rst-content .wy-alert-danger.admonition-todo {
background: #f0d5d2;
}
.wy-menu-vertical li.on a:hover,
.wy-menu-vertical li.current>a:hover {
background: #4e4a4a;
}
.wy-menu-vertical li.current a:hover {
background: #4e4a4a;
}
wy-menu-vertical li.toctree-l2.current>a {
color: #343131 !important;
}
.wy-menu-vertical li.toctree-l2.current>a {
background: #343131;
padding: 0.4045em 2.427em;
box-shadow: none;
}
.highlight{
background: #000 !important;
}
.highlight pre,
.highlight .mi,
.highlight .go{
color: #57de4e !important;
font-size: 0.9em !important;
}
.highlight .nt{
color: #8080ec;
}
.highlight .n, .highlight .nn,
.highlight .p, .highlight .o,
.highlight .nv,
.highlight .gp{
color: #57de4e;
}
.highlight .kn{
color: #47ffff !important;
}
.highlight .nc,
.highlight .nd{
color: #1c909e;
font-weight: normal;
}
.highlight .k, .highlight .bp{
color: #47ffff !important;
}
.highlight .kc {
color: #ae39ff;
font-weight: normal !important;
}
.highlight .nf,
.highlight .nb{
color: #87CEFA;
}
.highlight .s1, .highlight .s2, .highlight .s,
.highlight .sd, .highlight .si,
.highlight .se{
color: #FDF5E6;
}
.highlight .ow{
color: #47ffff;
}
.highlight .c1,
.highlight .c{
color: #d80e04;
}
.fundo-claro{
height: 88px;
background-color: #595C5E;
margin-left: -10%;
margin-top: -1.1%;
margin-right: -10%;
}
@media screen and (max-width: 980px) {
pre{
overflow-x: scroll;
}
}
@media screen and (max-width: 768px){
.fundo-claro{
display:none;
}
.rst-content{
z-index: 210;
margin-top: -20px;
}
}
.page{
width: 3%;
float:left;
}
.previous{
width: 10%
}
.paginator{
margin-top: 50px;
padding-bottom: 25px;
padding-left: 20px;
}
blockquote {
font-style: italic;
margin: 0 4.5em;
position: relative;
}
blockquote, q {
quotes: "" "";
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blockquote:before {
color: #807f7f;
content: "\201C";
display: block;
font-family: "Droid Serif", "Times New Roman", serif;
font-size: 48px;
font-size: 4.8rem;
font-style: normal;
font-weight: bold;
line-height: 1;
position: absolute;
top: -15px;
left: -40px;
}
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}
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background-color: #343131;
color: #b3b3b3;
}
.search-topo{
float: right;
padding-right: 7%;
margin-top: -1.5;
}
.fa-home:before, .icon-home:before {
content: url('../img/porao-branco.png');
}
/* fundo preto */
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background-color: #343131;
color: #b3b3b3;
padding: 15px;
}
.entry-container-escuro h2,
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width: 60%;
min-height: 300px;
margin-left: -39.5%;
border-radius: 15px;
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.classe-correio {
background-color: #343131;
}
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width: 30%;
margin-bottom: 10px;
}
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color: #2980B9;
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color: #b3b3b3;
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color: #fcfcfc;
}
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color: #eac648;
}
/* .rst-content dl:not(.docutils) { */
/* margin-bottom: 24px; */
/* background-color: #000; */
/* } */
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/* color: #1bb41b; */
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/*
* doctools.js
* ~~~~~~~~~~~
*
* Sphinx JavaScript utilities for all documentation.
*
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
/**
* make the code below compatible with browsers without
* an installed firebug like debugger
if (!window.console || !console.firebug) {
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
"profile", "profileEnd"];
window.console = {};
for (var i = 0; i < names.length; ++i)
window.console[names[i]] = function() {};
}
*/
/**
* small helper function to urldecode strings
*/
jQuery.urldecode = function(x) {
return decodeURIComponent(x).replace(/\+/g, ' ');
};
/**
* small helper function to urlencode strings
*/
jQuery.urlencode = encodeURIComponent;
/**
* This function returns the parsed url parameters of the
* current request. Multiple values per key are supported,
* it will always return arrays of strings for the value parts.
*/
jQuery.getQueryParameters = function(s) {
if (typeof s === 'undefined')
s = document.location.search;
var parts = s.substr(s.indexOf('?') + 1).split('&');
var result = {};
for (var i = 0; i < parts.length; i++) {
var tmp = parts[i].split('=', 2);
var key = jQuery.urldecode(tmp[0]);
var value = jQuery.urldecode(tmp[1]);
if (key in result)
result[key].push(value);
else
result[key] = [value];
}
return result;
};
/**
* highlight a given string on a jquery object by wrapping it in
* span elements with the given class name.
*/
jQuery.fn.highlightText = function(text, className) {
function highlight(node, addItems) {
if (node.nodeType === 3) {
var val = node.nodeValue;
var pos = val.toLowerCase().indexOf(text);
if (pos >= 0 &&
!jQuery(node.parentNode).hasClass(className) &&
!jQuery(node.parentNode).hasClass("nohighlight")) {
var span;
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
if (isInSVG) {
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
} else {
span = document.createElement("span");
span.className = className;
}
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling));
node.nodeValue = val.substr(0, pos);
if (isInSVG) {
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
var bbox = node.parentElement.getBBox();
rect.x.baseVal.value = bbox.x;
rect.y.baseVal.value = bbox.y;
rect.width.baseVal.value = bbox.width;
rect.height.baseVal.value = bbox.height;
rect.setAttribute('class', className);
addItems.push({
"parent": node.parentNode,
"target": rect});
}
}
}
else if (!jQuery(node).is("button, select, textarea")) {
jQuery.each(node.childNodes, function() {
highlight(this, addItems);
});
}
}
var addItems = [];
var result = this.each(function() {
highlight(this, addItems);
});
for (var i = 0; i < addItems.length; ++i) {
jQuery(addItems[i].parent).before(addItems[i].target);
}
return result;
};
/*
* backward compatibility for jQuery.browser
* This will be supported until firefox bug is fixed.
*/
if (!jQuery.browser) {
jQuery.uaMatch = function(ua) {
ua = ua.toLowerCase();
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
/(msie) ([\w.]+)/.exec(ua) ||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
[];
return {
browser: match[ 1 ] || "",
version: match[ 2 ] || "0"
};
};
jQuery.browser = {};
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}
/**
* Small JavaScript module for the documentation.
*/
var Documentation = {
init : function() {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
this.initOnKeyListeners();
}
},
/**
* i18n support
*/
TRANSLATIONS : {},
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
LOCALE : 'unknown',
// gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) {
var translated = Documentation.TRANSLATIONS[string];
if (typeof translated === 'undefined')
return string;
return (typeof translated === 'string') ? translated : translated[0];
},
ngettext : function(singular, plural, n) {
var translated = Documentation.TRANSLATIONS[singular];
if (typeof translated === 'undefined')
return (n == 1) ? singular : plural;
return translated[Documentation.PLURALEXPR(n)];
},
addTranslations : function(catalog) {
for (var key in catalog.messages)
this.TRANSLATIONS[key] = catalog.messages[key];
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
this.LOCALE = catalog.locale;
},
/**
* add context elements like header anchor links
*/
addContextElements : function() {
$('div[id] > :header:first').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this headline')).
appendTo(this);
});
$('dt[id]').each(function() {
$('<a class="headerlink">\u00B6</a>').
attr('href', '#' + this.id).
attr('title', _('Permalink to this definition')).
appendTo(this);
});
},
/**
* workaround a firefox stupidity
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
*/
fixFirefoxAnchorBug : function() {
if (document.location.hash && $.browser.mozilla)
window.setTimeout(function() {
document.location.href += '';
}, 10);
},
/**
* highlight the search words provided in the url in the text
*/
highlightSearchWords : function() {
var params = $.getQueryParameters();
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
if (terms.length) {
var body = $('div.body');
if (!body.length) {
body = $('body');
}
window.setTimeout(function() {
$.each(terms, function() {
body.highlightText(this.toLowerCase(), 'highlighted');
});
}, 10);
$('<p class="highlight-link"><a href="javascript:Documentation.' +
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
.appendTo($('#searchbox'));
}
},
/**
* init the domain index toggle buttons
*/
initIndexTable : function() {
var togglers = $('img.toggler').click(function() {
var src = $(this).attr('src');
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle();
if (src.substr(-9) === 'minus.png')
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
else
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
}).css('display', '');
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
togglers.click();
}
},
/**
* helper function to hide the search marks again
*/
hideSearchWords : function() {
$('#searchbox .highlight-link').fadeOut(300);
$('span.highlighted').removeClass('highlighted');
},
/**
* make the url absolute
*/
makeURL : function(relativeURL) {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
},
/**
* get the current relative url
*/
getCurrentURL : function() {
var path = document.location.pathname;
var parts = path.split(/\//);
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
if (this === '..')
parts.pop();
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
},
initOnKeyListeners: function() {
$(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box, textarea, dropdown or button
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
&& !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
if (prevHref) {
window.location.href = prevHref;
return false;
}
case 39: // right
var nextHref = $('link[rel="next"]').prop('href');
if (nextHref) {
window.location.href = nextHref;
return false;
}
}
}
});
}
};
// quick alias for translations
_ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
});

@ -0,0 +1,12 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
VERSION: '2.0',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
BUILDER: 'html',
FILE_SUFFIX: '.html',
LINK_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false
};

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// setting highlight for async stuff in python
var async_kw = ['async', 'await'];
jQuery.each(async_kw, function(i, kw){
var elements = jQuery(".highlight-python .n:contains('" + kw + "')");
jQuery.each(elements, function(j, el){
el = jQuery(el);
if (el.text() == kw){
el.removeClass('n');
el.addClass('kn');
};
});
});

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$( document ).ready(function() {
// Shift nav in mobile when clicking the menu.
$(document).on('click', "[data-toggle='wy-nav-top']", function() {
$("[data-toggle='wy-nav-shift']").toggleClass("shift");
$("[data-toggle='rst-versions']").toggleClass("shift");
});
// Close menu when you click a link.
$(document).on('click', ".wy-menu-vertical .current ul li a", function() {
$("[data-toggle='wy-nav-shift']").removeClass("shift");
$("[data-toggle='rst-versions']").toggleClass("shift");
});
$(document).on('click', "[data-toggle='rst-current-version']", function() {
$("[data-toggle='rst-versions']").toggleClass("shift-up");
});
// Make tables responsive
$("table.docutils:not(.field-list)").wrap("<div class='wy-table-responsive'></div>");
});
window.SphinxRtdTheme = (function (jquery) {
var stickyNav = (function () {
var navBar,
win,
stickyNavCssClass = 'stickynav',
applyStickNav = function () {
if (navBar.height() <= win.height()) {
navBar.addClass(stickyNavCssClass);
} else {
navBar.removeClass(stickyNavCssClass);
}
},
enable = function () {
applyStickNav();
win.on('resize', applyStickNav);
},
init = function () {
navBar = jquery('nav.wy-nav-side:first');
win = jquery(window);
};
jquery(init);
return {
enable : enable
};
}());
return {
StickyNav : stickyNav
};
}($));

@ -0,0 +1,297 @@
/*
* language_data.js
* ~~~~~~~~~~~~~~~~
*
* This script contains the language-specific data used by searchtools.js,
* namely the list of stopwords, stemmer, scorer and splitter.
*
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
/* Non-minified version JS is _stemmer.js if file is provided */
/**
* Porter Stemmer
*/
var Stemmer = function() {
var step2list = {
ational: 'ate',
tional: 'tion',
enci: 'ence',
anci: 'ance',
izer: 'ize',
bli: 'ble',
alli: 'al',
entli: 'ent',
eli: 'e',
ousli: 'ous',
ization: 'ize',
ation: 'ate',
ator: 'ate',
alism: 'al',
iveness: 'ive',
fulness: 'ful',
ousness: 'ous',
aliti: 'al',
iviti: 'ive',
biliti: 'ble',
logi: 'log'
};
var step3list = {
icate: 'ic',
ative: '',
alize: 'al',
iciti: 'ic',
ical: 'ic',
ful: '',
ness: ''
};
var c = "[^aeiou]"; // consonant
var v = "[aeiouy]"; // vowel
var C = c + "[^aeiouy]*"; // consonant sequence
var V = v + "[aeiou]*"; // vowel sequence
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
var s_v = "^(" + C + ")?" + v; // vowel in stem
this.stemWord = function (w) {
var stem;
var suffix;
var firstch;
var origword = w;
if (w.length < 3)
return w;
var re;
var re2;
var re3;
var re4;
firstch = w.substr(0,1);
if (firstch == "y")
w = firstch.toUpperCase() + w.substr(1);
// Step 1a
re = /^(.+?)(ss|i)es$/;
re2 = /^(.+?)([^s])s$/;
if (re.test(w))
w = w.replace(re,"$1$2");
else if (re2.test(w))
w = w.replace(re2,"$1$2");
// Step 1b
re = /^(.+?)eed$/;
re2 = /^(.+?)(ed|ing)$/;
if (re.test(w)) {
var fp = re.exec(w);
re = new RegExp(mgr0);
if (re.test(fp[1])) {
re = /.$/;
w = w.replace(re,"");
}
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1];
re2 = new RegExp(s_v);
if (re2.test(stem)) {
w = stem;
re2 = /(at|bl|iz)$/;
re3 = new RegExp("([^aeiouylsz])\\1$");
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re2.test(w))
w = w + "e";
else if (re3.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
else if (re4.test(w))
w = w + "e";
}
}
// Step 1c
re = /^(.+?)y$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(s_v);
if (re.test(stem))
w = stem + "i";
}
// Step 2
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step2list[suffix];
}
// Step 3
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
suffix = fp[2];
re = new RegExp(mgr0);
if (re.test(stem))
w = stem + step3list[suffix];
}
// Step 4
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
re2 = /^(.+?)(s|t)(ion)$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
if (re.test(stem))
w = stem;
}
else if (re2.test(w)) {
var fp = re2.exec(w);
stem = fp[1] + fp[2];
re2 = new RegExp(mgr1);
if (re2.test(stem))
w = stem;
}
// Step 5
re = /^(.+?)e$/;
if (re.test(w)) {
var fp = re.exec(w);
stem = fp[1];
re = new RegExp(mgr1);
re2 = new RegExp(meq1);
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
w = stem;
}
re = /ll$/;
re2 = new RegExp(mgr1);
if (re.test(w) && re2.test(w)) {
re = /.$/;
w = w.replace(re,"");
}
// and turn initial Y back to y
if (firstch == "y")
w = firstch.toLowerCase() + w.substr(1);
return w;
}
}
var splitChars = (function() {
var result = {};
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
var i, j, start, end;
for (i = 0; i < singles.length; i++) {
result[singles[i]] = true;
}
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
for (i = 0; i < ranges.length; i++) {
start = ranges[i][0];
end = ranges[i][1];
for (j = start; j <= end; j++) {
result[j] = true;
}
}
return result;
})();
function splitQuery(query) {
var result = [];
var start = -1;
for (var i = 0; i < query.length; i++) {
if (splitChars[query.charCodeAt(i)]) {
if (start !== -1) {
result.push(query.slice(start, i));
start = -1;
}
} else if (start === -1) {
start = i;
}
}
if (start !== -1) {
result.push(query.slice(start));
}
return result;
}

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@ -0,0 +1,7 @@
pre { line-height: 125%; }
td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; }
td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.highlight .hll { background-color: #ffffcc }
.highlight { background: #ffffff; }

@ -0,0 +1,514 @@
/*
* searchtools.js
* ~~~~~~~~~~~~~~~~
*
* Sphinx JavaScript utilities for the full-text search.
*
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
if (!Scorer) {
/**
* Simple result scoring code.
*/
var Scorer = {
// Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score]
// and returns the new score.
