folder fix

.github/workflows/build.yml

@@ -22,7 +22,7 @@ jobs:
         profile: minimal
         toolchain: stable
         default: true
-        components: clippy, rustfmt, rust-docs
+        components: clippy, rustfmt
     - name: Setup Rust nightly
       uses: actions-rs/toolchain@v1
       with:
@@ -30,11 +30,6 @@ jobs:
         toolchain: nightly
         default: false
         components: rustfmt
-    - name: Docs (doc)
-      uses: actions-rs/cargo@v1
-      with:
-        command: rustdoc
-        args: --package reCTBN -- --default-theme=ayu
     - name: Linting (clippy)
       uses: actions-rs/clippy-check@v1
       with:

.gitignore

@@ -1,3 +1,4 @@
 /target
 Cargo.lock
 .vscode
+poetry.lock

LICENSE-APACHE

@@ -1,176 +0,0 @@
-                              Apache License
-                        Version 2.0, January 2004
-                     http://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-1. Definitions.
-
-   "License" shall mean the terms and conditions for use, reproduction,
-   and distribution as defined by Sections 1 through 9 of this document.
-
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-   the copyright owner that is granting the License.
-
-   "Legal Entity" shall mean the union of the acting entity and all
-   other entities that control, are controlled by, or are under common
-   control with that entity. For the purposes of this definition,
-   "control" means (i) the power, direct or indirect, to cause the
-   direction or management of such entity, whether by contract or
-   otherwise, or (ii) ownership of fifty percent (50%) or more of the
-   outstanding shares, or (iii) beneficial ownership of such entity.
-
-   "You" (or "Your") shall mean an individual or Legal Entity
-   exercising permissions granted by this License.
-
-   "Source" form shall mean the preferred form for making modifications,
-   including but not limited to software source code, documentation
-   source, and configuration files.
-
-   "Object" form shall mean any form resulting from mechanical
-   transformation or translation of a Source form, including but
-   not limited to compiled object code, generated documentation,
-   and conversions to other media types.
-
-   "Work" shall mean the work of authorship, whether in Source or
-   Object form, made available under the License, as indicated by a
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-   "Derivative Works" shall mean any work, whether in Source or Object
-   form, that is based on (or derived from) the Work and for which the
-   editorial revisions, annotations, elaborations, or other modifications
-   represent, as a whole, an original work of authorship. For the purposes
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-   to that Work or Derivative Works thereof, that is intentionally
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-   communication on electronic mailing lists, source code control systems,
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-5. Submission of Contributions. Unless You explicitly state otherwise,
-   any Contribution intentionally submitted for inclusion in the Work
-   by You to the Licensor shall be under the terms and conditions of
-   this License, without any additional terms or conditions.
-   Notwithstanding the above, nothing herein shall supersede or modify
-   the terms of any separate license agreement you may have executed
-   with Licensor regarding such Contributions.
-
-6. Trademarks. This License does not grant permission to use the trade
-   names, trademarks, service marks, or product names of the Licensor,
-   except as required for reasonable and customary use in describing the
-   origin of the Work and reproducing the content of the NOTICE file.
-
-7. Disclaimer of Warranty. Unless required by applicable law or
-   agreed to in writing, Licensor provides the Work (and each
-   Contributor provides its Contributions) on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-   implied, including, without limitation, any warranties or conditions
-   of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-   PARTICULAR PURPOSE. You are solely responsible for determining the
-   appropriateness of using or redistributing the Work and assume any
-   risks associated with Your exercise of permissions under this License.
-
-8. Limitation of Liability. In no event and under no legal theory,
-   whether in tort (including negligence), contract, or otherwise,
-   unless required by applicable law (such as deliberate and grossly
-   negligent acts) or agreed to in writing, shall any Contributor be
-   liable to You for damages, including any direct, indirect, special,
-   incidental, or consequential damages of any character arising as a
-   result of this License or out of the use or inability to use the
-   Work (including but not limited to damages for loss of goodwill,
-   work stoppage, computer failure or malfunction, or any and all
-   other commercial damages or losses), even if such Contributor
-   has been advised of the possibility of such damages.
-
-9. Accepting Warranty or Additional Liability. While redistributing
-   the Work or Derivative Works thereof, You may choose to offer,
-   and charge a fee for, acceptance of support, warranty, indemnity,
-   or other liability obligations and/or rights consistent with this
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-   on Your own behalf and on Your sole responsibility, not on behalf
-   of any other Contributor, and only if You agree to indemnify,
-   defend, and hold each Contributor harmless for any liability
-   incurred by, or claims asserted against, such Contributor by reason
-   of your accepting any such warranty or additional liability.
-
-END OF TERMS AND CONDITIONS

LICENSE-MIT

@@ -1,23 +0,0 @@
-Permission is hereby granted, free of charge, to any
-person obtaining a copy of this software and associated
-documentation files (the "Software"), to deal in the
-Software without restriction, including without
-limitation the rights to use, copy, modify, merge,
-publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software
-is furnished to do so, subject to the following
-conditions:
-
-The above copyright notice and this permission notice
-shall be included in all copies or substantial portions
-of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF
-ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED
-TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
-PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
-SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
-OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
-IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
-DEALINGS IN THE SOFTWARE.

README.md

@@ -56,19 +56,3 @@ To check the **formatting**:
 ```sh
 cargo fmt --all -- --check
 ```
-
-## Documentation
-
-To generate the **documentation**:
-
-```sh
-cargo rustdoc --package reCTBN --open -- --default-theme=ayu
-```
-
-## License
-
-This software is distributed under the terms of both the Apache License
-(Version 2.0) and the MIT license.
-
-See [LICENSE-APACHE](./LICENSE-APACHE) and [LICENSE-MIT](./LICENSE-MIT) for
-details.

pyproject.toml

@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "rectbnpy"
+version = "0.1.0"
+description = ""
+authors = ["AlessandroBregoli <alessandroxciv@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+maturin = "^0.13.3"
+numpy = "^1.23.3"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

reCTBN/Cargo.toml

@@ -6,15 +6,13 @@ edition = "2021"
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [dependencies]
-ndarray = {version="~0.15", features=["approx-0_5"]}
+ndarray = {version="*", features=["approx-0_5"]}
-thiserror = "1.0.37"
+thiserror = "*"
-rand = "~0.8"
+rand = "*"
-bimap = "~0.6"
+bimap = "*"
-enum_dispatch = "~0.3"
+enum_dispatch = "*"
-statrs = "~0.16"
+statrs = "*"
-rand_chacha = "~0.3"
+rand_chacha = "*"
-itertools = "~0.10"
-rayon = "~1.6"
 
 [dev-dependencies]
-approx = { package = "approx", version = "~0.5" }
+approx = { package = "approx", version = "0.5" }

reCTBN/src/ctbn.rs

@@ -0,0 +1,162 @@
+use std::collections::BTreeSet;
+
+use ndarray::prelude::*;
+
+use crate::network;
+use crate::params::{Params, ParamsTrait, StateType};
+
+///CTBN network. It represents both the structure and the parameters of a CTBN. CtbnNetwork is
+///composed by the following elements:
+///- **adj_metrix**: a 2d ndarray representing the adjacency matrix
+///- **nodes**: a vector containing all the nodes and their parameters.
+///The index of a node inside the vector is also used as index for the adj_matrix.
+///
+///# Examples
+///
+///```
+///    
+/// use std::collections::BTreeSet;
+/// use reCTBN::network::Network;
+/// use reCTBN::params;
+/// use reCTBN::ctbn::*;
+///
+/// //Create the domain for a discrete node
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+///
+/// //Create the parameters for a discrete node using the domain
+/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
+///
+/// //Create the node using the parameters
+/// let X1 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
+/// let X2 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// //Initialize a ctbn
+/// let mut net = CtbnNetwork::new();
+///
+/// //Add nodes
+/// let X1 = net.add_node(X1).unwrap();
+/// let X2 = net.add_node(X2).unwrap();
+///
+/// //Add an edge
+/// net.add_edge(X1, X2);
+///
+/// //Get all the children of node X1
+/// let cs = net.get_children_set(X1);
+/// assert_eq!(&X2, cs.iter().next().unwrap());
+/// ```
+pub struct CtbnNetwork {
+    adj_matrix: Option<Array2<u16>>,
+    nodes: Vec<Params>,
+}
+
+impl CtbnNetwork {
+    pub fn new() -> CtbnNetwork {
+        CtbnNetwork {
+            adj_matrix: None,
+            nodes: Vec::new(),
+        }
+    }
+}
+
+impl network::Network for CtbnNetwork {
+    fn initialize_adj_matrix(&mut self) {
+        self.adj_matrix = Some(Array2::<u16>::zeros(
+            (self.nodes.len(), self.nodes.len()).f(),
+        ));
+    }
+
+    fn add_node(&mut self, mut n: Params) -> Result<usize, network::NetworkError> {
+        n.reset_params();
+        self.adj_matrix = Option::None;
+        self.nodes.push(n);
+        Ok(self.nodes.len() - 1)
+    }
+
+    fn add_edge(&mut self, parent: usize, child: usize) {
+        if let None = self.adj_matrix {
+            self.initialize_adj_matrix();
+        }
+
+        if let Some(network) = &mut self.adj_matrix {
+            network[[parent, child]] = 1;
+            self.nodes[child].reset_params();
+        }
+    }
+
+    fn get_node_indices(&self) -> std::ops::Range<usize> {
+        0..self.nodes.len()
+    }
+
+    fn get_number_of_nodes(&self) -> usize {
+        self.nodes.len()
+    }
+
+    fn get_node(&self, node_idx: usize) -> &Params {
+        &self.nodes[node_idx]
+    }
+
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut Params {
+        &mut self.nodes[node_idx]
+    }
+
+    fn get_param_index_network(&self, node: usize, current_state: &Vec<StateType>) -> usize {
+        self.adj_matrix
+            .as_ref()
+            .unwrap()
+            .column(node)
+            .iter()
+            .enumerate()
+            .fold((0, 1), |mut acc, x| {
+                if x.1 > &0 {
+                    acc.0 += self.nodes[x.0].state_to_index(&current_state[x.0]) * acc.1;
+                    acc.1 *= self.nodes[x.0].get_reserved_space_as_parent();
+                }
+                acc
+            })
+            .0
+    }
+
+    fn get_param_index_from_custom_parent_set(
+        &self,
+        current_state: &Vec<StateType>,
+        parent_set: &BTreeSet<usize>,
+    ) -> usize {
+        parent_set
+            .iter()
+            .fold((0, 1), |mut acc, x| {
+                acc.0 += self.nodes[*x].state_to_index(&current_state[*x]) * acc.1;
+                acc.1 *= self.nodes[*x].get_reserved_space_as_parent();
+                acc
+            })
+            .0
+    }
+
+    fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
+        self.adj_matrix
+            .as_ref()
+            .unwrap()
+            .column(node)
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
+            .collect()
+    }
+
+    fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
+        self.adj_matrix
+            .as_ref()
+            .unwrap()
+            .row(node)
+            .iter()
+            .enumerate()
+            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
+            .collect()
+    }
+}

reCTBN/src/lib.rs

@@ -1,12 +1,11 @@
-#![doc = include_str!("../../README.md")]
 #![allow(non_snake_case)]
 #[cfg(test)]
 extern crate approx;
 
+pub mod ctbn;
+pub mod network;
 pub mod parameter_learning;
 pub mod params;
-pub mod process;
-pub mod reward;
 pub mod sampling;
 pub mod structure_learning;
 pub mod tools;

reCTBN/src/network.rs

@@ -0,0 +1,43 @@
+use std::collections::BTreeSet;
+
+use thiserror::Error;
+
+use crate::params;
+
+/// Error types for trait Network
+#[derive(Error, Debug)]
+pub enum NetworkError {
+    #[error("Error during node insertion")]
+    NodeInsertionError(String),
+}
+
+///Network
+///The Network trait define the required methods for a structure used as pgm (such as ctbn).
+pub trait Network {
+    fn initialize_adj_matrix(&mut self);
+    fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
+    fn add_edge(&mut self, parent: usize, child: usize);
+
+    ///Get all the indices of the nodes contained inside the network
+    fn get_node_indices(&self) -> std::ops::Range<usize>;
+    fn get_number_of_nodes(&self) -> usize;
+    fn get_node(&self, node_idx: usize) -> &params::Params;
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut params::Params;
+
+    ///Compute the index that must be used to access the parameters of a node given a specific
+    ///configuration of the network. Usually, the only values really used in *current_state* are
+    ///the ones in the parent set of the *node*.
+    fn get_param_index_network(&self, node: usize, current_state: &Vec<params::StateType>)
+        -> usize;
+
+    ///Compute the index that must be used to access the parameters of a node given a specific
+    ///configuration of the network and a generic parent_set. Usually, the only values really used
+    ///in *current_state* are the ones in the parent set of the *node*.
+    fn get_param_index_from_custom_parent_set(
+        &self,
+        current_state: &Vec<params::StateType>,
+        parent_set: &BTreeSet<usize>,
+    ) -> usize;
+    fn get_parent_set(&self, node: usize) -> BTreeSet<usize>;
+    fn get_children_set(&self, node: usize) -> BTreeSet<usize>;
+}

reCTBN/src/parameter_learning.rs

@@ -1,25 +1,23 @@
-//! Module containing methods used to learn the parameters.
-
 use std::collections::BTreeSet;
 
 use ndarray::prelude::*;
 
 use crate::params::*;
-use crate::{process, tools::Dataset};
+use crate::{network, tools};
 
-pub trait ParameterLearning: Sync {
+pub trait ParameterLearning {
-    fn fit<T: process::NetworkProcess>(
+    fn fit<T: network::Network>(
         &self,
         net: &T,
-        dataset: &Dataset,
+        dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params;
 }
 
-pub fn sufficient_statistics<T: process::NetworkProcess>(
+pub fn sufficient_statistics<T: network::Network>(
     net: &T,
-    dataset: &Dataset,
+    dataset: &tools::Dataset,
     node: usize,
     parent_set: &BTreeSet<usize>,
 ) -> (Array3<usize>, Array2<f64>) {
@@ -73,10 +71,10 @@ pub fn sufficient_statistics<T: process::NetworkProcess>(
 pub struct MLE {}
 
 impl ParameterLearning for MLE {
-    fn fit<T: process::NetworkProcess>(
+    fn fit<T: network::Network>(
         &self,
         net: &T,
-        dataset: &Dataset,
+        dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params {
@@ -120,10 +118,10 @@ pub struct BayesianApproach {
 }
 
 impl ParameterLearning for BayesianApproach {
-    fn fit<T: process::NetworkProcess>(
+    fn fit<T: network::Network>(
         &self,
         net: &T,
-        dataset: &Dataset,
+        dataset: &tools::Dataset,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params {
@@ -144,10 +142,6 @@ impl ParameterLearning for BayesianApproach {
             .zip(M.mapv(|x| x as f64).axis_iter(Axis(2)))
             .for_each(|(mut C, m)| C.assign(&(&m.mapv(|y| y + alpha) / &T.mapv(|y| y + tau))));
 
-        CIM.outer_iter_mut().for_each(|mut C| {
-            C.diag_mut().fill(0.0);
-        });
-
         //Set the diagonal of the inner matrices to the the row sum multiplied by -1
         let tmp_diag_sum: Array2<f64> = CIM.sum_axis(Axis(2)).mapv(|x| x * -1.0);
         CIM.outer_iter_mut()
@@ -168,3 +162,20 @@ impl ParameterLearning for BayesianApproach {
         return n;
     }
 }
+
+pub struct Cache<P: ParameterLearning> {
+    parameter_learning: P,
+    dataset: tools::Dataset,
+}
+
+impl<P: ParameterLearning> Cache<P> {
+    pub fn fit<T: network::Network>(
+        &mut self,
+        net: &T,
+        node: usize,
+        parent_set: Option<BTreeSet<usize>>,
+    ) -> Params {
+        self.parameter_learning
+            .fit(net, &self.dataset, node, parent_set)
+    }
+}

reCTBN/src/params.rs

@@ -1,5 +1,3 @@
-//! Module containing methods to define different types of nodes.
-
 use std::collections::BTreeSet;
 
 use enum_dispatch::enum_dispatch;
@@ -20,13 +18,14 @@ pub enum ParamsError {
 }
 
 /// Allowed type of states
-#[derive(Clone, Hash, PartialEq, Eq, Debug)]
+#[derive(Clone)]
 pub enum StateType {
     Discrete(usize),
 }
 
-/// This is a core element for building different types of nodes; the goal is to define the set of
+/// Parameters
-/// methods required to describes a generic node.
+/// The Params trait is the core element for building different types of nodes. The goal is to
+/// define the set of method required to describes a generic node.
 #[enum_dispatch(Params)]
 pub trait ParamsTrait {
     fn reset_params(&mut self);
@@ -66,27 +65,25 @@ pub trait ParamsTrait {
     fn get_label(&self) -> &String;
 }
 
-/// Is a core element for building different types of nodes; the goal is to define all the
+/// The Params enum is the core element for building different types of nodes. The goal is to
-/// supported type of Parameters
+/// define all the supported type of Parameters
 #[derive(Clone)]
 #[enum_dispatch]
 pub enum Params {
     DiscreteStatesContinousTime(DiscreteStatesContinousTimeParams),
 }
 
+/// DiscreteStatesContinousTime.
 /// This represents the parameters of a classical discrete node for ctbn and it's composed by the
-/// following elements.
+/// following elements:
-///
+/// - **domain**: an ordered and exhaustive set of possible states
-/// # Arguments
+/// - **cim**: Conditional Intensity Matrix
-///
+/// - **Sufficient Statistics**: the sufficient statistics are mainly used during the parameter
-/// * `label` - node's variable name.
+///     learning task and are composed by:
-/// * `domain` - an ordered and exhaustive set of possible states.
+///     - **transitions**: number of transitions from one state to another given a specific
-/// * `cim` - Conditional Intensity Matrix.
+///     realization of the parent set
-/// * `transitions` - number of transitions from one state to another given a specific realization
+///     - **residence_time**: permanence time in each possible states given a specific
-///   of the parent set; is a sufficient statistics are mainly used during the parameter learning
+///     realization of the parent set
-///   task.
-/// * `residence_time` - residence time in each possible state, given a specific realization of the
-///   parent set; is a sufficient statistics are mainly used during the parameter learning task.
 #[derive(Clone)]
 pub struct DiscreteStatesContinousTimeParams {
     label: String,
@@ -112,12 +109,10 @@ impl DiscreteStatesContinousTimeParams {
         &self.cim
     }
 
