+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
classes package
+
+
Submodules
+
+
+
classes.abstract_importer module
+
+-
+class
classes.abstract_importer.AbstractImporter(file_path: str)
+Bases: abc.ABC
+Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
+
+- Parameters
+file_path (str) – the file path
+- _concatenated_samples
+Dataframe containing the concatenation of all the processed trajectories
+- _df_structure
+Dataframe containing the structure of the network (edges)
+- _df_variables
+Dataframe containing the nodes cardinalities
+- _sorter
+A list containing the columns header (excluding the time column) of the _concatenated_samples
+
+
+
Warning
+
The class members _df_variables and _df_structure 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]
+
+
Note
+
If you don’t have prior network structure just leave _df_structure set to None.
+
+
Note
+
See :class:JsonImporter for an example implementation
+
+-
+
build_list_of_samples_array(data_frame: pandas.core.frame.DataFrame) → List
+Builds a List containing the columns of data_frame and converts them to a numpy array.
+
+- Parameters
+data_frame (pandas.Dataframe) – the dataframe from which the columns have to be extracted and converted
+- Returns
+the resulting list of numpy arrays
+- Return type
+List
+
+
+
+
+-
+abstract
build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
+Initializes the _sorter 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.
+
+- Parameters
+sample_frame (pandas.DataFrame) – The dataframe from which extract the header
+- Returns
+A list containing the processed header.
+- Return type
+List
+
+
+
+
+-
+
clear_concatenated_frame() → None
+Removes all values in the dataframe concatenated_samples.
+
+
+
+-
+
compute_row_delta_in_all_samples_frames(df_samples_list: List) → None
+Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list
+df_samples_list.
+Concatenates the result in the dataframe concatanated_samples
+
+- Parameters
+df_samples_list (List) – the datframe’s list to be processed and concatenated
+
+
+
Warning
+
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)
+
+
Note
+
After the call of this method the class member concatanated_samples will contain all processed
+and merged trajectories
+
+
+
+-
+
compute_row_delta_sigle_samples_frame(sample_frame: pandas.core.frame.DataFrame, columns_header: List, shifted_cols_header: List) → pandas.core.frame.DataFrame
+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.
+
+- Parameters
+
+sample_frame (pandas.Dataframe) – the traj to be processed
columns_header (List) – the original header of sample_frame
shifted_cols_header (List) – a copy of columns_header with changed names of the contents
+- Returns
+The processed dataframe
+- Return type
+pandas.Dataframe
+
+
+
Warning
+
the Dataframe sample_frame has to follow the column structure of this header:
+Header of sample_frame = [Time | Variable values]
+
+
+
+-
+property
concatenated_samples
+
+
+
+-
+abstract
dataset_id() → object
+If the original dataset contains multiple dataset, this method returns a unique id to identify the current
+dataset
+
+
+
+-
+property
file_path
+
+
+
+-
+property
sorter
+
+
+
+-
+property
structure
+
+
+
+-
+property
variables
+
+
+
+
+
+
+
classes.cache module
+
+-
+class
classes.cache.Cache
+Bases: object
+This class acts as a cache of SetOfCims objects for a node.
+
+- _list_of_sets_of_parents
+a list of Sets objects of the parents to which the cim in cache at SAME
+index is related
+- _actual_cache
+a list of setOfCims objects
+
+
+-
+
clear() → None
+Clear the contents both of _actual_cache and _list_of_sets_of_parents.
+
+
+
+-
+
find(parents_comb: Set) → classes.set_of_cims.SetOfCims
+Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims
+related to that parents_comb.
+
+- Parameters
+parents_comb (Set) – the parents related to that SetOfCims
+- Returns
+A SetOfCims object if the parents_comb index is found in _list_of_sets_of_parents.
+None otherwise.
+- Return type
+SetOfCims
+
+
+
+
+-
+
put(parents_comb: Set, socim: classes.set_of_cims.SetOfCims) → None
+Place in cache the SetOfCims object, and the related symbolic index parents_comb in
+_list_of_sets_of_parents.
+
+- Parameters
+-
+
+
+
+
+
+
+
+
+
classes.conditional_intensity_matrix module
+
+-
+class
classes.conditional_intensity_matrix.ConditionalIntensityMatrix(state_residence_times: numpy.array, state_transition_matrix: numpy.array)
+Bases: object
+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.