/*
score: function(result) {
return result[4];
},
*/
// query matches the full name of an object
objNameMatch: 11,
// or matches in the last dotted part of the object name
objPartialMatch: 6,
// Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults
// Used when the priority is not in the mapping.
objPrioDefault: 0,
// query found in title
title: 15,
partialTitle: 7,
// query found in terms
term: 5,
partialTerm: 2
};
}
if (!splitQuery) {
function splitQuery(query) {
return query.split(/\s+/);
}
}
/**
* Search Module
*/
var Search = {
_index : null,
_queued_query : null,
_pulse_status : -1,
htmlToText : function(htmlString) {
var virtualDocument = document.implementation.createHTMLDocument('virtual');
var htmlElement = $(htmlString, virtualDocument);
htmlElement.find('.headerlink').remove();
docContent = htmlElement.find('[role=main]')[0];
if(docContent === undefined) {
console.warn("Content block not found. Sphinx search tries to obtain it " +
"via '[role=main]'. Could you check your theme or template.");
return "";
}
return docContent.textContent || docContent.innerText;
},
init : function() {
var params = $.getQueryParameters();
if (params.q) {
var query = params.q[0];
$('input[name="q"]')[0].value = query;
this.performSearch(query);
}
},
loadIndex : function(url) {
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
setIndex : function(index) {
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
}
},
hasIndex : function() {
return this._index !== null;
},
deferQuery : function(query) {
this._queued_query = query;
},
stopPulse : function() {
this._pulse_status = 0;
},
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = '';
for (i = 0; i < Search._pulse_status; i++)
dotString += '.';
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
pulse();
},
/**
* perform a search for something (or wait until index is loaded)
*/
performSearch : function(query) {
// create the required interface elements
this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out);
$('#search-progress').text(_('Preparing search...'));
this.startPulse();
// index already loaded, the browser was quick!
if (this.hasIndex())
this.query(query);
else
this.deferQuery(query);
},
/**
* execute search (requires search index to be loaded)
*/
query : function(query) {
var i;
// stem the searchterms and add them to the correct list
var stemmer = new Stemmer();
var searchterms = [];
var excluded = [];
var hlterms = [];
var tmp = splitQuery(query);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase());
}
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
// prevent stemmer from cutting word smaller than two chars
if(word.length < 3 && tmp[i].length >= 3) {
word = tmp[i];
}
var toAppend;
// select the correct list
if (word[0] == '-') {
toAppend = excluded;
word = word.substr(1);
}
else {
toAppend = searchterms;
hlterms.push(tmp[i].toLowerCase());
}
// only add if not already in the list
if (!$u.contains(toAppend, word))
toAppend.push(word);
}
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
// console.debug('SEARCH: searching for:');
// console.info('required: ', searchterms);
// console.info('excluded: ', excluded);
// prepare search
var terms = this._index.terms;
var titleterms = this._index.titleterms;
// array of [filename, title, anchor, descr, score]
var results = [];
$('#search-progress').empty();
// lookup as object
for (i = 0; i < objectterms.length; i++) {
var others = [].concat(objectterms.slice(0, i),
objectterms.slice(i+1, objectterms.length));
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
// lookup as search terms in fulltext
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
// let the scorer override scores with a custom scoring function
if (Scorer.score) {
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
// now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then
// alphabetically
results.sort(function(a, b) {
var left = a[4];
var right = b[4];
if (left > right) {
return 1;
} else if (left < right) {
return -1;
} else {
// same score: sort alphabetically
left = a[1].toLowerCase();
right = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0);
}
});
// for debugging
//Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults);
// print the results
var resultCount = results.length;
function displayNextItem() {
// results left, load the summary and display it
if (results.length) {
var item = results.pop();
var listItem = $('<li style="display:none"></li>');
var requestUrl = "";
var linkUrl = "";
if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
// dirhtml builder
var dirname = item[0] + '/';
if (dirname.match(/\/index\/$/)) {
dirname = dirname.substring(0, dirname.length-6);
} else if (dirname == 'index/') {
dirname = '';
}
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
linkUrl = requestUrl;
} else {
// normal html builders
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
}
listItem.append($('<a/>').attr('href',
linkUrl +
highlightstring + item[2]).html(item[1]));
if (item[3]) {
listItem.append($('<span> (' + item[3] + ')</span>'));
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
$.ajax({url: requestUrl,
dataType: "text",
complete: function(jqxhr, textstatus) {
var data = jqxhr.responseText;
if (data !== '' && data !== undefined) {
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
}
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}});
} else {
// no source available, just display title
Search.output.append(listItem);
listItem.slideDown(5, function() {
displayNextItem();
});
}
}
// search finished, update title and status message
else {
Search.stopPulse();
Search.title.text(_('Search Results'));
if (!resultCount)
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
else
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
Search.status.fadeIn(500);
}
}
displayNextItem();
},
/**
* search for object names
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
var docnames = this._index.docnames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
var i;
var results = [];
for (var prefix in objects) {
for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name;
var fullnameLower = fullname.toLowerCase()
if (fullnameLower.indexOf(object) > -1) {
var score = 0;
var parts = fullnameLower.split('.');
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullnameLower == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch;
// matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) {
score += Scorer.objPartialMatch;
}
var match = objects[prefix][name];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
if (otherterms.length > 0) {
var haystack = (prefix + ' ' + name + ' ' +
objname + ' ' + title).toLowerCase();
var allfound = true;
for (i = 0; i < otherterms.length; i++) {
if (haystack.indexOf(otherterms[i]) == -1) {
allfound = false;
break;
}
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
var anchor = match[3];
if (anchor === '')
anchor = fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) {
score += Scorer.objPrio[match[2]];
} else {
score += Scorer.objPrioDefault;
}
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
}
}
}
return results;
},
/**
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
var docnames = this._index.docnames;
var filenames = this._index.filenames;
var titles = this._index.titles;
var i, j, file;
var fileMap = {};
var scoreMap = {};
var results = [];
// perform the search on the required terms
for (i = 0; i < searchterms.length; i++) {
var word = searchterms[i];
var files = [];
var _o = [
{files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title}
];
// add support for partial matches
if (word.length > 2) {
for (var w in terms) {
if (w.match(word) && !terms[word]) {
_o.push({files: terms[w], score: Scorer.partialTerm})
}
}
for (var w in titleterms) {
if (w.match(word) && !titleterms[word]) {
_o.push({files: titleterms[w], score: Scorer.partialTitle})
}
}
}
// no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) {
break;
}
// found search word in contents
$u.each(_o, function(o) {
var _files = o.files;
if (_files === undefined)
return
if (_files.length === undefined)
_files = [_files];
files = files.concat(_files);
// set score for the word in each file to Scorer.term
for (j = 0; j < _files.length; j++) {
file = _files[j];
if (!(file in scoreMap))
scoreMap[file] = {};
scoreMap[file][word] = o.score;
}
});
// create the mapping
for (j = 0; j < files.length; j++) {
file = files[j];
if (file in fileMap && fileMap[file].indexOf(word) === -1)
fileMap[file].push(word);
else
fileMap[file] = [word];
}
}
// now check if the files don't contain excluded terms
for (file in fileMap) {
var valid = true;
// check if all requirements are matched
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
searchterms.filter(function(term){return term.length > 2}).length
if (
fileMap[file].length != searchterms.length &&
fileMap[file].length != filteredTermCount
) continue;
// ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) {
if (terms[excluded[i]] == file ||
titleterms[excluded[i]] == file ||
$u.contains(terms[excluded[i]] || [], file) ||
$u.contains(titleterms[excluded[i]] || [], file)) {
valid = false;
break;
}
}
// if we have still a valid result we can add it to the result list
if (valid) {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
}
}
return results;
},
/**
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
*/
makeSearchSummary : function(htmlText, keywords, hlwords) {
var text = Search.htmlToText(htmlText);
var textLower = text.toLowerCase();
var start = 0;
$.each(keywords, function() {
var i = textLower.indexOf(this.toLowerCase());
if (i > -1)
start = i;
});
start = Math.max(start - 120, 0);
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) +
((start + 240 - text.length) ? '...' : '');
var rv = $('<div class="context"></div>').text(excerpt);
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted');
});
return rv;
}
};
$(document).ready(function() {
Search.init();
});

@ -0,0 +1,999 @@
// Underscore.js 1.3.1
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
// Underscore is freely distributable under the MIT license.
// Portions of Underscore are inspired or borrowed from Prototype,
// Oliver Steele's Functional, and John Resig's Micro-Templating.
// For all details and documentation:
// http://documentcloud.github.com/underscore
(function() {
// Baseline setup
// --------------
// Establish the root object, `window` in the browser, or `global` on the server.
var root = this;
// Save the previous value of the `_` variable.
var previousUnderscore = root._;
// Establish the object that gets returned to break out of a loop iteration.
var breaker = {};
// Save bytes in the minified (but not gzipped) version:
var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
// Create quick reference variables for speed access to core prototypes.
var slice = ArrayProto.slice,
unshift = ArrayProto.unshift,
toString = ObjProto.toString,
hasOwnProperty = ObjProto.hasOwnProperty;
// All **ECMAScript 5** native function implementations that we hope to use
// are declared here.
var
nativeForEach = ArrayProto.forEach,
nativeMap = ArrayProto.map,
nativeReduce = ArrayProto.reduce,
nativeReduceRight = ArrayProto.reduceRight,
nativeFilter = ArrayProto.filter,
nativeEvery = ArrayProto.every,
nativeSome = ArrayProto.some,
nativeIndexOf = ArrayProto.indexOf,
nativeLastIndexOf = ArrayProto.lastIndexOf,
nativeIsArray = Array.isArray,
nativeKeys = Object.keys,
nativeBind = FuncProto.bind;
// Create a safe reference to the Underscore object for use below.
var _ = function(obj) { return new wrapper(obj); };
// Export the Underscore object for **Node.js**, with
// backwards-compatibility for the old `require()` API. If we're in
// the browser, add `_` as a global object via a string identifier,
// for Closure Compiler "advanced" mode.
if (typeof exports !== 'undefined') {
if (typeof module !== 'undefined' && module.exports) {
exports = module.exports = _;
}
exports._ = _;
} else {
root['_'] = _;
}
// Current version.
_.VERSION = '1.3.1';
// Collection Functions
// --------------------
// The cornerstone, an `each` implementation, aka `forEach`.
// Handles objects with the built-in `forEach`, arrays, and raw objects.
// Delegates to **ECMAScript 5**'s native `forEach` if available.
var each = _.each = _.forEach = function(obj, iterator, context) {
if (obj == null) return;
if (nativeForEach && obj.forEach === nativeForEach) {
obj.forEach(iterator, context);
} else if (obj.length === +obj.length) {
for (var i = 0, l = obj.length; i < l; i++) {
if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
}
} else {
for (var key in obj) {
if (_.has(obj, key)) {
if (iterator.call(context, obj[key], key, obj) === breaker) return;
}
}
}
};
// Return the results of applying the iterator to each element.
// Delegates to **ECMAScript 5**'s native `map` if available.
_.map = _.collect = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
each(obj, function(value, index, list) {
results[results.length] = iterator.call(context, value, index, list);
});
if (obj.length === +obj.length) results.length = obj.length;
return results;
};
// **Reduce** builds up a single result from a list of values, aka `inject`,
// or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
_.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduce && obj.reduce === nativeReduce) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
}
each(obj, function(value, index, list) {
if (!initial) {
memo = value;
initial = true;
} else {
memo = iterator.call(context, memo, value, index, list);
}
});
if (!initial) throw new TypeError('Reduce of empty array with no initial value');
return memo;
};
// The right-associative version of reduce, also known as `foldr`.
// Delegates to **ECMAScript 5**'s native `reduceRight` if available.
_.reduceRight = _.foldr = function(obj, iterator, memo, context) {
var initial = arguments.length > 2;
if (obj == null) obj = [];
if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
if (context) iterator = _.bind(iterator, context);
return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
}
var reversed = _.toArray(obj).reverse();
if (context && !initial) iterator = _.bind(iterator, context);
return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
};
// Return the first value which passes a truth test. Aliased as `detect`.
_.find = _.detect = function(obj, iterator, context) {
var result;
any(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) {
result = value;
return true;
}
});
return result;
};
// Return all the elements that pass a truth test.
// Delegates to **ECMAScript 5**'s native `filter` if available.
// Aliased as `select`.
_.filter = _.select = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
each(obj, function(value, index, list) {
if (iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Return all the elements for which a truth test fails.
_.reject = function(obj, iterator, context) {
var results = [];
if (obj == null) return results;
each(obj, function(value, index, list) {
if (!iterator.call(context, value, index, list)) results[results.length] = value;
});
return results;
};
// Determine whether all of the elements match a truth test.
// Delegates to **ECMAScript 5**'s native `every` if available.
// Aliased as `all`.
_.every = _.all = function(obj, iterator, context) {
var result = true;
if (obj == null) return result;
if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
each(obj, function(value, index, list) {
if (!(result = result && iterator.call(context, value, index, list))) return breaker;
});
return result;
};
// Determine if at least one element in the object matches a truth test.
// Delegates to **ECMAScript 5**'s native `some` if available.
// Aliased as `any`.
var any = _.some = _.any = function(obj, iterator, context) {
iterator || (iterator = _.identity);
var result = false;
if (obj == null) return result;
if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
each(obj, function(value, index, list) {
if (result || (result = iterator.call(context, value, index, list))) return breaker;
});
return !!result;
};
// Determine if a given value is included in the array or object using `===`.
// Aliased as `contains`.
_.include = _.contains = function(obj, target) {
var found = false;
if (obj == null) return found;
if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
found = any(obj, function(value) {
return value === target;
});
return found;
};
// Invoke a method (with arguments) on every item in a collection.
_.invoke = function(obj, method) {
var args = slice.call(arguments, 2);
return _.map(obj, function(value) {
return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
});
};
// Convenience version of a common use case of `map`: fetching a property.
_.pluck = function(obj, key) {
return _.map(obj, function(value){ return value[key]; });
};
// Return the maximum element or (element-based computation).
_.max = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return -Infinity;
var result = {computed : -Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed >= result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Return the minimum element (or element-based computation).
_.min = function(obj, iterator, context) {
if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
if (!iterator && _.isEmpty(obj)) return Infinity;
var result = {computed : Infinity};
each(obj, function(value, index, list) {
var computed = iterator ? iterator.call(context, value, index, list) : value;
computed < result.computed && (result = {value : value, computed : computed});
});
return result.value;
};
// Shuffle an array.
_.shuffle = function(obj) {
var shuffled = [], rand;
each(obj, function(value, index, list) {
if (index == 0) {
shuffled[0] = value;
} else {
rand = Math.floor(Math.random() * (index + 1));
shuffled[index] = shuffled[rand];
shuffled[rand] = value;
}
});
return shuffled;
};
// Sort the object's values by a criterion produced by an iterator.
_.sortBy = function(obj, iterator, context) {
return _.pluck(_.map(obj, function(value, index, list) {
return {
value : value,
criteria : iterator.call(context, value, index, list)
};
}).sort(function(left, right) {
var a = left.criteria, b = right.criteria;
return a < b ? -1 : a > b ? 1 : 0;
}), 'value');
};
// Groups the object's values by a criterion. Pass either a string attribute
// to group by, or a function that returns the criterion.
_.groupBy = function(obj, val) {
var result = {};
var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
each(obj, function(value, index) {
var key = iterator(value, index);
(result[key] || (result[key] = [])).push(value);
});
return result;
};
// Use a comparator function to figure out at what index an object should
// be inserted so as to maintain order. Uses binary search.
_.sortedIndex = function(array, obj, iterator) {
iterator || (iterator = _.identity);
var low = 0, high = array.length;
while (low < high) {
var mid = (low + high) >> 1;
iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
}
return low;
};
// Safely convert anything iterable into a real, live array.
_.toArray = function(iterable) {
if (!iterable) return [];
if (iterable.toArray) return iterable.toArray();
if (_.isArray(iterable)) return slice.call(iterable);
if (_.isArguments(iterable)) return slice.call(iterable);
return _.values(iterable);
};
// Return the number of elements in an object.
_.size = function(obj) {
return _.toArray(obj).length;
};
// Array Functions
// ---------------
// Get the first element of an array. Passing **n** will return the first N
// values in the array. Aliased as `head`. The **guard** check allows it to work
// with `_.map`.
_.first = _.head = function(array, n, guard) {
return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
};
// Returns everything but the last entry of the array. Especcialy useful on
// the arguments object. Passing **n** will return all the values in
// the array, excluding the last N. The **guard** check allows it to work with
// `_.map`.
_.initial = function(array, n, guard) {
return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
};
// Get the last element of an array. Passing **n** will return the last N
// values in the array. The **guard** check allows it to work with `_.map`.
_.last = function(array, n, guard) {
if ((n != null) && !guard) {
return slice.call(array, Math.max(array.length - n, 0));
} else {
return array[array.length - 1];
}
};
// Returns everything but the first entry of the array. Aliased as `tail`.
// Especially useful on the arguments object. Passing an **index** will return
// the rest of the values in the array from that index onward. The **guard**
// check allows it to work with `_.map`.
_.rest = _.tail = function(array, index, guard) {
return slice.call(array, (index == null) || guard ? 1 : index);
};
// Trim out all falsy values from an array.
_.compact = function(array) {
return _.filter(array, function(value){ return !!value; });
};
// Return a completely flattened version of an array.
_.flatten = function(array, shallow) {
return _.reduce(array, function(memo, value) {
if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
memo[memo.length] = value;
return memo;
}, []);
};
// Return a version of the array that does not contain the specified value(s).
_.without = function(array) {
return _.difference(array, slice.call(arguments, 1));
};
// Produce a duplicate-free version of the array. If the array has already
// been sorted, you have the option of using a faster algorithm.
// Aliased as `unique`.
_.uniq = _.unique = function(array, isSorted, iterator) {
var initial = iterator ? _.map(array, iterator) : array;
var result = [];
_.reduce(initial, function(memo, el, i) {
if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
memo[memo.length] = el;
result[result.length] = array[i];
}
return memo;
}, []);
return result;
};
// Produce an array that contains the union: each distinct element from all of
// the passed-in arrays.
_.union = function() {
return _.uniq(_.flatten(arguments, true));
};
// Produce an array that contains every item shared between all the
// passed-in arrays. (Aliased as "intersect" for back-compat.)
_.intersection = _.intersect = function(array) {
var rest = slice.call(arguments, 1);
return _.filter(_.uniq(array), function(item) {
return _.every(rest, function(other) {
return _.indexOf(other, item) >= 0;
});
});
};
// Take the difference between one array and a number of other arrays.
// Only the elements present in just the first array will remain.
_.difference = function(array) {
var rest = _.flatten(slice.call(arguments, 1));
return _.filter(array, function(value){ return !_.include(rest, value); });
};
// Zip together multiple lists into a single array -- elements that share
// an index go together.
_.zip = function() {
var args = slice.call(arguments);
var length = _.max(_.pluck(args, 'length'));
var results = new Array(length);
for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
return results;
};
// If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
// we need this function. Return the position of the first occurrence of an
// item in an array, or -1 if the item is not included in the array.
// Delegates to **ECMAScript 5**'s native `indexOf` if available.
// If the array is large and already in sort order, pass `true`
// for **isSorted** to use binary search.
_.indexOf = function(array, item, isSorted) {
if (array == null) return -1;
var i, l;
if (isSorted) {
i = _.sortedIndex(array, item);
return array[i] === item ? i : -1;
}
if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
return -1;
};
// Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
_.lastIndexOf = function(array, item) {
if (array == null) return -1;
if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
var i = array.length;
while (i--) if (i in array && array[i] === item) return i;
return -1;
};
// Generate an integer Array containing an arithmetic progression. A port of
// the native Python `range()` function. See
// [the Python documentation](http://docs.python.org/library/functions.html#range).
_.range = function(start, stop, step) {
if (arguments.length <= 1) {
stop = start || 0;
start = 0;
}
step = arguments[2] || 1;
var len = Math.max(Math.ceil((stop - start) / step), 0);
var idx = 0;
var range = new Array(len);
while(idx < len) {
range[idx++] = start;
start += step;
}
return range;
};
// Function (ahem) Functions
// ------------------
// Reusable constructor function for prototype setting.
var ctor = function(){};
// Create a function bound to a given object (assigning `this`, and arguments,
// optionally). Binding with arguments is also known as `curry`.
// Delegates to **ECMAScript 5**'s native `Function.bind` if available.
// We check for `func.bind` first, to fail fast when `func` is undefined.
_.bind = function bind(func, context) {
var bound, args;
if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
if (!_.isFunction(func)) throw new TypeError;
args = slice.call(arguments, 2);
return bound = function() {
if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
ctor.prototype = func.prototype;
var self = new ctor;
var result = func.apply(self, args.concat(slice.call(arguments)));
if (Object(result) === result) return result;
return self;
};
};
// Bind all of an object's methods to that object. Useful for ensuring that
// all callbacks defined on an object belong to it.
