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@ -3,7 +3,7 @@ |
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use std::collections::BTreeSet; |
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use ndarray::{Array3, Axis}; |
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use statrs::distribution::{ChiSquared, ContinuousCDF}; |
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use statrs::distribution::{ChiSquared, ContinuousCDF, FisherSnedecor}; |
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use crate::params::*; |
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use crate::{network, parameter_learning}; |
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@ -37,7 +37,61 @@ pub struct ChiSquare { |
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alpha: f64, |
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} |
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pub struct F {} |
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pub struct F { |
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alpha: f64, |
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} |
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impl F { |
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pub fn new(alpha: f64) -> F { |
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F { alpha } |
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} |
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pub fn compare_matrices( |
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&self, |
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i: usize, |
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M1: &Array3<usize>, |
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cim_1: &Array3<f64>, |
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j: usize, |
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M2: &Array3<usize>, |
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cim_2: &Array3<f64>, |
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) -> bool { |
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let M1 = M1.index_axis(Axis(0), i).mapv(|x| x as f64); |
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let M2 = M2.index_axis(Axis(0), j).mapv(|x| x as f64); |
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let cim_1 = cim_1.index_axis(Axis(0), i); |
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let cim_2 = cim_2.index_axis(Axis(0), j); |
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let r1 = M1.sum_axis(Axis(1)); |
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let r2 = M2.sum_axis(Axis(1)); |
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let q1 = cim_1.diag(); |
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let q2 = cim_2.diag(); |
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for idx in 0..r1.shape()[0] { |
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let s = q2[idx] / q1[idx]; |
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let F = FisherSnedecor::new(r1[idx], r2[idx]); |
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let lim_sx = F.as_ref().expect("REASON").cdf(self.alpha / 2.0); |
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let lim_dx = F.as_ref().expect("REASON").cdf(1.0 - (self.alpha / 2.0)); |
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if s < lim_sx || s > lim_dx { |
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return false; |
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} |
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} |
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true |
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} |
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} |
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impl HypothesisTest for F { |
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fn call<T, P>( |
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&self, |
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net: &T, |
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child_node: usize, |
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parent_node: usize, |
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separation_set: &BTreeSet<usize>, |
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cache: &mut parameter_learning::Cache<P>, |
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) -> bool |
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where |
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T: network::Network, |
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P: parameter_learning::ParameterLearning, |
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{ |
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true |
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} |
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} |
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impl ChiSquare { |
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pub fn new(alpha: f64) -> ChiSquare { |
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