+
+- Parameters
+-
+
+- _cim
+the actual cim of the node
+
+
+-
+property
cim
+
+
+
+-
+
compute_cim_coefficients() → None
+Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x].
+The class member _cim will contain the computed cim
+
+
+
+-
+property
state_residence_times
+
+
+
+-
+property
state_transition_matrix
+
+
+
+
+
+
+
classes.json_importer module
+
+-
+class
classes.json_importer.JsonImporter(file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, variables_key: str)
+Bases: classes.abstract_importer.AbstractImporter
+Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
+the data in json extension.
+
+- Parameters
+
+file_path (string) – the path of the file that contains tha data to be imported
samples_label (string) – the reference key for the samples in the trajectories
structure_label (string) – the reference key for the structure of the network data
variables_label (string) – the reference key for the cardinalites of the nodes data
time_key (string) – the key used to identify the timestamps in each trajectory
variables_key (string) – the key used to identify the names of the variables in the net
+- _array_indx
+the index of the outer JsonArray to extract the data from
+- _df_samples_list
+a Dataframe list in which every dataframe contains a trajectory
+- _raw_data
+The raw contents of the json file to import
+
+
+-
+
build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
+Implements the abstract method build_sorter of the AbstractImporter for this dataset.
+
+
+
+-
+
clear_data_frame_list() → None
+Removes all values present in the dataframes in the list _df_samples_list.
+
+
+
+-
+
dataset_id() → object
+If the original dataset contains multiple dataset, this method returns a unique id to identify the current
+dataset
+
+
+
+-
+
import_data(indx: int) → None
+Implements the abstract method of AbstractImporter.
+
+- Parameters
+indx (int) – the index of the outer JsonArray to extract the data from
+
+
+
+
+-
+
import_sampled_cims(raw_data: List, indx: int, cims_key: str) → Dict
+Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
+as keys for the set of CIMS of a particular node.
+
+- Parameters
+
+raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
cims_key (string) – the key where the json object cims are placed
+- Returns
+a dictionary containing the sampled CIMS for all the variables in the net
+- Return type
+Dictionary
+
+
+
+
+-
+
import_structure(raw_data: List) → pandas.core.frame.DataFrame
+Imports in a dataframe the data in the list raw_data at the key _structure_label
+
+- Parameters
+raw_data (List) – List of Dicts
+- Returns
+Dataframe containg the starting node a ending node of every arc of the network
+- Return type
+pandas.Dataframe
+
+
+
+
+-
+
import_trajectories(raw_data: List) → List
+Imports the trajectories from the list of dicts raw_data.
+
+- Parameters
+raw_data (List) – List of Dicts
+- Returns
+List of dataframes containing all the trajectories
+- Return type
+List
+
+
+
+
+-
+
import_variables(raw_data: List) → pandas.core.frame.DataFrame
+Imports the data in raw_data at the key _variables_label.
+
+- Parameters
+raw_data (List) – List of Dicts
+- Returns
+Datframe containg the variables simbolic labels and their cardinalities
+- Return type
+pandas.Dataframe
+
+
+
+
+-
+
normalize_trajectories(raw_data: List, indx: int, trajectories_key: str) → List
+Extracts the trajectories in raw_data at the index index at the key trajectories key.
+
+- Parameters
+
+raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
trajectories_key (string) – the key of the trajectories objects
+- Returns
+A list of daframes containg the trajectories
+- Return type
+List
+
+
+
+
+-
+
one_level_normalizing(raw_data: List, indx: int, key: str) → pandas.core.frame.DataFrame
+Extracts the one-level nested data in the list raw_data at the index indx at the key key.
+
+- Parameters
+
+raw_data (List) – List of Dicts
indx (int) – The index of the array from which the data have to be extracted
key (string) – the key for the Dicts from which exctract data
+- Returns
+A normalized dataframe
+- Return type
+pandas.Datframe
+
+
+
+
+-
+
read_json_file() → List
+Reads the JSON file in the path self.filePath.
+
+- Returns
+The contents of the json file
+- Return type
+List
+
+
+
+
+
+
+
+
classes.network_graph module
+
+-
+class
classes.network_graph.NetworkGraph(graph_struct: classes.structure.Structure)
+Bases: object
+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
+
+- Parameters
+graph_struct (Structure) – the Structure object from which infos about the net will be extracted
+- _graph
+directed graph
+- _aggregated_info_about_nodes_parents
+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.
