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@ -1,25 +0,0 @@ |
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use crate::params::*; |
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pub struct Node { |
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pub params: Params, |
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pub label: String |
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} |
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impl Node { |
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pub fn new(params: Params, label: String) -> Node { |
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Node{ |
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params: params, |
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label:label |
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} |
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} |
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} |
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impl PartialEq for Node { |
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fn eq(&self, other: &Node) -> bool{ |
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self.label == other.label |
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} |
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} |
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@ -1,263 +1,365 @@ |
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mod utils; |
mod utils; |
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use utils::*; |
use utils::*; |
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use reCTBN::parameter_learning::*; |
use ndarray::arr3; |
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use reCTBN::ctbn::*; |
use reCTBN::ctbn::*; |
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use reCTBN::network::Network; |
use reCTBN::network::Network; |
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use reCTBN::node; |
use reCTBN::parameter_learning::*; |
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use reCTBN::params; |
use reCTBN::{params, tools::*}; |
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use reCTBN::tools::*; |
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use ndarray::arr3; |
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use std::collections::BTreeSet; |
use std::collections::BTreeSet; |
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#[macro_use] |
#[macro_use] |
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extern crate approx; |
extern crate approx; |
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fn learn_binary_cim<T: ParameterLearning>(pl: T) { |
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fn learn_binary_cim<T: ParameterLearning> (pl: T) { |
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let mut net = CtbnNetwork::new(); |
let mut net = CtbnNetwork::new(); |
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let n1 = net |
let n1 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n1"),2)) |
.add_node(generate_discrete_time_continous_node(String::from("n1"), 2)) |
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.unwrap(); |
.unwrap(); |
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let n2 = net |
let n2 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n2"),2)) |
.add_node(generate_discrete_time_continous_node(String::from("n2"), 2)) |
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.unwrap(); |
.unwrap(); |
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net.add_edge(n1, n2); |
net.add_edge(n1, n2); |
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match &mut net.get_node_mut(n1).params { |
match &mut net.get_node_mut(n1) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]]))); |
assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]]))); |
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} |
} |
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} |
} |
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match &mut net.get_node_mut(n2).params { |
match &mut net.get_node_mut(n2) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
assert_eq!( |
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[[-1.0, 1.0], [4.0, -4.0]], |
Ok(()), |
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[[-6.0, 6.0], [2.0, -2.0]], |
param.set_cim(arr3(&[ |
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]))); |
[[-1.0, 1.0], [4.0, -4.0]], |
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[[-6.0, 6.0], [2.0, -2.0]], |
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])) |
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); |
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} |
} |
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} |
} |
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let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259),); |
let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259)); |
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let (CIM, M, T) = pl.fit(&net, &data, 1, None); |
let (CIM, M, T) = pl.fit(&net, &data, 1, None); |
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
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assert_eq!(CIM.shape(), [2, 2, 2]); |
assert_eq!(CIM.shape(), [2, 2, 2]); |
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assert!(CIM.abs_diff_eq(&arr3(&[ |
assert!(CIM.abs_diff_eq( |
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[[-1.0, 1.0], [4.0, -4.0]], |
&arr3(&[[[-1.0, 1.0], [4.0, -4.0]], [[-6.0, 6.0], [2.0, -2.0]],]), |
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[[-6.0, 6.0], [2.0, -2.0]], |
0.1 |
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]), 0.1)); |
)); |
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} |
} |
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#[test] |
#[test] |
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fn learn_binary_cim_MLE() { |
fn learn_binary_cim_MLE() { |
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let mle = MLE{}; |
let mle = MLE {}; |
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learn_binary_cim(mle); |
learn_binary_cim(mle); |
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} |
} |
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#[test] |
#[test] |
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fn learn_binary_cim_BA() { |
fn learn_binary_cim_BA() { |
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let ba = BayesianApproach{ |
let ba = BayesianApproach { alpha: 1, tau: 1.0 }; |
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alpha: 1, |
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tau: 1.0}; |
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learn_binary_cim(ba); |
learn_binary_cim(ba); |
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} |
} |
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fn learn_ternary_cim<T: ParameterLearning> (pl: T) { |
fn learn_ternary_cim<T: ParameterLearning>(pl: T) { |
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let mut net = CtbnNetwork::new(); |
let mut net = CtbnNetwork::new(); |
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let n1 = net |
let n1 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n1"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n1"), 3)) |
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.unwrap(); |
.unwrap(); |
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let n2 = net |
let n2 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n2"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n2"), 3)) |
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.unwrap(); |
.unwrap(); |
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net.add_edge(n1, n2); |
net.add_edge(n1, n2); |
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match &mut net.get_node_mut(n1).params { |
match &mut net.get_node_mut(n1) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0],
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assert_eq!( |
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[1.5, -2.0, 0.5], |
Ok(()), |
