#![allow(non_snake_case)]
mod utils;
use ndarray::arr3;
use reCTBN::process::ctbn::*;
use reCTBN::process::NetworkProcess;
use reCTBN::parameter_learning::*;
use reCTBN::params;
use reCTBN::params::Params::DiscreteStatesContinousTime;
use reCTBN::tools::*;
use utils::*;
extern crate approx;
use crate::approx::AbsDiffEq;
fn learn_binary_cim<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
let n1 = net
.add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
.unwrap();
let n2 = net
.add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
.unwrap();
net.add_edge(n1, n2);
match &mut net.get_node_mut(n1) {
params::Params::DiscreteStatesContinousTime(param) => {
assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]])));
}
}
match &mut net.get_node_mut(n2) {
params::Params::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(arr3(&[
[
[-1.0, 1.0],
[4.0, -4.0]
],
[
[-6.0, 6.0],
[2.0, -2.0]
],
]))
);
}
}
let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
let p = match pl.fit(&net, &data, 1, None) {
params::Params::DiscreteStatesContinousTime(p) => p,
};
assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
&arr3(&[
[
[-1.0, 1.0],
[4.0, -4.0]
],
[
[-6.0, 6.0],
[2.0, -2.0]
],
]),
0.1
));
}
fn generate_nodes(
net: &mut CtbnNetwork,
nodes_cardinality: usize,
nodes_domain_cardinality: usize
) {
for node_label in 0..nodes_cardinality {
net.add_node(
generate_discrete_time_continous_node(
node_label.to_string(),
nodes_domain_cardinality,
)
).unwrap();
}
}
fn learn_binary_cim_gen<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
generate_nodes(&mut net, 2, 2);
net.add_edge(0, 1);
let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
1.0..6.0,
Some(6813071588535822)
);
cim_generator.generate_parameters(&mut net);
let p_gen = match net.get_node(1) {
DiscreteStatesContinousTime(p_gen) => p_gen,
};
let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
let p_tj = match pl.fit(&net, &data, 1, None) {
DiscreteStatesContinousTime(p_tj) => p_tj,
};
assert_eq!(
p_tj.get_cim().as_ref().unwrap().shape(),
p_gen.get_cim().as_ref().unwrap().shape()
);
assert!(
p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
&p_gen.get_cim().as_ref().unwrap(),
0.1
)
);
}
#[test]
fn learn_binary_cim_MLE() {
let mle = MLE {};
learn_binary_cim(mle);
}
#[test]
fn learn_binary_cim_MLE_gen() {
let mle = MLE {};
learn_binary_cim_gen(mle);
}
#[test]
fn learn_binary_cim_BA() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_binary_cim(ba);
}
#[test]
fn learn_binary_cim_BA_gen() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_binary_cim_gen(ba);
}
fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
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();
net.add_edge(n1, n2);
match &mut net.get_node_mut(n1) {
params::Params::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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]
],
]))
);
}
}
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
let p = match pl.fit(&net, &data, 1, None) {
params::Params::DiscreteStatesContinousTime(p) => p,
};
assert_eq!(p.get_cim().as_ref().unwrap().shape(), [3, 3, 3]);
assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
&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]
],
]),
0.1
));
}
fn learn_ternary_cim_gen<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
generate_nodes(&mut net, 2, 3);
net.add_edge(0, 1);
let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
4.0..6.0,
Some(6813071588535822)
);
cim_generator.generate_parameters(&mut net);
let p_gen = match net.get_node(1) {
DiscreteStatesContinousTime(p_gen) => p_gen,
};
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
let p_tj = match pl.fit(&net, &data, 1, None) {
DiscreteStatesContinousTime(p_tj) => p_tj,
};
assert_eq!(
p_tj.get_cim().as_ref().unwrap().shape(),
p_gen.get_cim().as_ref().unwrap().shape()
);
assert!(
p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
&p_gen.get_cim().as_ref().unwrap(),
0.1
)
);
}
#[test]
fn learn_ternary_cim_MLE() {
let mle = MLE {};
learn_ternary_cim(mle);
}
#[test]
fn learn_ternary_cim_MLE_gen() {
let mle = MLE {};