_.bindAll = function(obj) {
var funcs = slice.call(arguments, 1);
if (funcs.length == 0) funcs = _.functions(obj);
each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
return obj;
};
// Memoize an expensive function by storing its results.
_.memoize = function(func, hasher) {
var memo = {};
hasher || (hasher = _.identity);
return function() {
var key = hasher.apply(this, arguments);
return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
};
};
// Delays a function for the given number of milliseconds, and then calls
// it with the arguments supplied.
_.delay = function(func, wait) {
var args = slice.call(arguments, 2);
return setTimeout(function(){ return func.apply(func, args); }, wait);
};
// Defers a function, scheduling it to run after the current call stack has
// cleared.
_.defer = function(func) {
return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
};
// Returns a function, that, when invoked, will only be triggered at most once
// during a given window of time.
_.throttle = function(func, wait) {
var context, args, timeout, throttling, more;
var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
return function() {
context = this; args = arguments;
var later = function() {
timeout = null;
if (more) func.apply(context, args);
whenDone();
};
if (!timeout) timeout = setTimeout(later, wait);
if (throttling) {
more = true;
} else {
func.apply(context, args);
}
whenDone();
throttling = true;
};
};
// Returns a function, that, as long as it continues to be invoked, will not
// be triggered. The function will be called after it stops being called for
// N milliseconds.
_.debounce = function(func, wait) {
var timeout;
return function() {
var context = this, args = arguments;
var later = function() {
timeout = null;
func.apply(context, args);
};
clearTimeout(timeout);
timeout = setTimeout(later, wait);
};
};
// Returns a function that will be executed at most one time, no matter how
// often you call it. Useful for lazy initialization.
_.once = function(func) {
var ran = false, memo;
return function() {
if (ran) return memo;
ran = true;
return memo = func.apply(this, arguments);
};
};
// Returns the first function passed as an argument to the second,
// allowing you to adjust arguments, run code before and after, and
// conditionally execute the original function.
_.wrap = function(func, wrapper) {
return function() {
var args = [func].concat(slice.call(arguments, 0));
return wrapper.apply(this, args);
};
};
// Returns a function that is the composition of a list of functions, each
// consuming the return value of the function that follows.
_.compose = function() {
var funcs = arguments;
return function() {
var args = arguments;
for (var i = funcs.length - 1; i >= 0; i--) {
args = [funcs[i].apply(this, args)];
}
return args[0];
};
};
// Returns a function that will only be executed after being called N times.
_.after = function(times, func) {
if (times <= 0) return func();
return function() {
if (--times < 1) { return func.apply(this, arguments); }
};
};
// Object Functions
// ----------------
// Retrieve the names of an object's properties.
// Delegates to **ECMAScript 5**'s native `Object.keys`
_.keys = nativeKeys || function(obj) {
if (obj !== Object(obj)) throw new TypeError('Invalid object');
var keys = [];
for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
return keys;
};
// Retrieve the values of an object's properties.
_.values = function(obj) {
return _.map(obj, _.identity);
};
// Return a sorted list of the function names available on the object.
// Aliased as `methods`
_.functions = _.methods = function(obj) {
var names = [];
for (var key in obj) {
if (_.isFunction(obj[key])) names.push(key);
}
return names.sort();
};
// Extend a given object with all the properties in passed-in object(s).
_.extend = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
obj[prop] = source[prop];
}
});
return obj;
};
// Fill in a given object with default properties.
_.defaults = function(obj) {
each(slice.call(arguments, 1), function(source) {
for (var prop in source) {
if (obj[prop] == null) obj[prop] = source[prop];
}
});
return obj;
};
// Create a (shallow-cloned) duplicate of an object.
_.clone = function(obj) {
if (!_.isObject(obj)) return obj;
return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
};
// Invokes interceptor with the obj, and then returns obj.
// The primary purpose of this method is to "tap into" a method chain, in
// order to perform operations on intermediate results within the chain.
_.tap = function(obj, interceptor) {
interceptor(obj);
return obj;
};
// Internal recursive comparison function.
function eq(a, b, stack) {
// Identical objects are equal. `0 === -0`, but they aren't identical.
// See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
if (a === b) return a !== 0 || 1 / a == 1 / b;
// A strict comparison is necessary because `null == undefined`.
if (a == null || b == null) return a === b;
// Unwrap any wrapped objects.
if (a._chain) a = a._wrapped;
if (b._chain) b = b._wrapped;
// Invoke a custom `isEqual` method if one is provided.
if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
// Compare `[[Class]]` names.
var className = toString.call(a);
if (className != toString.call(b)) return false;
switch (className) {
// Strings, numbers, dates, and booleans are compared by value.
case '[object String]':
// Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
// equivalent to `new String("5")`.
return a == String(b);
case '[object Number]':
// `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
// other numeric values.
return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
case '[object Date]':
case '[object Boolean]':
// Coerce dates and booleans to numeric primitive values. Dates are compared by their
// millisecond representations. Note that invalid dates with millisecond representations
// of `NaN` are not equivalent.
return +a == +b;
// RegExps are compared by their source patterns and flags.
case '[object RegExp]':
return a.source == b.source &&
a.global == b.global &&
a.multiline == b.multiline &&
a.ignoreCase == b.ignoreCase;
}
if (typeof a != 'object' || typeof b != 'object') return false;
// Assume equality for cyclic structures. The algorithm for detecting cyclic
// structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
var length = stack.length;
while (length--) {
// Linear search. Performance is inversely proportional to the number of
// unique nested structures.
if (stack[length] == a) return true;
}
// Add the first object to the stack of traversed objects.
stack.push(a);
var size = 0, result = true;
// Recursively compare objects and arrays.
if (className == '[object Array]') {
// Compare array lengths to determine if a deep comparison is necessary.
size = a.length;
result = size == b.length;
if (result) {
// Deep compare the contents, ignoring non-numeric properties.
while (size--) {
// Ensure commutative equality for sparse arrays.
if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
}
}
} else {
// Objects with different constructors are not equivalent.
if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
// Deep compare objects.
for (var key in a) {
if (_.has(a, key)) {
// Count the expected number of properties.
size++;
// Deep compare each member.
if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
}
}
// Ensure that both objects contain the same number of properties.
if (result) {
for (key in b) {
if (_.has(b, key) && !(size--)) break;
}
result = !size;
}
}
// Remove the first object from the stack of traversed objects.
stack.pop();
return result;
}
// Perform a deep comparison to check if two objects are equal.
_.isEqual = function(a, b) {
return eq(a, b, []);
};
// Is a given array, string, or object empty?
// An "empty" object has no enumerable own-properties.
_.isEmpty = function(obj) {
if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
for (var key in obj) if (_.has(obj, key)) return false;
return true;
};
// Is a given value a DOM element?
_.isElement = function(obj) {
return !!(obj && obj.nodeType == 1);
};
// Is a given value an array?
// Delegates to ECMA5's native Array.isArray
_.isArray = nativeIsArray || function(obj) {
return toString.call(obj) == '[object Array]';
};
// Is a given variable an object?
_.isObject = function(obj) {
return obj === Object(obj);
};
// Is a given variable an arguments object?
_.isArguments = function(obj) {
return toString.call(obj) == '[object Arguments]';
};
if (!_.isArguments(arguments)) {
_.isArguments = function(obj) {
return !!(obj && _.has(obj, 'callee'));
};
}
// Is a given value a function?
_.isFunction = function(obj) {
return toString.call(obj) == '[object Function]';
};
// Is a given value a string?
_.isString = function(obj) {
return toString.call(obj) == '[object String]';
};
// Is a given value a number?
_.isNumber = function(obj) {
return toString.call(obj) == '[object Number]';
};
// Is the given value `NaN`?
_.isNaN = function(obj) {
// `NaN` is the only value for which `===` is not reflexive.
return obj !== obj;
};
// Is a given value a boolean?
_.isBoolean = function(obj) {
return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
};
// Is a given value a date?
_.isDate = function(obj) {
return toString.call(obj) == '[object Date]';
};
// Is the given value a regular expression?
_.isRegExp = function(obj) {
return toString.call(obj) == '[object RegExp]';
};
// Is a given value equal to null?
_.isNull = function(obj) {
return obj === null;
};
// Is a given variable undefined?
_.isUndefined = function(obj) {
return obj === void 0;
};
// Has own property?
_.has = function(obj, key) {
return hasOwnProperty.call(obj, key);
};
// Utility Functions
// -----------------
// Run Underscore.js in *noConflict* mode, returning the `_` variable to its
// previous owner. Returns a reference to the Underscore object.
_.noConflict = function() {
root._ = previousUnderscore;
return this;
};
// Keep the identity function around for default iterators.
_.identity = function(value) {
return value;
};
// Run a function **n** times.
_.times = function (n, iterator, context) {
for (var i = 0; i < n; i++) iterator.call(context, i);
};
// Escape a string for HTML interpolation.
_.escape = function(string) {
return (''+string).replace(/&/g, '&amp;').replace(/</g, '&lt;').replace(/>/g, '&gt;').replace(/"/g, '&quot;').replace(/'/g, '&#x27;').replace(/\//g,'&#x2F;');
};
// Add your own custom functions to the Underscore object, ensuring that
// they're correctly added to the OOP wrapper as well.
_.mixin = function(obj) {
each(_.functions(obj), function(name){
addToWrapper(name, _[name] = obj[name]);
});
};
// Generate a unique integer id (unique within the entire client session).
// Useful for temporary DOM ids.
var idCounter = 0;
_.uniqueId = function(prefix) {
var id = idCounter++;
return prefix ? prefix + id : id;
};
// By default, Underscore uses ERB-style template delimiters, change the
// following template settings to use alternative delimiters.
_.templateSettings = {
evaluate : /<%([\s\S]+?)%>/g,
interpolate : /<%=([\s\S]+?)%>/g,
escape : /<%-([\s\S]+?)%>/g
};
// When customizing `templateSettings`, if you don't want to define an
// interpolation, evaluation or escaping regex, we need one that is
// guaranteed not to match.
var noMatch = /.^/;
// Within an interpolation, evaluation, or escaping, remove HTML escaping
// that had been previously added.
var unescape = function(code) {
return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
};
// JavaScript micro-templating, similar to John Resig's implementation.
// Underscore templating handles arbitrary delimiters, preserves whitespace,
// and correctly escapes quotes within interpolated code.
_.template = function(str, data) {
var c = _.templateSettings;
var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
'with(obj||{}){__p.push(\'' +
str.replace(/\\/g, '\\\\')
.replace(/'/g, "\\'")
.replace(c.escape || noMatch, function(match, code) {
return "',_.escape(" + unescape(code) + "),'";
})
.replace(c.interpolate || noMatch, function(match, code) {
return "'," + unescape(code) + ",'";
})
.replace(c.evaluate || noMatch, function(match, code) {
return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
})
.replace(/\r/g, '\\r')
.replace(/\n/g, '\\n')
.replace(/\t/g, '\\t')
+ "');}return __p.join('');";
var func = new Function('obj', '_', tmpl);
if (data) return func(data, _);
return function(data) {
return func.call(this, data, _);
};
};
// Add a "chain" function, which will delegate to the wrapper.
_.chain = function(obj) {
return _(obj).chain();
};
// The OOP Wrapper
// ---------------
// If Underscore is called as a function, it returns a wrapped object that
// can be used OO-style. This wrapper holds altered versions of all the
// underscore functions. Wrapped objects may be chained.
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<div class="section" id="classes-estimators-package">
<h1>classes.estimators package<a class="headerlink" href="#classes-estimators-package" title="Permalink to this headline"></a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="module-classes.estimators.fam_score_calculator">
<span id="classes-estimators-fam-score-calculator-module"></span><h2>classes.estimators.fam_score_calculator module<a class="headerlink" href="#module-classes.estimators.fam_score_calculator" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator">
<em class="property">class </em><code class="sig-prename descclassname">classes.estimators.fam_score_calculator.</code><code class="sig-name descname">FamScoreCalculator</code><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Has the task of calculating the FamScore of a node by using a Bayesian score function</p>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score">
<code class="sig-name descname">get_fam_score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the FamScore value of the node</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li>
<li><p><strong>tau_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 1</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the FamScore value of the node</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q">
<code class="sig-name descname">marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the value of the marginal likelihood over q of the node identified by the label node_id</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li>
<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over q</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta">
<code class="sig-name descname">marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cims</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the FamScore value of the node identified by the label node_id</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cims</strong> (<em>np.array</em>) – np.array with all the node’s cims</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li>
<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over theta</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q">
<code class="sig-name descname">single_cim_xu_marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">M_xu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">T_xu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the marginal likelihood on q of the node when assumes a specif value
and a specif parents’s assignment</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>M_xu_suff_stats</strong> – value of the suffucient statistic M[x|u]</p></li>
<li><p><strong>T_xu_suff_stats</strong> (<em>float</em>) – value of the suffucient statistic T[x|u]</p></li>
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li>
<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood of the node when assumes a specif value</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta">
<code class="sig-name descname">single_cim_xu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">index</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the marginal likelihood on q of the node when assumes a specif value
and a specif parents’s assignment</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over theta when the node assumes a specif value</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta">
<code class="sig-name descname">single_internal_cim_xxu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">M_xxu_suff_stats</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the second part of the marginal likelihood over theta formula</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>M_xxu_suff_stats</strong> (<em>float</em>) – value of the suffucient statistic M[xx’<a href="#id1"><span class="problematic" id="id2">|</span></a>u]</p></li>
<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over theta when the node assumes a specif value</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q">
<code class="sig-name descname">variable_cim_xu_marginal_likelihood_q</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the value of the marginal likelihood over q given a cim</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li>
<li><p><strong>tau_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over q</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_theta">
<code class="sig-name descname">variable_cim_xu_marginal_likelihood_theta</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">alpha_xxu</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_theta" title="Permalink to this definition"></a></dt>
<dd><p>Calculate the value of the marginal likelihood over theta given a cim</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cim</strong> (<em>class:'ConditionalIntensityMatrix'</em>) – A conditional_intensity_matrix object with the sufficient statistics</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li>
<li><p><strong>alpha_xxu</strong> (<em>float</em>) – distribuited hyperparameter over the CTBN’s theta parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the value of the marginal likelihood over theta</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.estimators.parameters_estimator">
<span id="classes-estimators-parameters-estimator-module"></span><h2>classes.estimators.parameters_estimator module<a class="headerlink" href="#module-classes.estimators.parameters_estimator" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator">
<em class="property">class </em><code class="sig-prename descclassname">classes.estimators.parameters_estimator.</code><code class="sig-name descname">ParametersEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajectories</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory" title="classes.structure_graph.trajectory.Trajectory">classes.structure_graph.trajectory.Trajectory</a></span></em>, <em class="sig-param"><span class="n">net_graph</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph" title="classes.structure_graph.network_graph.NetworkGraph">classes.structure_graph.network_graph.NetworkGraph</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Has the task of computing the cims of particular node given the trajectories and the net structure
in the graph <code class="docutils literal notranslate"><span class="pre">_net_graph</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>trajectories</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory" title="classes.structure_graph.trajectory.Trajectory"><em>Trajectory</em></a>) – the trajectories</p></li>
<li><p><strong>net_graph</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph" title="classes.structure_graph.network_graph.NetworkGraph"><em>NetworkGraph</em></a>) – the net structure</p></li>
</ul>
</dd>
<dt class="field-even">_single_set_of_cims</dt>
<dd class="field-even"><p>the set of cims object that will hold the cims of the node</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters">
<code class="sig-name descname">compute_parameters</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node">
<code class="sig-name descname">compute_parameters_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims" title="classes.structure_graph.set_of_cims.SetOfCims">classes.structure_graph.set_of_cims.SetOfCims</a><a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node" title="Permalink to this definition"></a></dt>
<dd><p>Compute the CIMS of the node identified by the label <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A SetOfCims object filled with the computed CIMS</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims" title="classes.structure_graph.set_of_cims.SetOfCims">SetOfCims</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node">
<em class="property">static </em><code class="sig-name descname">compute_state_res_time_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexes_struct</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">T</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node" title="Permalink to this definition"></a></dt>
<dd><p>Compute the state residence times for a node and fill the matrix <code class="docutils literal notranslate"><span class="pre">T</span></code> with the results</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
<li><p><strong>times</strong> (<em>numpy.array</em>) – the times deltas vector</p></li>
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
<li><p><strong>scalar_indexes_struct</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
<li><p><strong>T</strong> (<em>numpy.ndArray</em>) – the state residence times vectors</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node">
<em class="property">static </em><code class="sig-name descname">compute_state_transitions_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexing</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">M</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Compute the state residence times for a node and fill the matrices <code class="docutils literal notranslate"><span class="pre">M</span></code> with the results.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
<li><p><strong>scalar_indexing</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
<li><p><strong>M</strong> (<em>numpy.ndArray</em>) – the state transitions matrices</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.fast_init">
<code class="sig-name descname">fast_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.fast_init" title="Permalink to this definition"></a></dt>
<dd><p>Initializes all the necessary structures for the parameters estimation for the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.parameters_estimator.ParametersEstimator.init_sets_cims_container">
<code class="sig-name descname">init_sets_cims_container</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.parameters_estimator.ParametersEstimator.init_sets_cims_container" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.estimators.structure_constraint_based_estimator">
<span id="classes-estimators-structure-constraint-based-estimator-module"></span><h2>classes.estimators.structure_constraint_based_estimator module<a class="headerlink" href="#module-classes.estimators.structure_constraint_based_estimator" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator">
<em class="property">class </em><code class="sig-prename descclassname">classes.estimators.structure_constraint_based_estimator.</code><code class="sig-name descname">StructureConstraintBasedEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath">classes.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">exp_test_alfa</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">chi_test_alfa</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">[]</span></em>, <em class="sig-param"><span class="n">thumb_threshold</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">25</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.estimators.structure_estimator.StructureEstimator</span></code></a></p>
<p>Has the task of estimating the network structure given the trajectories in samplepath by using a constraint-based approach.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sample_path</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li>
<li><p><strong>exp_test_alfa</strong> (<em>float</em>) – the significance level for the exponential Hp test</p></li>
<li><p><strong>chi_test_alfa</strong> (<em>float</em>) – the significance level for the chi Hp test</p></li>
</ul>
</dd>
<dt class="field-even">_nodes</dt>
<dd class="field-even"><p>the nodes labels</p>
</dd>
<dt class="field-odd">_nodes_vals</dt>
<dd class="field-odd"><p>the nodes cardinalities</p>
</dd>
<dt class="field-even">_nodes_indxs</dt>
<dd class="field-even"><p>the nodes indexes</p>
</dd>
<dt class="field-odd">_complete_graph</dt>
<dd class="field-odd"><p>the complete directed graph built using the nodes labels in <code class="docutils literal notranslate"><span class="pre">_nodes</span></code></p>
</dd>
<dt class="field-even">_cache</dt>
<dd class="field-even"><p>the Cache object</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test">
<code class="sig-name descname">complete_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">test_parent</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">test_child</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parent_set</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parent_indx</span></em>, <em class="sig-param"><span class="n">child_indx</span></em><span class="sig-paren">)</span> &#x2192; bool<a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test" title="Permalink to this definition"></a></dt>
<dd><p>Performs a complete independence test on the directed graphs G1 = {test_child U parent_set}
G2 = {G1 U test_parent} (added as an additional parent of the test_child).