+- _time_scalar_indexing_structure
+the indexing structure for state res time estimation
+- _transition_scalar_indexing_structure
+the indexing structure for transition computation
+- _time_filtering
+the columns filtering structure used in the computation of the state res times
+- _transition_filtering
+the columns filtering structure used in the computation of the transition
+from one state to another
+- _p_combs_structure
+all the possible parents states combination for the node of interest
+
+
+-
+
add_edges(list_of_edges: List) → None
+Add the edges to the _graph contained in the list list_of_edges.
+
+- Parameters
+list_of_edges (List) – the list containing of tuples containing the edges
+
+
+
+
+-
+
add_nodes(list_of_nodes: List) → None
+Adds the nodes to the _graph contained in the list of nodes list_of_nodes.
+Sets all the properties that identify a nodes (index, positional index, cardinality)
+
+- Parameters
+list_of_nodes (List) – the nodes to add to _graph
+
+
+
+
+-
+static
build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray
+Builds the combinatorial structure that contains the combinations of all the values contained in
+parents_values.
+
+- Parameters
+parents_values (List) – the cardinalities of the nodes
+- Returns
+A numpy matrix containing a grid of the combinations
+- Return type
+numpy.ndArray
+
+
+
+
+-
+static
build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.ndarray
+Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs
+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
+
+
+
+-
+static
build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray
+Builds an indexing structure for the computation of state residence times values.
+
+- Parameters
+-
+
+- Returns
+The time indexing structure
+- Return type
+numpy.ndArray
+
+
+
+
+-
+static
build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.ndarray
+Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs
+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
+
+
+
+-
+static
build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray
+Builds an indexing structure for the computation of state transitions values.
+
+- Parameters
+-
+
+- Returns
+The transition indexing structure
+- Return type
+numpy.ndArray
+
+
+
+
+-
+
clear_indexing_filtering_structures() → None
+Initialize all the filtering/indexing structures.
+
+
+
+-
+property
edges
+
+
+
+-
+
fast_init(node_id: str) → None
+Initializes all the necessary structures for parameters estimation of the node identified by the label
+node_id
+
+- Parameters
+node_id (string) – the label of the node
+
+
+
+
+-
+
get_node_indx(node_id) → int
+
+
+
+-
+
get_ordered_by_indx_set_of_parents(node: str) → Tuple
+Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
+(parents_labels, parents_indexes, parents_cardinalities).
+
+- Parameters
+node (string) – the label of the node
+- Returns
+a tuple containing all the parent set infos
+- Return type
+Tuple
+
+
+
+
+-
+
get_parents_by_id(node_id) → List
+Returns a list of labels of the parents of the node node_id
+
+- Parameters
+node_id (string) – the node label
+- Returns
+a List of labels of the parents
+- Return type
+List
+
+
+
+
+-
+
get_positional_node_indx(node_id) → int
+
+
+
+-
+
get_states_number(node_id) → int
+
+
+
+-
+property
nodes
+
+
+
+-
+property
nodes_indexes
+
+
+
+-
+property
nodes_values
+
+
+
+-
+property
p_combs
+
+
+
+-
+
remove_node(node_id: str) → None
+Remove the node node_id from all the class members.
+Initialize all the filtering/indexing structures.
+
+
+
+-
+property
time_filtering
+
+
+
+-
+property
time_scalar_indexing_strucure
+
+
+
+-
+property
transition_filtering
+
+
+
+-
+property
transition_scalar_indexing_structure
+
+
+
+
+
+
+
classes.parameters_estimator module
+
+-
+class
classes.parameters_estimator.ParametersEstimator(trajectories: classes.trajectory.Trajectory, net_graph: classes.network_graph.NetworkGraph)
+Bases: object
+Has the task of computing the cims of particular node given the trajectories and the net structure
+in the graph _net_graph.
+
+- Parameters
+-
+
+- _single_set_of_cims
+the set of cims object that will hold the cims of the node
+
+
+-
+
compute_parameters_for_node(node_id: str) → classes.set_of_cims.SetOfCims
+Compute the CIMS of the node identified by the label node_id.