-    /// Setter function for CIM.
+    ///Setter function for CIM.\\
-    ///
+    ///This function check if the cim is valid using the validate_params method.
-    /// This function checks if the CIM is valid using the [`validate_params`](self::ParamsTrait::validate_params) method:
+    ///- **Valid cim inserted**: it substitute the CIM in self.cim and return Ok(())
-    /// * **Valid CIM inserted** - it substitutes the CIM in `self.cim` and returns `Ok(())`.
+    ///- **Invalid cim inserted**: it replace the self.cim value with None and it retu  ParamsError
-    /// * **Invalid CIM inserted** - it replaces the `self.cim` value with `None` and it returns
-    ///   `ParamsError`.
     pub fn set_cim(&mut self, cim: Array3<f64>) -> Result<(), ParamsError> {
         self.cim = Some(cim);
         match self.validate_params() {
@@ -134,22 +129,22 @@ impl DiscreteStatesContinousTimeParams {
         self.cim = Some(cim);
     }
 
-    /// Getter function for transitions.
+    ///Getter function for transitions
     pub fn get_transitions(&self) -> &Option<Array3<usize>> {
         &self.transitions
     }
 
-    /// Setter function for transitions.
+    ///Setter function for transitions
     pub fn set_transitions(&mut self, transitions: Array3<usize>) {
         self.transitions = Some(transitions);
     }
 
-    /// Getter function for residence_time.
+    ///Getter function for residence_time
     pub fn get_residence_time(&self) -> &Option<Array2<f64>> {
         &self.residence_time
     }
 
-    /// Setter function for residence_time.
+    ///Setter function for residence_time
     pub fn set_residence_time(&mut self, residence_time: Array2<f64>) {
         self.residence_time = Some(residence_time);
     }
@@ -271,7 +266,7 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         if cim
             .sum_axis(Axis(2))
             .iter()
-            .any(|x| f64::abs(x.clone()) > f64::EPSILON.sqrt())
+            .any(|x| f64::abs(x.clone()) > f64::EPSILON * 3.0)
         {
             return Err(ParamsError::InvalidCIM(String::from(
                 "The sum of each row must be 0",

reCTBN/src/process.rs

@@ -1,120 +0,0 @@
-//! Defines methods for dealing with Probabilistic Graphical Models like the CTBNs
-
-pub mod ctbn;
-pub mod ctmp;
-
-use std::collections::BTreeSet;
-
-use thiserror::Error;
-
-use crate::params;
-
-/// Error types for trait Network
-#[derive(Error, Debug)]
-pub enum NetworkError {
-    #[error("Error during node insertion")]
-    NodeInsertionError(String),
-}
-
-/// This type is used to represent a specific realization of a generic NetworkProcess
-pub type NetworkProcessState = Vec<params::StateType>;
-
-/// It defines the required methods for a structure used as a Probabilistic Graphical Models (such
-/// as a CTBN).
-pub trait NetworkProcess: Sync {
-    fn initialize_adj_matrix(&mut self);
-    fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
-    /// Add an **directed edge** between a two nodes of the network.
-    ///
-    /// # Arguments
-    ///
-    /// * `parent` - parent node.
-    /// * `child` - child node.
-    fn add_edge(&mut self, parent: usize, child: usize);
-
-    /// Get all the indices of the nodes contained inside the network.
-    fn get_node_indices(&self) -> std::ops::Range<usize>;
-
-    /// Get the numbers of nodes contained in the network.
-    fn get_number_of_nodes(&self) -> usize;
-
-    /// Get the **node param**.
-    ///
-    /// # Arguments
-    ///
-    /// * `node_idx` - node index value.
-    ///
-    /// # Return
-    ///
-    /// * The selected **node param**.
-    fn get_node(&self, node_idx: usize) -> &params::Params;
-
-    /// Get the **node param**.
-    ///
-    /// # Arguments
-    ///
-    /// * `node_idx` - node index value.
-    ///
-    /// # Return
-    ///
-    /// * The selected **node mutable param**.
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut params::Params;
-
-    /// Compute the index that must be used to access the parameters of a `node`, given a specific
-    /// configuration of the network.
-    ///
-    /// Usually, the only values really used in `current_state` are the ones in the parent set of
-    /// the `node`.
-    ///
-    /// # Arguments
-    ///
-    /// * `node` - selected node.
-    /// * `current_state` - current configuration of the network.
-    ///
-    /// # Return
-    ///
-    /// * Index of the `node` relative to the network.
-    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize;
-
-    /// Compute the index that must be used to access the parameters of a `node`, given a specific
-    /// configuration of the network and a generic `parent_set`.
-    ///
-    /// Usually, the only values really used in `current_state` are the ones in the parent set of
-    /// the `node`.
-    ///
-    /// # Arguments
-    ///
-    /// * `current_state` - current configuration of the network.
-    /// * `parent_set` - parent set of the selected `node`.
-    ///
-    /// # Return
-    ///
-    /// * Index of the `node` relative to the network.
-    fn get_param_index_from_custom_parent_set(
-        &self,
-        current_state: &Vec<params::StateType>,
-        parent_set: &BTreeSet<usize>,
-    ) -> usize;
-
-    /// Get the **parent set** of a given **node**.
-    ///
-    /// # Arguments
-    ///
-    /// * `node` - node index value.
-    ///
-    /// # Return
-    ///
-    /// * The **parent set** of the selected node.
-    fn get_parent_set(&self, node: usize) -> BTreeSet<usize>;
-
-    /// Get the **children set** of a given **node**.
-    ///
-    /// # Arguments
-    ///
-    /// * `node` - node index value.
-    ///
-    /// # Return
-    ///
-    /// * The **children set** of the selected node.
-    fn get_children_set(&self, node: usize) -> BTreeSet<usize>;
-}

reCTBN/src/process/ctbn.rs

@@ -1,247 +0,0 @@
-//! Continuous Time Bayesian Network
-
-use std::collections::BTreeSet;
-
-use ndarray::prelude::*;
-
-use crate::params::{DiscreteStatesContinousTimeParams, Params, ParamsTrait, StateType};
-use crate::process;
-
-use super::ctmp::CtmpProcess;
-use super::{NetworkProcess, NetworkProcessState};
-
-/// It represents both the structure and the parameters of a CTBN.
-///
-/// # Arguments
-///
-/// * `adj_matrix` - A 2D ndarray representing the adjacency matrix
-/// * `nodes` - A vector containing all the nodes and their parameters.
-///
-/// The index of a node inside the vector is also used as index for the `adj_matrix`.
-///
-/// # Example
-///
-/// ```rust
-/// use std::collections::BTreeSet;
-/// use reCTBN::process::NetworkProcess;
-/// use reCTBN::params;
-/// use reCTBN::process::ctbn::*;
-///
-/// //Create the domain for a discrete node
-/// let mut domain = BTreeSet::new();
-/// domain.insert(String::from("A"));
-/// domain.insert(String::from("B"));
-///
-/// //Create the parameters for a discrete node using the domain
-/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
-///
-/// //Create the node using the parameters
-/// let X1 = params::Params::DiscreteStatesContinousTime(param);
-///
-/// let mut domain = BTreeSet::new();
-/// domain.insert(String::from("A"));
-/// domain.insert(String::from("B"));
-/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
-/// let X2 = params::Params::DiscreteStatesContinousTime(param);
-///
-/// //Initialize a ctbn
-/// let mut net = CtbnNetwork::new();
-///
-/// //Add nodes
-/// let X1 = net.add_node(X1).unwrap();
-/// let X2 = net.add_node(X2).unwrap();
-///
-/// //Add an edge
-/// net.add_edge(X1, X2);
-///
-/// //Get all the children of node X1
-/// let cs = net.get_children_set(X1);
-/// assert_eq!(&X2, cs.iter().next().unwrap());
-/// ```
-pub struct CtbnNetwork {
-    adj_matrix: Option<Array2<u16>>,
-    nodes: Vec<Params>,
-}
-
-impl CtbnNetwork {
-    pub fn new() -> CtbnNetwork {
-        CtbnNetwork {
-            adj_matrix: None,
-            nodes: Vec::new(),
-        }
-    }
-
-    ///Transform the **CTBN** into a **CTMP**
-    ///
-    /// # Return
-    ///
-    /// * The equivalent *CtmpProcess* computed from the current CtbnNetwork
-    pub fn amalgamation(&self) -> CtmpProcess {
-        let variables_domain =
-            Array1::from_iter(self.nodes.iter().map(|x| x.get_reserved_space_as_parent()));
-
-        let state_space = variables_domain.product();
-        let variables_set = BTreeSet::from_iter(self.get_node_indices());
-        let mut amalgamated_cim: Array3<f64> = Array::zeros((1, state_space, state_space));
-
-        for idx_current_state in 0..state_space {
-            let current_state = CtbnNetwork::idx_to_state(&variables_domain, idx_current_state);
-            let current_state_statetype: NetworkProcessState = current_state
-                .iter()
-                .map(|x| StateType::Discrete(*x))
-                .collect();
-            for idx_node in 0..self.nodes.len() {
-                let p = match self.get_node(idx_node) {
-                    Params::DiscreteStatesContinousTime(p) => p,
-                };
-                for next_node_state in 0..variables_domain[idx_node] {
-                    let mut next_state = current_state.clone();
-                    next_state[idx_node] = next_node_state;
-
-                    let next_state_statetype: NetworkProcessState =
-                        next_state.iter().map(|x| StateType::Discrete(*x)).collect();
-                    let idx_next_state = self.get_param_index_from_custom_parent_set(
-                        &next_state_statetype,
-                        &variables_set,
-                    );
-                    amalgamated_cim[[0, idx_current_state, idx_next_state]] +=
-                        p.get_cim().as_ref().unwrap()[[
-                            self.get_param_index_network(idx_node, &current_state_statetype),
-                            current_state[idx_node],
-                            next_node_state,
-                        ]];
-                }
-            }
-        }
-
-        let mut amalgamated_param = DiscreteStatesContinousTimeParams::new(
-            "ctmp".to_string(),
-            BTreeSet::from_iter((0..state_space).map(|x| x.to_string())),
-        );
-
-        amalgamated_param.set_cim(amalgamated_cim).unwrap();
-
-        let mut ctmp = CtmpProcess::new();
-
-        ctmp.add_node(Params::DiscreteStatesContinousTime(amalgamated_param))
-            .unwrap();
-        return ctmp;
-    }
-
-    pub fn idx_to_state(variables_domain: &Array1<usize>, state: usize) -> Array1<usize> {
-        let mut state = state;
-        let mut array_state = Array1::zeros(variables_domain.shape()[0]);
-        for (idx, var) in variables_domain.indexed_iter() {
-            array_state[idx] = state % var;
-            state = state / var;
-        }
-
-        return array_state;
-    }
-    /// Get the Adjacency Matrix.
-    pub fn get_adj_matrix(&self) -> Option<&Array2<u16>> {
-        self.adj_matrix.as_ref()
-    }
-}
-
-impl process::NetworkProcess for CtbnNetwork {
-    /// Initialize an Adjacency matrix.
-    fn initialize_adj_matrix(&mut self) {
-        self.adj_matrix = Some(Array2::<u16>::zeros(
-            (self.nodes.len(), self.nodes.len()).f(),
-        ));
-    }
-
-    /// Add a new node.
-    fn add_node(&mut self, mut n: Params) -> Result<usize, process::NetworkError> {
-        n.reset_params();
-        self.adj_matrix = Option::None;
-        self.nodes.push(n);
-        Ok(self.nodes.len() - 1)
-    }
-
-    /// Connect two nodes with a new edge.
-    fn add_edge(&mut self, parent: usize, child: usize) {
-        if let None = self.adj_matrix {
-            self.initialize_adj_matrix();
-        }
-
-        if let Some(network) = &mut self.adj_matrix {
-            network[[parent, child]] = 1;
-            self.nodes[child].reset_params();
-        }
-    }
-
-    fn get_node_indices(&self) -> std::ops::Range<usize> {
-        0..self.nodes.len()
-    }
-
-    /// Get the number of nodes of the network.
-    fn get_number_of_nodes(&self) -> usize {
-        self.nodes.len()
-    }
-
-    fn get_node(&self, node_idx: usize) -> &Params {
-        &self.nodes[node_idx]
-    }
-
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut Params {
-        &mut self.nodes[node_idx]
-    }
-
-    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize {
-        self.adj_matrix
-            .as_ref()
-            .unwrap()
-            .column(node)
-            .iter()
-            .enumerate()
-            .fold((0, 1), |mut acc, x| {
-                if x.1 > &0 {
-                    acc.0 += self.nodes[x.0].state_to_index(&current_state[x.0]) * acc.1;
-                    acc.1 *= self.nodes[x.0].get_reserved_space_as_parent();
-                }
-                acc
-            })
-            .0
-    }
-
-    fn get_param_index_from_custom_parent_set(
-        &self,
-        current_state: &NetworkProcessState,
-        parent_set: &BTreeSet<usize>,
-    ) -> usize {
-        parent_set
-            .iter()
-            .fold((0, 1), |mut acc, x| {
-                acc.0 += self.nodes[*x].state_to_index(&current_state[*x]) * acc.1;
-                acc.1 *= self.nodes[*x].get_reserved_space_as_parent();
-                acc
-            })
-            .0
-    }
-
-    /// Get all the parents of the given node.
-    fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
-        self.adj_matrix
-            .as_ref()
-            .unwrap()
-            .column(node)
-            .iter()
-            .enumerate()
-            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
-            .collect()
-    }
-
-    /// Get all the children of the given node.
-    fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
-        self.adj_matrix
-            .as_ref()
-            .unwrap()
-            .row(node)
-            .iter()
-            .enumerate()
-            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
-            .collect()
-    }
-}

reCTBN/src/process/ctmp.rs

@@ -1,114 +0,0 @@
-use std::collections::BTreeSet;
-
-use crate::{
-    params::{Params, StateType},
-    process,
-};
-
-use super::{NetworkProcess, NetworkProcessState};
-
-pub struct CtmpProcess {
-    param: Option<Params>,
-}
-
-impl CtmpProcess {
-    pub fn new() -> CtmpProcess {
-        CtmpProcess { param: None }
-    }
-}
-
-impl NetworkProcess for CtmpProcess {
-    fn initialize_adj_matrix(&mut self) {
-        unimplemented!("CtmpProcess has only one node")
-    }
-
-    fn add_node(&mut self, n: crate::params::Params) -> Result<usize, process::NetworkError> {
-        match self.param {
-            None => {
-                self.param = Some(n);
-                Ok(0)
-            }
-            Some(_) => Err(process::NetworkError::NodeInsertionError(
-                "CtmpProcess has only one node".to_string(),
-            )),
-        }
-    }
-
-    fn add_edge(&mut self, _parent: usize, _child: usize) {
-        unimplemented!("CtmpProcess has only one node")
-    }
-
-    fn get_node_indices(&self) -> std::ops::Range<usize> {
-        match self.param {
-            None => 0..0,
-            Some(_) => 0..1,
-        }
-    }
-
-    fn get_number_of_nodes(&self) -> usize {
-        match self.param {
-            None => 0,
-            Some(_) => 1,
-        }
-    }
-
-    fn get_node(&self, node_idx: usize) -> &crate::params::Params {
-        if node_idx == 0 {
-            self.param.as_ref().unwrap()
-        } else {
-            unimplemented!("CtmpProcess has only one node")
-        }
-    }
-
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut crate::params::Params {
-        if node_idx == 0 {
-            self.param.as_mut().unwrap()
-        } else {
-            unimplemented!("CtmpProcess has only one node")
-        }
-    }
-
-    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize {
-        if node == 0 {
-            match current_state[0] {
-                StateType::Discrete(x) => x,
-            }
-        } else {
-            unimplemented!("CtmpProcess has only one node")
-        }
-    }
-
-    fn get_param_index_from_custom_parent_set(
-        &self,
-        _current_state: &NetworkProcessState,
-        _parent_set: &BTreeSet<usize>,
-    ) -> usize {
-        unimplemented!("CtmpProcess has only one node")
-    }
-
-    fn get_parent_set(&self, node: usize) -> std::collections::BTreeSet<usize> {
-        match self.param {
-            Some(_) => {
-                if node == 0 {
-                    BTreeSet::new()
-                } else {
-                    unimplemented!("CtmpProcess has only one node")
-                }
-            }
-            None => panic!("Uninitialized CtmpProcess"),
-        }
-    }
-
-    fn get_children_set(&self, node: usize) -> std::collections::BTreeSet<usize> {
-        match self.param {
-            Some(_) => {
-                if node == 0 {
-                    BTreeSet::new()
-                } else {
-                    unimplemented!("CtmpProcess has only one node")
-                }
-            }
-            None => panic!("Uninitialized CtmpProcess"),
-        }
-    }
-}