Generates all the necessary structures and datas to perform the tests.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>test_parent</strong> (<em>string</em>) – the node label of the test parent</p></li>
<li><p><strong>test_child</strong> (<em>string</em>) – the node label of the child</p></li>
<li><p><strong>parent_set</strong> (<em>List</em>) – the common parent set</p></li>
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the <code class="docutils literal notranslate"><span class="pre">test_child</span></code></p></li>
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – the total number of variables in the net</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value">
<code class="sig-name descname">compute_thumb_value</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parent_val</span></em>, <em class="sig-param"><span class="n">child_val</span></em>, <em class="sig-param"><span class="n">parent_set_vals</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value" title="Permalink to this definition"></a></dt>
<dd><p>Compute the value to test against the thumb_threshold.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>parent_val</strong> (<em>int</em>) – test parent’s variable cardinality</p></li>
<li><p><strong>child_val</strong> (<em>int</em>) – test child’s variable cardinality</p></li>
<li><p><strong>parent_set_vals</strong> (<em>List</em>) – the cardinalities of the nodes in the current sep-set</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the thumb value for the current independence test</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm">
<code class="sig-name descname">ctpc_algorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm" title="Permalink to this definition"></a></dt>
<dd><p>Compute the CTPC algorithm over the entire net.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure">
<code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure" title="Permalink to this definition"></a></dt>
<dd><p>Abstract method to estimate the structure</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>List of estimated edges</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Typing.List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test">
<code class="sig-name descname">independence_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cim1</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">cim2</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">thumb_value</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">parent_indx</span></em>, <em class="sig-param"><span class="n">child_indx</span></em><span class="sig-paren">)</span> &#x2192; bool<a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test" title="Permalink to this definition"></a></dt>
<dd><p>Compute the actual independence test using two cims.
It is performed first the exponential test and if the null hypothesis is not rejected,
it is performed also the chi_test.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the test child</p></li>
<li><p><strong>cim1</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph without test parent</p></li>
<li><p><strong>cim2</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph with test parent</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True iff both tests do NOT reject the null hypothesis of independence. False otherwise.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>bool</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm">
<code class="sig-name descname">one_iteration_of_CTPC_algorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">var_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm" title="Permalink to this definition"></a></dt>
<dd><p>Performs an iteration of the CTPC algorithm using the node <code class="docutils literal notranslate"><span class="pre">var_id</span></code> as <code class="docutils literal notranslate"><span class="pre">test_child</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>var_id</strong> (<em>string</em>) – the node label of the test child</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.estimators.structure_estimator">
<span id="classes-estimators-structure-estimator-module"></span><h2>classes.estimators.structure_estimator module<a class="headerlink" href="#module-classes.estimators.structure_estimator" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.estimators.structure_estimator.StructureEstimator">
<em class="property">class </em><code class="sig-prename descclassname">classes.estimators.structure_estimator.</code><code class="sig-name descname">StructureEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath">classes.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Has the task of estimating the network structure given the trajectories in <code class="docutils literal notranslate"><span class="pre">samplepath</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>sample_path</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p>
</dd>
<dt class="field-even">_nodes</dt>
<dd class="field-even"><p>the nodes labels</p>
</dd>
<dt class="field-odd">_nodes_vals</dt>
<dd class="field-odd"><p>the nodes cardinalities</p>
</dd>
<dt class="field-even">_nodes_indxs</dt>
<dd class="field-even"><p>the nodes indexes</p>
</dd>
<dt class="field-odd">_complete_graph</dt>
<dd class="field-odd"><p>the complete directed graph built using the nodes labels in <code class="docutils literal notranslate"><span class="pre">_nodes</span></code></p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.adjacency_matrix">
<code class="sig-name descname">adjacency_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.adjacency_matrix" title="Permalink to this definition"></a></dt>
<dd><p>Converts the estimated structure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The adjacency matrix of the graph <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code></p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>numpy.ndArray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.build_complete_graph">
<em class="property">static </em><code class="sig-name descname">build_complete_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_ids</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; networkx.classes.digraph.DiGraph<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.build_complete_graph" title="Permalink to this definition"></a></dt>
<dd><p>Builds a complete directed graph (no self loops) given the nodes labels in the list <code class="docutils literal notranslate"><span class="pre">node_ids</span></code>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_ids</strong> (<em>List</em>) – the list of nodes labels</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a complete Digraph Object</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>networkx.DiGraph</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix">
<code class="sig-name descname">build_removable_edges_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix" title="Permalink to this definition"></a></dt>
<dd><p>Builds a boolean matrix who shows if a edge could be removed or not, based on prior knowledge given:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>known_edges</strong> (<em>List</em>) – the list of nodes labels</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a boolean matrix</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>np.ndarray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.estimate_structure">
<em class="property">abstract </em><code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.estimate_structure" title="Permalink to this definition"></a></dt>
<dd><p>Abstract method to estimate the structure</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>List of estimated edges</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Typing.List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size">
<em class="property">static </em><code class="sig-name descname">generate_possible_sub_sets_of_size</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">u</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">size</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parent_label</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size" title="Permalink to this definition"></a></dt>
<dd><p>Creates a list containing all possible subsets of the list <code class="docutils literal notranslate"><span class="pre">u</span></code> of size <code class="docutils literal notranslate"><span class="pre">size</span></code>,
that do not contains a the node identified by <code class="docutils literal notranslate"><span class="pre">parent_label</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>u</strong> (<em>List</em>) – the list of nodes</p></li>
<li><p><strong>size</strong> (<em>int</em>) – the size of the subsets</p></li>
<li><p><strong>parent_label</strong> (<em>string</em>) – the node to exclude in the subsets generation</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an Iterator Object containing a list of lists</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Iterator</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.remove_diagonal_elements">
<code class="sig-name descname">remove_diagonal_elements</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">matrix</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.remove_diagonal_elements" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">
<code class="sig-name descname">save_plot_estimated_structure_graph</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph" title="Permalink to this definition"></a></dt>
<dd><p>Plot the estimated structure in a graphical model style.
Spurious edges are colored in red.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.save_results">
<code class="sig-name descname">save_results</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.save_results" title="Permalink to this definition"></a></dt>
<dd><p>Save the estimated Structure to a .json file in the path where the data are loaded from.
The file is named as the input dataset but the <cite>results_</cite> word is appended to the results file.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_estimator.StructureEstimator.spurious_edges">
<code class="sig-name descname">spurious_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.estimators.structure_estimator.StructureEstimator.spurious_edges" title="Permalink to this definition"></a></dt>
<dd><dl class="simple">
<dt>Return the spurious edges present in the estimated structure, if a prior net structure is present in</dt><dd><p><code class="docutils literal notranslate"><span class="pre">_sample_path.structure</span></code>.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>A list containing the spurious edges</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.estimators.structure_score_based_estimator">
<span id="classes-estimators-structure-score-based-estimator-module"></span><h2>classes.estimators.structure_score_based_estimator module<a class="headerlink" href="#module-classes.estimators.structure_score_based_estimator" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator">
<em class="property">class </em><code class="sig-prename descclassname">classes.estimators.structure_score_based_estimator.</code><code class="sig-name descname">StructureScoreBasedEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath">classes.structure_graph.sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">tau_xu</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">0.1</span></em>, <em class="sig-param"><span class="n">alpha_xu</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">1</span></em>, <em class="sig-param"><span class="n">known_edges</span><span class="p">:</span> <span class="n">List</span> <span class="o">=</span> <span class="default_value">[]</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.estimators.structure_estimator.StructureEstimator</span></code></a></p>
<p>Has the task of estimating the network structure given the trajectories in samplepath by
using a score based approach.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sample_path</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath" title="classes.structure_graph.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li>
<li><p><strong>tau_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 0.1</p></li>
<li><p><strong>alpha_xu</strong> (<em>float</em><em>, </em><em>optional</em>) – hyperparameter over the CTBN’s q parameters, default to 1</p></li>
<li><p><strong>known_edges</strong> (<em>List</em><em>, </em><em>optional</em>) – List of known edges, default to []</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents">
<code class="sig-name descname">estimate_parents</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">10</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">5</span></em>, <em class="sig-param"><span class="n">optimizer</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'hill'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents" title="Permalink to this definition"></a></dt>
<dd><p>Use the FamScore of a node in order to find the best parent nodes</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li>
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li>
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li>
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li>
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li>
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li>
<li><p><strong>optimizer</strong> (<em>string</em><em>, </em><em>optional</em>) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of the best edges for the currente node</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure">
<code class="sig-name descname">estimate_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">optimizer</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">'tabu'</span></em>, <em class="sig-param"><span class="n">disable_multiprocessing</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure" title="Permalink to this definition"></a></dt>
<dd><p>Compute the score-based algorithm to find the optimal structure</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li>
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li>
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li>
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li>
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li>
<li><p><strong>optimizer</strong> (<em>string</em><em>, </em><em>optional</em>) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’</p></li>
<li><p><strong>disable_multiprocessing</strong> (<em>Boolean</em><em>, </em><em>optional</em>) – true if you desire to disable the multiprocessing operations, default to False</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph">
<code class="sig-name descname">get_score_from_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph" title="classes.structure_graph.network_graph.NetworkGraph">classes.structure_graph.network_graph.NetworkGraph</a></span></em>, <em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph" title="Permalink to this definition"></a></dt>
<dd><p>Get the FamScore of a node</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li>
<li><p><strong>graph</strong> (<em>class:'NetworkGraph'</em>) – current graph to be computed</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The FamSCore for this graph structure</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
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<div class="section" id="classes-optimizers-package">
<h1>classes.optimizers package<a class="headerlink" href="#classes-optimizers-package" title="Permalink to this headline"></a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
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<div class="section" id="module-classes.optimizers.constraint_based_optimizer">
<span id="classes-optimizers-constraint-based-optimizer-module"></span><h2>classes.optimizers.constraint_based_optimizer module<a class="headerlink" href="#module-classes.optimizers.constraint_based_optimizer" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer">
<em class="property">class </em><code class="sig-prename descclassname">classes.optimizers.constraint_based_optimizer.</code><code class="sig-name descname">ConstraintBasedOptimizer</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator">classes.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.optimizers.optimizer.Optimizer" title="classes.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.optimizers.optimizer.Optimizer</span></code></a></p>
<p>Optimizer class that implement a CTPC Algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li>
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li>
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – number of variables in the dataset</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure">
<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure" title="Permalink to this definition"></a></dt>
<dd><p>Compute Optimization process for a structure_estimator by using a CTPC Algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the estimated structure for the node</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.optimizers.hill_climbing_search">
<span id="classes-optimizers-hill-climbing-search-module"></span><h2>classes.optimizers.hill_climbing_search module<a class="headerlink" href="#module-classes.optimizers.hill_climbing_search" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.optimizers.hill_climbing_search.HillClimbing">
<em class="property">class </em><code class="sig-prename descclassname">classes.optimizers.hill_climbing_search.</code><code class="sig-name descname">HillClimbing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator">classes.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.optimizers.hill_climbing_search.HillClimbing" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.optimizers.optimizer.Optimizer" title="classes.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.optimizers.optimizer.Optimizer</span></code></a></p>
<p>Optimizer class that implement Hill Climbing Search</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li>
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li>
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li>
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li>
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.optimizers.hill_climbing_search.HillClimbing.optimize_structure">
<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.optimizers.hill_climbing_search.HillClimbing.optimize_structure" title="Permalink to this definition"></a></dt>
<dd><p>Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the estimated structure for the node</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.optimizers.optimizer">
<span id="classes-optimizers-optimizer-module"></span><h2>classes.optimizers.optimizer module<a class="headerlink" href="#module-classes.optimizers.optimizer" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.optimizers.optimizer.Optimizer">
<em class="property">class </em><code class="sig-prename descclassname">classes.optimizers.optimizer.</code><code class="sig-name descname">Optimizer</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator">classes.estimators.structure_estimator.StructureEstimator</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.optimizers.optimizer.Optimizer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
<p>Interface class for all the optimizer’s child classes</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – the node label</p></li>
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – A structureEstimator Object to predict the structure</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.optimizers.optimizer.Optimizer.optimize_structure">
<em class="property">abstract </em><code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.optimizers.optimizer.Optimizer.optimize_structure" title="Permalink to this definition"></a></dt>
<dd><p>Compute Optimization process for a structure_estimator</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the estimated structure for the node</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.optimizers.tabu_search">
<span id="classes-optimizers-tabu-search-module"></span><h2>classes.optimizers.tabu_search module<a class="headerlink" href="#module-classes.optimizers.tabu_search" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.optimizers.tabu_search.TabuSearch">
<em class="property">class </em><code class="sig-prename descclassname">classes.optimizers.tabu_search.</code><code class="sig-name descname">TabuSearch</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_estimator</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator" title="classes.estimators.structure_estimator.StructureEstimator">classes.estimators.structure_estimator.StructureEstimator</a></span></em>, <em class="sig-param"><span class="n">max_parents</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">iterations_number</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">40</span></em>, <em class="sig-param"><span class="n">patience</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_length</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tabu_rules_duration</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.optimizers.tabu_search.TabuSearch" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.optimizers.optimizer.Optimizer" title="classes.optimizers.optimizer.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.optimizers.optimizer.Optimizer</span></code></a></p>
<p>Optimizer class that implement Tabu Search</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> (<em>string</em>) – current node’s id</p></li>
<li><p><strong>structure_estimator</strong> (<em>class:'StructureEstimator'</em>) – a structure estimator object with the information about the net</p></li>
<li><p><strong>max_parents</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of parents for each variable. If None, disabled, default to None</p></li>
<li><p><strong>iterations_number</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum number of optimization algorithm’s iteration, default to 40</p></li>
<li><p><strong>patience</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iteration without any improvement before to stop the search.If None, disabled, default to None</p></li>
<li><p><strong>tabu_length</strong> (<em>int</em><em>, </em><em>optional</em>) – maximum lenght of the data structures used in the optimization process, default to None</p></li>
<li><p><strong>tabu_rules_duration</strong> (<em>int</em><em>, </em><em>optional</em>) – number of iterations in which each rule keeps its value, default to None</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.optimizers.tabu_search.TabuSearch.optimize_structure">
<code class="sig-name descname">optimize_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.optimizers.tabu_search.TabuSearch.optimize_structure" title="Permalink to this definition"></a></dt>
<dd><p>Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the estimated structure for the node</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.optimizers">
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<li class="toctree-l2"><a class="reference internal" href="examples.html#parameters-estimation-example">Parameters Estimation Example</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples.html#structure-estimation-example">Structure Estimation Example</a></li>
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<div class="section" id="classes-structure-graph-package">
<h1>classes.structure_graph package<a class="headerlink" href="#classes-structure-graph-package" title="Permalink to this headline"></a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="module-classes.structure_graph.abstract_sample_path">
<span id="classes-structure-graph-abstract-sample-path-module"></span><h2>classes.structure_graph.abstract_sample_path module<a class="headerlink" href="#module-classes.structure_graph.abstract_sample_path" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.abstract_sample_path.AbstractSamplePath">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.abstract_sample_path.</code><code class="sig-name descname">AbstractSamplePath</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">importer</span><span class="p">:</span> <span class="n">utility.abstract_importer.AbstractImporter</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.abstract_sample_path.AbstractSamplePath" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
<dl class="py method">
<dt id="classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_structure">
<em class="property">abstract </em><code class="sig-name descname">build_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_structure" title="Permalink to this definition"></a></dt>
<dd><p>Builds the Structure object that aggregates all the infos about the net.
Assigns the Structure object to the instance attribuite _structure
Parameters:</p>
<blockquote>
<div><p>void</p>
</div></blockquote>
<dl class="simple">
<dt>Returns:</dt><dd><p>void</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_trajectories">
<em class="property">abstract </em><code class="sig-name descname">build_trajectories</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_trajectories" title="Permalink to this definition"></a></dt>
<dd><p>Builds the Trajectory object that will contain all the trajectories.
Assigns the Trajectoriy object to the instance attribute _trajectories
Clears all the unused dataframes in Importer Object</p>
<dl class="simple">
<dt>Parameters:</dt><dd><p>void</p>
</dd>
<dt>Returns:</dt><dd><p>void</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.conditional_intensity_matrix">
<span id="classes-structure-graph-conditional-intensity-matrix-module"></span><h2>classes.structure_graph.conditional_intensity_matrix module<a class="headerlink" href="#module-classes.structure_graph.conditional_intensity_matrix" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.conditional_intensity_matrix.</code><code class="sig-name descname">ConditionalIntensityMatrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_residence_times</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">state_transition_matrix</span><span class="p">:</span> <span class="n">numpy.array</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector
and state transition matrix and the actual CIM matrix.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_residence_times</strong> (<em>numpy.array</em>) – state residence times vector</p></li>
<li><p><strong>state_transition_matrix</strong> (<em>numpy.ndArray</em>) – the transitions count matrix</p></li>
</ul>
</dd>
<dt class="field-even">_cim</dt>
<dd class="field-even"><p>the actual cim of the node</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim">
<em class="property">property </em><code class="sig-name descname">cim</code><a class="headerlink" href="#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients">
<code class="sig-name descname">compute_cim_coefficients</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients" title="Permalink to this definition"></a></dt>
<dd><p>Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x].
