Defined the `compare_matrices` function for the F-test

reCTBN/src/structure_learning/hypothesis_test.rs

@@ -3,7 +3,7 @@
 use std::collections::BTreeSet;
 
 use ndarray::{Array3, Axis};
-use statrs::distribution::{ChiSquared, ContinuousCDF};
+use statrs::distribution::{ChiSquared, ContinuousCDF, FisherSnedecor};
 
 use crate::params::*;
 use crate::{network, parameter_learning};
@@ -37,7 +37,61 @@ pub struct ChiSquare {
     alpha: f64,
 }
 
-pub struct F {}
+pub struct F {
+    alpha: f64,
+}
+
+impl F {
+    pub fn new(alpha: f64) -> F {
+        F { alpha }
+    }
+
+    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]);
+            let lim_sx = F.as_ref().expect("REASON").cdf(self.alpha / 2.0);
+            let lim_dx = F.as_ref().expect("REASON").cdf(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>,
+        cache: &mut parameter_learning::Cache<P>,
+    ) -> bool
+    where
+        T: network::Network,
+        P: parameter_learning::ParameterLearning,
+    {
+        true
+    }
+}
 
 impl ChiSquare {
     pub fn new(alpha: f64) -> ChiSquare {