Laying grounds for CTPC

reCTBN/src/structure_learning/constraint_based_algorithm.rs

@@ -1,5 +1,39 @@
 //! Module containing constraint based algorithms like CTPC and Hiton.
 
-//pub struct CTPC {
+use super::hypothesis_test::*;
-//
+use crate::structure_learning::StructureLearningAlgorithm;
-//}
+use crate::{process, tools};
+use crate::parameter_learning::{Cache, ParameterLearning};
+
+pub struct CTPC<P: ParameterLearning> {
+    Ftest: F,
+    Chi2test: ChiSquare,
+    cache: Cache<P>,
+
+}
+
+impl<P: ParameterLearning> CTPC<P> {
+    pub fn new(Ftest: F, Chi2test: ChiSquare, cache: Cache<P>) -> CTPC<P> {
+        CTPC {
+            Chi2test,
+            Ftest,
+            cache,
+        }
+    }
+}
+
+impl<P: ParameterLearning> StructureLearningAlgorithm for CTPC<P> {
+    fn fit_transform<T>(&self, net: T, dataset: &tools::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
+    }
+}