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[0.4, 0.6, -1.0]]]))); |
param.set_cim(arr3(&[[ |
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[-3.0, 2.0, 1.0], |
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[1.5, -2.0, 0.5], |
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[0.4, 0.6, -1.0] |
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]])) |
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); |
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} |
} |
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} |
} |
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match &mut net.get_node_mut(n2).params { |
match &mut net.get_node_mut(n2) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
assert_eq!( |
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[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
Ok(()), |
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[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
param.set_cim(arr3(&[ |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
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]))); |
[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
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])) |
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); |
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} |
} |
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} |
} |
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let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),); |
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259)); |
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let (CIM, M, T) = pl.fit(&net, &data, 1, None); |
let (CIM, M, T) = pl.fit(&net, &data, 1, None); |
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
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assert_eq!(CIM.shape(), [3, 3, 3]); |
assert_eq!(CIM.shape(), [3, 3, 3]); |
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assert!(CIM.abs_diff_eq(&arr3(&[ |
assert!(CIM.abs_diff_eq( |
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[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
&arr3(&[ |
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[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
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]), 0.1)); |
[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
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]), |
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0.1 |
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)); |
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} |
} |
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#[test] |
#[test] |
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fn learn_ternary_cim_MLE() { |
fn learn_ternary_cim_MLE() { |
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let mle = MLE{}; |
let mle = MLE {}; |
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learn_ternary_cim(mle); |
learn_ternary_cim(mle); |
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} |
} |
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#[test] |
#[test] |
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fn learn_ternary_cim_BA() { |
fn learn_ternary_cim_BA() { |
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let ba = BayesianApproach{ |
let ba = BayesianApproach { alpha: 1, tau: 1.0 }; |
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alpha: 1, |
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tau: 1.0}; |
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learn_ternary_cim(ba); |
learn_ternary_cim(ba); |
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} |
} |
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fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) { |
fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) { |
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let mut net = CtbnNetwork::new(); |
let mut net = CtbnNetwork::new(); |
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let n1 = net |
let n1 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n1"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n1"), 3)) |
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.unwrap(); |
.unwrap(); |
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let n2 = net |
let n2 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n2"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n2"), 3)) |
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.unwrap(); |
.unwrap(); |
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net.add_edge(n1, n2); |
net.add_edge(n1, n2); |
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match &mut net.get_node_mut(n1).params { |
match &mut net.get_node_mut(n1) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0],
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assert_eq!( |
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[1.5, -2.0, 0.5], |
Ok(()), |
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[0.4, 0.6, -1.0]]]))); |
param.set_cim(arr3(&[[ |
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[-3.0, 2.0, 1.0], |
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[1.5, -2.0, 0.5], |
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[0.4, 0.6, -1.0] |
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]])) |
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); |
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} |
} |
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} |
} |
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match &mut net.get_node_mut(n2).params { |
match &mut net.get_node_mut(n2) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
assert_eq!( |
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[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
Ok(()), |
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[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
param.set_cim(arr3(&[ |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
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]))); |
[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
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])) |
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); |
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} |
} |
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} |
} |
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let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),); |
let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259)); |
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let (CIM, M, T) = pl.fit(&net, &data, 0, None); |
let (CIM, M, T) = pl.fit(&net, &data, 0, None); |
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
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assert_eq!(CIM.shape(), [1, 3, 3]); |
assert_eq!(CIM.shape(), [1, 3, 3]); |
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assert!(CIM.abs_diff_eq(&arr3(&[[[-3.0, 2.0, 1.0],
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assert!(CIM.abs_diff_eq( |
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[1.5, -2.0, 0.5], |
&arr3(&[[[-3.0, 2.0, 1.0], [1.5, -2.0, 0.5], [0.4, 0.6, -1.0]]]), |
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[0.4, 0.6, -1.0]]]), 0.1)); |
0.1 |
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)); |
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} |
} |
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#[test] |
#[test] |
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fn learn_ternary_cim_no_parents_MLE() { |
fn learn_ternary_cim_no_parents_MLE() { |
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let mle = MLE{}; |
let mle = MLE {}; |
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learn_ternary_cim_no_parents(mle); |
learn_ternary_cim_no_parents(mle); |
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} |
} |
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#[test] |
#[test] |
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fn learn_ternary_cim_no_parents_BA() { |
fn learn_ternary_cim_no_parents_BA() { |