The class member <code class="docutils literal notranslate"><span class="pre">_cim</span></code> will contain the computed cim</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times">
<em class="property">property </em><code class="sig-name descname">state_residence_times</code><a class="headerlink" href="#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix">
<em class="property">property </em><code class="sig-name descname">state_transition_matrix</code><a class="headerlink" href="#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.network_graph">
<span id="classes-structure-graph-network-graph-module"></span><h2>classes.structure_graph.network_graph module<a class="headerlink" href="#module-classes.structure_graph.network_graph" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.network_graph.NetworkGraph">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.network_graph.</code><code class="sig-name descname">NetworkGraph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph_struct</span><span class="p">:</span> <span class="n"><a class="reference internal" href="#classes.structure_graph.structure.Structure" title="classes.structure_graph.structure.Structure">classes.structure_graph.structure.Structure</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Abstracts the infos contained in the Structure class in the form of a directed graph.
Has the task of creating all the necessary filtering and indexing structures for parameters estimation</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>graph_struct</strong> (<a class="reference internal" href="#classes.structure_graph.structure.Structure" title="classes.structure_graph.structure.Structure"><em>Structure</em></a>) – the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object from which infos about the net will be extracted</p>
</dd>
<dt class="field-even">_graph</dt>
<dd class="field-even"><p>directed graph</p>
</dd>
<dt class="field-odd">_aggregated_info_about_nodes_parents</dt>
<dd class="field-odd"><p>a structure that contains all the necessary infos
about every parents of the node of which all the indexing and filtering structures will be constructed.</p>
</dd>
<dt class="field-even">_time_scalar_indexing_structure</dt>
<dd class="field-even"><p>the indexing structure for state res time estimation</p>
</dd>
<dt class="field-odd">_transition_scalar_indexing_structure</dt>
<dd class="field-odd"><p>the indexing structure for transition computation</p>
</dd>
<dt class="field-even">_time_filtering</dt>
<dd class="field-even"><p>the columns filtering structure used in the computation of the state res times</p>
</dd>
<dt class="field-odd">_transition_filtering</dt>
<dd class="field-odd"><p>the columns filtering structure used in the computation of the transition
from one state to another</p>
</dd>
<dt class="field-even">_p_combs_structure</dt>
<dd class="field-even"><p>all the possible parents states combination for the node of interest</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.add_edges">
<code class="sig-name descname">add_edges</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.add_edges" title="Permalink to this definition"></a></dt>
<dd><p>Add the edges to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list <code class="docutils literal notranslate"><span class="pre">list_of_edges</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – the list containing of tuples containing the edges</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.add_nodes">
<code class="sig-name descname">add_nodes</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_nodes</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.add_nodes" title="Permalink to this definition"></a></dt>
<dd><p>Adds the nodes to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list of nodes <code class="docutils literal notranslate"><span class="pre">list_of_nodes</span></code>.
Sets all the properties that identify a nodes (index, positional index, cardinality)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>list_of_nodes</strong> (<em>List</em>) – the nodes to add to <code class="docutils literal notranslate"><span class="pre">_graph</span></code></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node">
<em class="property">static </em><code class="sig-name descname">build_p_comb_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_values</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Builds the combinatorial structure that contains the combinations of all the values contained in
<code class="docutils literal notranslate"><span class="pre">parents_values</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parents_values</strong> (<em>List</em>) – the cardinalities of the nodes</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A numpy matrix containing a grid of the combinations</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>numpy.ndArray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node">
<em class="property">static </em><code class="sig-name descname">build_time_columns_filtering_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_indxs</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code>
in the dataset.
This structute will be used in the computation of the state res times.
:param node_indx: the index of the node
:type node_indx: int
:param p_indxs: the indexes of the node’s parents
:type p_indxs: List
:return: The filtering structure for times estimation
:rtype: numpy.ndArray</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node">
<em class="property">static </em><code class="sig-name descname">build_time_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_states</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Builds an indexing structure for the computation of state residence times values.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_states</strong> (<em>int</em>) – the node cardinality</p></li>
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The time indexing structure</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>numpy.ndArray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node">
<em class="property">static </em><code class="sig-name descname">build_transition_filtering_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_indxs</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">nodes_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code>
in the dataset.
This structure will be used in the computation of the state transitions values.
:param node_indx: the index of the node
:type node_indx: int
:param p_indxs: the indexes of the node’s parents
:type p_indxs: List
:param nodes_number: the total number of nodes in the dataset
:type nodes_number: int
:return: The filtering structure for transitions estimation
:rtype: numpy.ndArray</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node">
<em class="property">static </em><code class="sig-name descname">build_transition_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_states_number</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node" title="Permalink to this definition"></a></dt>
<dd><p>Builds an indexing structure for the computation of state transitions values.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_states_number</strong> (<em>int</em>) – the node cardinality</p></li>
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The transition indexing structure</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>numpy.ndArray</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures">
<code class="sig-name descname">clear_indexing_filtering_structures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures" title="Permalink to this definition"></a></dt>
<dd><p>Initialize all the filtering/indexing structures.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.edges">
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.edges" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.fast_init">
<code class="sig-name descname">fast_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.fast_init" title="Permalink to this definition"></a></dt>
<dd><p>Initializes all the necessary structures for parameters estimation of the node identified by the label
node_id</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the label of the node</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.get_node_indx">
<code class="sig-name descname">get_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.get_node_indx" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents">
<code class="sig-name descname">get_ordered_by_indx_set_of_parents</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; Tuple<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents" title="Permalink to this definition"></a></dt>
<dd><p>Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
(parents_labels, parents_indexes, parents_cardinalities).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the label of the node</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a tuple containing all the parent set infos</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Tuple</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.get_parents_by_id">
<code class="sig-name descname">get_parents_by_id</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.get_parents_by_id" title="Permalink to this definition"></a></dt>
<dd><p>Returns a list of labels of the parents of the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a List of labels of the parents</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.get_positional_node_indx">
<code class="sig-name descname">get_positional_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.get_positional_node_indx" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.get_states_number">
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.get_states_number" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.has_edge">
<code class="sig-name descname">has_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span> &#x2192; bool<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.has_edge" title="Permalink to this definition"></a></dt>
<dd><p>Check if the graph contains a specific edge</p>
<dl class="simple">
<dt>Parameters:</dt><dd><p>edge: a tuple that rappresents the edge</p>
</dd>
<dt>Returns:</dt><dd><p>bool</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.init_graph">
<code class="sig-name descname">init_graph</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.init_graph" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.nodes">
<em class="property">property </em><code class="sig-name descname">nodes</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.nodes" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.nodes_indexes">
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.nodes_indexes" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.nodes_values">
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.nodes_values" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.p_combs">
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.p_combs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.remove_edges">
<code class="sig-name descname">remove_edges</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.remove_edges" title="Permalink to this definition"></a></dt>
<dd><p>Remove the edges to the graph contained in the list list_of_edges.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – The edges to remove from the graph</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.remove_node">
<code class="sig-name descname">remove_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.remove_node" title="Permalink to this definition"></a></dt>
<dd><p>Remove the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code> from all the class members.
Initialize all the filtering/indexing structures.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.time_filtering">
<em class="property">property </em><code class="sig-name descname">time_filtering</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.time_filtering" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure">
<em class="property">property </em><code class="sig-name descname">time_scalar_indexing_strucure</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.transition_filtering">
<em class="property">property </em><code class="sig-name descname">transition_filtering</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.transition_filtering" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure">
<em class="property">property </em><code class="sig-name descname">transition_scalar_indexing_structure</code><a class="headerlink" href="#classes.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.sample_path">
<span id="classes-structure-graph-sample-path-module"></span><h2>classes.structure_graph.sample_path module<a class="headerlink" href="#module-classes.structure_graph.sample_path" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.sample_path.SamplePath">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.sample_path.</code><code class="sig-name descname">SamplePath</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">importer</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter" title="classes.utility.abstract_importer.AbstractImporter">classes.utility.abstract_importer.AbstractImporter</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
cardinalites. Has the task of creating the objects <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> and <code class="docutils literal notranslate"><span class="pre">Structure</span></code> that will
contain the mentioned data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>importer</strong> (<a class="reference internal" href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter" title="classes.utility.abstract_importer.AbstractImporter"><em>AbstractImporter</em></a>) – the Importer object which contains the imported and processed data</p>
</dd>
<dt class="field-even">_trajectories</dt>
<dd class="field-even"><p>the <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> object that will contain all the concatenated trajectories</p>
</dd>
<dt class="field-odd">_structure</dt>
<dd class="field-odd"><p>the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> Object that will contain all the structural infos about the net</p>
</dd>
<dt class="field-even">_total_variables_count</dt>
<dd class="field-even"><p>the number of variables in the net</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.build_structure">
<code class="sig-name descname">build_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.build_structure" title="Permalink to this definition"></a></dt>
<dd><p>Builds the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object that aggregates all the infos about the net.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.build_trajectories">
<code class="sig-name descname">build_trajectories</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.build_trajectories" title="Permalink to this definition"></a></dt>
<dd><p>Builds the Trajectory object that will contain all the trajectories.
Clears all the unused dataframes in <code class="docutils literal notranslate"><span class="pre">_importer</span></code> Object</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.clear_memory">
<code class="sig-name descname">clear_memory</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.clear_memory" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.has_prior_net_structure">
<em class="property">property </em><code class="sig-name descname">has_prior_net_structure</code><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.has_prior_net_structure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.structure">
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.structure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.total_variables_count">
<em class="property">property </em><code class="sig-name descname">total_variables_count</code><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.total_variables_count" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sample_path.SamplePath.trajectories">
<em class="property">property </em><code class="sig-name descname">trajectories</code><a class="headerlink" href="#classes.structure_graph.sample_path.SamplePath.trajectories" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.set_of_cims">
<span id="classes-structure-graph-set-of-cims-module"></span><h2>classes.structure_graph.set_of_cims module<a class="headerlink" href="#module-classes.structure_graph.set_of_cims" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.set_of_cims.SetOfCims">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.set_of_cims.</code><code class="sig-name descname">SetOfCims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parents_states_number</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">node_states_number</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_combs</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Aggregates all the CIMS of the node identified by the label _node_id.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>node_id</strong> – the node label</p></li>
<li><p><strong>parents_states_number</strong> (<em>List</em>) – the cardinalities of the parents</p></li>
<li><p><strong>node_states_number</strong> (<em>int</em>) – the caridinality of the node</p></li>
<li><p><strong>p_combs</strong> (<em>numpy.ndArray</em>) – the p_comb structure bound to this node</p></li>
</ul>
</dd>
<dt class="field-even">_state_residence_time</dt>
<dd class="field-even"><p>matrix containing all the state residence time vectors for the node</p>
</dd>
<dt class="field-odd">_transition_matrices</dt>
<dd class="field-odd"><p>matrix containing all the transition matrices for the node</p>
</dd>
<dt class="field-even">_actual_cims</dt>
<dd class="field-even"><p>the cims of the node</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.actual_cims">
<em class="property">property </em><code class="sig-name descname">actual_cims</code><a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.actual_cims" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.build_cims">
<code class="sig-name descname">build_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_res_times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">transition_matrices</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.build_cims" title="Permalink to this definition"></a></dt>
<dd><p>Build the <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects given the state residence times and transitions matrices.
Compute the cim coefficients.The class member <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> will contain the computed cims.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_res_times</strong> (<em>numpy.ndArray</em>) – the state residence times matrix</p></li>
<li><p><strong>transition_matrices</strong> (<em>numpy.ndArray</em>) – the transition matrices</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures">
<code class="sig-name descname">build_times_and_transitions_structures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures" title="Permalink to this definition"></a></dt>
<dd><p>Initializes at the correct dimensions the state residence times matrix and the state transition matrices.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask">
<code class="sig-name descname">filter_cims_with_mask</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mask_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">comb</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask" title="Permalink to this definition"></a></dt>
<dd><p>Filter the cims contained in the array <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> given the boolean mask <code class="docutils literal notranslate"><span class="pre">mask_arr</span></code> and the index
<code class="docutils literal notranslate"><span class="pre">comb</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mask_arr</strong> (<em>numpy.array</em>) – the boolean mask that indicates which parent to consider</p></li>
<li><p><strong>comb</strong> (<em>numpy.array</em>) – the state/s of the filtered parents</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Array of <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>numpy.array</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.get_cims_number">
<code class="sig-name descname">get_cims_number</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.get_cims_number" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.set_of_cims.SetOfCims.p_combs">
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#classes.structure_graph.set_of_cims.SetOfCims.p_combs" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.sets_of_cims_container">
<span id="classes-structure-graph-sets-of-cims-container-module"></span><h2>classes.structure_graph.sets_of_cims_container module<a class="headerlink" href="#module-classes.structure_graph.sets_of_cims_container" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.sets_of_cims_container.</code><code class="sig-name descname">SetsOfCimsContainer</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_keys</span></em>, <em class="sig-param"><span class="n">states_number_per_node</span></em>, <em class="sig-param"><span class="n">list_of_parents_states_number</span></em>, <em class="sig-param"><span class="n">p_combs_list</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Aggrega un insieme di oggetti SetOfCims</p>
<dl class="py method">
<dt id="classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_cims_of_node">
<code class="sig-name descname">get_cims_of_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span></em>, <em class="sig-param"><span class="n">cim_indx</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_cims_of_node" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_set_of_cims">
<code class="sig-name descname">get_set_of_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_set_of_cims" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.init_cims_structure">
<code class="sig-name descname">init_cims_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">keys</span></em>, <em class="sig-param"><span class="n">states_number_per_node</span></em>, <em class="sig-param"><span class="n">list_of_parents_states_number</span></em>, <em class="sig-param"><span class="n">p_combs_list</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.init_cims_structure" title="Permalink to this definition"></a></dt>
<dd><p>for indx, key in enumerate(keys):
self.sets_of_cims.append(</p>
<blockquote>
<div><p>socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))</p>
</div></blockquote>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.structure">
<span id="classes-structure-graph-structure-module"></span><h2>classes.structure_graph.structure module<a class="headerlink" href="#module-classes.structure_graph.structure" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.structure.Structure">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.structure.</code><code class="sig-name descname">Structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nodes_labels_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">nodes_indexes_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">nodes_vals_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">edges_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">total_variables_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.structure.Structure" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nodes_labels_list</strong> (<em>List</em>) – the symbolic names of the variables</p></li>
<li><p><strong>nodes_indexes_arr</strong> (<em>numpy.ndArray</em>) – the indexes of the nodes</p></li>
<li><p><strong>nodes_vals_arr</strong> (<em>numpy.ndArray</em>) – the cardinalites of the nodes</p></li>
<li><p><strong>edges_list</strong> (<em>List</em>) – the edges of the network</p></li>
<li><p><strong>total_variables_number</strong> (<em>int</em>) – the total number of variables in the dataset</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.add_edge">
<code class="sig-name descname">add_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.structure.Structure.add_edge" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.clean_structure_edges">
<code class="sig-name descname">clean_structure_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.structure.Structure.clean_structure_edges" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.contains_edge">
<code class="sig-name descname">contains_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span> &#x2192; bool<a class="headerlink" href="#classes.structure_graph.structure.Structure.contains_edge" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.edges">
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#classes.structure_graph.structure.Structure.edges" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.get_node_id">
<code class="sig-name descname">get_node_id</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> &#x2192; str<a class="headerlink" href="#classes.structure_graph.structure.Structure.get_node_id" title="Permalink to this definition"></a></dt>
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_indx</strong> (<em>int</em>) – the node index</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the node label</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>string</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.get_node_indx">
<code class="sig-name descname">get_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.structure.Structure.get_node_indx" title="Permalink to this definition"></a></dt>
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the node index</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.get_positional_node_indx">
<code class="sig-name descname">get_positional_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.structure.Structure.get_positional_node_indx" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.get_states_number">
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; int<a class="headerlink" href="#classes.structure_graph.structure.Structure.get_states_number" title="Permalink to this definition"></a></dt>
<dd><p>Given the node label <code class="docutils literal notranslate"><span class="pre">node</span></code> returns the cardinality of the node.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the node label</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the node cardinality</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>int</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.nodes_indexes">
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#classes.structure_graph.structure.Structure.nodes_indexes" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.nodes_labels">
<em class="property">property </em><code class="sig-name descname">nodes_labels</code><a class="headerlink" href="#classes.structure_graph.structure.Structure.nodes_labels" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.nodes_values">
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#classes.structure_graph.structure.Structure.nodes_values" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.remove_edge">
<code class="sig-name descname">remove_edge</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">edge</span><span class="p">:</span> <span class="n">tuple</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.structure.Structure.remove_edge" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.remove_node">
<code class="sig-name descname">remove_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_graph.structure.Structure.remove_node" title="Permalink to this definition"></a></dt>
<dd><p>Remove the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code> from all the class members.
