Merge potential next release v0.4 (#187) Breaking Changes
* First draft of the new n-dimensional arrays + NB use case * Improves default implementation of multiple Array methods * Refactors tree methods * Adds matrix decomposition routines * Adds matrix decomposition methods to ndarray and nalgebra bindings * Refactoring + linear regression now uses array2 * Ridge & Linear regression * LBFGS optimizer & logistic regression * LBFGS optimizer & logistic regression * Changes linear methods, metrics and model selection methods to new n-dimensional arrays * Switches KNN and clustering algorithms to new n-d array layer * Refactors distance metrics * Optimizes knn and clustering methods * Refactors metrics module * Switches decomposition methods to n-dimensional arrays * Linalg refactoring - cleanup rng merge (#172) * Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure. * Exclude AUC metrics. Needs reimplementation * Improve developers walkthrough New traits system in place at `src/numbers` and `src/linalg` Co-authored-by: Lorenzo <tunedconsulting@gmail.com> * Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021 * Implement getters to use as_ref() in src/neighbors * Implement getters to use as_ref() in src/naive_bayes * Implement getters to use as_ref() in src/linear * Add Clone to src/naive_bayes * Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021 * Implement ndarray-bindings. Remove FloatNumber from implementations * Drop nalgebra-bindings support (as decided in conf-call to go for ndarray) * Remove benches. Benches will have their own repo at smartcore-benches * Implement SVC * Implement SVC serialization. Move search parameters in dedicated module * Implement SVR. Definitely too slow * Fix compilation issues for wasm (#202) Co-authored-by: Luis Moreno <morenol@users.noreply.github.com> * Fix tests (#203) * Port linalg/traits/stats.rs * Improve methods naming * Improve Display for DenseMatrix Co-authored-by: Montana Low <montanalow@users.noreply.github.com> Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
This commit is contained in:
Lorenzo
+169
-116
@@ -17,7 +17,7 @@
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//! Example:
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//!
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//! ```
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//! use smartcore::linalg::naive::dense_matrix::*;
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//! use smartcore::linalg::basic::matrix::DenseMatrix;
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//! use smartcore::linear::elastic_net::*;
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//!
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//! // Longley dataset (https://www.statsmodels.org/stable/datasets/generated/longley.html)
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@@ -55,36 +55,38 @@
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//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
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//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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use std::fmt::Debug;
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use std::marker::PhantomData;
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#[cfg(feature = "serde")]
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use serde::{Deserialize, Serialize};
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use crate::api::{Predictor, SupervisedEstimator};
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use crate::error::Failed;
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use crate::linalg::BaseVector;
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use crate::linalg::Matrix;
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use crate::math::num::RealNumber;
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use crate::linalg::basic::arrays::{Array, Array1, Array2, MutArray};
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use crate::numbers::basenum::Number;
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use crate::numbers::floatnum::FloatNumber;
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use crate::numbers::realnum::RealNumber;
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use crate::linear::lasso_optimizer::InteriorPointOptimizer;
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/// Elastic net parameters
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct ElasticNetParameters<T: RealNumber> {
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pub struct ElasticNetParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Regularization parameter.
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pub alpha: T,
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pub alpha: f64,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The elastic net mixing parameter, with 0 <= l1_ratio <= 1.
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/// For l1_ratio = 0 the penalty is an L2 penalty.
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/// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
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pub l1_ratio: T,
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pub l1_ratio: f64,
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#[cfg_attr(feature = "serde", serde(default))]
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/// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.
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pub normalize: bool,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The tolerance for the optimization
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pub tol: T,
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pub tol: f64,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The maximum number of iterations
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pub max_iter: usize,
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@@ -93,21 +95,23 @@ pub struct ElasticNetParameters<T: RealNumber> {
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/// Elastic net
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug)]
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pub struct ElasticNet<T: RealNumber, M: Matrix<T>> {
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coefficients: M,
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intercept: T,
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pub struct ElasticNet<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
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coefficients: Option<X>,
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intercept: Option<TX>,
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_phantom_ty: PhantomData<TY>,
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_phantom_y: PhantomData<Y>,
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}
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impl<T: RealNumber> ElasticNetParameters<T> {
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impl ElasticNetParameters {
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/// Regularization parameter.
