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Added tests for BA and MLE

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pull/19/head
AlessandroBregoli 3 years ago
parent cc8071ca07
commit 9a27d794fc
2 changed files with 141 additions and 12 deletions
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  1. 4
      src/parameter_learning.rs
  2. 149
      tests/parameter_learning.rs

4
src/parameter_learning.rs
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@ -114,8 +114,8 @@ impl ParameterLearning for MLE {
}
pub struct BayesianApproach {
default_alpha: usize,
default_tau: f64
pub default_alpha: usize,
pub default_tau: f64
}
impl ParameterLearning for BayesianApproach {

149
tests/parameter_learning.rs
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@ -15,8 +15,7 @@ use std::collections::BTreeSet;
extern crate approx;
#[test]
fn learn_binary_cim_MLE() {
fn learn_binary_cim<T: ParameterLearning> (pl: T) {
let mut net = CtbnNetwork::init();
let n1 = net
.add_node(generate_discrete_time_continous_node(String::from("n1"),2))
@ -42,8 +41,7 @@ fn learn_binary_cim_MLE() {
}
let data = trajectory_generator(&net, 100, 100.0);
let mle = MLE{};
let (CIM, M, T) = mle.fit(&net, &data, 1, None);
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(&[
@ -52,9 +50,22 @@ fn learn_binary_cim_MLE() {
]), 0.2));
}
#[test]
fn learn_binary_cim_MLE() {
let mle = MLE{};
learn_binary_cim(mle);
}
#[test]
fn learn_ternary_cim_MLE() {
fn learn_binary_cim_BA() {
let ba = BayesianApproach{
default_alpha: 1,
default_tau: 1.0};
learn_binary_cim(ba);
}
fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
let mut net = CtbnNetwork::init();
let n1 = net
.add_node(generate_discrete_time_continous_node(String::from("n1"),3))
@ -83,8 +94,7 @@ fn learn_ternary_cim_MLE() {
}
let data = trajectory_generator(&net, 100, 200.0);
let mle = MLE{};
let (CIM, M, T) = mle.fit(&net, &data, 1, None);
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]);
assert!(CIM.abs_diff_eq(&arr3(&[
@ -94,8 +104,23 @@ fn learn_ternary_cim_MLE() {
]), 0.2));
}
#[test]
fn learn_ternary_cim_MLE_no_parents() {
fn learn_ternary_cim_MLE() {
let mle = MLE{};
learn_ternary_cim(mle);
}
#[test]
fn learn_ternary_cim_BA() {
let ba = BayesianApproach{
default_alpha: 1,
default_tau: 1.0};
learn_ternary_cim(ba);
}
fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
let mut net = CtbnNetwork::init();
let n1 = net
.add_node(generate_discrete_time_continous_node(String::from("n1"),3))
@ -124,11 +149,115 @@ fn learn_ternary_cim_MLE_no_parents() {
}
let data = trajectory_generator(&net, 100, 200.0);
let mle = MLE{};
let (CIM, M, T) = mle.fit(&net, &data, 0, None);
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]]]), 0.2));
}
#[test]
fn learn_ternary_cim_no_parents_MLE() {
let mle = MLE{};
learn_ternary_cim_no_parents(mle);
}
#[test]
fn learn_ternary_cim_no_parents_BA() {
let ba = BayesianApproach{
default_alpha: 1,
default_tau: 1.0};
learn_ternary_cim_no_parents(ba);
}
fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) {
let mut net = CtbnNetwork::init();
let n1 = net
.add_node(generate_discrete_time_continous_node(String::from("n1"),3))
.unwrap();
let n2 = net
.add_node(generate_discrete_time_continous_node(String::from("n2"),3))
.unwrap();
let n3 = net
.add_node(generate_discrete_time_continous_node(String::from("n3"),4))
.unwrap();
net.add_edge(n1, n2);
net.add_edge(n1, n3);
net.add_edge(n2, n3);
match &mut net.get_node_mut(n1).params {
params::Params::DiscreteStatesContinousTime(param) => {
param.cim = Some(arr3(&[[[-3.0, 2.0, 1.0],
[1.5, -2.0, 0.5],
[0.4, 0.6, -1.0]]]));
}
}
match &mut net.get_node_mut(n2).params {
params::Params::DiscreteStatesContinousTime(param) => {
param.cim = Some(arr3(&[
[[-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]],
]));
}
}
match &mut net.get_node_mut(n3).params {
params::Params::DiscreteStatesContinousTime(param) => {
param.cim = Some(arr3(&[
[[-1.0, 0.5, 0.3, 0.2], [0.5, -4.0, 2.5, 1.0], [2.5, 0.5, -4.0, 1.0], [0.7, 0.2, 0.1, -1.0]],
[[-6.0, 2.0, 3.0, 1.0], [1.5, -3.0, 0.5, 1.0], [2.0, 1.3, -5.0 , 1.7], [2.5, 0.5, 1.0, -4.0]],
[[-1.3, 0.3, 0.1, 0.9], [1.4, -4.0, 0.5, 2.1], [1.0, 1.5, -3.0, 0.5], [0.4, 0.3, 0.1, -0.8]],
[[-2.0, 1.0, 0.7, 0.3], [1.3, -5.9, 2.7, 1.9], [2.0, 1.5, -4.0, 0.5], [0.2, 0.7, 0.1, -1.0]],
[[-6.0, 1.0, 2.0, 3.0], [0.5, -3.0, 1.0, 1.5], [1.4, 2.1, -4.3, 0.8], [0.5, 1.0, 2.5, -4.0]],
[[-1.3, 0.9, 0.3, 0.1], [0.1, -1.3, 0.2, 1.0], [0.5, 1.0, -3.0, 1.5], [0.1, 0.4, 0.3, -0.8]],
[[-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]],
]));
}
}
let data = trajectory_generator(&net, 300, 200.0);
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]);
assert!(CIM.abs_diff_eq(&arr3(&[
[[-1.0, 0.5, 0.3, 0.2], [0.5, -4.0, 2.5, 1.0], [2.5, 0.5, -4.0, 1.0], [0.7, 0.2, 0.1, -1.0]],
[[-6.0, 2.0, 3.0, 1.0], [1.5, -3.0, 0.5, 1.0], [2.0, 1.3, -5.0 , 1.7], [2.5, 0.5, 1.0, -4.0]],
[[-1.3, 0.3, 0.1, 0.9], [1.4, -4.0, 0.5, 2.1], [1.0, 1.5, -3.0, 0.5], [0.4, 0.3, 0.1, -0.8]],
[[-2.0, 1.0, 0.7, 0.3], [1.3, -5.9, 2.7, 1.9], [2.0, 1.5, -4.0, 0.5], [0.2, 0.7, 0.1, -1.0]],
[[-6.0, 1.0, 2.0, 3.0], [0.5, -3.0, 1.0, 1.5], [1.4, 2.1, -4.3, 0.8], [0.5, 1.0, 2.5, -4.0]],
[[-1.3, 0.9, 0.3, 0.1], [0.1, -1.3, 0.2, 1.0], [0.5, 1.0, -3.0, 1.5], [0.1, 0.4, 0.3, -0.8]],
[[-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));
}
#[test]
fn learn_mixed_discrete_cim_MLE() {
let mle = MLE{};
learn_mixed_discrete_cim(mle);
}
#[test]
fn learn_mixed_discrete_cim_BA() {
let ba = BayesianApproach{
default_alpha: 1,
default_tau: 1.0};
learn_mixed_discrete_cim(ba);
}

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