Merge branch 'meta-license' into 'dev'

Double licensed this project: MIT and APACHE 2.0

LICENSE-APACHE

@@ -0,0 +1,176 @@
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+
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LICENSE-MIT

@@ -0,0 +1,23 @@
+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

@@ -64,3 +64,11 @@ 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.

reCTBN/Cargo.toml

@@ -18,8 +18,3 @@ rayon = "~1.6"
 
 [dev-dependencies]
 approx = { package = "approx", version = "~0.5" }
-criterion = "0.4.*"
-
-[[bench]]
-name = "structure_learning"
-harness = false

reCTBN/benches/structure_learning.rs

@@ -1,88 +0,0 @@
-#![allow(non_snake_case)]
-
-use std::collections::BTreeSet;
-use std::time::Duration;
-use criterion::{criterion_group, criterion_main, Criterion};
-
-use reCTBN::params::DiscreteStatesContinousTimeParams;
-use reCTBN::params::Params::DiscreteStatesContinousTime;
-use reCTBN::process::NetworkProcess;
-use reCTBN::parameter_learning::BayesianApproach;
-use reCTBN::process::ctbn::CtbnNetwork;
-use reCTBN::structure_learning::constraint_based_algorithm::CTPC;
-use reCTBN::structure_learning::hypothesis_test::{ChiSquare, F};
-use reCTBN::structure_learning::StructureLearningAlgorithm;
-use reCTBN::tools::trajectory_generator;
-use reCTBN::tools::Dataset;
-use reCTBN::tools::RandomGraphGenerator;
-use reCTBN::tools::RandomParametersGenerator;
-use reCTBN::tools::UniformGraphGenerator;
-use reCTBN::tools::UniformParametersGenerator;
-
-
-fn uniform_parameters_generator_right_densities_ctmp() -> (CtbnNetwork, Dataset) {
-    let mut net = CtbnNetwork::new();
-    let nodes_cardinality = 5;
-    let domain_cardinality = 3;
-    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 = 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(3.0..7.0, Some(7641630759785120));
-
-    // Generate CIMs with uniformly distributed parameters.
-    cim_generator.generate_parameters(&mut net);
-
-    let dataset = trajectory_generator(&net, 300, 200.0, Some(30230423));
-
-    return (net, dataset);
-}
-
-fn structure_learning_CTPC(net: CtbnNetwork, dataset: &Dataset) {
-    // 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
-    ctpc.fit_transform(net, dataset);
-}
-
-pub fn criterion_benchmark_ctpc(c: &mut Criterion) {
-    let mut group = c.benchmark_group("structure_learning_CTPC");
-    // Configure Criterion.rs to detect smaller differences and increase sample size to improve
-    // precision and counteract the resulting noise.
-    group.sample_size(10).measurement_time(Duration::from_secs(20));
-    group.bench_function("CTPC", move |b| {
-        b.iter_batched(
-            || uniform_parameters_generator_right_densities_ctmp(),
-            |(net, dataset)| structure_learning_CTPC(net, &dataset),
-            criterion::BatchSize::PerIteration,
-        )
-    });
-    group.finish();
-}
-
-criterion_group!(benches, criterion_benchmark_ctpc);
-criterion_main!(benches);

reCTBN/src/process/ctbn.rs

@@ -138,6 +138,10 @@ impl CtbnNetwork {
 
         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 {