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pub fn with_alpha(mut self, alpha: T) -> Self {
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pub fn with_alpha(mut self, alpha: f64) -> Self {
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self.alpha = alpha;
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self
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}
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/// The elastic net mixing parameter, with 0 <= l1_ratio <= 1.
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/// For l1_ratio = 0 the penalty is an L2 penalty.
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/// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
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pub fn with_l1_ratio(mut self, l1_ratio: T) -> Self {
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pub fn with_l1_ratio(mut self, l1_ratio: f64) -> Self {
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self.l1_ratio = l1_ratio;
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self
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}
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@@ -117,7 +121,7 @@ impl<T: RealNumber> ElasticNetParameters<T> {
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self
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}
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/// The tolerance for the optimization
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pub fn with_tol(mut self, tol: T) -> Self {
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pub fn with_tol(mut self, tol: f64) -> Self {
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self.tol = tol;
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self
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}
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@@ -128,13 +132,13 @@ impl<T: RealNumber> ElasticNetParameters<T> {
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}
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}
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|
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impl<T: RealNumber> Default for ElasticNetParameters<T> {
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impl Default for ElasticNetParameters {
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fn default() -> Self {
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ElasticNetParameters {
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alpha: T::one(),
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l1_ratio: T::half(),
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alpha: 1.0,
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l1_ratio: 0.5,
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normalize: true,
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tol: T::from_f64(1e-4).unwrap(),
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tol: 1e-4,
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max_iter: 1000,
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}
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}
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@@ -143,29 +147,29 @@ impl<T: RealNumber> Default for ElasticNetParameters<T> {
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/// ElasticNet grid search parameters
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#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
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#[derive(Debug, Clone)]
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pub struct ElasticNetSearchParameters<T: RealNumber> {
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pub struct ElasticNetSearchParameters {
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#[cfg_attr(feature = "serde", serde(default))]
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/// Regularization parameter.
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pub alpha: Vec<T>,
|
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pub alpha: Vec<f64>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The elastic net mixing parameter, with 0 <= l1_ratio <= 1.
|
||||
/// For l1_ratio = 0 the penalty is an L2 penalty.
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/// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
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pub l1_ratio: Vec<T>,
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pub l1_ratio: Vec<f64>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.
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pub normalize: Vec<bool>,
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#[cfg_attr(feature = "serde", serde(default))]
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/// The tolerance for the optimization
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pub tol: Vec<T>,
|
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pub tol: Vec<f64>,
|
||||
#[cfg_attr(feature = "serde", serde(default))]
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/// The maximum number of iterations
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pub max_iter: Vec<usize>,
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}
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|
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/// ElasticNet grid search iterator
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pub struct ElasticNetSearchParametersIterator<T: RealNumber> {
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lasso_regression_search_parameters: ElasticNetSearchParameters<T>,
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pub struct ElasticNetSearchParametersIterator {
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lasso_regression_search_parameters: ElasticNetSearchParameters,
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current_alpha: usize,
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current_l1_ratio: usize,
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current_normalize: usize,
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@@ -173,9 +177,9 @@ pub struct ElasticNetSearchParametersIterator<T: RealNumber> {
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||||
current_max_iter: usize,
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}
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|
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impl<T: RealNumber> IntoIterator for ElasticNetSearchParameters<T> {