learn_ternary_cim_gen(mle);
}
#[test]
fn learn_ternary_cim_BA() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_ternary_cim(ba);
}
#[test]
fn learn_ternary_cim_BA_gen() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_ternary_cim_gen(ba);
}
fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
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();
net.add_edge(n1, n2);
match &mut net.get_node_mut(n1) {
params::Params::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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]
],
]))
);
}
}
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
let p = match pl.fit(&net, &data, 0, None) {
params::Params::DiscreteStatesContinousTime(p) => p,
};
assert_eq!(p.get_cim().as_ref().unwrap().shape(), [1, 3, 3]);
assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
&arr3(&[
[
[-3.0, 2.0, 1.0],
[1.5, -2.0, 0.5],
[0.4, 0.6, -1.0]
],
]),
0.1
));
}
fn learn_ternary_cim_no_parents_gen<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
generate_nodes(&mut net, 2, 3);
net.add_edge(0, 1);
let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
1.0..6.0,
Some(6813071588535822)
);
cim_generator.generate_parameters(&mut net);
let p_gen = match net.get_node(0) {
DiscreteStatesContinousTime(p_gen) => p_gen,
};
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
let p_tj = match pl.fit(&net, &data, 0, None) {
DiscreteStatesContinousTime(p_tj) => p_tj,
};
assert_eq!(
p_tj.get_cim().as_ref().unwrap().shape(),
p_gen.get_cim().as_ref().unwrap().shape()
);
assert!(
p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
&p_gen.get_cim().as_ref().unwrap(),
0.1
)
);
}
#[test]
fn learn_ternary_cim_no_parents_MLE() {
let mle = MLE {};
learn_ternary_cim_no_parents(mle);
}
#[test]
fn learn_ternary_cim_no_parents_MLE_gen() {
let mle = MLE {};
learn_ternary_cim_no_parents_gen(mle);
}
#[test]
fn learn_ternary_cim_no_parents_BA() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_ternary_cim_no_parents(ba);
}
#[test]
fn learn_ternary_cim_no_parents_BA_gen() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_ternary_cim_no_parents_gen(ba);
}
fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
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::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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::DiscreteStatesContinousTime(param) => {
assert_eq!(
Ok(()),
param.set_cim(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, 300.0, Some(6347747169756259));
let p = match pl.fit(&net, &data, 2, None) {
params::Params::DiscreteStatesContinousTime(p) => p,
};
assert_eq!(p.get_cim().as_ref().unwrap().shape(), [9, 4, 4]);
assert!(p.get_cim().as_ref().unwrap().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
));
}
fn learn_mixed_discrete_cim_gen<T: ParameterLearning>(pl: T) {
let mut net = CtbnNetwork::new();
generate_nodes(&mut net, 2, 3);
net.add_node(
generate_discrete_time_continous_node(
String::from("3"),
4
)
).unwrap();
net.add_edge(0, 1);
net.add_edge(0, 2);
net.add_edge(1, 2);
let mut cim_generator: UniformParametersGenerator = RandomParametersGenerator::new(
1.0..8.0,
Some(6813071588535822)
);
cim_generator.generate_parameters(&mut net);
let p_gen = match net.get_node(2) {
DiscreteStatesContinousTime(p_gen) => p_gen,
};
let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
let p_tj = match pl.fit(&net, &data, 2, None) {
DiscreteStatesContinousTime(p_tj) => p_tj,
};
assert_eq!(
p_tj.get_cim().as_ref().unwrap().shape(),
p_gen.get_cim().as_ref().unwrap().shape()
);
assert!(
p_tj.get_cim().as_ref().unwrap().abs_diff_eq(
&p_gen.get_cim().as_ref().unwrap(),
0.2
)
);
}
#[test]
fn learn_mixed_discrete_cim_MLE() {
let mle = MLE {};
learn_mixed_discrete_cim(mle);
}
#[test]
fn learn_mixed_discrete_cim_MLE_gen() {
let mle = MLE {};
learn_mixed_discrete_cim_gen(mle);
}
#[test]
fn learn_mixed_discrete_cim_BA() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_mixed_discrete_cim(ba);
}
#[test]
fn learn_mixed_discrete_cim_BA_gen() {
let ba = BayesianApproach { alpha: 1, tau: 1.0 };
learn_mixed_discrete_cim_gen(ba);
}