+
+- Parameters
+node_id (string) – the node label
+- Returns
+A SetOfCims object filled with the computed CIMS
+- Return type
+SetOfCims
+
+
+
+
+-
+static
compute_state_res_time_for_node(times: numpy.ndarray, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexes_struct: numpy.ndarray, T: numpy.ndarray) → None
+Compute the state residence times for a node and fill the matrix T with the results
+
+- Parameters
+
+node_indx (int) – the index of the node
times (numpy.array) – the times deltas vector
trajectory (numpy.ndArray) – the trajectory
cols_filter (numpy.array) – the columns filtering structure
scalar_indexes_struct (numpy.array) – the indexing structure
T (numpy.ndArray) – the state residence times vectors
+
+
+
+
+-
+static
compute_state_transitions_for_a_node(node_indx: int, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexing: numpy.ndarray, M: numpy.ndarray) → None
+Compute the state residence times for a node and fill the matrices M with the results.
+
+- Parameters
+
+node_indx (int) – the index of the node
trajectory (numpy.ndArray) – the trajectory
cols_filter (numpy.array) – the columns filtering structure
scalar_indexing (numpy.array) – the indexing structure
M (numpy.ndArray) – the state transitions matrices
+
+
+
+
+-
+
fast_init(node_id: str) → None
+Initializes all the necessary structures for the parameters estimation for the node node_id.
+
+- Parameters
+node_id (string) – the node label
+
+
+
+
+
+
+
+
classes.sample_path module
+
+-
+class
classes.sample_path.SamplePath(importer: classes.abstract_importer.AbstractImporter)
+Bases: object
+Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
+cardinalites. Has the task of creating the objects Trajectory and Structure that will
+contain the mentioned data.
+
+- Parameters
+importer (AbstractImporter) – the Importer object which contains the imported and processed data
+- _trajectories
+the Trajectory object that will contain all the concatenated trajectories
+- _structure
+the Structure Object that will contain all the structural infos about the net
+- _total_variables_count
+the number of variables in the net
+
+
+-
+
build_structure() → None
+Builds the Structure object that aggregates all the infos about the net.
+
+
+
+-
+
build_trajectories() → None
+Builds the Trajectory object that will contain all the trajectories.
+Clears all the unused dataframes in _importer Object
+
+
+
+-
+property
structure
+
+
+
+-
+property
total_variables_count
+
+
+
+-
+property
trajectories
+
+
+
+
+
+
+
classes.set_of_cims module
+
+-
+class
classes.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray)
+Bases: object
+Aggregates all the CIMS of the node identified by the label _node_id.
+
+- Parameters
+
+node_id – the node label
parents_states_number (List) – the cardinalities of the parents
node_states_number (int) – the caridinality of the node
p_combs (numpy.ndArray) – the p_comb structure bound to this node
+- _state_residence_time
+matrix containing all the state residence time vectors for the node
+- _transition_matrices
+matrix containing all the transition matrices for the node
+- _actual_cims
+the cims of the node
+
+
+-
+property
actual_cims
+
+
+
+-
+
build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.ndarray) → None
+Build the ConditionalIntensityMatrix objects given the state residence times and transitions matrices.
+Compute the cim coefficients.The class member _actual_cims will contain the computed cims.
+
+- Parameters
+-
+
+
+
+
+
+-
+
build_times_and_transitions_structures() → None
+Initializes at the correct dimensions the state residence times matrix and the state transition matrices.
+
+
+
+-
+
filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray
+Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index
+comb.
+
+- Parameters
+-
+
+- Returns
+Array of ConditionalIntensityMatrix objects
+- Return type
+numpy.array
+
+
+
+
+-
+
get_cims_number()
+
+
+
+-
+property
p_combs
+
+
+
+
+
+
+
classes.structure module
+
+-
+class
classes.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)
+Bases: object
+Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)
+
+- Parameters
+
+nodes_labels_list (List) – the symbolic names of the variables
nodes_indexes_arr (numpy.ndArray) – the indexes of the nodes
nodes_vals_arr (numpy.ndArray) – the cardinalites of the nodes
edges_list (List) – the edges of the network
total_variables_number (int) – the total number of variables in the dataset
+
+
+-
+property
edges
+
+
+
+-
+
get_node_id(node_indx: int) → str
+Given the node_index returns the node label.
+
+- Parameters
+node_indx (int) – the node index
+- Returns
+the node label
+- Return type
+string
+
+
+
+
+-
+
get_node_indx(node_id: str) → int
+Given the node_index returns the node label.