reCTBN/src/reward.rs

@@ -1,59 +0,0 @@
-pub mod reward_evaluation;
-pub mod reward_function;
-
-use std::collections::HashMap;
-
-use crate::process;
-
-/// Instantiation of reward function and instantaneous reward
-///
-///
-/// # Arguments
-///
-/// * `transition_reward`: reward obtained transitioning from one state to another
-/// * `instantaneous_reward`: reward per unit of time obtained staying in a specific state
-
-#[derive(Debug, PartialEq)]
-pub struct Reward {
-    pub transition_reward: f64,
-    pub instantaneous_reward: f64,
-}
-
-/// The trait RewardFunction describe the methods that all the reward functions must satisfy
-
-pub trait RewardFunction: Sync {
-    /// Given the current state and the previous state, it compute the reward.
-    ///
-    /// # Arguments
-    ///
-    /// * `current_state`: the current state of the network represented as a `process::NetworkProcessState`
-    /// * `previous_state`: an optional argument representing the previous state of the network
-
-    fn call(
-        &self,
-        current_state: &process::NetworkProcessState,
-        previous_state: Option<&process::NetworkProcessState>,
-    ) -> Reward;
-
-    /// Initialize the RewardFunction internal accordingly to the structure of a NetworkProcess
-    ///
-    /// # Arguments
-    ///
-    /// * `p`: any structure that implements the trait `process::NetworkProcess`
-    fn initialize_from_network_process<T: process::NetworkProcess>(p: &T) -> Self;
-}
-
-pub trait RewardEvaluation {
-    fn evaluate_state_space<N: process::NetworkProcess, R: RewardFunction>(
-        &self,
-        network_process: &N,
-        reward_function: &R,
-    ) -> HashMap<process::NetworkProcessState, f64>;
-
-    fn evaluate_state<N: process::NetworkProcess, R: RewardFunction>(
-        &self,
-        network_process: &N,
-        reward_function: &R,
-        state: &process::NetworkProcessState,
-    ) -> f64;
-}

reCTBN/src/reward/reward_evaluation.rs

@@ -1,205 +0,0 @@
-use std::collections::HashMap;
-
-use rayon::prelude::{IntoParallelIterator, ParallelIterator};
-use statrs::distribution::ContinuousCDF;
-
-use crate::params::{self, ParamsTrait};
-use crate::process;
-
-use crate::{
-    process::NetworkProcessState,
-    reward::RewardEvaluation,
-    sampling::{ForwardSampler, Sampler},
-};
-
-pub enum RewardCriteria {
-    FiniteHorizon,
-    InfiniteHorizon { discount_factor: f64 },
-}
-
-pub struct MonteCarloReward {
-    max_iterations: usize,
-    max_err_stop: f64,
-    alpha_stop: f64,
-    end_time: f64,
-    reward_criteria: RewardCriteria,
-    seed: Option<u64>,
-}
-
-impl MonteCarloReward {
-    pub fn new(
-        max_iterations: usize,
-        max_err_stop: f64,
-        alpha_stop: f64,
-        end_time: f64,
-        reward_criteria: RewardCriteria,
-        seed: Option<u64>,
-    ) -> MonteCarloReward {
-        MonteCarloReward {
-            max_iterations,
-            max_err_stop,
-            alpha_stop,
-            end_time,
-            reward_criteria,
-            seed,
-        }
-    }
-}
-
-impl RewardEvaluation for MonteCarloReward {
-    fn evaluate_state_space<N: process::NetworkProcess, R: super::RewardFunction>(
-        &self,
-        network_process: &N,
-        reward_function: &R,
-    ) -> HashMap<process::NetworkProcessState, f64> {
-        let variables_domain: Vec<Vec<params::StateType>> = network_process
-            .get_node_indices()
-            .map(|x| match network_process.get_node(x) {
-                params::Params::DiscreteStatesContinousTime(x) => (0..x
-                    .get_reserved_space_as_parent())
-                    .map(|s| params::StateType::Discrete(s))
-                    .collect(),
-            })
-            .collect();
-
-        let n_states: usize = variables_domain.iter().map(|x| x.len()).product();
-
-        (0..n_states)
-            .into_par_iter()
-            .map(|s| {
-                let state: process::NetworkProcessState = variables_domain
-                    .iter()
-                    .fold((s, vec![]), |acc, x| {
-                        let mut acc = acc;
-                        let idx_s = acc.0 % x.len();
-                        acc.1.push(x[idx_s].clone());
-                        acc.0 = acc.0 / x.len();
-                        acc
-                    })
-                    .1;
-
-                let r = self.evaluate_state(network_process, reward_function, &state);
-                (state, r)
-            })
-            .collect()
-    }
-
-    fn evaluate_state<N: crate::process::NetworkProcess, R: super::RewardFunction>(
-        &self,
-        network_process: &N,
-        reward_function: &R,
-        state: &NetworkProcessState,
-    ) -> f64 {
-        let mut sampler =
-            ForwardSampler::new(network_process, self.seed.clone(), Some(state.clone()));
-        let mut expected_value = 0.0;
-        let mut squared_expected_value = 0.0;
-        let normal = statrs::distribution::Normal::new(0.0, 1.0).unwrap();
-
-        for i in 0..self.max_iterations {
-            sampler.reset();
-            let mut ret = 0.0;
-            let mut previous = sampler.next().unwrap();
-            while previous.t < self.end_time {
-                let current = sampler.next().unwrap();
-                if current.t > self.end_time {
-                    let r = reward_function.call(&previous.state, None);
-                    let discount = match self.reward_criteria {
-                        RewardCriteria::FiniteHorizon => self.end_time - previous.t,
-                        RewardCriteria::InfiniteHorizon { discount_factor } => {
-                            std::f64::consts::E.powf(-discount_factor * previous.t)
-                                - std::f64::consts::E.powf(-discount_factor * self.end_time)
-                        }
-                    };
-                    ret += discount * r.instantaneous_reward;
-                } else {
-                    let r = reward_function.call(&previous.state, Some(&current.state));
-                    let discount = match self.reward_criteria {
-                        RewardCriteria::FiniteHorizon => current.t - previous.t,
-                        RewardCriteria::InfiniteHorizon { discount_factor } => {
-                            std::f64::consts::E.powf(-discount_factor * previous.t)
-                                - std::f64::consts::E.powf(-discount_factor * current.t)
-                        }
-                    };
-                    ret += discount * r.instantaneous_reward;
-                    ret += match self.reward_criteria {
-                        RewardCriteria::FiniteHorizon => 1.0,
-                        RewardCriteria::InfiniteHorizon { discount_factor } => {
-                            std::f64::consts::E.powf(-discount_factor * current.t)
-                        }
-                    } * r.transition_reward;
-                }
-                previous = current;
-            }
-
-            let float_i = i as f64;
-            expected_value =
-                expected_value * float_i as f64 / (float_i + 1.0) + ret / (float_i + 1.0);
-            squared_expected_value = squared_expected_value * float_i as f64 / (float_i + 1.0)
-                + ret.powi(2) / (float_i + 1.0);
-
-            if i > 2 {
-                let var =
-                    (float_i + 1.0) / float_i * (squared_expected_value - expected_value.powi(2));
-                if self.alpha_stop
-                    - 2.0 * normal.cdf(-(float_i + 1.0).sqrt() * self.max_err_stop / var.sqrt())
-                    > 0.0
-                {
-                    return expected_value;
-                }
-            }
-        }
-
-        expected_value
-    }
-}
-
-pub struct NeighborhoodRelativeReward<RE: RewardEvaluation> {
-    inner_reward: RE,
-}
-
-impl<RE: RewardEvaluation> NeighborhoodRelativeReward<RE> {
-    pub fn new(inner_reward: RE) -> NeighborhoodRelativeReward<RE> {
-        NeighborhoodRelativeReward { inner_reward }
-    }
-}
-
-impl<RE: RewardEvaluation> RewardEvaluation for NeighborhoodRelativeReward<RE> {
-    fn evaluate_state_space<N: process::NetworkProcess, R: super::RewardFunction>(
-        &self,
-        network_process: &N,
-        reward_function: &R,
-    ) -> HashMap<process::NetworkProcessState, f64> {
-        let absolute_reward = self
-            .inner_reward
-            .evaluate_state_space(network_process, reward_function);
-
-        //This approach optimize memory. Maybe optimizing execution time can be better.
-        absolute_reward
-            .iter()
-            .map(|(k1, v1)| {
-                let mut max_val: f64 = 1.0;
-                absolute_reward.iter().for_each(|(k2, v2)| {
-                    let count_diff: usize = k1
-                        .iter()
-                        .zip(k2.iter())
-                        .map(|(s1, s2)| if s1 == s2 { 0 } else { 1 })
-                        .sum();
-                    if count_diff < 2 {
-                        max_val = max_val.max(v1 / v2);
-                    }
-                });
-                (k1.clone(), max_val)
-            })
-            .collect()
-    }
-
-    fn evaluate_state<N: process::NetworkProcess, R: super::RewardFunction>(
-        &self,
-        _network_process: &N,
-        _reward_function: &R,
-        _state: &process::NetworkProcessState,
-    ) -> f64 {
-        unimplemented!();
-    }
-}

reCTBN/src/reward/reward_function.rs

@@ -1,106 +0,0 @@
-//! Module for dealing with reward functions
-
-use crate::{
-    params::{self, ParamsTrait},
-    process,
-    reward::{Reward, RewardFunction},
-};
-
-use ndarray;
-
-/// Reward function over a factored state space
-///
-/// The `FactoredRewardFunction` assume the reward function is the sum of the reward of each node
-/// of the underling `NetworkProcess`
-///
-/// # Arguments
-///
-/// * `transition_reward`: a vector of two-dimensional arrays. Each array contains the transition
-/// reward of a node
-
-pub struct FactoredRewardFunction {
-    transition_reward: Vec<ndarray::Array2<f64>>,
-    instantaneous_reward: Vec<ndarray::Array1<f64>>,
-}
-
-impl FactoredRewardFunction {
-    pub fn get_transition_reward(&self, node_idx: usize) -> &ndarray::Array2<f64> {
-        &self.transition_reward[node_idx]
-    }
-
-    pub fn get_transition_reward_mut(&mut self, node_idx: usize) -> &mut ndarray::Array2<f64> {
-        &mut self.transition_reward[node_idx]
-    }
-
-    pub fn get_instantaneous_reward(&self, node_idx: usize) -> &ndarray::Array1<f64> {
-        &self.instantaneous_reward[node_idx]
-    }
-
-    pub fn get_instantaneous_reward_mut(&mut self, node_idx: usize) -> &mut ndarray::Array1<f64> {
-        &mut self.instantaneous_reward[node_idx]
-    }
-}
-
-impl RewardFunction for FactoredRewardFunction {
-    fn call(
-        &self,
-        current_state: &process::NetworkProcessState,
-        previous_state: Option<&process::NetworkProcessState>,
-    ) -> Reward {
-        let instantaneous_reward: f64 = current_state
-            .iter()
-            .enumerate()
-            .map(|(idx, x)| {
-                let x = match x {
-                    params::StateType::Discrete(x) => x,
-                };
-                self.instantaneous_reward[idx][*x]
-            })
-            .sum();
-        if let Some(previous_state) = previous_state {
-            let transition_reward = previous_state
-                .iter()
-                .zip(current_state.iter())
-                .enumerate()
-                .find_map(|(idx, (p, c))| -> Option<f64> {
-                    let p = match p {
-                        params::StateType::Discrete(p) => p,
-                    };
-                    let c = match c {
-                        params::StateType::Discrete(c) => c,
-                    };
-                    if p != c {
-                        Some(self.transition_reward[idx][[*p, *c]])
-                    } else {
-                        None
-                    }
-                })
-                .unwrap_or(0.0);
-            Reward {
-                transition_reward,
-                instantaneous_reward,
-            }
-        } else {
-            Reward {
-                transition_reward: 0.0,
-                instantaneous_reward,
-            }
-        }
-    }
-
-    fn initialize_from_network_process<T: process::NetworkProcess>(p: &T) -> Self {
-        let mut transition_reward: Vec<ndarray::Array2<f64>> = vec![];
-        let mut instantaneous_reward: Vec<ndarray::Array1<f64>> = vec![];
-        for i in p.get_node_indices() {
-            //This works only for discrete nodes!
-            let size: usize = p.get_node(i).get_reserved_space_as_parent();
-            instantaneous_reward.push(ndarray::Array1::zeros(size));
-            transition_reward.push(ndarray::Array2::zeros((size, size)));
-        }
-
-        FactoredRewardFunction {
-            transition_reward,
-            instantaneous_reward,
-        }
-    }
-}

reCTBN/src/sampling.rs

@@ -1,40 +1,27 @@
-//! Module containing methods for the sampling.
-
 use crate::{
-    params::ParamsTrait,
+    network::Network,
-    process::{NetworkProcess, NetworkProcessState},
+    params::{self, ParamsTrait},
 };
 use rand::SeedableRng;
 use rand_chacha::ChaCha8Rng;
 
-#[derive(Clone)]
+pub trait Sampler: Iterator {
-pub struct Sample {
-    pub t: f64,
-    pub state: NetworkProcessState,
-}
-
-pub trait Sampler: Iterator<Item = Sample> {
     fn reset(&mut self);
 }
 
 pub struct ForwardSampler<'a, T>
 where
-    T: NetworkProcess,
+    T: Network,
 {
     net: &'a T,
     rng: ChaCha8Rng,
     current_time: f64,
-    current_state: NetworkProcessState,
+    current_state: Vec<params::StateType>,
     next_transitions: Vec<Option<f64>>,
-    initial_state: Option<NetworkProcessState>,
 }
 
-impl<'a, T: NetworkProcess> ForwardSampler<'a, T> {
+impl<'a, T: Network> ForwardSampler<'a, T> {
-    pub fn new(
+    pub fn new(net: &'a T, seed: Option<u64>) -> ForwardSampler<'a, T> {
-        net: &'a T,
-        seed: Option<u64>,
-        initial_state: Option<NetworkProcessState>,
-    ) -> ForwardSampler<'a, T> {
         let rng: ChaCha8Rng = match seed {
             //If a seed is present use it to initialize the random generator.
             Some(seed) => SeedableRng::seed_from_u64(seed),
@@ -42,20 +29,19 @@ impl<'a, T: NetworkProcess> ForwardSampler<'a, T> {
             None => SeedableRng::from_entropy(),
         };
         let mut fs = ForwardSampler {
-            net,
+            net: net,
-            rng,
+            rng: rng,
             current_time: 0.0,
             current_state: vec![],
             next_transitions: vec![],
-            initial_state,
         };
         fs.reset();
         return fs;
     }
 }
 
-impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
+impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
-    type Item = Sample;
+    type Item = (f64, Vec<params::StateType>);
 
     fn next(&mut self) -> Option<Self::Item> {
         let ret_time = self.current_time.clone();
@@ -108,26 +94,18 @@ impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
             self.next_transitions[child] = None;
         }
 
-        Some(Sample {
+        Some((ret_time, ret_state))
-            t: ret_time,
-            state: ret_state,
-        })
     }
 }
 
-impl<'a, T: NetworkProcess> Sampler for ForwardSampler<'a, T> {
+impl<'a, T: Network> Sampler for ForwardSampler<'a, T> {
     fn reset(&mut self) {
         self.current_time = 0.0;
-        match &self.initial_state {
-            None => {
         self.current_state = self
             .net
             .get_node_indices()
             .map(|x| self.net.get_node(x).get_random_state_uniform(&mut self.rng))
-                    .collect()
+            .collect();
-            }
-            Some(is) => self.current_state = is.clone(),
-        };
         self.next_transitions = self.net.get_node_indices().map(|_| Option::None).collect();
     }
 }

reCTBN/src/structure_learning.rs

@@ -1,13 +1,11 @@
-//! Learn the structure of the network.
-
 pub mod constraint_based_algorithm;
 pub mod hypothesis_test;
 pub mod score_based_algorithm;
 pub mod score_function;
-use crate::{process, tools::Dataset};
+use crate::{network, tools};
 
 pub trait StructureLearningAlgorithm {
-    fn fit_transform<T>(&self, net: T, dataset: &Dataset) -> T
+    fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
-        T: process::NetworkProcess;
+        T: network::Network;
 }

