Decreased epsilon to `0.1` with a new seed

tests/parameter_learning.rs

@@ -40,14 +40,14 @@ fn learn_binary_cim<T: ParameterLearning> (pl: T) {
         }
     }
 
-    let data = trajectory_generator(&net, 100, 100.0, Some(1234),);
+    let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259),);
     let (CIM, M, T) = pl.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [2, 2, 2]);
     assert!(CIM.abs_diff_eq(&arr3(&[
                 [[-1.0, 1.0], [4.0, -4.0]],
                 [[-6.0, 6.0], [2.0, -2.0]],
-            ]), 0.3));
+            ]), 0.1));
 }
 
 #[test]
@@ -93,7 +93,7 @@ fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
         }
     }
 
-    let data = trajectory_generator(&net, 100, 200.0, Some(1234),);
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),);
     let (CIM, M, T) = pl.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [3, 3, 3]);
@@ -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]],
-            ]), 0.3));
+            ]), 0.1));
 }
 
 
@@ -148,13 +148,13 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
         }
     }
 
-    let data = trajectory_generator(&net, 100, 200.0, Some(1234),);
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),);
     let (CIM, M, T) = pl.fit(&net, &data, 0, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, 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]]]), 0.3));
+                                  [0.4, 0.6, -1.0]]]), 0.1));
 }
 
 
@@ -228,7 +228,7 @@ fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) {
     }
 
 
-    let data = trajectory_generator(&net, 300, 300.0, Some(1234),);
+    let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259),);
     let (CIM, M, T) = pl.fit(&net, &data, 2, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [9, 4, 4]);
@@ -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]],
-            ]), 0.3));
+            ]), 0.1));
 }
 
 #[test]

tests/params.rs

@@ -23,7 +23,7 @@ fn test_uniform_generation() {
     let param = create_ternary_discrete_time_continous_param();
     let mut states = Array1::<usize>::zeros(10000);
 
-    let mut rng = ChaCha8Rng::seed_from_u64(123456);
+    let mut rng = ChaCha8Rng::seed_from_u64(6347747169756259);
 
     states.mapv_inplace(|_| {
         if let StateType::Discrete(val) = param.get_random_state_uniform(&mut rng) {
@@ -42,7 +42,7 @@ fn test_random_generation_state() {
     let param = create_ternary_discrete_time_continous_param();
     let mut states = Array1::<usize>::zeros(10000);
 
-    let mut rng = ChaCha8Rng::seed_from_u64(123456);
+    let mut rng = ChaCha8Rng::seed_from_u64(6347747169756259);
 
     states.mapv_inplace(|_| {
         if let StateType::Discrete(val) = param.get_random_state(1, 0, &mut rng).unwrap() {
@@ -63,7 +63,7 @@ fn test_random_generation_residence_time() {
     let param = create_ternary_discrete_time_continous_param();
     let mut states = Array1::<f64>::zeros(10000);
 
-    let mut rng = ChaCha8Rng::seed_from_u64(123456);
+    let mut rng = ChaCha8Rng::seed_from_u64(6347747169756259);
 
     states.mapv_inplace(|_| param.get_random_residence_time(1, 0, &mut rng).unwrap());
 

tests/tools.rs

@@ -36,7 +36,7 @@ fn run_sampling() {
         }
     }
 
-    let data = trajectory_generator(&net, 4, 1.0, Some(1234),);
+    let data = trajectory_generator(&net, 4, 1.0, Some(6347747169756259),);
 
     assert_eq!(4, data.trajectories.len());
     assert_relative_eq!(1.0, data.trajectories[0].time[data.trajectories[0].time.len()-1]);