+
+- Parameters
+node_id (string) – the node label
+- Returns
+the node index
+- Return type
+int
+
+
+
+
+-
+
get_positional_node_indx(node_id: str) → int
+
+
+
+-
+
get_states_number(node: str) → int
+Given the node label node returns the cardinality of the node.
+
+- Parameters
+node (string) – the node label
+- Returns
+the node cardinality
+- Return type
+int
+
+
+
+
+-
+property
nodes_indexes
+
+
+
+-
+property
nodes_labels
+
+
+
+-
+property
nodes_values
+
+
+
+-
+
remove_node(node_id: str) → None
+Remove the node node_id from all the class members.
+The class member _total_variables_number since it refers to the total number of variables in the dataset.
+
+
+
+-
+property
total_variables_number
+
+
+
+
+
+
+
classes.structure_estimator module
+
+-
+class
classes.structure_estimator.StructureEstimator(sample_path: classes.sample_path.SamplePath, exp_test_alfa: float, chi_test_alfa: float)
+Bases: object
+Has the task of estimating the network structure given the trajectories in samplepath.
+
+- Parameters
+
+sample_path (SamplePath) – the _sample_path object containing the trajectories and the real structure
exp_test_alfa (float) – the significance level for the exponential Hp test
chi_test_alfa (float) – the significance level for the chi Hp test
+- _nodes
+the nodes labels
+- _nodes_vals
+the nodes cardinalities
+- _nodes_indxs
+the nodes indexes
+- _complete_graph
+the complete directed graph built using the nodes labels in _nodes
+- _cache
+the Cache object
+
+
+-
+
adjacency_matrix() → numpy.ndarray
+Converts the estimated structrure _complete_graph to a boolean adjacency matrix representation.
+
+- Returns
+The adjacency matrix of the graph _complete_graph
+- Return type
+numpy.ndArray
+
+
+
+
+-
+static
build_complete_graph(node_ids: List) → networkx.classes.digraph.DiGraph
+Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
+
+- Parameters
+node_ids (List) – the list of nodes labels
+- Returns
+a complete Digraph Object
+- Return type
+networkx.DiGraph
+
+
+
+
+-
+
complete_test(test_parent: str, test_child: str, parent_set: List, child_states_numb: int, tot_vars_count: int) → bool
+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.
+
+- Parameters
+
+test_parent (string) – the node label of the test parent
test_child (string) – the node label of the child
parent_set (List) – the common parent set
child_states_numb (int) – the cardinality of the test_child
tot_vars_count (int) – the total number of variables in the net
+- Returns
+True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise
+- Return type
+bool
+
+
+
+
+-
+
ctpc_algorithm() → None
+Compute the CTPC algorithm over the entire net.
+
+
+
+-
+static
generate_possible_sub_sets_of_size(u: List, size: int, parent_label: str) → Iterator
+Creates a list containing all possible subsets of the list u of size size,
+that do not contains a the node identified by parent_label.
+
+- Parameters
+
+u (List) – the list of nodes
size (int) – the size of the subsets
parent_label (string) – the node to exclude in the subsets generation
+- Returns
+an Iterator Object containing a list of lists
+- Return type
+Iterator
+
+
+
+
+-
+
independence_test(child_states_numb: int, cim1: classes.conditional_intensity_matrix.ConditionalIntensityMatrix, cim2: classes.conditional_intensity_matrix.ConditionalIntensityMatrix) → bool
+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.
+
+- Parameters
+-
+
+- Returns
+True iff both tests do NOT reject the null hypothesis of independence. False otherwise.
+- Return type
+bool
+
+
+
+
+-
+
one_iteration_of_CTPC_algorithm(var_id: str, tot_vars_count: int) → None
+Performs an iteration of the CTPC algorithm using the node var_id as test_child.
+
+- Parameters
+-
+
+
+
+
+
+-
+
save_results() → None
+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 results_ word is appended to the results file.
+
+
+
+
+
+
+
classes.trajectory module
+
+-
+class
classes.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)
+Bases: object
+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).
+
+- Parameters
+-
+
+- _actual_trajectory
+the trajectory containing also the duplicated/shifted values
+- _times
+the array containing the time deltas
+
+
+-
+property
complete_trajectory
+
+
+
+-
+
size()
+
+
+
+-
+property
times
+
+
+
+-
+property
trajectory
+
+
+
+
+
+
+
Module contents
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