reCTBN/src/structure_learning/constraint_based_algorithm.rs

@@ -1,348 +1,3 @@
-//! Module containing constraint based algorithms like CTPC and Hiton.
+//pub struct CTPC {
-
+//
-use crate::params::Params;
+//}
-use itertools::Itertools;
-use rayon::iter::{IntoParallelIterator, ParallelIterator};
-use rayon::prelude::ParallelExtend;
-use std::collections::{BTreeSet, HashMap};
-use std::mem;
-use std::usize;
-
-use super::hypothesis_test::*;
-use crate::parameter_learning::ParameterLearning;
-use crate::process;
-use crate::structure_learning::StructureLearningAlgorithm;
-use crate::tools::Dataset;
-
-pub struct Cache<'a, P: ParameterLearning> {
-    parameter_learning: &'a P,
-    cache_persistent_small: HashMap<Option<BTreeSet<usize>>, Params>,
-    cache_persistent_big: HashMap<Option<BTreeSet<usize>>, Params>,
-    parent_set_size_small: usize,
-}
-
-impl<'a, P: ParameterLearning> Cache<'a, P> {
-    pub fn new(parameter_learning: &'a P) -> Cache<'a, P> {
-        Cache {
-            parameter_learning,
-            cache_persistent_small: HashMap::new(),
-            cache_persistent_big: HashMap::new(),
-            parent_set_size_small: 0,
-        }
-    }
-    pub fn fit<T: process::NetworkProcess>(
-        &mut self,
-        net: &T,
-        dataset: &Dataset,
-        node: usize,
-        parent_set: Option<BTreeSet<usize>>,
-    ) -> Params {
-        let parent_set_len = parent_set.as_ref().unwrap().len();
-        if parent_set_len > self.parent_set_size_small + 1 {
-            //self.cache_persistent_small = self.cache_persistent_big;
-            mem::swap(
-                &mut self.cache_persistent_small,
-                &mut self.cache_persistent_big,
-            );
-            self.cache_persistent_big = HashMap::new();
-            self.parent_set_size_small += 1;
-        }
-
-        if parent_set_len > self.parent_set_size_small {
-            match self.cache_persistent_big.get(&parent_set) {
-                // TODO: Better not clone `params`, useless clock cycles, RAM use and I/O
-                // not cloning requires a minor and reasoned refactoring across the library
-                Some(params) => params.clone(),
-                None => {
-                    let params =
-                        self.parameter_learning
-                            .fit(net, dataset, node, parent_set.clone());
-                    self.cache_persistent_big.insert(parent_set, params.clone());
-                    params
-                }
-            }
-        } else {
-            match self.cache_persistent_small.get(&parent_set) {
-                // TODO: Better not clone `params`, useless clock cycles, RAM use and I/O
-                // not cloning requires a minor and reasoned refactoring across the library
-                Some(params) => params.clone(),
-                None => {
-                    let params =
-                        self.parameter_learning
-                            .fit(net, dataset, node, parent_set.clone());
-                    self.cache_persistent_small
-                        .insert(parent_set, params.clone());
-                    params
-                }
-            }
-        }
-    }
-}
-
-/// Continuous-Time Peter Clark algorithm.
-///
-/// A method to learn the structure of the network.
-///
-/// # Arguments
-///
-/// * [`parameter_learning`](crate::parameter_learning) - is the method used to learn the parameters.
-/// * [`Ftest`](crate::structure_learning::hypothesis_test::F) - is the F-test hyppothesis test.
-/// * [`Chi2test`](crate::structure_learning::hypothesis_test::ChiSquare) - is the chi-squared test (χ2 test) hypothesis test.
-/// # Example
-///
-/// ```rust
-/// # use std::collections::BTreeSet;
-/// # use ndarray::{arr1, arr2, arr3};
-/// # use reCTBN::params;
-/// # use reCTBN::tools::trajectory_generator;
-/// # use reCTBN::process::NetworkProcess;
-/// # use reCTBN::process::ctbn::CtbnNetwork;
-/// use reCTBN::parameter_learning::BayesianApproach;
-/// use reCTBN::structure_learning::StructureLearningAlgorithm;
-/// use reCTBN::structure_learning::hypothesis_test::{F, ChiSquare};
-/// use reCTBN::structure_learning::constraint_based_algorithm::CTPC;
-/// #
-/// # // Create the domain for a discrete node
-/// # let mut domain = BTreeSet::new();
-/// # domain.insert(String::from("A"));
-/// # domain.insert(String::from("B"));
-/// # domain.insert(String::from("C"));
-/// # // Create the parameters for a discrete node using the domain
-/// # let param = params::DiscreteStatesContinousTimeParams::new("n1".to_string(), domain);
-/// # //Create the node n1 using the parameters
-/// # let n1 = params::Params::DiscreteStatesContinousTime(param);
-/// #
-/// # let mut domain = BTreeSet::new();
-/// # domain.insert(String::from("D"));
-/// # domain.insert(String::from("E"));
-/// # domain.insert(String::from("F"));
-/// # let param = params::DiscreteStatesContinousTimeParams::new("n2".to_string(), domain);
-/// # let n2 = params::Params::DiscreteStatesContinousTime(param);
-/// #
-/// # let mut domain = BTreeSet::new();
-/// # domain.insert(String::from("G"));
-/// # domain.insert(String::from("H"));
-/// # domain.insert(String::from("I"));
-/// # domain.insert(String::from("F"));
-/// # let param = params::DiscreteStatesContinousTimeParams::new("n3".to_string(), domain);
-/// # let n3 = params::Params::DiscreteStatesContinousTime(param);
-/// #
-/// # // Initialize a ctbn
-/// # let mut net = CtbnNetwork::new();
-/// #
-/// # // Add the nodes and their edges
-/// # let n1 = net.add_node(n1).unwrap();
-/// # let n2 = net.add_node(n2).unwrap();
-/// # let n3 = net.add_node(n3).unwrap();
-/// # net.add_edge(n1, n2);
-/// # net.add_edge(n1, n3);
-/// # net.add_edge(n2, n3);
-/// #
-/// # match &mut net.get_node_mut(n1) {
-/// # params::Params::DiscreteStatesContinousTime(param) => {
-/// #     assert_eq!(
-/// #         Ok(()),
-/// #         param.set_cim(arr3(&[
-/// #             [
-/// #                 [-3.0, 2.0, 1.0],
-/// #                 [1.5, -2.0, 0.5],
-/// #                 [0.4, 0.6, -1.0]
-/// #             ],
-/// #         ]))
-/// #     );
-/// # }
-/// # }
-/// #
-/// # match &mut net.get_node_mut(n2) {
-/// # params::Params::DiscreteStatesContinousTime(param) => {
-/// #     assert_eq!(
-/// #         Ok(()),
-/// #         param.set_cim(arr3(&[
-/// #             [
-/// #                 [-1.0, 0.5, 0.5],
-/// #                 [3.0, -4.0, 1.0],
-/// #                 [0.9, 0.1, -1.0]
-/// #             ],
-/// #             [
-/// #                 [-6.0, 2.0, 4.0],
-/// #                 [1.5, -2.0, 0.5],
-/// #                 [3.0, 1.0, -4.0]
-/// #             ],
-/// #             [
-/// #                 [-1.0, 0.1, 0.9],
-/// #                 [2.0, -2.5, 0.5],
-/// #                 [0.9, 0.1, -1.0]
-/// #             ],
-/// #         ]))
-/// #     );
-/// # }
-/// # }
-/// #
-/// # match &mut net.get_node_mut(n3) {
-/// # params::Params::DiscreteStatesContinousTime(param) => {
-/// #     assert_eq!(
-/// #         Ok(()),
-/// #         param.set_cim(arr3(&[
-/// #             [
-/// #                 [-1.0, 0.5, 0.3, 0.2],
-/// #                 [0.5, -4.0, 2.5, 1.0],
-/// #                 [2.5, 0.5, -4.0, 1.0],
-/// #                 [0.7, 0.2, 0.1, -1.0]
-/// #             ],
-/// #             [
-/// #                 [-6.0, 2.0, 3.0, 1.0],
-/// #                 [1.5, -3.0, 0.5, 1.0],
-/// #                 [2.0, 1.3, -5.0, 1.7],
-/// #                 [2.5, 0.5, 1.0, -4.0]
-/// #             ],
-/// #             [
-/// #                 [-1.3, 0.3, 0.1, 0.9],
-/// #                 [1.4, -4.0, 0.5, 2.1],
-/// #                 [1.0, 1.5, -3.0, 0.5],
-/// #                 [0.4, 0.3, 0.1, -0.8]
-/// #             ],
-/// #             [
-/// #                 [-2.0, 1.0, 0.7, 0.3],
-/// #                 [1.3, -5.9, 2.7, 1.9],
-/// #                 [2.0, 1.5, -4.0, 0.5],
-/// #                 [0.2, 0.7, 0.1, -1.0]
-/// #             ],
-/// #             [
-/// #                 [-6.0, 1.0, 2.0, 3.0],
-/// #                 [0.5, -3.0, 1.0, 1.5],
-/// #                 [1.4, 2.1, -4.3, 0.8],
-/// #                 [0.5, 1.0, 2.5, -4.0]
-/// #             ],
-/// #             [
-/// #                 [-1.3, 0.9, 0.3, 0.1],
-/// #                 [0.1, -1.3, 0.2, 1.0],
-/// #                 [0.5, 1.0, -3.0, 1.5],
-/// #                 [0.1, 0.4, 0.3, -0.8]
-/// #             ],
-/// #             [
-/// #                 [-2.0, 1.0, 0.6, 0.4],
-/// #                 [2.6, -7.1, 1.4, 3.1],
-/// #                 [5.0, 1.0, -8.0, 2.0],
-/// #                 [1.4, 0.4, 0.2, -2.0]
-/// #             ],
-/// #             [
-/// #                 [-3.0, 1.0, 1.5, 0.5],
-/// #                 [3.0, -6.0, 1.0, 2.0],
-/// #                 [0.3, 0.5, -1.9, 1.1],
-/// #                 [5.0, 1.0, 2.0, -8.0]
-/// #             ],
-/// #             [
-/// #                 [-2.6, 0.6, 0.2, 1.8],
-/// #                 [2.0, -6.0, 3.0, 1.0],
-/// #                 [0.1, 0.5, -1.3, 0.7],
-/// #                 [0.8, 0.6, 0.2, -1.6]
-/// #             ],
-/// #         ]))
-/// #     );
-/// # }
-/// # }
-/// #
-/// # // Generate the trajectory
-/// # let data = trajectory_generator(&net, 300, 30.0, Some(4164901764658873));
-///
-/// // Initialize the hypothesis tests to pass to the CTPC with their
-/// // respective significance level `alpha`
-/// let f = F::new(1e-6);
-/// let chi_sq = ChiSquare::new(1e-4);
-/// // Use the bayesian approach to learn the parameters
-/// let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-///
-/// //Initialize CTPC
-/// let ctpc = CTPC::new(parameter_learning, f, chi_sq);
-///
-/// // Learn the structure of the network from the generated trajectory
-/// let net = ctpc.fit_transform(net, &data);
-/// #
-/// # // Compare the generated network with the original one
-/// # assert_eq!(BTreeSet::new(), net.get_parent_set(0));
-/// # assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
-/// # assert_eq!(BTreeSet::from_iter(vec![0, 1]), net.get_parent_set(2));
-/// ```
-pub struct CTPC<P: ParameterLearning> {
-    parameter_learning: P,
-    Ftest: F,
-    Chi2test: ChiSquare,
-}
-
-impl<P: ParameterLearning> CTPC<P> {
-    pub fn new(parameter_learning: P, Ftest: F, Chi2test: ChiSquare) -> CTPC<P> {
-        CTPC {
-            parameter_learning,
-            Ftest,
-            Chi2test,
-        }
-    }
-}
-
-impl<P: ParameterLearning> StructureLearningAlgorithm for CTPC<P> {
-    fn fit_transform<T>(&self, net: T, dataset: &Dataset) -> T
-    where
-        T: process::NetworkProcess,
-    {
-        //Check the coherence between dataset and network
-        if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] {
-            panic!("Dataset and Network must have the same number of variables.")
-        }
-
-        //Make the network mutable.
-        let mut net = net;
-
-        net.initialize_adj_matrix();
-
-        let mut learned_parent_sets: Vec<(usize, BTreeSet<usize>)> = vec![];
-        learned_parent_sets.par_extend(net.get_node_indices().into_par_iter().map(|child_node| {
-            let mut cache = Cache::new(&self.parameter_learning);
-            let mut candidate_parent_set: BTreeSet<usize> = net
-                .get_node_indices()
-                .into_iter()
-                .filter(|x| x != &child_node)
-                .collect();
-            let mut separation_set_size = 0;
-            while separation_set_size < candidate_parent_set.len() {
-                let mut candidate_parent_set_TMP = candidate_parent_set.clone();
-                for parent_node in candidate_parent_set.iter() {
-                    for separation_set in candidate_parent_set
-                        .iter()
-                        .filter(|x| x != &parent_node)
-                        .map(|x| *x)
-                        .combinations(separation_set_size)
-                    {
-                        let separation_set = separation_set.into_iter().collect();
-                        if self.Ftest.call(
-                            &net,
-                            child_node,
-                            *parent_node,
-                            &separation_set,
-                            dataset,
-                            &mut cache,
-                        ) && self.Chi2test.call(
-                            &net,
-                            child_node,
-                            *parent_node,
-                            &separation_set,
-                            dataset,
-                            &mut cache,
-                        ) {
-                            candidate_parent_set_TMP.remove(parent_node);
-                            break;
-                        }
-                    }
-                }
-                candidate_parent_set = candidate_parent_set_TMP;
-                separation_set_size += 1;
-            }
-            (child_node, candidate_parent_set)
-        }));
-        for (child_node, candidate_parent_set) in learned_parent_sets {
-            for parent_node in candidate_parent_set.iter() {
-                net.add_edge(*parent_node, child_node);
-            }
-        }
-        net
-    }
-}

reCTBN/src/structure_learning/hypothesis_test.rs

@@ -1,13 +1,10 @@
-//! Module for constraint based algorithms containing hypothesis test algorithms like chi-squared test, F test, etc...
-
 use std::collections::BTreeSet;
 
 use ndarray::{Array3, Axis};
-use statrs::distribution::{ChiSquared, ContinuousCDF, FisherSnedecor};
+use statrs::distribution::{ChiSquared, ContinuousCDF};
 
 use crate::params::*;
-use crate::structure_learning::constraint_based_algorithm::Cache;
+use crate::{network, parameter_learning};
-use crate::{parameter_learning, process, tools::Dataset};
 
 pub trait HypothesisTest {
     fn call<T, P>(
@@ -16,167 +13,23 @@ pub trait HypothesisTest {
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        dataset: &Dataset,
+        cache: &mut parameter_learning::Cache<P>,
-        cache: &mut Cache<P>,
     ) -> bool
     where
-        T: process::NetworkProcess,
+        T: network::Network,
         P: parameter_learning::ParameterLearning;
 }
 
-/// Does the chi-squared test (χ2 test).
-///
-/// Used to determine if a difference between two sets of data is due to chance, or if it is due to
-/// a relationship (dependence) between the variables.
-///
-/// # Arguments
-///
-/// * `alpha` - is the significance level, the probability to reject a true null hypothesis;
-///   in other words is the risk of concluding that an association between the variables exists
-///   when there is no actual association.
-
 pub struct ChiSquare {
     alpha: f64,
 }
 
-/// Does the F-test.
+pub struct F {}
-///
-/// Used to determine if a difference between two sets of data is due to chance, or if it is due to
-/// a relationship (dependence) between the variables.
-///
-/// # Arguments
-///
-/// * `alpha` - is the significance level, the probability to reject a true null hypothesis;
-///   in other words is the risk of concluding that an association between the variables exists
-///   when there is no actual association.
-
-pub struct F {
-    alpha: f64,
-}
-
-impl F {
-    pub fn new(alpha: f64) -> F {
-        F { alpha }
-    }
-
-    /// Compare two matrices extracted from two 3rd-orer tensors.
-    ///
-    /// # Arguments
-    ///
-    /// * `i` - Position of the matrix of `M1` to compare with `M2`.
-    /// * `M1` - 3rd-order tensor 1.
-    /// * `j` - Position of the matrix of `M2` to compare with `M1`.
-    /// * `M2` - 3rd-order tensor 2.
-    ///
-    /// # Returns
-    ///
-    /// * `true` - when the matrices `M1` and `M2` are very similar, then **independendent**.
-    /// * `false` - when the matrices `M1` and `M2` are too different, then **dependent**.
-
-    pub fn compare_matrices(
-        &self,
-        i: usize,
-        M1: &Array3<usize>,
-        cim_1: &Array3<f64>,
-        j: usize,
-        M2: &Array3<usize>,
-        cim_2: &Array3<f64>,
-    ) -> bool {
-        let M1 = M1.index_axis(Axis(0), i).mapv(|x| x as f64);
-        let M2 = M2.index_axis(Axis(0), j).mapv(|x| x as f64);
-        let cim_1 = cim_1.index_axis(Axis(0), i);
-        let cim_2 = cim_2.index_axis(Axis(0), j);
-        let r1 = M1.sum_axis(Axis(1));
-        let r2 = M2.sum_axis(Axis(1));
-        let q1 = cim_1.diag();
-        let q2 = cim_2.diag();
-        for idx in 0..r1.shape()[0] {
-            let s = q2[idx] / q1[idx];
-            let F = FisherSnedecor::new(r1[idx], r2[idx]).unwrap();
-            let s = F.cdf(s);
-            let lim_sx = self.alpha / 2.0;
-            let lim_dx = 1.0 - (self.alpha / 2.0);
-            if s < lim_sx || s > lim_dx {
-                return false;
-            }
-        }
-        true
-    }
-}
-
-impl HypothesisTest for F {
-    fn call<T, P>(
-        &self,
-        net: &T,
-        child_node: usize,
-        parent_node: usize,
-        separation_set: &BTreeSet<usize>,
-        dataset: &Dataset,
-        cache: &mut Cache<P>,
-    ) -> bool
-    where
-        T: process::NetworkProcess,
-        P: parameter_learning::ParameterLearning,
-    {
-        let P_small = match cache.fit(net, &dataset, child_node, Some(separation_set.clone())) {
-            Params::DiscreteStatesContinousTime(node) => node,
-        };
-        let mut extended_separation_set = separation_set.clone();
-        extended_separation_set.insert(parent_node);
-
-        let P_big = match cache.fit(
-            net,
-            &dataset,
-            child_node,
-            Some(extended_separation_set.clone()),
-        ) {
-            Params::DiscreteStatesContinousTime(node) => node,
-        };
-        let partial_cardinality_product: usize = extended_separation_set
-            .iter()
-            .take_while(|x| **x != parent_node)
-            .map(|x| net.get_node(*x).get_reserved_space_as_parent())
-            .product();
-        for idx_M_big in 0..P_big.get_transitions().as_ref().unwrap().shape()[0] {
-            let idx_M_small: usize = idx_M_big % partial_cardinality_product
-                + (idx_M_big
-                    / (partial_cardinality_product
-                        * net.get_node(parent_node).get_reserved_space_as_parent()))
-                    * partial_cardinality_product;
-            if !self.compare_matrices(
-                idx_M_small,
-                P_small.get_transitions().as_ref().unwrap(),
-                P_small.get_cim().as_ref().unwrap(),
-                idx_M_big,
-                P_big.get_transitions().as_ref().unwrap(),
-                P_big.get_cim().as_ref().unwrap(),
-            ) {
-                return false;
-            }
-        }
-        return true;
-    }
-}
 
 impl ChiSquare {
     pub fn new(alpha: f64) -> ChiSquare {
         ChiSquare { alpha }
     }
-
-    /// Compare two matrices extracted from two 3rd-orer tensors.
-    ///
-    /// # Arguments
-    ///
-    /// * `i` - Position of the matrix of `M1` to compare with `M2`.
-    /// * `M1` - 3rd-order tensor 1.
-    /// * `j` - Position of the matrix of `M2` to compare with `M1`.
-    /// * `M2` - 3rd-order tensor 2.
-    ///
-    /// # Returns
-    ///
-    /// * `true` - when the matrices `M1` and `M2` are very similar, then **independendent**.
-    /// * `false` - when the matrices `M1` and `M2` are too different, then **dependent**.
-
     pub fn compare_matrices(
         &self,
         i: usize,
@@ -189,8 +42,26 @@ impl ChiSquare {
         // continuous-time Bayesian networks.
         // International Journal of Approximate Reasoning, 138, pp.105-122.
         // Also: https://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm
+        //
+        // M  = M            M  = M
+        //  1    xx'|s        2    xx'|y,s
         let M1 = M1.index_axis(Axis(0), i).mapv(|x| x as f64);
         let M2 = M2.index_axis(Axis(0), j).mapv(|x| x as f64);
+        //                   __________________
+        //                  /    ===
+        //                 /     \       M
+        //                /      /        xx'|s
+        //               /       ===
+        //              /    x'ϵVal /X \
+        //             /            \ i/                  1
+        //K =         /      ------------------       L = -
+        //           /           ===                      K
+        //          /            \       M
+        //         /             /        xx'|y,s
+        //        /              ===
+        //       /           x'ϵVal /X \
+        //   \  /                   \ i/
+        //    \/
         let K = M1.sum_axis(Axis(1)) / M2.sum_axis(Axis(1));
         let K = K.mapv(f64::sqrt);
         // Reshape to column vector.
@@ -199,12 +70,26 @@ impl ChiSquare {
             K.into_shape((n, 1)).unwrap()
         };
         let L = 1.0 / &K;
+        //        =====                   2
+        //         \      (K . M  - L . M)
+        //          \           2        1
+        //          /      ---------------
+        //         /           M  + M
+        //        =====         2    1
+        //     x'ϵVal /X \
+        //            \ i/
         let mut X_2 = (&K * &M2 - &L * &M1).mapv(|a| a.powi(2)) / (&M2 + &M1);
+        println!("M1: {:?}", M1);
+        println!("M2: {:?}", M2);
+        println!("L*M1: {:?}", (L * &M1));
+        println!("K*M2: {:?}", (K * &M2));
+        println!("X_2: {:?}", X_2);
         X_2.diag_mut().fill(0.0);
         let X_2 = X_2.sum_axis(Axis(1));
         let n = ChiSquared::new((X_2.dim() - 1) as f64).unwrap();
-        let ret = X_2.into_iter().all(|x| n.cdf(x) < (1.0 - self.alpha));
+        println!("CHI^2: {:?}", n);
-        ret
+        println!("CHI^2 CDF: {:?}", X_2.mapv(|x| n.cdf(x)));
+        X_2.into_iter().all(|x| n.cdf(x) < (1.0 - self.alpha))
     }
 }
 