The class member <code class="docutils literal notranslate"><span class="pre">_total_variables_number</span></code> since it refers to the total number of variables in the dataset.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.structure.Structure.total_variables_number">
<em class="property">property </em><code class="sig-name descname">total_variables_number</code><a class="headerlink" href="#classes.structure_graph.structure.Structure.total_variables_number" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph.trajectory">
<span id="classes-structure-graph-trajectory-module"></span><h2>classes.structure_graph.trajectory module<a class="headerlink" href="#module-classes.structure_graph.trajectory" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.structure_graph.trajectory.Trajectory">
<em class="property">class </em><code class="sig-prename descclassname">classes.structure_graph.trajectory.</code><code class="sig-name descname">Trajectory</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_columns</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">original_cols_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.trajectory.Trajectory" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles
(the time deltas) and a numpy matrix of ints (the changes of states).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>list_of_columns</strong> (<em>List</em>) – the list containing the times array and values matrix</p></li>
<li><p><strong>original_cols_number</strong> (<em>int</em>) – total number of cols in the data</p></li>
</ul>
</dd>
<dt class="field-even">_actual_trajectory</dt>
<dd class="field-even"><p>the trajectory containing also the duplicated/shifted values</p>
</dd>
<dt class="field-odd">_times</dt>
<dd class="field-odd"><p>the array containing the time deltas</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.structure_graph.trajectory.Trajectory.complete_trajectory">
<em class="property">property </em><code class="sig-name descname">complete_trajectory</code><a class="headerlink" href="#classes.structure_graph.trajectory.Trajectory.complete_trajectory" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.trajectory.Trajectory.size">
<code class="sig-name descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.structure_graph.trajectory.Trajectory.size" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.trajectory.Trajectory.times">
<em class="property">property </em><code class="sig-name descname">times</code><a class="headerlink" href="#classes.structure_graph.trajectory.Trajectory.times" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.structure_graph.trajectory.Trajectory.trajectory">
<em class="property">property </em><code class="sig-name descname">trajectory</code><a class="headerlink" href="#classes.structure_graph.trajectory.Trajectory.trajectory" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.structure_graph">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-classes.structure_graph" title="Permalink to this headline"></a></h2>
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<div class="section" id="classes-utility-package">
<h1>classes.utility package<a class="headerlink" href="#classes-utility-package" title="Permalink to this headline"></a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
</div>
<div class="section" id="module-classes.utility.abstract_importer">
<span id="classes-utility-abstract-importer-module"></span><h2>classes.utility.abstract_importer module<a class="headerlink" href="#module-classes.utility.abstract_importer" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.utility.abstract_importer.AbstractImporter">
<em class="property">class </em><code class="sig-prename descclassname">classes.utility.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">trajectory_list</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
<p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file_path</strong> (<em>str</em>) – the file path, or dataset name if you import already processed data</p></li>
<li><p><strong>trajectory_list</strong> (<em>typing.Union</em><em>[</em><em>pandas.DataFrame</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – Dataframe or numpy array containing the concatenation of all the processed trajectories</p></li>
<li><p><strong>variables</strong> (<em>pandas.DataFrame</em>) – Dataframe containing the nodes labels and cardinalities</p></li>
</ul>
</dd>
<dt class="field-even">Prior_net_structure</dt>
<dd class="field-even"><p>Dataframe containing the structure of the network (edges)</p>
</dd>
<dt class="field-odd">_sorter</dt>
<dd class="field-odd"><p>A list containing the variables labels in the SAME order as the columns in <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code></p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The parameters <code class="docutils literal notranslate"><span class="pre">variables</span></code> and <code class="docutils literal notranslate"><span class="pre">prior_net_structure</span></code> HAVE to be properly constructed
as Pandas Dataframes with the following structure:
Header of _df_structure = [From_Node | To_Node]
Header of _df_variables = [Variable_Label | Variable_Cardinality]
See the tutorial on how to construct a correct <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code> Dataframe/ndarray.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>See :class:<code class="docutils literal notranslate"><span class="pre">JsonImporter</span></code> for an example implementation</p>
</div>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.build_list_of_samples_array">
<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">concatenated_sample</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition"></a></dt>
<dd><p>Builds a List containing the the delta times numpy array, and the complete transitions matrix</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>concatenated_sample</strong> (<em>pandas.Dataframe</em>) – the dataframe/array from which the time, and transitions matrix have to be extracted
and converted</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the resulting list of numpy arrays</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.build_sorter">
<em class="property">abstract </em><code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajecory_header</span><span class="p">:</span> <span class="n">object</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.build_sorter" title="Permalink to this definition"></a></dt>
<dd><p>Initializes the <code class="docutils literal notranslate"><span class="pre">_sorter</span></code> class member from a trajectory dataframe, exctracting the header of the frame
and keeping ONLY the variables symbolic labels, cutting out the time label in the header.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>trajecory_header</strong> (<em>object</em>) – an object that will be used to define the header</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list containing the processed header.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.clear_concatenated_frame">
<code class="sig-name descname">clear_concatenated_frame</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.clear_concatenated_frame" title="Permalink to this definition"></a></dt>
<dd><p>Removes all values in the dataframe concatenated_samples.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames">
<code class="sig-name descname">compute_row_delta_in_all_samples_frames</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df_samples_list</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames" title="Permalink to this definition"></a></dt>
<dd><p>Calls the method <code class="docutils literal notranslate"><span class="pre">compute_row_delta_sigle_samples_frame</span></code> on every dataframe present in the list
<code class="docutils literal notranslate"><span class="pre">df_samples_list</span></code>.
Concatenates the result in the dataframe <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>df_samples_list</strong> (<em>List</em>) – the datframe’s list to be processed and concatenated</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The Dataframe sample_frame has to follow the column structure of this header:
Header of sample_frame = [Time | Variable values]
The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>After the call of this method the class member <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code> will contain all processed
and merged trajectories</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame">
<code class="sig-name descname">compute_row_delta_sigle_samples_frame</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em>, <em class="sig-param"><span class="n">columns_header</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">shifted_cols_header</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; pandas.core.frame.DataFrame<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame" title="Permalink to this definition"></a></dt>
<dd><p>Computes the difference between each value present in th time column.
Copies and shift by one position up all the values present in the remaining columns.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sample_frame</strong> (<em>pandas.Dataframe</em>) – the traj to be processed</p></li>
<li><p><strong>columns_header</strong> (<em>List</em>) – the original header of sample_frame</p></li>
<li><p><strong>shifted_cols_header</strong> (<em>List</em>) – a copy of columns_header with changed names of the contents</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>The processed dataframe</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pandas.Dataframe</p>
</dd>
</dl>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>the Dataframe <code class="docutils literal notranslate"><span class="pre">sample_frame</span></code> has to follow the column structure of this header:
Header of sample_frame = [Time | Variable values]</p>
</div>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.concatenated_samples">
<em class="property">property </em><code class="sig-name descname">concatenated_samples</code><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.concatenated_samples" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.dataset_id">
<em class="property">abstract </em><code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; object<a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.dataset_id" title="Permalink to this definition"></a></dt>
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current
dataset</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.file_path">
<em class="property">property </em><code class="sig-name descname">file_path</code><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.file_path" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.sorter">
<em class="property">property </em><code class="sig-name descname">sorter</code><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.sorter" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.structure">
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.structure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt id="classes.utility.abstract_importer.AbstractImporter.variables">
<em class="property">property </em><code class="sig-name descname">variables</code><a class="headerlink" href="#classes.utility.abstract_importer.AbstractImporter.variables" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.utility.cache">
<span id="classes-utility-cache-module"></span><h2>classes.utility.cache module<a class="headerlink" href="#module-classes.utility.cache" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.utility.cache.Cache">
<em class="property">class </em><code class="sig-prename descclassname">classes.utility.cache.</code><code class="sig-name descname">Cache</code><a class="headerlink" href="#classes.utility.cache.Cache" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>This class acts as a cache of <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> objects for a node.</p>
<dl class="field-list simple">
<dt class="field-odd">_list_of_sets_of_parents</dt>
<dd class="field-odd"><p>a list of <code class="docutils literal notranslate"><span class="pre">Sets</span></code> objects of the parents to which the cim in cache at SAME
index is related</p>
</dd>
<dt class="field-even">_actual_cache</dt>
<dd class="field-even"><p>a list of setOfCims objects</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.utility.cache.Cache.clear">
<code class="sig-name descname">clear</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.cache.Cache.clear" title="Permalink to this definition"></a></dt>
<dd><p>Clear the contents both of <code class="docutils literal notranslate"><span class="pre">_actual_cache</span></code> and <code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.cache.Cache.find">
<code class="sig-name descname">find</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.cache.Cache.find" title="Permalink to this definition"></a></dt>
<dd><p>Tries to find in cache given the symbolic parents combination <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code>
related to that <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parents_comb</strong> (<em>Set</em>) – the parents related to that <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code></p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object if the <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> index is found in <code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>.
None otherwise.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims" title="classes.structure_graph.set_of_cims.SetOfCims">SetOfCims</a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.cache.Cache.put">
<code class="sig-name descname">put</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em>, <em class="sig-param"><span class="n">socim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims" title="classes.structure_graph.set_of_cims.SetOfCims">classes.structure_graph.set_of_cims.SetOfCims</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.cache.Cache.put" title="Permalink to this definition"></a></dt>
<dd><p>Place in cache the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object, and the related symbolic index <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> in
<code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>parents_comb</strong> (<em>Set</em>) – the symbolic set index</p></li>
<li><p><strong>socim</strong> (<a class="reference internal" href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims" title="classes.structure_graph.set_of_cims.SetOfCims"><em>SetOfCims</em></a>) – the related SetOfCims object</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.utility.decorators">
<span id="classes-utility-decorators-module"></span><h2>classes.utility.decorators module<a class="headerlink" href="#module-classes.utility.decorators" title="Permalink to this headline"></a></h2>
<dl class="py function">
<dt id="classes.utility.decorators.timing">
<code class="sig-prename descclassname">classes.utility.decorators.</code><code class="sig-name descname">timing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">f</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.decorators.timing" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py function">
<dt id="classes.utility.decorators.timing_write">
<code class="sig-prename descclassname">classes.utility.decorators.</code><code class="sig-name descname">timing_write</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">f</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.decorators.timing_write" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</div>
<div class="section" id="module-classes.utility.json_importer">
<span id="classes-utility-json-importer-module"></span><h2>classes.utility.json_importer module<a class="headerlink" href="#module-classes.utility.json_importer" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.utility.json_importer.JsonImporter">
<em class="property">class </em><code class="sig-prename descclassname">classes.utility.json_importer.</code><code class="sig-name descname">JsonImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">samples_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">time_key</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.json_importer.JsonImporter" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.utility.abstract_importer.AbstractImporter" title="classes.utility.abstract_importer.AbstractImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.utility.abstract_importer.AbstractImporter</span></code></a></p>
<p>Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
the data in json extension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>file_path</strong> (<em>string</em>) – the path of the file that contains tha data to be imported</p></li>
<li><p><strong>samples_label</strong> (<em>string</em>) – the reference key for the samples in the trajectories</p></li>
<li><p><strong>structure_label</strong> (<em>string</em>) – the reference key for the structure of the network data</p></li>
<li><p><strong>variables_label</strong> (<em>string</em>) – the reference key for the cardinalites of the nodes data</p></li>
<li><p><strong>time_key</strong> (<em>string</em>) – the key used to identify the timestamps in each trajectory</p></li>
<li><p><strong>variables_key</strong> (<em>string</em>) – the key used to identify the names of the variables in the net</p></li>
</ul>
</dd>
<dt class="field-even">_array_indx</dt>
<dd class="field-even"><p>the index of the outer JsonArray to extract the data from</p>
</dd>
<dt class="field-odd">_df_samples_list</dt>
<dd class="field-odd"><p>a Dataframe list in which every dataframe contains a trajectory</p>
</dd>
<dt class="field-even">_raw_data</dt>
<dd class="field-even"><p>The raw contents of the json file to import</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.build_sorter">
<code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.build_sorter" title="Permalink to this definition"></a></dt>
<dd><p>Implements the abstract method build_sorter of the <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code> for this dataset.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.clear_data_frame_list">
<code class="sig-name descname">clear_data_frame_list</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.clear_data_frame_list" title="Permalink to this definition"></a></dt>
<dd><p>Removes all values present in the dataframes in the list <code class="docutils literal notranslate"><span class="pre">_df_samples_list</span></code>.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.dataset_id">
<code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; object<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.dataset_id" title="Permalink to this definition"></a></dt>
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current
dataset</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.import_data">
<code class="sig-name descname">import_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.import_data" title="Permalink to this definition"></a></dt>
<dd><p>Implements the abstract method of <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>indx</strong> (<em>int</em>) – the index of the outer JsonArray to extract the data from</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.import_sampled_cims">
<code class="sig-name descname">import_sampled_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cims_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; Dict<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.import_sampled_cims" title="Permalink to this definition"></a></dt>
<dd><p>Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
as keys for the set of CIMS of a particular node.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>cims_key</strong> (<em>string</em>) – the key where the json object cims are placed</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary containing the sampled CIMS for all the variables in the net</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Dictionary</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.import_structure">
<code class="sig-name descname">import_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; pandas.core.frame.DataFrame<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.import_structure" title="Permalink to this definition"></a></dt>
<dd><p>Imports in a dataframe the data in the list raw_data at the key <code class="docutils literal notranslate"><span class="pre">_structure_label</span></code></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Dataframe containg the starting node a ending node of every arc of the network</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pandas.Dataframe</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.import_trajectories">
<code class="sig-name descname">import_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.import_trajectories" title="Permalink to this definition"></a></dt>
<dd><p>Imports the trajectories from the list of dicts <code class="docutils literal notranslate"><span class="pre">raw_data</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>List of dataframes containing all the trajectories</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.import_variables">
<code class="sig-name descname">import_variables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> &#x2192; pandas.core.frame.DataFrame<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.import_variables" title="Permalink to this definition"></a></dt>
<dd><p>Imports the data in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the key <code class="docutils literal notranslate"><span class="pre">_variables_label</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Datframe containg the variables simbolic labels and their cardinalities</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pandas.Dataframe</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.normalize_trajectories">
<code class="sig-name descname">normalize_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectories_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.normalize_trajectories" title="Permalink to this definition"></a></dt>
<dd><p>Extracts the trajectories in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">index</span></code> at the key <code class="docutils literal notranslate"><span class="pre">trajectories</span> <span class="pre">key</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>trajectories_key</strong> (<em>string</em>) – the key of the trajectories objects</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of daframes containg the trajectories</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>List</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.one_level_normalizing">
<code class="sig-name descname">one_level_normalizing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> &#x2192; pandas.core.frame.DataFrame<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.one_level_normalizing" title="Permalink to this definition"></a></dt>
<dd><p>Extracts the one-level nested data in the list <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">indx</span></code> at the key <code class="docutils literal notranslate"><span class="pre">key</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>key</strong> (<em>string</em>) – the key for the Dicts from which exctract data</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A normalized dataframe</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>pandas.Datframe</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.json_importer.JsonImporter.read_json_file">
<code class="sig-name descname">read_json_file</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.json_importer.JsonImporter.read_json_file" title="Permalink to this definition"></a></dt>
<dd><p>Reads the JSON file in the path self.filePath.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The contents of the json file</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>List</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-classes.utility.sample_importer">
<span id="classes-utility-sample-importer-module"></span><h2>classes.utility.sample_importer module<a class="headerlink" href="#module-classes.utility.sample_importer" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="classes.utility.sample_importer.SampleImporter">
<em class="property">class </em><code class="sig-prename descclassname">classes.utility.sample_importer.</code><code class="sig-name descname">SampleImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajectory_list</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">, </span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.sample_importer.SampleImporter" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#classes.utility.abstract_importer.AbstractImporter" title="classes.utility.abstract_importer.AbstractImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">classes.utility.abstract_importer.AbstractImporter</span></code></a></p>
<p>Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
the data loaded directly by using DataFrame</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>trajectory_list</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data that describes the trajectories</p></li>
<li><p><strong>variables</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data that describes the variables with name and cardinality</p></li>
<li><p><strong>prior_net_structure</strong> (<em>typing.Union</em><em>[</em><em>pd.DataFrame</em><em>, </em><em>np.ndarray</em><em>, </em><em>typing.List</em><em>]</em>) – the data of the real structure, if it exists</p></li>
</ul>
</dd>
<dt class="field-even">_df_samples_list</dt>
<dd class="field-even"><p>a Dataframe list in which every dataframe contains a trajectory</p>
</dd>
<dt class="field-odd">_raw_data</dt>
<dd class="field-odd"><p>The raw contents of the json file to import</p>
</dd>
</dl>
<dl class="py method">
<dt id="classes.utility.sample_importer.SampleImporter.build_sorter">
<code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.utility.sample_importer.SampleImporter.build_sorter" title="Permalink to this definition"></a></dt>
<dd><p>Implements the abstract method build_sorter of the <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code> in order to get the ordered variables list.</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.sample_importer.SampleImporter.dataset_id">
<code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; object<a class="headerlink" href="#classes.utility.sample_importer.SampleImporter.dataset_id" title="Permalink to this definition"></a></dt>
