From 62fcbd466a08d7dead12c48a40c8200f3fa57698 Mon Sep 17 00:00:00 2001
From: meliurwen <meliurwen@gmail.com>
Date: Tue, 12 Apr 2022 09:33:07 +0200
Subject: [PATCH] Removed `rand::thread_rng` overriding the ChaCha's `rng`,
 increased the epsilon from 0.2 to 0.3 in the tests

---
 src/params.rs               | 3 ---
 tests/parameter_learning.rs | 8 ++++----
 2 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/src/params.rs b/src/params.rs
index 6173d75..f0e5efa 100644
--- a/src/params.rs
+++ b/src/params.rs
@@ -1,7 +1,6 @@
 use enum_dispatch::enum_dispatch;
 use ndarray::prelude::*;
 use rand::Rng;
-use rand::rngs::ThreadRng;
 use std::collections::{BTreeSet, HashMap};
 use thiserror::Error;
 use rand_chacha::ChaCha8Rng;
@@ -149,7 +148,6 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         // https://en.wikipedia.org/wiki/Exponential_distribution#Generating_exponential_variates
         match &self.cim {
             Option::Some(cim) => {
-                let mut rng = rand::thread_rng();
                 let lambda = cim[[u, state, state]] * -1.0;
                 let x: f64 = rng.gen_range(0.0..=1.0);
                 Ok(-x.ln() / lambda)
@@ -166,7 +164,6 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         // https://en.wikipedia.org/wiki/Multinomial_distribution#Sampling_from_a_multinomial_distribution
         match &self.cim {
             Option::Some(cim) => {
-                let mut rng = rand::thread_rng();
                 let lambda = cim[[u, state, state]] * -1.0;
                 let urand: f64 = rng.gen_range(0.0..=1.0);
 
diff --git a/tests/parameter_learning.rs b/tests/parameter_learning.rs
index af57291..adff6e8 100644
--- a/tests/parameter_learning.rs
+++ b/tests/parameter_learning.rs
@@ -47,7 +47,7 @@ fn learn_binary_cim<T: ParameterLearning> (pl: T) {
     assert!(CIM.abs_diff_eq(&arr3(&[
                 [[-1.0, 1.0], [4.0, -4.0]],
                 [[-6.0, 6.0], [2.0, -2.0]],
-            ]), 0.2));
+            ]), 0.3));
 }
 
 #[test]
@@ -101,7 +101,7 @@ fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
                 [[-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]],
-            ]), 0.2));
+            ]), 0.3));
 }
 
 
@@ -154,7 +154,7 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
     assert_eq!(CIM.shape(), [1, 3, 3]);
     assert!(CIM.abs_diff_eq(&arr3(&[[[-3.0, 2.0, 1.0], 
                                   [1.5, -2.0, 0.5],
-                                  [0.4, 0.6, -1.0]]]), 0.2));
+                                  [0.4, 0.6, -1.0]]]), 0.3));
 }
 
 
@@ -244,7 +244,7 @@ fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) {
                 [[-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]],
-            ]), 0.2));
+            ]), 0.3));
 }
 
 #[test]