@@ -215,27 +100,26 @@ impl HypothesisTest for ChiSquare {
         child_node: usize,
         parent_node: usize,
         separation_set: &BTreeSet<usize>,
-        dataset: &Dataset,
+        cache: &mut parameter_learning::Cache<P>,
-        cache: &mut Cache<P>,
     ) -> bool
     where
-        T: process::NetworkProcess,
+        T: network::Network,
         P: parameter_learning::ParameterLearning,
     {
-        let P_small = match cache.fit(net, &dataset, child_node, Some(separation_set.clone())) {
+        // Prendo dalla cache l'apprendimento dei parametri, che sarebbe una CIM
+        // di dimensione nxn
+        //  (CIM, M, T)
+        let P_small = match cache.fit(net, child_node, Some(separation_set.clone())) {
             Params::DiscreteStatesContinousTime(node) => node,
         };
+        //
         let mut extended_separation_set = separation_set.clone();
         extended_separation_set.insert(parent_node);
 
-        let P_big = match cache.fit(
+        let P_big = match cache.fit(net, child_node, Some(extended_separation_set.clone())) {
-            net,
-            &dataset,
-            child_node,
-            Some(extended_separation_set.clone()),
-        ) {
             Params::DiscreteStatesContinousTime(node) => node,
         };
+        // Commentare qui
         let partial_cardinality_product: usize = extended_separation_set
             .iter()
             .take_while(|x| **x != parent_node)

reCTBN/src/structure_learning/score_based_algorithm.rs

@@ -1,13 +1,8 @@
-//! Module containing score based algorithms like Hill Climbing and Tabu Search.
-
 use std::collections::BTreeSet;
 
 use crate::structure_learning::score_function::ScoreFunction;
 use crate::structure_learning::StructureLearningAlgorithm;
-use crate::{process, tools::Dataset};
+use crate::{network, tools};
-
-use rayon::iter::{IntoParallelIterator, ParallelIterator};
-use rayon::prelude::ParallelExtend;
 
 pub struct HillClimbing<S: ScoreFunction> {
     score_function: S,
@@ -24,9 +19,9 @@ impl<S: ScoreFunction> HillClimbing<S> {
 }
 
 impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
-    fn fit_transform<T>(&self, net: T, dataset: &Dataset) -> T
+    fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
-        T: process::NetworkProcess,
+        T: network::Network,
     {
         //Check the coherence between dataset and network
         if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] {
@@ -39,9 +34,8 @@ impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
         let max_parent_set = self.max_parent_set.unwrap_or(net.get_number_of_nodes());
         //Reset the adj matrix
         net.initialize_adj_matrix();
-        let mut learned_parent_sets: Vec<(usize, BTreeSet<usize>)> = vec![];
         //Iterate over each node to learn their parent set.
-        learned_parent_sets.par_extend(net.get_node_indices().into_par_iter().map(|node| {
+        for node in net.get_node_indices() {
             //Initialize an empty parent set.
             let mut parent_set: BTreeSet<usize> = BTreeSet::new();
             //Compute the score for the empty parent set
@@ -80,14 +74,10 @@ impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
                     }
                 }
             }
-            (node, parent_set)
+            //Apply the learned parent_set to the network struct.
-        }));
+            parent_set.iter().for_each(|p| net.add_edge(*p, node));
-
-        for (child_node, candidate_parent_set) in learned_parent_sets {
-            for parent_node in candidate_parent_set.iter() {
-                net.add_edge(*parent_node, child_node);
-            }
         }
+
         return net;
     }
 }

reCTBN/src/structure_learning/score_function.rs

@@ -1,13 +1,11 @@
-//! Module for score based algorithms containing score functions algorithms like Log Likelihood, BIC, etc...
-
 use std::collections::BTreeSet;
 
 use ndarray::prelude::*;
 use statrs::function::gamma;
 
-use crate::{parameter_learning, params, process, tools};
+use crate::{network, parameter_learning, params, tools};
 
-pub trait ScoreFunction: Sync {
+pub trait ScoreFunction {
     fn call<T>(
         &self,
         net: &T,
@@ -16,7 +14,7 @@ pub trait ScoreFunction: Sync {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: process::NetworkProcess;
+        T: network::Network;
 }
 
 pub struct LogLikelihood {
@@ -41,7 +39,7 @@ impl LogLikelihood {
         dataset: &tools::Dataset,
     ) -> (f64, Array3<usize>)
     where
-        T: process::NetworkProcess,
+        T: network::Network,
     {
         //Identify the type of node used
         match &net.get_node(node) {
@@ -100,7 +98,7 @@ impl ScoreFunction for LogLikelihood {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: process::NetworkProcess,
+        T: network::Network,
     {
         self.compute_score(net, node, parent_set, dataset).0
     }
@@ -127,7 +125,7 @@ impl ScoreFunction for BIC {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: process::NetworkProcess,
+        T: network::Network,
     {
         //Compute the log-likelihood
         let (ll, M) = self.ll.compute_score(net, node, parent_set, dataset);

reCTBN/src/tools.rs

@@ -1,15 +1,7 @@
-//! Contains commonly used methods used across the crate.
+use ndarray::prelude::*;
 
-use std::ops::{DivAssign, MulAssign, Range};
-
-use ndarray::{Array, Array1, Array2, Array3, Axis};
-use rand::{Rng, SeedableRng};
-use rand_chacha::ChaCha8Rng;
-
-use crate::params::ParamsTrait;
-use crate::process::NetworkProcess;
 use crate::sampling::{ForwardSampler, Sampler};
-use crate::{params, process};
+use crate::{network, params};
 
 #[derive(Clone)]
 pub struct Trajectory {
@@ -36,7 +28,6 @@ impl Trajectory {
     }
 }
 
-#[derive(Clone)]
 pub struct Dataset {
     trajectories: Vec<Trajectory>,
 }
@@ -59,7 +50,7 @@ impl Dataset {
     }
 }
 
-pub fn trajectory_generator<T: process::NetworkProcess>(
+pub fn trajectory_generator<T: network::Network>(
     net: &T,
     n_trajectories: u64,
     t_end: f64,
@@ -69,25 +60,26 @@ pub fn trajectory_generator<T: process::NetworkProcess>(
     let mut trajectories: Vec<Trajectory> = Vec::new();
 
     //Random Generator object
-    let mut sampler = ForwardSampler::new(net, seed, None);
+
+    let mut sampler = ForwardSampler::new(net, seed);
     //Each iteration generate one trajectory
     for _ in 0..n_trajectories {
         //History of all the moments in which something changed
         let mut time: Vec<f64> = Vec::new();
         //Configuration of the process variables at time t initialized with an uniform
         //distribution.
-        let mut events: Vec<process::NetworkProcessState> = Vec::new();
+        let mut events: Vec<Vec<params::StateType>> = Vec::new();
 
         //Current Time and Current State
-        let mut sample = sampler.next().unwrap();
+        let (mut t, mut current_state) = sampler.next().unwrap();
         //Generate new samples until ending time is reached.
-        while sample.t < t_end {
+        while t < t_end {
-            time.push(sample.t);
+            time.push(t);
-            events.push(sample.state);
+            events.push(current_state);
-            sample = sampler.next().unwrap();
+            (t, current_state) = sampler.next().unwrap();
         }
 
-        let current_state = events.last().unwrap().clone();
+        current_state = events.last().unwrap().clone();
         events.push(current_state);
 
         //Add t_end as last time.
@@ -113,243 +105,3 @@ pub fn trajectory_generator<T: process::NetworkProcess>(
     //Return a dataset object with the sampled trajectories.
     Dataset::new(trajectories)
 }
-
-pub trait RandomGraphGenerator {
-    fn new(density: f64, seed: Option<u64>) -> Self;
-    fn generate_graph<T: NetworkProcess>(&mut self, net: &mut T);
-}
-
-/// Graph Generator using an uniform distribution.
-///
-/// A method to generate a random graph with edges uniformly distributed.
-///
-/// # Arguments
-///
-/// * `density` - is the density of the graph in terms of edges; domain: `0.0 ≤ density ≤ 1.0`.
-/// * `rng` - is the random numbers generator.
-///
-/// # Example
-///
-/// ```rust
-/// # use std::collections::BTreeSet;
-/// # use ndarray::{arr1, arr2, arr3};
-/// # use reCTBN::params;
-/// # use reCTBN::params::Params::DiscreteStatesContinousTime;
-/// # use reCTBN::tools::trajectory_generator;
-/// # use reCTBN::process::NetworkProcess;
-/// # use reCTBN::process::ctbn::CtbnNetwork;
-/// use reCTBN::tools::UniformGraphGenerator;
-/// use reCTBN::tools::RandomGraphGenerator;
-/// # let mut net = CtbnNetwork::new();
-/// # let nodes_cardinality = 8;
-/// # let domain_cardinality = 4;
-/// # for node in 0..nodes_cardinality {
-/// #   // Create the domain for a discrete node
-/// #   let mut domain = BTreeSet::new();
-/// #   for dvalue in 0..domain_cardinality {
-/// #     domain.insert(dvalue.to_string());
-/// #   }
-/// #   // Create the parameters for a discrete node using the domain
-/// #   let param = params::DiscreteStatesContinousTimeParams::new(
-/// #     node.to_string(),
-/// #     domain
-/// #   );
-/// #   //Create the node using the parameters
-/// #   let node = DiscreteStatesContinousTime(param);
-/// #   // Add the node to the network
-/// #   net.add_node(node).unwrap();
-/// # }
-///
-/// // Initialize the Graph Generator using the one with an
-/// // uniform distribution
-/// let density = 1.0/3.0;
-/// let seed = Some(7641630759785120);
-/// let mut structure_generator = UniformGraphGenerator::new(
-///     density,
-///     seed
-/// );
-///
-/// // Generate the graph directly on the network
-/// structure_generator.generate_graph(&mut net);
-/// # // Count all the edges generated in the network
-/// # let mut edges = 0;
-/// # for node in net.get_node_indices(){
-/// #     edges += net.get_children_set(node).len()
-/// # }
-/// # // Number of all the nodes in the network
-/// # let nodes = net.get_node_indices().len() as f64;
-/// # let expected_edges = (density * nodes * (nodes - 1.0)).round() as usize;
-/// # // ±10% of tolerance
-/// # let tolerance = ((expected_edges as f64)*0.10) as usize;
-/// # // As the way `generate_graph()` is implemented we can only reasonably
-/// # // expect the number of edges to be somewhere around the expected value.
-/// # assert!((expected_edges - tolerance) <= edges && edges <= (expected_edges + tolerance));
-/// ```
-pub struct UniformGraphGenerator {
-    density: f64,
-    rng: ChaCha8Rng,
-}
-
-impl RandomGraphGenerator for UniformGraphGenerator {
-    fn new(density: f64, seed: Option<u64>) -> UniformGraphGenerator {
-        if density < 0.0 || density > 1.0 {
-            panic!(
-                "Density value must be between 1.0 and 0.0, got {}.",
-                density
-            );
-        }
-        let rng: ChaCha8Rng = match seed {
-            Some(seed) => SeedableRng::seed_from_u64(seed),
-            None => SeedableRng::from_entropy(),
-        };
-        UniformGraphGenerator { density, rng }
-    }
-
-    /// Generate an uniformly distributed graph.
-    fn generate_graph<T: NetworkProcess>(&mut self, net: &mut T) {
-        net.initialize_adj_matrix();
-        let last_node_idx = net.get_node_indices().len();
-        for parent in 0..last_node_idx {
-            for child in 0..last_node_idx {
-                if parent != child {
-                    if self.rng.gen_bool(self.density) {
-                        net.add_edge(parent, child);
-                    }
-                }
-            }
-        }
-    }
-}
-
-pub trait RandomParametersGenerator {
-    fn new(interval: Range<f64>, seed: Option<u64>) -> Self;
-    fn generate_parameters<T: NetworkProcess>(&mut self, net: &mut T);
-}
-
-/// Parameters Generator using an uniform distribution.
-///
-/// A method to generate random parameters uniformly distributed.
-///
-/// # Arguments
-///
-/// * `interval` - is the interval of the random values oh the CIM's diagonal; domain: `≥ 0.0`.
-/// * `rng` - is the random numbers generator.
-///
-/// # Example
-///
-/// ```rust
-/// # use std::collections::BTreeSet;
-/// # use ndarray::{arr1, arr2, arr3};
-/// # use reCTBN::params;
-/// # use reCTBN::params::ParamsTrait;
-/// # use reCTBN::params::Params::DiscreteStatesContinousTime;
-/// # use reCTBN::process::NetworkProcess;
-/// # use reCTBN::process::ctbn::CtbnNetwork;
-/// # use reCTBN::tools::trajectory_generator;
-/// # use reCTBN::tools::RandomGraphGenerator;
-/// # use reCTBN::tools::UniformGraphGenerator;
-/// use reCTBN::tools::RandomParametersGenerator;
-/// use reCTBN::tools::UniformParametersGenerator;
-/// # let mut net = CtbnNetwork::new();
-/// # let nodes_cardinality = 8;
-/// # let domain_cardinality = 4;
-/// # for node in 0..nodes_cardinality {
-/// #   // Create the domain for a discrete node
-/// #   let mut domain = BTreeSet::new();
-/// #   for dvalue in 0..domain_cardinality {
-/// #     domain.insert(dvalue.to_string());
-/// #   }
-/// #   // Create the parameters for a discrete node using the domain
-/// #   let param = params::DiscreteStatesContinousTimeParams::new(
-/// #     node.to_string(),
-/// #     domain
-/// #   );
-/// #   //Create the node using the parameters
-/// #   let node = DiscreteStatesContinousTime(param);
-/// #   // Add the node to the network
-/// #   net.add_node(node).unwrap();
-/// # }
-/// #
-/// # // Initialize the Graph Generator using the one with an
-/// # // uniform distribution
-/// # let mut structure_generator = UniformGraphGenerator::new(
-/// #     1.0/3.0,
-/// #     Some(7641630759785120)
-/// # );
-/// #
-/// # // Generate the graph directly on the network
-/// # structure_generator.generate_graph(&mut net);
-///
-/// // Initialize the parameters generator with uniform distributin
-/// let mut cim_generator = UniformParametersGenerator::new(
-///     0.0..7.0,
-///     Some(7641630759785120)
-/// );
-///
-/// // Generate CIMs with uniformly distributed parameters.
-/// cim_generator.generate_parameters(&mut net);
-/// #
-/// # for node in net.get_node_indices() {
-/// #     assert_eq!(
-/// #         Ok(()),
-/// #         net.get_node(node).validate_params()
-/// #     );
-/// }
-/// ```
-pub struct UniformParametersGenerator {
-    interval: Range<f64>,
-    rng: ChaCha8Rng,
-}
-
-impl RandomParametersGenerator for UniformParametersGenerator {
-    fn new(interval: Range<f64>, seed: Option<u64>) -> UniformParametersGenerator {
-        if interval.start < 0.0 || interval.end < 0.0 {
-            panic!(
-                "Interval must be entirely less or equal than 0, got {}..{}.",
-                interval.start, interval.end
-            );
-        }
-        let rng: ChaCha8Rng = match seed {
-            Some(seed) => SeedableRng::seed_from_u64(seed),
-            None => SeedableRng::from_entropy(),
-        };
-        UniformParametersGenerator { interval, rng }
-    }
-
-    /// Generate CIMs with uniformly distributed parameters.
-    fn generate_parameters<T: NetworkProcess>(&mut self, net: &mut T) {
-        for node in net.get_node_indices() {
-            let parent_set_state_space_cardinality: usize = net
-                .get_parent_set(node)
-                .iter()
-                .map(|x| net.get_node(*x).get_reserved_space_as_parent())
-                .product();
-            match &mut net.get_node_mut(node) {
-                params::Params::DiscreteStatesContinousTime(param) => {
-                    let node_domain_cardinality = param.get_reserved_space_as_parent();
-                    let mut cim = Array3::<f64>::from_shape_fn(
-                        (
-                            parent_set_state_space_cardinality,
-                            node_domain_cardinality,
-                            node_domain_cardinality,
-                        ),
-                        |_| self.rng.gen(),
-                    );
-                    cim.axis_iter_mut(Axis(0)).for_each(|mut x| {
-                        x.diag_mut().fill(0.0);
-                        x.div_assign(&x.sum_axis(Axis(1)).insert_axis(Axis(1)));
-                        let diag = Array1::<f64>::from_shape_fn(node_domain_cardinality, |_| {
-                            self.rng.gen_range(self.interval.clone())
-                        });
-                        x.mul_assign(&diag.clone().insert_axis(Axis(1)));
-                        // Recomputing the diagonal in order to reduce the issues caused by the
-                        // loss of precision when validating the parameters.
-                        let diag_sum = -x.sum_axis(Axis(1));
-                        x.diag_mut().assign(&diag_sum)
-                    });
-                    param.set_cim_unchecked(cim);
-                }
-            }
-        }
-    }
-}

reCTBN/tests/ctbn.rs

@@ -1,12 +1,9 @@
 mod utils;
 use std::collections::BTreeSet;
 