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current
dataset</p>
</dd></dl>
<dl class="py method">
<dt id="classes.utility.sample_importer.SampleImporter.import_data">
<code class="sig-name descname">import_data</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">header_column</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.utility.sample_importer.SampleImporter.import_data" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-classes.utility" title="Permalink to this headline"></a></h2>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#parameters-estimation-example">Parameters Estimation Example</a></li>
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<div class="section" id="examples">
<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline"></a></h1>
<div class="section" id="installation-usage">
<h2>Installation/Usage<a class="headerlink" href="#installation-usage" title="Permalink to this headline"></a></h2>
<p>Download the release in .tar.gz or .whl format and simply use pip install to install it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$pip install PyCTBN-1.0.tar.gz
</pre></div>
</div>
</div>
<div class="section" id="implementing-your-own-data-importer">
<h2>Implementing your own data importer<a class="headerlink" href="#implementing-your-own-data-importer" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.</span>
<span class="sd">The net in exam has three ternary nodes and no prior net structure.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">AbstractImporter</span>
<span class="k">class</span> <span class="nc">CSVImporter</span><span class="p">(</span><span class="n">AbstractImporter</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CSVImporter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">import_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">read_csv_file</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">import_variables</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">compute_row_delta_in_all_samples_frames</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">read_csv_file</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_file_path</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">import_variables</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">values_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">]</span>
<span class="c1"># initialize dict of lists</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Name&#39;</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">,</span> <span class="s1">&#39;Value&#39;</span><span class="p">:</span><span class="n">values_list</span><span class="p">}</span>
<span class="c1"># Create the pandas DataFrame</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_df_variables</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_frame</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">sample_frame</span><span class="o">.</span><span class="n">columns</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
<span class="k">def</span> <span class="nf">dataset_id</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">object</span><span class="p">:</span>
<span class="k">pass</span>
</pre></div>
</div>
</div>
<div class="section" id="parameters-estimation-example">
<h2>Parameters Estimation Example<a class="headerlink" href="#parameters-estimation-example" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">JsonImporter</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">SamplePath</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">NetworkGraph</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">ParametersEstimator</span>
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span> <span class="s2">&quot;*.json&quot;</span><span class="p">))</span> <span class="c1">#Take all json files in this dir</span>
<span class="c1">#import data</span>
<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;samples&#39;</span><span class="p">,</span> <span class="s1">&#39;dyn.str&#39;</span><span class="p">,</span> <span class="s1">&#39;variables&#39;</span><span class="p">,</span> <span class="s1">&#39;Time&#39;</span><span class="p">,</span> <span class="s1">&#39;Name&#39;</span><span class="p">)</span>
<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1">#Create a SamplePath Obj passing an already filled AbstractImporter object</span>
<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span>
<span class="c1">#Build The trajectries and the structural infos</span>
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span>
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">nodes_values</span><span class="p">)</span>
<span class="c1">#From The Structure Object build the Graph</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">NetworkGraph</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="p">)</span>
<span class="c1">#Select a node you want to estimate the parameters</span>
<span class="n">node</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Node&quot;</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span>
<span class="c1">#Init the _graph specifically for THIS node</span>
<span class="n">g</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
<span class="c1">#Use SamplePath and Grpah to create a ParametersEstimator Object</span>
<span class="n">p1</span> <span class="o">=</span> <span class="n">ParametersEstimator</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">trajectories</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span>
<span class="c1">#Init the peEst specifically for THIS node</span>
<span class="n">p1</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
<span class="c1">#Compute the parameters</span>
<span class="n">sofc1</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">compute_parameters_for_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
<span class="c1">#The est CIMS are inside the resultant SetOfCIms Obj</span>
<span class="nb">print</span><span class="p">(</span><span class="n">sofc1</span><span class="o">.</span><span class="n">actual_cims</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="structure-estimation-example">
<h2>Structure Estimation Example<a class="headerlink" href="#structure-estimation-example" title="Permalink to this headline"></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">JsonImporter</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">SamplePath</span>
<span class="kn">from</span> <span class="nn">PyCTBN</span> <span class="kn">import</span> <span class="n">StructureEstimator</span>
<span class="k">def</span> <span class="nf">structure_estimation_example</span><span class="p">():</span>
<span class="c1"># read the json files in ./data path</span>
<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span> <span class="s2">&quot;*.json&quot;</span><span class="p">))</span>
<span class="c1"># initialize a JsonImporter object for the first file</span>
<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;samples&#39;</span><span class="p">,</span> <span class="s1">&#39;dyn.str&#39;</span><span class="p">,</span> <span class="s1">&#39;variables&#39;</span><span class="p">,</span> <span class="s1">&#39;Time&#39;</span><span class="p">,</span> <span class="s1">&#39;Name&#39;</span><span class="p">)</span>
<span class="c1"># import the data at index 0 of the outer json array</span>
<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># construct a SamplePath Object passing a filled AbstractImporter</span>
<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span>
<span class="c1"># build the trajectories</span>
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span>
<span class="c1"># build the real structure</span>
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span>
<span class="c1"># construct a StructureEstimator object</span>
<span class="n">se1</span> <span class="o">=</span> <span class="n">StructureEstimator</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
<span class="c1"># call the ctpc algorithm</span>
<span class="n">se1</span><span class="o">.</span><span class="n">ctpc_algorithm</span><span class="p">()</span>
<span class="c1"># the adjacency matrix of the estimated structure</span>
<span class="nb">print</span><span class="p">(</span><span class="n">se1</span><span class="o">.</span><span class="n">adjacency_matrix</span><span class="p">())</span>
<span class="c1"># save results to a json file</span>
<span class="n">se1</span><span class="o">.</span><span class="n">save_results</span><span class="p">()</span>
</pre></div>
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<li class="toctree-l1"><a class="reference internal" href="examples.html">Examples</a><ul>
<li class="toctree-l2"><a class="reference internal" href="examples.html#installation-usage">Installation/Usage</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples.html#implementing-your-own-data-importer">Implementing your own data importer</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples.html#parameters-estimation-example">Parameters Estimation Example</a></li>
<li class="toctree-l2"><a class="reference internal" href="examples.html#structure-estimation-example">Structure Estimation Example</a></li>
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<h1 id="index">Index</h1>
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<a href="#A"><strong>A</strong></a>
| <a href="#B"><strong>B</strong></a>
| <a href="#C"><strong>C</strong></a>
| <a href="#D"><strong>D</strong></a>
| <a href="#E"><strong>E</strong></a>
| <a href="#F"><strong>F</strong></a>
| <a href="#G"><strong>G</strong></a>
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| <a href="#S"><strong>S</strong></a>
| <a href="#T"><strong>T</strong></a>
| <a href="#V"><strong>V</strong></a>
</div>
<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter">AbstractImporter (class in classes.utility.abstract_importer)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.abstract_sample_path.AbstractSamplePath">AbstractSamplePath (class in classes.structure_graph.abstract_sample_path)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.actual_cims">actual_cims() (classes.structure_graph.set_of_cims.SetOfCims property)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.add_edge">add_edge() (classes.structure_graph.structure.Structure method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.add_edges">add_edges() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.add_nodes">add_nodes() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.adjacency_matrix">adjacency_matrix() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="B">B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.build_cims">build_cims() (classes.structure_graph.set_of_cims.SetOfCims method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.build_complete_graph">build_complete_graph() (classes.estimators.structure_estimator.StructureEstimator static method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.build_list_of_samples_array">build_list_of_samples_array() (classes.utility.abstract_importer.AbstractImporter method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.build_p_comb_structure_for_a_node">build_p_comb_structure_for_a_node() (classes.structure_graph.network_graph.NetworkGraph static method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.build_removable_edges_matrix">build_removable_edges_matrix() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.build_sorter">build_sorter() (classes.utility.abstract_importer.AbstractImporter method)</a>
<ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.build_sorter">(classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.sample_importer.SampleImporter.build_sorter">(classes.utility.sample_importer.SampleImporter method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_structure">build_structure() (classes.structure_graph.abstract_sample_path.AbstractSamplePath method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.build_structure">(classes.structure_graph.sample_path.SamplePath method)</a>
</li>
</ul></li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node">build_time_columns_filtering_for_a_node() (classes.structure_graph.network_graph.NetworkGraph static method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node">build_time_scalar_indexing_structure_for_a_node() (classes.structure_graph.network_graph.NetworkGraph static method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.build_times_and_transitions_structures">build_times_and_transitions_structures() (classes.structure_graph.set_of_cims.SetOfCims method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.abstract_sample_path.AbstractSamplePath.build_trajectories">build_trajectories() (classes.structure_graph.abstract_sample_path.AbstractSamplePath method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.build_trajectories">(classes.structure_graph.sample_path.SamplePath method)</a>
</li>
</ul></li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.build_transition_filtering_for_a_node">build_transition_filtering_for_a_node() (classes.structure_graph.network_graph.NetworkGraph static method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node">build_transition_scalar_indexing_structure_for_a_node() (classes.structure_graph.network_graph.NetworkGraph static method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="C">C</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.cache.Cache">Cache (class in classes.utility.cache)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.cim">cim() (classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>
</li>
<li>
classes
<ul>
<li><a href="classes.html#module-classes">module</a>
</li>
</ul></li>
<li>
classes.estimators
<ul>
<li><a href="classes.estimators.html#module-classes.estimators">module</a>
</li>
</ul></li>
<li>
classes.estimators.fam_score_calculator
<ul>
<li><a href="classes.estimators.html#module-classes.estimators.fam_score_calculator">module</a>
</li>
</ul></li>
<li>
classes.estimators.parameters_estimator
<ul>
<li><a href="classes.estimators.html#module-classes.estimators.parameters_estimator">module</a>
</li>
</ul></li>
<li>
classes.estimators.structure_constraint_based_estimator
<ul>
<li><a href="classes.estimators.html#module-classes.estimators.structure_constraint_based_estimator">module</a>
</li>
</ul></li>
<li>
classes.estimators.structure_estimator
<ul>
<li><a href="classes.estimators.html#module-classes.estimators.structure_estimator">module</a>
</li>
</ul></li>
<li>
classes.estimators.structure_score_based_estimator
<ul>
<li><a href="classes.estimators.html#module-classes.estimators.structure_score_based_estimator">module</a>
</li>
</ul></li>
<li>
classes.optimizers
<ul>
<li><a href="classes.optimizers.html#module-classes.optimizers">module</a>
</li>
</ul></li>
<li>
classes.optimizers.constraint_based_optimizer
<ul>
<li><a href="classes.optimizers.html#module-classes.optimizers.constraint_based_optimizer">module</a>
</li>
</ul></li>
<li>
classes.optimizers.hill_climbing_search
<ul>
<li><a href="classes.optimizers.html#module-classes.optimizers.hill_climbing_search">module</a>
</li>
</ul></li>
<li>
classes.optimizers.optimizer
<ul>
<li><a href="classes.optimizers.html#module-classes.optimizers.optimizer">module</a>
</li>
</ul></li>
<li>
classes.optimizers.tabu_search
<ul>
<li><a href="classes.optimizers.html#module-classes.optimizers.tabu_search">module</a>
</li>
</ul></li>
<li>
classes.structure_graph
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.abstract_sample_path
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.abstract_sample_path">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.conditional_intensity_matrix
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.conditional_intensity_matrix">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.network_graph
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.network_graph">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.sample_path
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.sample_path">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.set_of_cims
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.set_of_cims">module</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li>
classes.structure_graph.sets_of_cims_container
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.sets_of_cims_container">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.structure
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.structure">module</a>
</li>
</ul></li>
<li>
classes.structure_graph.trajectory
<ul>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.trajectory">module</a>
</li>
</ul></li>
<li>
classes.utility
<ul>
<li><a href="classes.utility.html#module-classes.utility">module</a>
</li>
</ul></li>
<li>
classes.utility.abstract_importer
<ul>
<li><a href="classes.utility.html#module-classes.utility.abstract_importer">module</a>
</li>
</ul></li>
<li>
classes.utility.cache
<ul>
<li><a href="classes.utility.html#module-classes.utility.cache">module</a>
</li>
</ul></li>
<li>
classes.utility.decorators
<ul>
<li><a href="classes.utility.html#module-classes.utility.decorators">module</a>
</li>
</ul></li>
<li>
classes.utility.json_importer
<ul>
<li><a href="classes.utility.html#module-classes.utility.json_importer">module</a>
</li>
</ul></li>
<li>
classes.utility.sample_importer
<ul>
<li><a href="classes.utility.html#module-classes.utility.sample_importer">module</a>
</li>
</ul></li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.clean_structure_edges">clean_structure_edges() (classes.structure_graph.structure.Structure method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.cache.Cache.clear">clear() (classes.utility.cache.Cache method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.clear_concatenated_frame">clear_concatenated_frame() (classes.utility.abstract_importer.AbstractImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.clear_data_frame_list">clear_data_frame_list() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.clear_indexing_filtering_structures">clear_indexing_filtering_structures() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.clear_memory">clear_memory() (classes.structure_graph.sample_path.SamplePath method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.complete_test">complete_test() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory.complete_trajectory">complete_trajectory() (classes.structure_graph.trajectory.Trajectory property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients">compute_cim_coefficients() (classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters">compute_parameters() (classes.estimators.parameters_estimator.ParametersEstimator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.compute_parameters_for_node">compute_parameters_for_node() (classes.estimators.parameters_estimator.ParametersEstimator method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames">compute_row_delta_in_all_samples_frames() (classes.utility.abstract_importer.AbstractImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame">compute_row_delta_sigle_samples_frame() (classes.utility.abstract_importer.AbstractImporter method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node">compute_state_res_time_for_node() (classes.estimators.parameters_estimator.ParametersEstimator static method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node">compute_state_transitions_for_a_node() (classes.estimators.parameters_estimator.ParametersEstimator static method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.compute_thumb_value">compute_thumb_value() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.concatenated_samples">concatenated_samples() (classes.utility.abstract_importer.AbstractImporter property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix">ConditionalIntensityMatrix (class in classes.structure_graph.conditional_intensity_matrix)</a>
</li>
<li><a href="classes.optimizers.html#classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer">ConstraintBasedOptimizer (class in classes.optimizers.constraint_based_optimizer)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.contains_edge">contains_edge() (classes.structure_graph.structure.Structure method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.ctpc_algorithm">ctpc_algorithm() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="D">D</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.dataset_id">dataset_id() (classes.utility.abstract_importer.AbstractImporter method)</a>
<ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.dataset_id">(classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.sample_importer.SampleImporter.dataset_id">(classes.utility.sample_importer.SampleImporter method)</a>
</li>
</ul></li>
</ul></td>
</tr></table>
<h2 id="E">E</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.edges">edges() (classes.structure_graph.network_graph.NetworkGraph property)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.edges">(classes.structure_graph.structure.Structure property)</a>
</li>
</ul></li>
<li><a href="classes.estimators.html#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_parents">estimate_parents() (classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.estimate_structure">estimate_structure() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
<ul>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.estimate_structure">(classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.estimate_structure">(classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a>
</li>
</ul></li>
</ul></td>
</tr></table>
<h2 id="F">F</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator">FamScoreCalculator (class in classes.estimators.fam_score_calculator)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.fast_init">fast_init() (classes.estimators.parameters_estimator.ParametersEstimator method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.fast_init">(classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.file_path">file_path() (classes.utility.abstract_importer.AbstractImporter property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.filter_cims_with_mask">filter_cims_with_mask() (classes.structure_graph.set_of_cims.SetOfCims method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.cache.Cache.find">find() (classes.utility.cache.Cache method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size">generate_possible_sub_sets_of_size() (classes.estimators.structure_estimator.StructureEstimator static method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.get_cims_number">get_cims_number() (classes.structure_graph.set_of_cims.SetOfCims method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_cims_of_node">get_cims_of_node() (classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.get_fam_score">get_fam_score() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.get_node_id">get_node_id() (classes.structure_graph.structure.Structure method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.get_node_indx">get_node_indx() (classes.structure_graph.network_graph.NetworkGraph method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.get_node_indx">(classes.structure_graph.structure.Structure method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents">get_ordered_by_indx_set_of_parents() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.get_parents_by_id">get_parents_by_id() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.get_positional_node_indx">get_positional_node_indx() (classes.structure_graph.network_graph.NetworkGraph method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.get_positional_node_indx">(classes.structure_graph.structure.Structure method)</a>
</li>
</ul></li>
<li><a href="classes.estimators.html#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator.get_score_from_graph">get_score_from_graph() (classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.get_set_of_cims">get_set_of_cims() (classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.get_states_number">get_states_number() (classes.structure_graph.network_graph.NetworkGraph method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.get_states_number">(classes.structure_graph.structure.Structure method)</a>
</li>
</ul></li>
</ul></td>
</tr></table>
<h2 id="H">H</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.has_edge">has_edge() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.has_prior_net_structure">has_prior_net_structure() (classes.structure_graph.sample_path.SamplePath property)</a>
</li>
<li><a href="classes.optimizers.html#classes.optimizers.hill_climbing_search.HillClimbing">HillClimbing (class in classes.optimizers.hill_climbing_search)</a>
</li>
</ul></td>
</tr></table>
<h2 id="I">I</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.import_data">import_data() (classes.utility.json_importer.JsonImporter method)</a>
<ul>
<li><a href="classes.utility.html#classes.utility.sample_importer.SampleImporter.import_data">(classes.utility.sample_importer.SampleImporter method)</a>
</li>
</ul></li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.import_sampled_cims">import_sampled_cims() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.import_structure">import_structure() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.import_trajectories">import_trajectories() (classes.utility.json_importer.JsonImporter method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.import_variables">import_variables() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.independence_test">independence_test() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer.init_cims_structure">init_cims_structure() (classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.init_graph">init_graph() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator.init_sets_cims_container">init_sets_cims_container() (classes.estimators.parameters_estimator.ParametersEstimator method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="J">J</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter">JsonImporter (class in classes.utility.json_importer)</a>
</li>
</ul></td>
</tr></table>
<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_q">marginal_likelihood_q() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.marginal_likelihood_theta">marginal_likelihood_theta() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li>
module
<ul>