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let ba = BayesianApproach{ |
let ba = BayesianApproach { alpha: 1, tau: 1.0 }; |
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alpha: 1, |
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tau: 1.0}; |
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learn_ternary_cim_no_parents(ba); |
learn_ternary_cim_no_parents(ba); |
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} |
} |
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fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) { |
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fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) { |
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let mut net = CtbnNetwork::new(); |
let mut net = CtbnNetwork::new(); |
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let n1 = net |
let n1 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n1"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n1"), 3)) |
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.unwrap(); |
.unwrap(); |
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let n2 = net |
let n2 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n2"),3)) |
.add_node(generate_discrete_time_continous_node(String::from("n2"), 3)) |
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.unwrap(); |
.unwrap(); |
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let n3 = net |
let n3 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n3"),4)) |
.add_node(generate_discrete_time_continous_node(String::from("n3"), 4)) |
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.unwrap(); |
.unwrap(); |
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net.add_edge(n1, n2); |
net.add_edge(n1, n2); |
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net.add_edge(n1, n3); |
net.add_edge(n1, n3); |
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net.add_edge(n2, n3); |
net.add_edge(n2, n3); |
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match &mut net.get_node_mut(n1).params { |
match &mut net.get_node_mut(n1) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0],
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assert_eq!( |
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[1.5, -2.0, 0.5], |
Ok(()), |
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[0.4, 0.6, -1.0]]]))); |
param.set_cim(arr3(&[[ |
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[-3.0, 2.0, 1.0], |
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[1.5, -2.0, 0.5], |
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[0.4, 0.6, -1.0] |
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]])) |
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); |
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} |
} |
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} |
} |
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match &mut net.get_node_mut(n2).params { |
match &mut net.get_node_mut(n2) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
assert_eq!( |
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[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
Ok(()), |
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[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
param.set_cim(arr3(&[ |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
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]))); |
[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
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])) |
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); |
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} |
} |
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} |
} |
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match &mut net.get_node_mut(n3) { |
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match &mut net.get_node_mut(n3).params { |
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params::Params::DiscreteStatesContinousTime(param) => { |
params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
assert_eq!( |
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[[-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]], |
Ok(()), |
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[[-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]], |
param.set_cim(arr3(&[ |
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[[-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]], |
[ |
||||||
|
[-1.0, 0.5, 0.3, 0.2], |
||||||
[[-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]], |
[0.5, -4.0, 2.5, 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]], |
[2.5, 0.5, -4.0, 1.0], |
||||||
[[-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]], |
[0.7, 0.2, 0.1, -1.0] |
||||||
|
], |
||||||
[[-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]], |
[-6.0, 2.0, 3.0, 1.0], |
||||||
[[-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]], |
[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 data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259),); |
|
||||||
let (CIM, M, T) = pl.fit(&net, &data, 2, None); |
let (CIM, M, T) = pl.fit(&net, &data, 2, None); |
||||||
print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T); |
||||||
assert_eq!(CIM.shape(), [9, 4, 4]); |
assert_eq!(CIM.shape(), [9, 4, 4]); |
||||||
assert!(CIM.abs_diff_eq(&arr3(&[ |
assert!(CIM.abs_diff_eq( |
||||||
[[-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]], |
&arr3(&[ |
||||||
[[-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]], |
[-1.0, 0.5, 0.3, 0.2], |
||||||
|
[0.5, -4.0, 2.5, 1.0], |
||||||
[[-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]], |
[2.5, 0.5, -4.0, 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]], |
[0.7, 0.2, 0.1, -1.0] |
||||||
[[-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]], |
[-6.0, 2.0, 3.0, 1.0], |
||||||
[[-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]], |
[1.5, -3.0, 0.5, 1.0], |
||||||
[[-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]], |
[2.0, 1.3, -5.0, 1.7], |
||||||
]), 0.1)); |
[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] |
||||||
|
], |
||||||
|
]), |
||||||
|
0.1 |
||||||
|
)); |
||||||
} |
} |
||||||
|
|
||||||
#[test] |
#[test] |
||||||
fn learn_mixed_discrete_cim_MLE() { |
fn learn_mixed_discrete_cim_MLE() { |
||||||
let mle = MLE{}; |
let mle = MLE {}; |
||||||
learn_mixed_discrete_cim(mle); |
learn_mixed_discrete_cim(mle); |
||||||
} |
} |
||||||
|
|
||||||
|
|
||||||
#[test] |
#[test] |
||||||
fn learn_mixed_discrete_cim_BA() { |
fn learn_mixed_discrete_cim_BA() { |
||||||
let ba = BayesianApproach{ |
let ba = BayesianApproach { alpha: 1, tau: 1.0 }; |
||||||
alpha: 1, |
|
||||||
tau: 1.0}; |
|
||||||
learn_mixed_discrete_cim(ba); |
learn_mixed_discrete_cim(ba); |
||||||
} |
} |
||||||
|
@ -1,16 +1,17 @@ |
|||||||
use reCTBN::params; |
use reCTBN::params; |
||||||
use reCTBN::node; |
|
||||||
use std::collections::BTreeSet; |
use std::collections::BTreeSet; |
||||||
|
|
||||||
pub fn generate_discrete_time_continous_node(name: String, cardinality: usize) -> node::Node { |
pub fn generate_discrete_time_continous_node(label: String, cardinality: usize) -> params::Params { |
||||||
node::Node::new(params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_param(cardinality)), name) |
params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_params(label, cardinality)) |
||||||
} |
} |
||||||
|
|
||||||
|
|
||||||
pub fn generate_discrete_time_continous_param(cardinality: usize) -> params::DiscreteStatesContinousTimeParams{ |
pub fn generate_discrete_time_continous_params(label: String, cardinality: usize) -> params::DiscreteStatesContinousTimeParams{ |
||||||
let domain: BTreeSet<String> = (0..cardinality).map(|x| x.to_string()).collect(); |
let domain: BTreeSet<String> = (0..cardinality).map(|x| x.to_string()).collect(); |
||||||
params::DiscreteStatesContinousTimeParams::new(domain) |
params::DiscreteStatesContinousTimeParams::new(label, domain) |
||||||
} |
} |
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Loading…
Reference in new issue