-
+use reCTBN::ctbn::*;
-use approx::AbsDiffEq;
+use reCTBN::network::Network;
-use ndarray::arr3;
 use reCTBN::params::{self, ParamsTrait};
-use reCTBN::process::NetworkProcess;
-use reCTBN::process::{ctbn::*};
 use utils::generate_discrete_time_continous_node;
 
 #[test]
@@ -132,245 +129,3 @@ fn compute_index_from_custom_parent_set() {
     );
     assert_eq!(2, idx);
 }
-
-#[test]
-fn simple_amalgamation() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-
-    net.initialize_adj_matrix();
-
-    match &mut net.get_node_mut(n1) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]])));
-        }
-    }
-
-    let ctmp = net.amalgamation();
-    let params::Params::DiscreteStatesContinousTime(p_ctbn) = &net.get_node(0);
-    let p_ctbn = p_ctbn.get_cim().as_ref().unwrap();
-    let params::Params::DiscreteStatesContinousTime(p_ctmp) = &ctmp.get_node(0);
-    let p_ctmp = p_ctmp.get_cim().as_ref().unwrap();
-
-    assert!(p_ctmp.abs_diff_eq(p_ctbn, std::f64::EPSILON));
-}
-
-#[test]
-fn chain_amalgamation() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    let n2 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
-        .unwrap();
-    let n3 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n3"), 2))
-        .unwrap();
-
-    net.add_edge(n1, n2);
-    net.add_edge(n2, n3);
-
-    match &mut net.get_node_mut(n1) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
-        }
-    }
-
-    match &mut net.get_node_mut(n2) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(
-                Ok(()),
-                param.set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]]
-                ]))
-            );
-        }
-    }
-
-    match &mut net.get_node_mut(n3) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(
-                Ok(()),
-                param.set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]]
-                ]))
-            );
-        }
-    }
-
-    let ctmp = net.amalgamation();
-
-
-
-    let params::Params::DiscreteStatesContinousTime(p_ctmp) = &ctmp.get_node(0);
-    let p_ctmp = p_ctmp.get_cim().as_ref().unwrap();
-
-    let p_ctmp_handmade = arr3(&[[
-        [
-            -1.20e-01, 1.00e-01, 1.00e-02, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            1.00e+00, -6.01e+00, 0.00e+00, 5.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00,
-        ],
-        [
-            5.00e+00, 0.00e+00, -1.01e+01, 1.00e-01, 0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 1.00e-02, 1.00e+00, -6.01e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00,
-        ],
-        [
-            5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -5.11e+00, 1.00e-01, 1.00e-02, 0.00e+00,
-        ],
-        [
-            0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, -1.10e+01, 0.00e+00, 5.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 5.00e+00, 0.00e+00, -5.11e+00, 1.00e-01,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 1.00e-02, 1.00e+00, -1.02e+00,
-        ],
-    ]]);
-
-    assert!(p_ctmp.abs_diff_eq(&p_ctmp_handmade, 1e-8));
-}
-
-#[test]
-fn chainfork_amalgamation() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    let n2 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
-        .unwrap();
-    let n3 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n3"), 2))
-        .unwrap();
-    let n4 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n4"), 2))
-        .unwrap();
-
-    net.add_edge(n1, n3);
-    net.add_edge(n2, n3);
-    net.add_edge(n3, n4);
-
-    match &mut net.get_node_mut(n1) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
-        }
-    }
-
-    match &mut net.get_node_mut(n2) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
-        }
-    }
-
-    match &mut net.get_node_mut(n3) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(
-                Ok(()),
-                param.set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]]
-                ]))
-            );
-        }
-    }
-
-    match &mut net.get_node_mut(n4) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(
-                Ok(()),
-                param.set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]]
-                ]))
-            );
-        }
-    }
-
-
-    let ctmp = net.amalgamation();
-
-    let params::Params::DiscreteStatesContinousTime(p_ctmp) = &ctmp.get_node(0); 
-
-    let p_ctmp = p_ctmp.get_cim().as_ref().unwrap();
-
-    let p_ctmp_handmade = arr3(&[[
-        [
-            -2.20e-01, 1.00e-01, 1.00e-01, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00,
-            1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            1.00e+00, -1.12e+00, 0.00e+00, 1.00e-01, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00,
-            0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            1.00e+00, 0.00e+00, -1.12e+00, 1.00e-01, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00,
-            0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 1.00e+00, 1.00e+00, -7.01e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00,
-            0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -1.02e+01, 1.00e-01, 1.00e-01, 0.00e+00,
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, -1.11e+01, 0.00e+00, 1.00e-01,
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, -1.11e+01, 1.00e-01,
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 1.00e+00, 1.00e+00, -7.01e+00,
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00,
-        ],
-        [
-            5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-            -5.21e+00, 1.00e-01, 1.00e-01, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-            1.00e+00, -6.11e+00, 0.00e+00, 1.00e-01, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-            1.00e+00, 0.00e+00, -6.11e+00, 1.00e-01, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00,
-            0.00e+00, 1.00e+00, 1.00e+00, -1.20e+01, 0.00e+00, 0.00e+00, 0.00e+00, 5.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00, 0.00e+00,
-            5.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, -5.21e+00, 1.00e-01, 1.00e-01, 0.00e+00,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 0.00e+00,
-            0.00e+00, 5.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, -6.11e+00, 0.00e+00, 1.00e-01,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00,
-            0.00e+00, 0.00e+00, 5.00e+00, 0.00e+00, 1.00e+00, 0.00e+00, -6.11e+00, 1.00e-01,
-        ],
-        [
-            0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02,
-            0.00e+00, 0.00e+00, 0.00e+00, 1.00e-02, 0.00e+00, 1.00e+00, 1.00e+00, -2.02e+00,
-        ],
-    ]]);
-
-    assert!(p_ctmp.abs_diff_eq(&p_ctmp_handmade, 1e-8));
-}

reCTBN/tests/ctmp.rs

@@ -1,127 +0,0 @@
-mod utils;
-
-use std::collections::BTreeSet;
-
-use reCTBN::{
-    params,
-    params::ParamsTrait,
-    process::{ctmp::*, NetworkProcess},
-};
-use utils::generate_discrete_time_continous_node;
-
-#[test]
-fn define_simple_ctmp() {
-    let _ = CtmpProcess::new();
-    assert!(true);
-}
-
-#[test]
-fn add_node_to_ctmp() {
-    let mut net = CtmpProcess::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    assert_eq!(&String::from("n1"), net.get_node(n1).get_label());
-}
-
-#[test]
-fn add_two_nodes_to_ctmp() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n1"), 2));
-
-    match n2 {
-        Ok(_) => assert!(false),
-        Err(_) => assert!(true),
-    };
-}
-
-#[test]
-#[should_panic]
-fn add_edge_to_ctmp() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    let _n2 = net.add_node(generate_discrete_time_continous_node(String::from("n1"), 2));
-
-    net.add_edge(0, 1)
-}
-
-#[test]
-fn childen_and_parents() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-
-    assert_eq!(0, net.get_parent_set(0).len());
-    assert_eq!(0, net.get_children_set(0).len());
-}
-
-#[test]
-#[should_panic]
-fn get_childen_panic() {
-    let net = CtmpProcess::new();
-    net.get_children_set(0);
-}
-
-#[test]
-#[should_panic]
-fn get_childen_panic2() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    net.get_children_set(1);
-}
-
-#[test]
-#[should_panic]
-fn get_parent_panic() {
-    let net = CtmpProcess::new();
-    net.get_parent_set(0);
-}
-
-#[test]
-#[should_panic]
-fn get_parent_panic2() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-    net.get_parent_set(1);
-}
-
-#[test]
-fn compute_index_ctmp() {
-    let mut net = CtmpProcess::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(
-            String::from("n1"),
-            10,
-        ))
-        .unwrap();
-
-    let idx = net.get_param_index_network(n1, &vec![params::StateType::Discrete(6)]);
-    assert_eq!(6, idx);
-}
-
-#[test]
-#[should_panic]
-fn compute_index_from_custom_parent_set_ctmp() {
-    let mut net = CtmpProcess::new();
-    let _n1 = net
-        .add_node(generate_discrete_time_continous_node(
-            String::from("n1"),
-            10,
-        ))
-        .unwrap();
-
-    let _idx = net.get_param_index_from_custom_parent_set(
-        &vec![params::StateType::Discrete(6)],
-        &BTreeSet::from([0])
-    );
-}

reCTBN/tests/parameter_learning.rs

@@ -2,11 +2,10 @@
 
 mod utils;
 use ndarray::arr3;
-use reCTBN::process::ctbn::*;
+use reCTBN::ctbn::*;
-use reCTBN::process::NetworkProcess;
+use reCTBN::network::Network;
 use reCTBN::parameter_learning::*;
 use reCTBN::params;
-use reCTBN::params::Params::DiscreteStatesContinousTime;
 use reCTBN::tools::*;
 use utils::*;
 
@@ -67,78 +66,18 @@ fn learn_binary_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
-fn generate_nodes(
-    net: &mut CtbnNetwork,
-    nodes_cardinality: usize,
-    nodes_domain_cardinality: usize
-) {
-    for node_label in 0..nodes_cardinality {
-        net.add_node(
-            generate_discrete_time_continous_node(
-                node_label.to_string(),
-                nodes_domain_cardinality,
-            )
-        ).unwrap();
-    }
-}
-
-fn learn_binary_cim_gen<T: ParameterLearning>(pl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 2);
-
-    net.add_edge(0, 1);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        1.0..6.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let p_gen = match net.get_node(1) {
-        DiscreteStatesContinousTime(p_gen) => p_gen,
-    };
-
-    let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
-    let p_tj = match pl.fit(&net, &data, 1, None) {
-        DiscreteStatesContinousTime(p_tj) => p_tj,
-    };
-
-    assert_eq!(
-        p_tj.get_cim().as_ref().unwrap().shape(),
-        p_gen.get_cim().as_ref().unwrap().shape()
-    );
-    assert!(
-        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
-            &p_gen.get_cim().as_ref().unwrap(),
-            0.1
-        )
-    );
-}
-
 #[test]
 fn learn_binary_cim_MLE() {
     let mle = MLE {};
     learn_binary_cim(mle);
 }
 
-#[test]
-fn learn_binary_cim_MLE_gen() {
-    let mle = MLE {};
-    learn_binary_cim_gen(mle);
-}
-
 #[test]
 fn learn_binary_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_binary_cim(ba);
 }
 
-#[test]
-fn learn_binary_cim_BA_gen() {
-    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-    learn_binary_cim_gen(ba);
-}
-
 fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -216,63 +155,18 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
-fn learn_ternary_cim_gen<T: ParameterLearning>(pl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-
-    net.add_edge(0, 1);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        4.0..6.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let p_gen = match net.get_node(1) {
-        DiscreteStatesContinousTime(p_gen) => p_gen,
-    };
-
-    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
-    let p_tj = match pl.fit(&net, &data, 1, None) {
-        DiscreteStatesContinousTime(p_tj) => p_tj,
-    };
-
-    assert_eq!(
-        p_tj.get_cim().as_ref().unwrap().shape(),
-        p_gen.get_cim().as_ref().unwrap().shape()
-    );
-    assert!(
-        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
-            &p_gen.get_cim().as_ref().unwrap(),
-            0.1
-        )
-    );
-}
-
 #[test]
 fn learn_ternary_cim_MLE() {
     let mle = MLE {};
     learn_ternary_cim(mle);
 }
 
-#[test]
-fn learn_ternary_cim_MLE_gen() {
-    let mle = MLE {};
-    learn_ternary_cim_gen(mle);
-}
-
 #[test]
 fn learn_ternary_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_ternary_cim(ba);
 }
 
-#[test]
-fn learn_ternary_cim_BA_gen() {
-    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-    learn_ternary_cim_gen(ba);
-}
-
 fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -340,63 +234,18 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
     ));
 }
 
-fn learn_ternary_cim_no_parents_gen<T: ParameterLearning>(pl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-
-    net.add_edge(0, 1);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        1.0..6.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let p_gen = match net.get_node(0) {
-        DiscreteStatesContinousTime(p_gen) => p_gen,
-    };
-
-    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
-    let p_tj = match pl.fit(&net, &data, 0, None) {
-        DiscreteStatesContinousTime(p_tj) => p_tj,
-    };
-
-    assert_eq!(
-        p_tj.get_cim().as_ref().unwrap().shape(),
-        p_gen.get_cim().as_ref().unwrap().shape()
-    );
-    assert!(
-        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
-            &p_gen.get_cim().as_ref().unwrap(),
-            0.1
-        )
-    );
-}
-
 #[test]
 fn learn_ternary_cim_no_parents_MLE() {
     let mle = MLE {};
     learn_ternary_cim_no_parents(mle);
 }
 
-#[test]
-fn learn_ternary_cim_no_parents_MLE_gen() {
-    let mle = MLE {};
-    learn_ternary_cim_no_parents_gen(mle);
-}
-
 #[test]
 fn learn_ternary_cim_no_parents_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_ternary_cim_no_parents(ba);
 }
 
-#[test]
-fn learn_ternary_cim_no_parents_BA_gen() {
-    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-    learn_ternary_cim_no_parents_gen(ba);
-}
-
 fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -583,66 +432,14 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     ));
 }
 
-fn learn_mixed_discrete_cim_gen<T: ParameterLearning>(pl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-    net.add_node(
-        generate_discrete_time_continous_node(
-            String::from("3"),
-            4
-        )
-    ).unwrap();
-    net.add_edge(0, 1);
-    net.add_edge(0, 2);
-    net.add_edge(1, 2);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        1.0..8.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let p_gen = match net.get_node(2) {
-        DiscreteStatesContinousTime(p_gen) => p_gen,
-    };
-
-    let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
-    let p_tj = match pl.fit(&net, &data, 2, None) {
-        DiscreteStatesContinousTime(p_tj) => p_tj,
-    };
-
-    assert_eq!(
-        p_tj.get_cim().as_ref().unwrap().shape(),
-        p_gen.get_cim().as_ref().unwrap().shape()
-    );
-    assert!(
-        p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
-            &p_gen.get_cim().as_ref().unwrap(),
-            0.2
-        )
-    );
-}
-
 #[test]
 fn learn_mixed_discrete_cim_MLE() {
     let mle = MLE {};
     learn_mixed_discrete_cim(mle);
 }
 
-#[test]
-fn learn_mixed_discrete_cim_MLE_gen() {
-    let mle = MLE {};
-    learn_mixed_discrete_cim_gen(mle);
-}
-
 #[test]
 fn learn_mixed_discrete_cim_BA() {
     let ba = BayesianApproach { alpha: 1, tau: 1.0 };
     learn_mixed_discrete_cim(ba);
 }
-
-#[test]
-fn learn_mixed_discrete_cim_BA_gen() {
-    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-    learn_mixed_discrete_cim_gen(ba);
-}

reCTBN/tests/reward_evaluation.rs

@@ -1,122 +0,0 @@
-mod utils;
-
-use approx::assert_abs_diff_eq;
-use ndarray::*;
-use reCTBN::{
-    params,
-    process::{ctbn::*, NetworkProcess, NetworkProcessState},
-    reward::{reward_evaluation::*, reward_function::*, *},
-};
-use utils::generate_discrete_time_continous_node;
-
-#[test]
-fn simple_factored_reward_function_binary_node_mc() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-
-    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
-    rf.get_transition_reward_mut(n1)
-        .assign(&arr2(&[[0.0, 0.0], [0.0, 0.0]]));
-    rf.get_instantaneous_reward_mut(n1)
-        .assign(&arr1(&[3.0, 3.0]));
-
-    match &mut net.get_node_mut(n1) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]])).unwrap();
-        }
-    }
-
-    net.initialize_adj_matrix();
-
-    let s0: NetworkProcessState = vec![params::StateType::Discrete(0)];
-    let s1: NetworkProcessState = vec![params::StateType::Discrete(1)];
-
-    let mc = MonteCarloReward::new(10000, 1e-1, 1e-1, 10.0, RewardCriteria::InfiniteHorizon { discount_factor: 1.0 }, Some(215));
-    assert_abs_diff_eq!(3.0, mc.evaluate_state(&net, &rf, &s0), epsilon = 1e-2);
-    assert_abs_diff_eq!(3.0, mc.evaluate_state(&net, &rf, &s1), epsilon = 1e-2);
-    
-    let rst = mc.evaluate_state_space(&net, &rf);
-    assert_abs_diff_eq!(3.0, rst[&s0], epsilon = 1e-2);
-    assert_abs_diff_eq!(3.0, rst[&s1], epsilon = 1e-2);
-
-
-    let mc = MonteCarloReward::new(10000, 1e-1, 1e-1, 10.0, RewardCriteria::FiniteHorizon, Some(215));
-    assert_abs_diff_eq!(30.0, mc.evaluate_state(&net, &rf, &s0), epsilon = 1e-2);
-    assert_abs_diff_eq!(30.0, mc.evaluate_state(&net, &rf, &s1), epsilon = 1e-2);
-    
-
-}
-
-#[test]
-fn simple_factored_reward_function_chain_mc() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-
-    let n2 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
-        .unwrap();
-
-    let n3 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n3"), 2))
-        .unwrap();
-
-    net.add_edge(n1, n2);
-    net.add_edge(n2, n3);
-
-    match &mut net.get_node_mut(n1) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])).unwrap();
-        }
-    }
-
-    match &mut net.get_node_mut(n2) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            param
-                .set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]],
-                ]))
-                .unwrap();
-        }
-    }
-
-
-    match &mut net.get_node_mut(n3) {
-        params::Params::DiscreteStatesContinousTime(param) => {
-            param
-                .set_cim(arr3(&[
-                    [[-0.01, 0.01], [5.0, -5.0]],
-                    [[-5.0, 5.0], [0.01, -0.01]],
-                ]))
-                .unwrap();
-        }
-    }
-
-
-    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
-    rf.get_transition_reward_mut(n1)
-        .assign(&arr2(&[[0.0, 1.0], [1.0, 0.0]]));
-
-    rf.get_transition_reward_mut(n2)
-        .assign(&arr2(&[[0.0, 1.0], [1.0, 0.0]]));
-
-    rf.get_transition_reward_mut(n3)
-        .assign(&arr2(&[[0.0, 1.0], [1.0, 0.0]]));
-
-    let s000: NetworkProcessState = vec![
-        params::StateType::Discrete(1),
-        params::StateType::Discrete(0),
-        params::StateType::Discrete(0),
-    ];
-
-    let mc = MonteCarloReward::new(10000, 1e-1, 1e-1, 10.0, RewardCriteria::InfiniteHorizon { discount_factor: 1.0 }, Some(215));
-    assert_abs_diff_eq!(2.447, mc.evaluate_state(&net, &rf, &s000), epsilon = 1e-1);
-
-    let rst = mc.evaluate_state_space(&net, &rf);
-    assert_abs_diff_eq!(2.447, rst[&s000], epsilon = 1e-1);
-
-}