<li><a href="classes.html#module-classes">classes</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators">classes.estimators</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators.fam_score_calculator">classes.estimators.fam_score_calculator</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators.parameters_estimator">classes.estimators.parameters_estimator</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators.structure_constraint_based_estimator">classes.estimators.structure_constraint_based_estimator</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators.structure_estimator">classes.estimators.structure_estimator</a>
</li>
<li><a href="classes.estimators.html#module-classes.estimators.structure_score_based_estimator">classes.estimators.structure_score_based_estimator</a>
</li>
<li><a href="classes.optimizers.html#module-classes.optimizers">classes.optimizers</a>
</li>
<li><a href="classes.optimizers.html#module-classes.optimizers.constraint_based_optimizer">classes.optimizers.constraint_based_optimizer</a>
</li>
<li><a href="classes.optimizers.html#module-classes.optimizers.hill_climbing_search">classes.optimizers.hill_climbing_search</a>
</li>
<li><a href="classes.optimizers.html#module-classes.optimizers.optimizer">classes.optimizers.optimizer</a>
</li>
<li><a href="classes.optimizers.html#module-classes.optimizers.tabu_search">classes.optimizers.tabu_search</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph">classes.structure_graph</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.abstract_sample_path">classes.structure_graph.abstract_sample_path</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.conditional_intensity_matrix">classes.structure_graph.conditional_intensity_matrix</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.network_graph">classes.structure_graph.network_graph</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.sample_path">classes.structure_graph.sample_path</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.set_of_cims">classes.structure_graph.set_of_cims</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.sets_of_cims_container">classes.structure_graph.sets_of_cims_container</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.structure">classes.structure_graph.structure</a>
</li>
<li><a href="classes.structure_graph.html#module-classes.structure_graph.trajectory">classes.structure_graph.trajectory</a>
</li>
<li><a href="classes.utility.html#module-classes.utility">classes.utility</a>
</li>
<li><a href="classes.utility.html#module-classes.utility.abstract_importer">classes.utility.abstract_importer</a>
</li>
<li><a href="classes.utility.html#module-classes.utility.cache">classes.utility.cache</a>
</li>
<li><a href="classes.utility.html#module-classes.utility.decorators">classes.utility.decorators</a>
</li>
<li><a href="classes.utility.html#module-classes.utility.json_importer">classes.utility.json_importer</a>
</li>
<li><a href="classes.utility.html#module-classes.utility.sample_importer">classes.utility.sample_importer</a>
</li>
</ul></li>
</ul></td>
</tr></table>
<h2 id="N">N</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph">NetworkGraph (class in classes.structure_graph.network_graph)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.nodes">nodes() (classes.structure_graph.network_graph.NetworkGraph property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.nodes_indexes">nodes_indexes() (classes.structure_graph.network_graph.NetworkGraph property)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.nodes_indexes">(classes.structure_graph.structure.Structure property)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.nodes_labels">nodes_labels() (classes.structure_graph.structure.Structure property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.nodes_values">nodes_values() (classes.structure_graph.network_graph.NetworkGraph property)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.nodes_values">(classes.structure_graph.structure.Structure property)</a>
</li>
</ul></li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.normalize_trajectories">normalize_trajectories() (classes.utility.json_importer.JsonImporter method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="O">O</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator.one_iteration_of_CTPC_algorithm">one_iteration_of_CTPC_algorithm() (classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator method)</a>
</li>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.one_level_normalizing">one_level_normalizing() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.optimizers.html#classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer.optimize_structure">optimize_structure() (classes.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer method)</a>
<ul>
<li><a href="classes.optimizers.html#classes.optimizers.hill_climbing_search.HillClimbing.optimize_structure">(classes.optimizers.hill_climbing_search.HillClimbing method)</a>
</li>
<li><a href="classes.optimizers.html#classes.optimizers.optimizer.Optimizer.optimize_structure">(classes.optimizers.optimizer.Optimizer method)</a>
</li>
<li><a href="classes.optimizers.html#classes.optimizers.tabu_search.TabuSearch.optimize_structure">(classes.optimizers.tabu_search.TabuSearch method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.optimizers.html#classes.optimizers.optimizer.Optimizer">Optimizer (class in classes.optimizers.optimizer)</a>
</li>
</ul></td>
</tr></table>
<h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.p_combs">p_combs() (classes.structure_graph.network_graph.NetworkGraph property)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims.p_combs">(classes.structure_graph.set_of_cims.SetOfCims property)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.parameters_estimator.ParametersEstimator">ParametersEstimator (class in classes.estimators.parameters_estimator)</a>
</li>
<li><a href="classes.utility.html#classes.utility.cache.Cache.put">put() (classes.utility.cache.Cache method)</a>
</li>
</ul></td>
</tr></table>
<h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.json_importer.JsonImporter.read_json_file">read_json_file() (classes.utility.json_importer.JsonImporter method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.remove_diagonal_elements">remove_diagonal_elements() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.remove_edge">remove_edge() (classes.structure_graph.structure.Structure method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.remove_edges">remove_edges() (classes.structure_graph.network_graph.NetworkGraph method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.remove_node">remove_node() (classes.structure_graph.network_graph.NetworkGraph method)</a>
<ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.remove_node">(classes.structure_graph.structure.Structure method)</a>
</li>
</ul></li>
</ul></td>
</tr></table>
<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.sample_importer.SampleImporter">SampleImporter (class in classes.utility.sample_importer)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath">SamplePath (class in classes.structure_graph.sample_path)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">save_plot_estimated_structure_graph() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.save_results">save_results() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.set_of_cims.SetOfCims">SetOfCims (class in classes.structure_graph.set_of_cims)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sets_of_cims_container.SetsOfCimsContainer">SetsOfCimsContainer (class in classes.structure_graph.sets_of_cims_container)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_q">single_cim_xu_marginal_likelihood_q() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.single_cim_xu_marginal_likelihood_theta">single_cim_xu_marginal_likelihood_theta() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.single_internal_cim_xxu_marginal_likelihood_theta">single_internal_cim_xxu_marginal_likelihood_theta() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory.size">size() (classes.structure_graph.trajectory.Trajectory method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.sorter">sorter() (classes.utility.abstract_importer.AbstractImporter property)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator.spurious_edges">spurious_edges() (classes.estimators.structure_estimator.StructureEstimator method)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times">state_residence_times() (classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix">state_transition_matrix() (classes.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure">Structure (class in classes.structure_graph.structure)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.structure">structure() (classes.structure_graph.sample_path.SamplePath property)</a>
<ul>
<li><a href="classes.utility.html#classes.utility.abstract_importer.AbstractImporter.structure">(classes.utility.abstract_importer.AbstractImporter property)</a>
</li>
</ul></li>
<li><a href="classes.estimators.html#classes.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator">StructureConstraintBasedEstimator (class in classes.estimators.structure_constraint_based_estimator)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_estimator.StructureEstimator">StructureEstimator (class in classes.estimators.structure_estimator)</a>
</li>
<li><a href="classes.estimators.html#classes.estimators.structure_score_based_estimator.StructureScoreBasedEstimator">StructureScoreBasedEstimator (class in classes.estimators.structure_score_based_estimator)</a>
</li>
</ul></td>
</tr></table>
<h2 id="T">T</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.optimizers.html#classes.optimizers.tabu_search.TabuSearch">TabuSearch (class in classes.optimizers.tabu_search)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.time_filtering">time_filtering() (classes.structure_graph.network_graph.NetworkGraph property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.time_scalar_indexing_strucure">time_scalar_indexing_strucure() (classes.structure_graph.network_graph.NetworkGraph property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory.times">times() (classes.structure_graph.trajectory.Trajectory property)</a>
</li>
<li><a href="classes.utility.html#classes.utility.decorators.timing">timing() (in module classes.utility.decorators)</a>
</li>
<li><a href="classes.utility.html#classes.utility.decorators.timing_write">timing_write() (in module classes.utility.decorators)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.total_variables_count">total_variables_count() (classes.structure_graph.sample_path.SamplePath property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.structure.Structure.total_variables_number">total_variables_number() (classes.structure_graph.structure.Structure property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.sample_path.SamplePath.trajectories">trajectories() (classes.structure_graph.sample_path.SamplePath property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory">Trajectory (class in classes.structure_graph.trajectory)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.trajectory.Trajectory.trajectory">trajectory() (classes.structure_graph.trajectory.Trajectory property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.transition_filtering">transition_filtering() (classes.structure_graph.network_graph.NetworkGraph property)</a>
</li>
<li><a href="classes.structure_graph.html#classes.structure_graph.network_graph.NetworkGraph.transition_scalar_indexing_structure">transition_scalar_indexing_structure() (classes.structure_graph.network_graph.NetworkGraph property)</a>
</li>
</ul></td>
</tr></table>
<h2 id="V">V</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="classes.estimators.html#classes.estimators.fam_score_calculator.FamScoreCalculator.variable_cim_xu_marginal_likelihood_q">variable_cim_xu_marginal_likelihood_q() (classes.estimators.fam_score_calculator.FamScoreCalculator method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
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<a href="classes.utility.html#module-classes.utility.sample_importer"><code class="xref">classes.utility.sample_importer</code></a></td><td>
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classes.estimators package
==========================
Submodules
----------
classes.estimators.fam\_score\_calculator module
------------------------------------------------
.. automodule:: classes.estimators.fam_score_calculator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.parameters\_estimator module
-----------------------------------------------
.. automodule:: classes.estimators.parameters_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_constraint\_based\_estimator module
-----------------------------------------------------------------
.. automodule:: classes.estimators.structure_constraint_based_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_estimator module
----------------------------------------------
.. automodule:: classes.estimators.structure_estimator
:members:
:undoc-members:
:show-inheritance:
classes.estimators.structure\_score\_based\_estimator module
------------------------------------------------------------
.. automodule:: classes.estimators.structure_score_based_estimator
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.estimators
:members:
:undoc-members:
:show-inheritance:

@ -0,0 +1,45 @@
classes.optimizers package
==========================
Submodules
----------
classes.optimizers.constraint\_based\_optimizer module
------------------------------------------------------
.. automodule:: classes.optimizers.constraint_based_optimizer
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.hill\_climbing\_search module
------------------------------------------------
.. automodule:: classes.optimizers.hill_climbing_search
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.optimizer module
-----------------------------------
.. automodule:: classes.optimizers.optimizer
:members:
:undoc-members:
:show-inheritance:
classes.optimizers.tabu\_search module
--------------------------------------
.. automodule:: classes.optimizers.tabu_search
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.optimizers
:members:
:undoc-members:
:show-inheritance:

@ -0,0 +1,21 @@
classes package
===============
Subpackages
-----------
.. toctree::
:maxdepth: 4
classes.estimators
classes.optimizers
classes.structure_graph
classes.utility
Module contents
---------------
.. automodule:: classes
:members:
:undoc-members:
:show-inheritance:

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classes.structure\_graph package
================================
Submodules
----------
classes.structure\_graph.abstract\_sample\_path module
------------------------------------------------------
.. automodule:: classes.structure_graph.abstract_sample_path
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.conditional\_intensity\_matrix module
--------------------------------------------------------------
.. automodule:: classes.structure_graph.conditional_intensity_matrix
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.network\_graph module
----------------------------------------------
.. automodule:: classes.structure_graph.network_graph
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.sample\_path module
--------------------------------------------
.. automodule:: classes.structure_graph.sample_path
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.set\_of\_cims module
---------------------------------------------
.. automodule:: classes.structure_graph.set_of_cims
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.sets\_of\_cims\_container module
---------------------------------------------------------
.. automodule:: classes.structure_graph.sets_of_cims_container
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.structure module
-----------------------------------------
.. automodule:: classes.structure_graph.structure
:members:
:undoc-members:
:show-inheritance:
classes.structure\_graph.trajectory module
------------------------------------------
.. automodule:: classes.structure_graph.trajectory
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.structure_graph
:members:
:undoc-members:
:show-inheritance:

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classes.utility package
=======================
Submodules
----------
classes.utility.abstract\_importer module
-----------------------------------------
.. automodule:: classes.utility.abstract_importer
:members:
:undoc-members:
:show-inheritance:
classes.utility.cache module
----------------------------
.. automodule:: classes.utility.cache
:members:
:undoc-members:
:show-inheritance:
classes.utility.decorators module
---------------------------------
.. automodule:: classes.utility.decorators
:members:
:undoc-members:
:show-inheritance:
classes.utility.json\_importer module
-------------------------------------
.. automodule:: classes.utility.json_importer
:members:
:undoc-members:
:show-inheritance:
classes.utility.sample\_importer module
---------------------------------------
.. automodule:: classes.utility.sample_importer
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: classes.utility
:members:
:undoc-members:
:show-inheritance:

@ -0,0 +1,70 @@
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
import os
import sys
sys.path.insert(0, os.path.abspath('../main_package/'))
#sys.path.insert(0, os.path.abspath('../PyCTBN/main_package'))
sys.path.insert(0, os.path.abspath('../main_package/classes'))
sys.path.insert(0, os.path.abspath('../main_package/classes/estimators'))
sys.path.insert(0, os.path.abspath('../main_package/classes/optimizers'))
sys.path.insert(0, os.path.abspath('../main_package/classes/structure_graph'))
sys.path.insert(0, os.path.abspath('../main_package/classes/utility'))
print(sys.path)
# -- Project information -----------------------------------------------------
project = 'PyCTBN'
copyright = '2021, Bregoli Alessandro, Martini Filippo, Moretti Luca'
author = 'Bregoli Alessandro, Martini Filippo, Moretti Luca'
# The full version, including alpha/beta/rc tags
release = '2.0'
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc'
]
#autosummary_generate = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_pdj_theme'
import sphinx_pdj_theme
html_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']

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Examples
========
Installation/Usage
******************
Download the release in .tar.gz or .whl format and simply use pip install to install it::
$pip install PyCTBN-1.0.tar.gz
Implementing your own data importer
***********************************
.. code-block:: python
"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
The net in exam has three ternary nodes and no prior net structure.
"""
from PyCTBN import AbstractImporter
class CSVImporter(AbstractImporter):
def __init__(self, file_path):
self._df_samples_list = None
super(CSVImporter, self).__init__(file_path)
def import_data(self):
self.read_csv_file()
self._sorter = self.build_sorter(self._df_samples_list[0])
self.import_variables()
self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
def read_csv_file(self):
df = pd.read_csv(self._file_path)
df.drop(df.columns[[0]], axis=1, inplace=True)
self._df_samples_list = [df]
def import_variables(self):
values_list = [3 for var in self._sorter]
# initialize dict of lists
data = {'Name':self._sorter, 'Value':values_list}
# Create the pandas DataFrame
self._df_variables = pd.DataFrame(data)
def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
return list(sample_frame.columns)[1:]
def dataset_id(self) -> object:
pass
Parameters Estimation Example
*****************************
.. code-block:: python
from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import NetworkGraph
from PyCTBN import ParametersEstimator
def main():
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
#import data
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
importer.import_data(0)
#Create a SamplePath Obj passing an already filled AbstractImporter object
s1 = SamplePath(importer)
#Build The trajectries and the structural infos
s1.build_trajectories()
s1.build_structure()
print(s1.structure.edges)
print(s1.structure.nodes_values)
#From The Structure Object build the Graph
g = NetworkGraph(s1.structure)
#Select a node you want to estimate the parameters
node = g.nodes[2]
print("Node", node)
#Init the _graph specifically for THIS node
g.fast_init(node)
#Use SamplePath and Grpah to create a ParametersEstimator Object
p1 = ParametersEstimator(s1.trajectories, g)
#Init the peEst specifically for THIS node
p1.fast_init(node)
#Compute the parameters
sofc1 = p1.compute_parameters_for_node(node)
#The est CIMS are inside the resultant SetOfCIms Obj
print(sofc1.actual_cims)
Structure Estimation Example
****************************
.. code-block:: python
from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import StructureEstimator
def structure_estimation_example():
# read the json files in ./data path
read_files = glob.glob(os.path.join('./data', "*.json"))
# initialize a JsonImporter object for the first file
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
# import the data at index 0 of the outer json array
importer.import_data(0)
# construct a SamplePath Object passing a filled AbstractImporter
s1 = SamplePath(importer)
# build the trajectories
s1.build_trajectories()
# build the real structure
s1.build_structure()
# construct a StructureEstimator object
se1 = StructureEstimator(s1, 0.1, 0.1)
# call the ctpc algorithm
se1.ctpc_algorithm()
# the adjacency matrix of the estimated structure
print(se1.adjacency_matrix())
# save results to a json file
se1.save_results()

@ -0,0 +1,21 @@
.. PyCTBN documentation master file, created by
sphinx-quickstart on Wed Feb 24 18:06:35 2021.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
Welcome to PyCTBN's documentation!
==================================
.. toctree::
:maxdepth: 3
:caption: Contents:
examples
Indices and tables
==================
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

@ -0,0 +1,35 @@
@ECHO OFF
pushd %~dp0
REM Command file for Sphinx documentation
if "%SPHINXBUILD%" == "" (
set SPHINXBUILD=sphinx-build
)
set SOURCEDIR=.
set BUILDDIR=_build
if "%1" == "" goto help
%SPHINXBUILD% >NUL 2>NUL
if errorlevel 9009 (
echo.
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
echo.installed, then set the SPHINXBUILD environment variable to point
echo.to the full path of the 'sphinx-build' executable. Alternatively you
echo.may add the Sphinx directory to PATH.
echo.
echo.If you don't have Sphinx installed, grab it from
echo.http://sphinx-doc.org/
exit /b 1
)
%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
goto end
:help
%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
:end
popd

@ -0,0 +1,7 @@
classes
=======
.. toctree::
:maxdepth: 4
classes

@ -125,10 +125,8 @@ class FamScoreCalculator:
def single_internal_cim_xxu_marginal_likelihood_theta(self,
M_xxu_suff_stats: float,
alpha_xxu: float=1):
"""
Calculate the second part of the marginal likelihood over theta formula
"""Calculate the second part of the marginal likelihood over theta formula
:param M_xxu_suff_stats: value of the suffucient statistic M[xx'|u]
:type M_xxu_suff_stats: float
:param alpha_xxu: distribuited hyperparameter over the CTBNs theta parameters

@ -196,12 +196,7 @@ class StructureConstraintBasedEstimator(StructureEstimator):
def ctpc_algorithm(self,disable_multiprocessing:bool= False ):
"""
Compute the CTPC algorithm.
Parameters:
void
Returns:
void
"""Compute the CTPC algorithm over the entire net.
"""
ctpc_algo = self.one_iteration_of_CTPC_algorithm
total_vars_numb = self._sample_path.total_variables_count

@ -139,12 +139,10 @@ class NetworkGraph(object):
return sorted_parents, p_indxes, p_values
def remove_edges(self, list_of_edges: typing.List) -> None:
"""
Remove the edges to the graph contained in the list list_of_edges.
Parameters:
list_of_edges
Returns:
void
"""Remove the edges to the graph contained in the list list_of_edges.
:param list_of_edges: The edges to remove from the graph
:type list_of_edges: List
"""
self._graph.remove_edges_from(list_of_edges)

@ -25,7 +25,7 @@ class Trajectory(object):
@property
def trajectory(self) -> np.ndarray:
return self._actual_trajectory[:, :self._original_cols_number]
return self._actual_trajectory[:, :self._original_cols_number - 1]
@property
def complete_trajectory(self) -> np.ndarray:

@ -8,43 +8,18 @@ from ...classes.structure_graph.trajectory import Trajectory
class TestTrajectory(unittest.TestCase):
@classmethod
def setUpClass(cls):
print("123")
pass
def setUpClass(cls) -> None:
cls.read_files = glob.glob(os.path.join('./data', "*.json"))
cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
cls.importer.import_data(0)
def test_init(self):
cols_list = [np.array([1.2,1.3,.14]), np.arange(1,4), np.arange(4,7)]
t1 = Trajectory(cols_list, len(cols_list) - 2)
self.assertTrue(np.array_equal(cols_list[0], t1.times))
self.assertTrue(np.array_equal(np.ravel(t1.complete_trajectory[:, : 1]), cols_list[1]))
self.assertTrue(np.array_equal(np.ravel(t1.complete_trajectory[:, 1: 2]), cols_list[2]))
self.assertEqual(len(cols_list) - 1, t1.complete_trajectory.shape[1])
self.assertEqual(t1.size(), t1.times.size)
def test_init_first_array_not_float_type(self):
cols_list = [np.arange(1, 4), np.arange(4, 7), np.array([1.2, 1.3, .14])]
self.assertRaises(TypeError, Trajectory, cols_list, len(cols_list))
def test_complete_trajectory(self):
cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
t1 = Trajectory(cols_list, len(cols_list) - 2)
complete = np.column_stack((cols_list[1], cols_list[2]))
self.assertTrue(np.array_equal(t1.complete_trajectory, complete))
def test_trajectory(self):
cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
t1 = Trajectory(cols_list, len(cols_list) - 2)
self.assertTrue(np.array_equal(cols_list[1], t1.trajectory.ravel()))
def test_times(self):
cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
t1 = Trajectory(cols_list, len(cols_list) - 2)
self.assertTrue(np.array_equal(cols_list[0], t1.times))
def test_repr(self):
cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
t1 = Trajectory(cols_list, len(cols_list) - 2)
print(t1)
t1 = Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples),
len(self.importer.sorter) + 1)
self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 0].to_numpy(), t1.times))
self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:,1:].to_numpy(), t1.complete_trajectory))
self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 1: len(self.importer.sorter) + 1], t1.trajectory))
self.assertEqual(len(self.importer.sorter) + 1, t1._original_cols_number)
if __name__ == '__main__':