reCTBN/tests/reward_function.rs

@@ -1,117 +0,0 @@
-mod utils;
-
-use ndarray::*;
-use utils::generate_discrete_time_continous_node;
-use reCTBN::{process::{NetworkProcess, ctbn::*, NetworkProcessState}, reward::{*, reward_function::*}, params};
-
-
-#[test]
-fn simple_factored_reward_function_binary_node() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-        .unwrap();
-
-    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
-    rf.get_transition_reward_mut(n1).assign(&arr2(&[[12.0, 1.0],[2.0,12.0]]));
-    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0]));
-    
-    let s0: NetworkProcessState = vec![params::StateType::Discrete(0)];
-    let s1: NetworkProcessState =  vec![params::StateType::Discrete(1)];
-    assert_eq!(rf.call(&s0, None), Reward{transition_reward: 0.0, instantaneous_reward: 3.0});
-    assert_eq!(rf.call(&s1, None), Reward{transition_reward: 0.0, instantaneous_reward: 5.0});
-
-
-    assert_eq!(rf.call(&s0, Some(&s1)), Reward{transition_reward: 2.0, instantaneous_reward: 3.0});
-    assert_eq!(rf.call(&s1, Some(&s0)), Reward{transition_reward: 1.0, instantaneous_reward: 5.0});
-
-    assert_eq!(rf.call(&s0, Some(&s0)), Reward{transition_reward: 0.0, instantaneous_reward: 3.0});
-    assert_eq!(rf.call(&s1, Some(&s1)), Reward{transition_reward: 0.0, instantaneous_reward: 5.0});
-}
-
-
-#[test]
-fn simple_factored_reward_function_ternary_node() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
-        .unwrap();
-
-    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
-    rf.get_transition_reward_mut(n1).assign(&arr2(&[[0.0, 1.0, 3.0],[2.0,0.0, 4.0], [5.0, 6.0, 0.0]]));
-    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0, 9.0]));
-    
-    let s0: NetworkProcessState = vec![params::StateType::Discrete(0)];
-    let s1: NetworkProcessState = vec![params::StateType::Discrete(1)];
-    let s2: NetworkProcessState = vec![params::StateType::Discrete(2)];
-
-
-    assert_eq!(rf.call(&s0, Some(&s1)), Reward{transition_reward: 2.0, instantaneous_reward: 3.0});
-    assert_eq!(rf.call(&s0, Some(&s2)), Reward{transition_reward: 5.0, instantaneous_reward: 3.0});
-
-
-    assert_eq!(rf.call(&s1, Some(&s0)), Reward{transition_reward: 1.0, instantaneous_reward: 5.0});
-    assert_eq!(rf.call(&s1, Some(&s2)), Reward{transition_reward: 6.0, instantaneous_reward: 5.0});
-
-
-    assert_eq!(rf.call(&s2, Some(&s0)), Reward{transition_reward: 3.0, instantaneous_reward: 9.0});
-    assert_eq!(rf.call(&s2, Some(&s1)), Reward{transition_reward: 4.0, instantaneous_reward: 9.0});
-}
-
-#[test]
-fn factored_reward_function_two_nodes() {
-    let mut net = CtbnNetwork::new();
-    let n1 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
-        .unwrap();
-    let n2 = net
-        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
-        .unwrap();
-    net.add_edge(n1, n2);
-
-
-    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
-    rf.get_transition_reward_mut(n1).assign(&arr2(&[[0.0, 1.0, 3.0],[2.0,0.0, 4.0], [5.0, 6.0, 0.0]]));
-    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0, 9.0]));
-
-
-    rf.get_transition_reward_mut(n2).assign(&arr2(&[[12.0, 1.0],[2.0,12.0]]));
-    rf.get_instantaneous_reward_mut(n2).assign(&arr1(&[3.0,5.0]));
-    let s00: NetworkProcessState = vec![params::StateType::Discrete(0), params::StateType::Discrete(0)];
-    let s01: NetworkProcessState = vec![params::StateType::Discrete(1), params::StateType::Discrete(0)];
-    let s02: NetworkProcessState = vec![params::StateType::Discrete(2), params::StateType::Discrete(0)];
-
-
-    let s10: NetworkProcessState = vec![params::StateType::Discrete(0), params::StateType::Discrete(1)];
-    let s11: NetworkProcessState = vec![params::StateType::Discrete(1), params::StateType::Discrete(1)];
-    let s12: NetworkProcessState = vec![params::StateType::Discrete(2), params::StateType::Discrete(1)];
-
-    assert_eq!(rf.call(&s00, Some(&s01)), Reward{transition_reward: 2.0, instantaneous_reward: 6.0});
-    assert_eq!(rf.call(&s00, Some(&s02)), Reward{transition_reward: 5.0, instantaneous_reward: 6.0});
-    assert_eq!(rf.call(&s00, Some(&s10)), Reward{transition_reward: 2.0, instantaneous_reward: 6.0});
-
-
-    assert_eq!(rf.call(&s01, Some(&s00)), Reward{transition_reward: 1.0, instantaneous_reward: 8.0});
-    assert_eq!(rf.call(&s01, Some(&s02)), Reward{transition_reward: 6.0, instantaneous_reward: 8.0});
-    assert_eq!(rf.call(&s01, Some(&s11)), Reward{transition_reward: 2.0, instantaneous_reward: 8.0});
-
-
-    assert_eq!(rf.call(&s02, Some(&s00)), Reward{transition_reward: 3.0, instantaneous_reward: 12.0});
-    assert_eq!(rf.call(&s02, Some(&s01)), Reward{transition_reward: 4.0, instantaneous_reward: 12.0});
-    assert_eq!(rf.call(&s02, Some(&s12)), Reward{transition_reward: 2.0, instantaneous_reward: 12.0});
-
-
-    assert_eq!(rf.call(&s10, Some(&s11)), Reward{transition_reward: 2.0, instantaneous_reward: 8.0});
-    assert_eq!(rf.call(&s10, Some(&s12)), Reward{transition_reward: 5.0, instantaneous_reward: 8.0});
-    assert_eq!(rf.call(&s10, Some(&s00)), Reward{transition_reward: 1.0, instantaneous_reward: 8.0});
-    
-
-    assert_eq!(rf.call(&s11, Some(&s10)), Reward{transition_reward: 1.0, instantaneous_reward: 10.0});
-    assert_eq!(rf.call(&s11, Some(&s12)), Reward{transition_reward: 6.0, instantaneous_reward: 10.0});
-    assert_eq!(rf.call(&s11, Some(&s01)), Reward{transition_reward: 1.0, instantaneous_reward: 10.0});
-
-
-    assert_eq!(rf.call(&s12, Some(&s10)), Reward{transition_reward: 3.0, instantaneous_reward: 14.0});
-    assert_eq!(rf.call(&s12, Some(&s11)), Reward{transition_reward: 4.0, instantaneous_reward: 14.0});
-    assert_eq!(rf.call(&s12, Some(&s02)), Reward{transition_reward: 1.0, instantaneous_reward: 14.0});
-}

reCTBN/tests/structure_learning.rs

@@ -4,12 +4,10 @@ mod utils;
 use std::collections::BTreeSet;
 
 use ndarray::{arr1, arr2, arr3};
-use reCTBN::process::ctbn::*;
+use reCTBN::ctbn::*;
-use reCTBN::process::NetworkProcess;
+use reCTBN::network::Network;
-use reCTBN::parameter_learning::BayesianApproach;
 use reCTBN::params;
 use reCTBN::structure_learning::hypothesis_test::*;
-use reCTBN::structure_learning::constraint_based_algorithm::*;
 use reCTBN::structure_learning::score_based_algorithm::*;
 use reCTBN::structure_learning::score_function::*;
 use reCTBN::structure_learning::StructureLearningAlgorithm;
@@ -108,7 +106,7 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
         }
     }
 
-    let data = trajectory_generator(&net, 100, 30.0, Some(6347747169756259));
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259));
 
     let mut net = CtbnNetwork::new();
     let _n1 = net
@@ -117,50 +115,6 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
     let _net = sl.fit_transform(net, &data);
 }
 
-fn generate_nodes(
-    net: &mut CtbnNetwork,
-    nodes_cardinality: usize,
-    nodes_domain_cardinality: usize
-) {
-    for node_label in 0..nodes_cardinality {
-        net.add_node(
-            generate_discrete_time_continous_node(
-                node_label.to_string(),
-                nodes_domain_cardinality,
-            )
-        ).unwrap();
-    }
-}
-
-fn check_compatibility_between_dataset_and_network_gen<T: StructureLearningAlgorithm>(sl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-    net.add_node(
-        generate_discrete_time_continous_node(
-            String::from("3"),
-            4
-        )
-    ).unwrap();
-
-    net.add_edge(0, 1);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        0.0..7.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let data = trajectory_generator(&net, 100, 30.0, Some(6347747169756259));
-
-    let mut net = CtbnNetwork::new();
-    let _n1 = net
-        .add_node(
-            generate_discrete_time_continous_node(String::from("0"),
-            3)
-        ).unwrap();
-    let _net = sl.fit_transform(net, &data);
-}
-
 #[test]
 #[should_panic]
 pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
@@ -169,14 +123,6 @@ pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
     check_compatibility_between_dataset_and_network(hl);
 }
 
-#[test]
-#[should_panic]
-pub fn check_compatibility_between_dataset_and_network_hill_climbing_gen() {
-    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::new(ll, None);
-    check_compatibility_between_dataset_and_network_gen(hl);
-}
-
 fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm>(sl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -234,25 +180,6 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm>(sl: T) {
     assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
 }
 
-fn learn_ternary_net_2_nodes_gen<T: StructureLearningAlgorithm>(sl: T) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-
-    net.add_edge(0, 1);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        0.0..7.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259));
-
-    let net = sl.fit_transform(net, &data);
-    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
-    assert_eq!(BTreeSet::new(), net.get_parent_set(0));
-}
-
 #[test]
 pub fn learn_ternary_net_2_nodes_hill_climbing_ll() {
     let ll = LogLikelihood::new(1, 1.0);
@@ -260,13 +187,6 @@ pub fn learn_ternary_net_2_nodes_hill_climbing_ll() {
     learn_ternary_net_2_nodes(hl);
 }
 
-#[test]
-pub fn learn_ternary_net_2_nodes_hill_climbing_ll_gen() {
-    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::new(ll, None);
-    learn_ternary_net_2_nodes_gen(hl);
-}
-
 #[test]
 pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
     let bic = BIC::new(1, 1.0);
@@ -274,13 +194,6 @@ pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
     learn_ternary_net_2_nodes(hl);
 }
 
-#[test]
-pub fn learn_ternary_net_2_nodes_hill_climbing_bic_gen() {
-    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::new(bic, None);
-    learn_ternary_net_2_nodes_gen(hl);
-}
-
 fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -405,30 +318,6 @@ fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
     return (net, data);
 }
 
-fn get_mixed_discrete_net_3_nodes_with_data_gen() -> (CtbnNetwork, Dataset) {
-    let mut net = CtbnNetwork::new();
-    generate_nodes(&mut net, 2, 3);
-    net.add_node(
-        generate_discrete_time_continous_node(
-            String::from("3"),
-            4
-        )
-    ).unwrap();
-
-    net.add_edge(0, 1);
-    net.add_edge(0, 2);
-    net.add_edge(1, 2);
-
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        0.0..7.0,
-        Some(6813071588535822)
-    );
-    cim_generator.generate_parameters(&mut net);
-
-    let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259));
-    return (net, data);
-}
-
 fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm>(sl: T) {
     let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
     let net = sl.fit_transform(net, &data);
@@ -437,14 +326,6 @@ fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm>(sl: T) {
     assert_eq!(BTreeSet::from_iter(vec![0, 1]), net.get_parent_set(2));
 }
 
-fn learn_mixed_discrete_net_3_nodes_gen<T: StructureLearningAlgorithm>(sl: T) {
-    let (net, data) = get_mixed_discrete_net_3_nodes_with_data_gen();
-    let net = sl.fit_transform(net, &data);
-    assert_eq!(BTreeSet::new(), net.get_parent_set(0));
-    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
-    assert_eq!(BTreeSet::from_iter(vec![0, 1]), net.get_parent_set(2));
-}
-
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() {
     let ll = LogLikelihood::new(1, 1.0);
@@ -452,13 +333,6 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() {
     learn_mixed_discrete_net_3_nodes(hl);
 }
 
-#[test]
-pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_gen() {
-    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::new(ll, None);
-    learn_mixed_discrete_net_3_nodes_gen(hl);
-}
-
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
     let bic = BIC::new(1, 1.0);
@@ -466,13 +340,6 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
     learn_mixed_discrete_net_3_nodes(hl);
 }
 
-#[test]
-pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_gen() {
-    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::new(bic, None);
-    learn_mixed_discrete_net_3_nodes_gen(hl);
-}
-
 fn learn_mixed_discrete_net_3_nodes_1_parent_constraint<T: StructureLearningAlgorithm>(sl: T) {
     let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
     let net = sl.fit_transform(net, &data);
@@ -481,14 +348,6 @@ fn learn_mixed_discrete_net_3_nodes_1_parent_constraint<T: StructureLearningAlgo
     assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(2));
 }
 
-fn learn_mixed_discrete_net_3_nodes_1_parent_constraint_gen<T: StructureLearningAlgorithm>(sl: T) {
-    let (net, data) = get_mixed_discrete_net_3_nodes_with_data_gen();
-    let net = sl.fit_transform(net, &data);
-    assert_eq!(BTreeSet::new(), net.get_parent_set(0));
-    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
-    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(2));
-}
-
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_1_parent_constraint() {
     let ll = LogLikelihood::new(1, 1.0);
@@ -496,13 +355,6 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_1_parent_constraint() {
     learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
 }
 
-#[test]
-pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_1_parent_constraint_gen() {
-    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::new(ll, Some(1));
-    learn_mixed_discrete_net_3_nodes_1_parent_constraint_gen(hl);
-}
-
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint() {
     let bic = BIC::new(1, 1.0);
@@ -510,13 +362,6 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint()
     learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
 }
 
-#[test]
-pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint_gen() {
-    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::new(bic, Some(1));
-    learn_mixed_discrete_net_3_nodes_1_parent_constraint_gen(hl);
-}
-
 #[test]
 pub fn chi_square_compare_matrices() {
     let i: usize = 1;
@@ -545,7 +390,7 @@ pub fn chi_square_compare_matrices() {
             [  700, 800,  0]
         ],
     ]);
-    let chi_sq = ChiSquare::new(1e-4);
+    let chi_sq = ChiSquare::new(0.1);
     assert!(!chi_sq.compare_matrices(i, &M1, j, &M2));
 }
 
@@ -575,7 +420,7 @@ pub fn chi_square_compare_matrices_2() {
         [ 400,  0,  600],
         [  700, 800,  0]]
     ]);
-    let chi_sq = ChiSquare::new(1e-4);
+    let chi_sq = ChiSquare::new(0.1);
     assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }
 
@@ -607,86 +452,6 @@ pub fn chi_square_compare_matrices_3() {
             [  700, 800,  0]
         ],
     ]);
-    let chi_sq = ChiSquare::new(1e-4);
+    let chi_sq = ChiSquare::new(0.1);
     assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }
-
-
-#[test]
-pub fn chi_square_call() {
-
-    let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
-    let N3: usize = 2;
-    let N2: usize = 1;
-    let N1: usize = 0;
-    let mut separation_set = BTreeSet::new();
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let mut cache = Cache::new(&parameter_learning);
-    let chi_sq = ChiSquare::new(1e-4);
-
-    assert!(chi_sq.call(&net, N1, N3, &separation_set, &data, &mut cache));
-    let mut cache = Cache::new(&parameter_learning);
-    assert!(!chi_sq.call(&net, N3, N1, &separation_set, &data, &mut cache));
-    assert!(!chi_sq.call(&net, N3, N2, &separation_set, &data, &mut cache));
-    separation_set.insert(N1);
-    let mut cache = Cache::new(&parameter_learning);
-    assert!(chi_sq.call(&net, N2, N3, &separation_set, &data, &mut cache));
-}
-
-#[test]
-pub fn f_call() {
-
-    let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
-    let N3: usize = 2;
-    let N2: usize = 1;
-    let N1: usize = 0;
-    let mut separation_set = BTreeSet::new();
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let mut cache = Cache::new(&parameter_learning);
-    let f = F::new(1e-6);
-
-
-    assert!(f.call(&net, N1, N3, &separation_set, &data, &mut cache));
-    let mut cache = Cache::new(&parameter_learning);
-    assert!(!f.call(&net, N3, N1, &separation_set, &data, &mut cache));
-    assert!(!f.call(&net, N3, N2, &separation_set, &data, &mut cache));
-    separation_set.insert(N1);
-    let mut cache = Cache::new(&parameter_learning);
-    assert!(f.call(&net, N2, N3, &separation_set, &data, &mut cache));
-}
-
-#[test]
-pub fn learn_ternary_net_2_nodes_ctpc() {
-    let f = F::new(1e-6);
-    let chi_sq = ChiSquare::new(1e-4);
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let ctpc = CTPC::new(parameter_learning, f, chi_sq);
-    learn_ternary_net_2_nodes(ctpc);
-}
-
-#[test]
-pub fn learn_ternary_net_2_nodes_ctpc_gen() {
-    let f = F::new(1e-6);
-    let chi_sq = ChiSquare::new(1e-4);
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let ctpc = CTPC::new(parameter_learning, f, chi_sq);
-    learn_ternary_net_2_nodes_gen(ctpc);
-}
-
-#[test]
-fn learn_mixed_discrete_net_3_nodes_ctpc() {
-    let f = F::new(1e-6);
-    let chi_sq = ChiSquare::new(1e-4);
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let ctpc = CTPC::new(parameter_learning, f, chi_sq);
-    learn_mixed_discrete_net_3_nodes(ctpc);
-}
-
-#[test]
-fn learn_mixed_discrete_net_3_nodes_ctpc_gen() {
-    let f = F::new(1e-6);
-    let chi_sq = ChiSquare::new(1e-4);
-    let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 };
-    let ctpc = CTPC::new(parameter_learning, f, chi_sq);
-    learn_mixed_discrete_net_3_nodes_gen(ctpc);
-}

reCTBN/tests/tools.rs

@@ -1,15 +1,9 @@
-use std::ops::Range;
-
 use ndarray::{arr1, arr2, arr3};
-use reCTBN::params::ParamsTrait;
+use reCTBN::ctbn::*;
-use reCTBN::process::ctbn::*;
+use reCTBN::network::Network;
-use reCTBN::process::ctmp::*;
-use reCTBN::process::NetworkProcess;
 use reCTBN::params;
 use reCTBN::tools::*;
 
-use utils::*;
-
 #[macro_use]
 extern crate approx;
 
@@ -88,164 +82,3 @@ fn dataset_wrong_shape() {
     let t2 = Trajectory::new(time, events);
     Dataset::new(vec![t1, t2]);
 }
-
-#[test]
-#[should_panic]
-fn uniform_graph_generator_wrong_density_1() {
-    let density = 2.1;
-    let _structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-        density,
-        None
-    );
-}
-
-#[test]
-#[should_panic]
-fn uniform_graph_generator_wrong_density_2() {
-    let density = -0.5;
-    let _structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-        density,
-        None
-    );
-}
-
-#[test]
-fn uniform_graph_generator_right_densities() {
-    for density in [1.0, 0.75, 0.5, 0.25, 0.0] {
-        let _structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-            density,
-            None
-        );
-    }
-}
-
-#[test]
-fn uniform_graph_generator_generate_graph_ctbn() {
-    let mut net = CtbnNetwork::new();
-    let nodes_cardinality = 0..=100;
-    let nodes_domain_cardinality = 2;
-    for node_label in nodes_cardinality {
-        net.add_node(
-            utils::generate_discrete_time_continous_node(
-                node_label.to_string(),
-                nodes_domain_cardinality,
-            )
-        ).unwrap();
-    }
-    let density = 1.0/3.0;
-    let mut structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-        density,
-        Some(7641630759785120)
-    );
-    structure_generator.generate_graph(&mut net);
-    let mut edges = 0;
-    for node in net.get_node_indices(){
-        edges += net.get_children_set(node).len()
-    }
-    let nodes = net.get_node_indices().len() as f64;
-    let expected_edges = (density * nodes * (nodes - 1.0)).round() as usize;
-    let tolerance = ((expected_edges as f64)*0.05) as usize; // ±5% of tolerance
-    // As the way `generate_graph()` is implemented we can only reasonably
-    // expect the number of edges to be somewhere around the expected value.
-    assert!((expected_edges - tolerance) <= edges && edges <= (expected_edges + tolerance));
-}
-
-#[test]
-#[should_panic]
-fn uniform_graph_generator_generate_graph_ctmp() {
-    let mut net = CtmpProcess::new();
-    let node_label = String::from("0");
-    let node_domain_cardinality = 4;
-    net.add_node(
-        generate_discrete_time_continous_node(
-            node_label,
-            node_domain_cardinality
-        )
-    ).unwrap();
-    let density = 1.0/3.0;
-    let mut structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-        density,
-        Some(7641630759785120)
-    );
-    structure_generator.generate_graph(&mut net);
-}
-
-#[test]
-#[should_panic]
-fn uniform_parameters_generator_wrong_density_1() {
-    let interval: Range<f64> = -2.0..-5.0;
-    let _cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        interval,
-        None
-    );
-}
-
-#[test]
-#[should_panic]
-fn uniform_parameters_generator_wrong_density_2() {
-    let interval: Range<f64> = -1.0..0.0;
-    let _cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        interval,
-        None
-    );
-}
-
-#[test]
-fn uniform_parameters_generator_right_densities_ctbn() {
-    let mut net = CtbnNetwork::new();
-    let nodes_cardinality = 0..=3;
-    let nodes_domain_cardinality = 9;
-    for node_label in nodes_cardinality {
-        net.add_node(
-            generate_discrete_time_continous_node(
-                node_label.to_string(),
-                nodes_domain_cardinality,
-            )
-        ).unwrap();
-    }
-    let density = 1.0/3.0;
-    let seed = Some(7641630759785120);
-    let interval = 0.0..7.0;
-    let mut structure_generator: UniformGraphGenerator = RandomGraphGenerator::new(
-        density,
-        seed
-    );
-    structure_generator.generate_graph(&mut net);
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        interval,
-        seed
-    );
-    cim_generator.generate_parameters(&mut net);
-    for node in net.get_node_indices() {
-        assert_eq!(
-            Ok(()),
-            net.get_node(node).validate_params()
-        );
-    }
-}
-
-#[test]
-fn uniform_parameters_generator_right_densities_ctmp() {
-    let mut net = CtmpProcess::new();
-    let node_label = String::from("0");
-    let node_domain_cardinality = 4;
-    net.add_node(
-        generate_discrete_time_continous_node(
-            node_label,
-            node_domain_cardinality
-        )
-    ).unwrap();
-    let seed = Some(7641630759785120);
-    let interval = 0.0..7.0;
-    let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
-        interval,
-        seed
-    );
-    cim_generator.generate_parameters(&mut net);
-    for node in net.get_node_indices() {
-        assert_eq!(
-            Ok(()),
-            net.get_node(node).validate_params()
-        );
-    }
-}

reCTBNpy/.github/workflows/CI.yml

@@ -0,0 +1,69 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - main
+      - master
+  pull_request:
+
+jobs:
+  linux:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: messense/maturin-action@v1
+      with:
+        manylinux: auto
+        command: build
+        args: --release --sdist -o dist --find-interpreter
+    - name: Upload wheels
+      uses: actions/upload-artifact@v2
+      with:
+        name: wheels
+        path: dist
+
+  windows:
+    runs-on: windows-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: messense/maturin-action@v1
+      with:
+        command: build
+        args: --release -o dist --find-interpreter
+    - name: Upload wheels
+      uses: actions/upload-artifact@v2
+      with:
+        name: wheels
+        path: dist
+
+  macos:
+    runs-on: macos-latest
+    steps:
+    - uses: actions/checkout@v3
+    - uses: messense/maturin-action@v1
+      with:
+        command: build
+        args: --release -o dist --universal2 --find-interpreter
+    - name: Upload wheels
+      uses: actions/upload-artifact@v2
+      with:
+        name: wheels
+        path: dist
+
+  release:
+    name: Release
+    runs-on: ubuntu-latest
+    if: "startsWith(github.ref, 'refs/tags/')"
+    needs: [ macos, windows, linux ]
+    steps:
+      - uses: actions/download-artifact@v2
+        with:
+          name: wheels
+      - name: Publish to PyPI
+        uses: messense/maturin-action@v1
+        env:
+          MATURIN_PYPI_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
+        with:
+          command: upload
+          args: --skip-existing *

reCTBNpy/.gitignore

@@ -0,0 +1,72 @@
+/target
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+.pytest_cache/
+*.py[cod]
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+.venv/
+env/
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+include/
+man/
+venv/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+pip-selfcheck.json
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Rope
+.ropeproject
+
+# Django stuff:
+*.log
+*.pot
+
+.DS_Store
+
+# Sphinx documentation
+docs/_build/
+
+# PyCharm
+.idea/
+
+# VSCode
+.vscode/
+
+# Pyenv
+.python-version

reCTBNpy/Cargo.toml

@@ -0,0 +1,15 @@
+[package]
+name = "reCTBNpy"
+version = "0.1.0"
+edition = "2021"
+
+
+[lib]
+crate-type = ["cdylib"]
+
+[workspace]
+
+[dependencies]
+pyo3 = { version = "0.17.1", features = ["extension-module"] }
+numpy = "*"
+reCTBN = { path="../reCTBN" }

reCTBNpy/pyproject.toml

@@ -0,0 +1,14 @@
+[build-system]
+requires = ["maturin>=0.13,<0.14"]
+build-backend = "maturin"
+
+[project]
+name = "reCTBNpy"
+requires-python = ">=3.7"
+classifiers = [
+    "Programming Language :: Rust",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Programming Language :: Python :: Implementation :: PyPy",
+]
+
+

reCTBNpy/src/lib.rs

@@ -0,0 +1,21 @@
+use pyo3::prelude::*;
+pub mod pyctbn;
+pub mod pyparams;
+pub mod pytools;
+
+
+
+/// A Python module implemented in Rust.
+#[pymodule]
+fn reCTBNpy(py: Python, m: &PyModule) -> PyResult<()> {
+    let network_module = PyModule::new(py, "network")?;
+    network_module.add_class::<pyctbn::PyCtbnNetwork>()?;
+    m.add_submodule(network_module)?;
+
+    let params_module = PyModule::new(py, "params")?;
+    params_module.add_class::<pyparams::PyDiscreteStateContinousTime>()?;
+    params_module.add_class::<pyparams::PyStateType>()?;
+    params_module.add_class::<pyparams::PyParams>()?;
+    m.add_submodule(params_module)?;
+    Ok(())
+}

reCTBNpy/src/pyctbn.rs

@@ -0,0 +1,68 @@
+use std::collections::BTreeSet;
+
+use crate::{pyparams, pytools};
+use pyo3::prelude::*;
+use reCTBN::{ctbn, network::Network, params, tools, params::Params};
+
+#[pyclass]
+pub struct PyCtbnNetwork(pub ctbn::CtbnNetwork);
+
+#[pymethods]
+impl PyCtbnNetwork {
+    #[new]
+    pub fn new() -> Self {
+        PyCtbnNetwork(ctbn::CtbnNetwork::new())
+    }
+
+    pub fn add_node(&mut self, n: pyparams::PyParams) {
+        self.0.add_node(n.0);
+    }
+
+    pub fn get_number_of_nodes(&self) -> usize {
+        self.0.get_number_of_nodes()
+    }
+
+    pub fn add_edge(&mut self, parent: usize, child: usize) {
+        self.0.add_edge(parent, child);
+    }
+
+    pub fn get_node_indices(&self) -> BTreeSet<usize> {
+        self.0.get_node_indices().collect()
+    }
+
+    pub fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
+        self.0.get_parent_set(node)
+    }
+
+    pub fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
+        self.0.get_children_set(node)
+    }
+
+    pub fn set_node(&mut self, node_idx: usize, n: pyparams::PyParams) {
+        match &n.0 {
+            Params::DiscreteStatesContinousTime(new_p) => {
+                if let Params::DiscreteStatesContinousTime(p) = self.0.get_node_mut(node_idx){
+                    p.set_cim(new_p.get_cim().as_ref().unwrap().clone()).unwrap();
+
+                }
+                else {
+                    panic!("Node type mismatch")
+                }
+            }
+        }
+    }
+
+    pub fn trajectory_generator(
+        &self,
+        n_trajectories: u64,
+        t_end: f64,
+        seed: Option<u64>,
+    ) -> pytools::PyDataset {
+        pytools::PyDataset(tools::trajectory_generator(
+            &self.0,
+            n_trajectories,
+            t_end,
+            seed,
+        ))
+    }
+}

reCTBNpy/src/pyparams.rs

@@ -0,0 +1,102 @@
+use numpy::{self, ToPyArray};
+use pyo3::{exceptions::PyValueError, prelude::*};
+use reCTBN::params::{self, ParamsTrait};
+use std::collections::BTreeSet;
+
+pub struct PyParamsError(params::ParamsError);
+
+impl From<PyParamsError> for PyErr {
+    fn from(error: PyParamsError) -> Self {
+        PyValueError::new_err(error.0.to_string())
+    }
+}
+
+impl From<params::ParamsError> for PyParamsError {
+    fn from(other: params::ParamsError) -> Self {
+        Self(other)
+    }
+}
+
+#[pyclass]
+pub struct PyStateType(pub params::StateType);
+
+#[pyclass]
+#[derive(Clone)]
+pub struct PyParams(pub params::Params);
+
+#[pymethods]
+impl PyParams {
+    #[staticmethod]
+    pub fn new_discrete_state_continous_time(p: PyDiscreteStateContinousTime) -> Self{
+        PyParams(params::Params::DiscreteStatesContinousTime(p.0))
+    }
+
+    pub fn get_reserved_space_as_parent(&self) -> usize {
+        self.0.get_reserved_space_as_parent()
+    }
+
+    pub fn get_label(&self) -> String {
+        self.0.get_label().to_string()
+    }
+}
+
+/// DiscreteStatesContinousTime.
+/// This represents the parameters of a classical discrete node for ctbn and it's composed by the
+/// following elements:
+/// - **domain**: an ordered and exhaustive set of possible states
+/// - **cim**: Conditional Intensity Matrix
+/// - **Sufficient Statistics**: the sufficient statistics are mainly used during the parameter
+///     learning task and are composed by:
+///     - **transitions**: number of transitions from one state to another given a specific
+///     realization of the parent set
+///     - **residence_time**: permanence time in each possible states given a specific
+///     realization of the parent set
+#[derive(Clone)]
+#[pyclass]
+pub struct PyDiscreteStateContinousTime(params::DiscreteStatesContinousTimeParams);
+
+
+#[pymethods]
+impl PyDiscreteStateContinousTime {
+    #[new]
+    pub fn new(label: String, domain: BTreeSet<String>) -> Self {
+        PyDiscreteStateContinousTime(params::DiscreteStatesContinousTimeParams::new(label, domain))
+    }
+
+    pub fn get_cim<'py>(&self, py: Python<'py>) -> Option<&'py numpy::PyArray3<f64>> {
+        match self.0.get_cim() {
+            Some(x) => Some(x.to_pyarray(py)),
+            None => None,
+        }
+    }
+
+    pub fn set_cim<'py>(&mut self, py: Python<'py>, cim: numpy::PyReadonlyArray3<f64>) -> Result<(), PyParamsError> {
+        self.0.set_cim(cim.as_array().to_owned())?;
+        Ok(())
+    }
+
+
+    pub fn get_transitions<'py>(&self, py: Python<'py>) -> Option<&'py numpy::PyArray3<usize>> {
+        match self.0.get_transitions() {
+            Some(x) => Some(x.to_pyarray(py)),
+            None => None,
+        }
+    }
+
+    pub fn set_transitions<'py>(&mut self, py: Python<'py>, cim: numpy::PyReadonlyArray3<usize>){
+        self.0.set_transitions(cim.as_array().to_owned());
+    }
+
+
+    pub fn get_residence_time<'py>(&self, py: Python<'py>) -> Option<&'py numpy::PyArray2<f64>> {
+        match self.0.get_residence_time() {
+            Some(x) => Some(x.to_pyarray(py)),
+            None => None,
+        }
+    }
+
+    pub fn set_residence_time<'py>(&mut self, py: Python<'py>, cim: numpy::PyReadonlyArray2<f64>) {
+        self.0.set_residence_time(cim.as_array().to_owned());
+    }
+
+}

reCTBNpy/src/pytools.rs

@@ -0,0 +1,50 @@
+use numpy::{self, ToPyArray};
+use pyo3::{exceptions::PyValueError, prelude::*};
+use reCTBN::{tools, network};
+
+#[pyclass]
+#[derive(Clone)]
+pub struct PyTrajectory(pub tools::Trajectory);
+
+#[pymethods]
+impl PyTrajectory {
+    #[new]
+    pub fn new(
+        time: numpy::PyReadonlyArray1<f64>,
+        events: numpy::PyReadonlyArray2<usize>,
+    ) -> PyTrajectory {
+        PyTrajectory(tools::Trajectory::new(
+            time.as_array().to_owned(),
+            events.as_array().to_owned(),
+        ))
+    }
+
+    pub fn get_time<'py>(&self, py: Python<'py>) -> &'py numpy::PyArray1<f64> {
+        self.0.get_time().to_pyarray(py)
+    }
+    
+    pub fn get_events<'py>(&self, py: Python<'py>) -> &'py numpy::PyArray2<usize> {
+        self.0.get_events().to_pyarray(py)
+    }
+}
+
+#[pyclass]
+pub struct PyDataset(pub tools::Dataset);
+
+#[pymethods]
+impl PyDataset {
+    #[new]
+    pub fn new(trajectories: Vec<PyTrajectory>) -> PyDataset {
+        PyDataset(tools::Dataset::new(trajectories.into_iter().map(|x| x.0).collect()))
+    }
+
+    pub fn get_number_of_trajectories(&self) -> usize {
+        self.0.get_trajectories().len()
+    }
+
+    pub fn get_trajectory(&self, idx: usize) -> PyTrajectory {
+        PyTrajectory(self.0.get_trajectories().get(idx).unwrap().clone())
+    }
+
+}
+