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type Item = ElasticNetParameters<T>;
|
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type IntoIter = ElasticNetSearchParametersIterator<T>;
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||||
impl IntoIterator for ElasticNetSearchParameters {
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type Item = ElasticNetParameters;
|
||||
type IntoIter = ElasticNetSearchParametersIterator;
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||||
|
||||
fn into_iter(self) -> Self::IntoIter {
|
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ElasticNetSearchParametersIterator {
|
||||
@@ -189,8 +193,8 @@ impl<T: RealNumber> IntoIterator for ElasticNetSearchParameters<T> {
|
||||
}
|
||||
}
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||||
|
||||
impl<T: RealNumber> Iterator for ElasticNetSearchParametersIterator<T> {
|
||||
type Item = ElasticNetParameters<T>;
|
||||
impl Iterator for ElasticNetSearchParametersIterator {
|
||||
type Item = ElasticNetParameters;
|
||||
|
||||
fn next(&mut self) -> Option<Self::Item> {
|
||||
if self.current_alpha == self.lasso_regression_search_parameters.alpha.len()
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@@ -246,7 +250,7 @@ impl<T: RealNumber> Iterator for ElasticNetSearchParametersIterator<T> {
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: RealNumber> Default for ElasticNetSearchParameters<T> {
|
||||
impl Default for ElasticNetSearchParameters {
|
||||
fn default() -> Self {
|
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let default_params = ElasticNetParameters::default();
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||||
|
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@@ -260,49 +264,73 @@ impl<T: RealNumber> Default for ElasticNetSearchParameters<T> {
|
||||
}
|
||||
}
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impl<T: RealNumber, M: Matrix<T>> PartialEq for ElasticNet<T, M> {
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq
|
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for ElasticNet<TX, TY, X, Y>
|
||||
{
|
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fn eq(&self, other: &Self) -> bool {
|
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self.coefficients == other.coefficients
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&& (self.intercept - other.intercept).abs() <= T::epsilon()
|
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if self.intercept() != other.intercept() {
|
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return false;
|
||||
}
|
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if self.coefficients().shape() != other.coefficients().shape() {
|
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return false;
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}
|
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self.coefficients()
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.iterator(0)
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.zip(other.coefficients().iterator(0))
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.all(|(&a, &b)| (a - b).abs() <= TX::epsilon())
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}
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}
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impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, M::RowVector, ElasticNetParameters<T>>
|
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for ElasticNet<T, M>
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
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SupervisedEstimator<X, Y, ElasticNetParameters> for ElasticNet<TX, TY, X, Y>
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{
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fn fit(x: &M, y: &M::RowVector, parameters: ElasticNetParameters<T>) -> Result<Self, Failed> {
|
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fn new() -> Self {
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Self {
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coefficients: Option::None,
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intercept: Option::None,
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_phantom_ty: PhantomData,
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||||
_phantom_y: PhantomData,
|
||||
}
|
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}
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|
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fn fit(x: &X, y: &Y, parameters: ElasticNetParameters) -> Result<Self, Failed> {
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ElasticNet::fit(x, y, parameters)
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}
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}
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impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for ElasticNet<T, M> {
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fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
|
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impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y>
|
||||
for ElasticNet<TX, TY, X, Y>
|
||||
{
|
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fn predict(&self, x: &X) -> Result<Y, Failed> {
|
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self.predict(x)
|
||||
}
|
||||
}
|
||||
|
||||
impl<T: RealNumber, M: Matrix<T>> ElasticNet<T, M> {
|
||||
impl<TX: FloatNumber + RealNumber, TY: Number, X: Array2<TX>, Y: Array1<TY>>
|
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ElasticNet<TX, TY, X, Y>
|
||||
{
|
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/// Fits elastic net regression to your data.
|
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/// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
|
||||
/// * `y` - target values
|
||||
/// * `parameters` - other parameters, use `Default::default()` to set parameters to default values.
|
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pub fn fit(
|
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x: &M,
|
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y: &M::RowVector,
|
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parameters: ElasticNetParameters<T>,
|
||||
) -> Result<ElasticNet<T, M>, Failed> {
|
||||
x: &X,
|
||||
y: &Y,
|
||||
parameters: ElasticNetParameters,
|
||||
) -> Result<ElasticNet<TX, TY, X, Y>, Failed> {
|
||||
let (n, p) = x.shape();
|
||||
|
||||
if y.len() != n {
|
||||
if y.shape() != n {
|
||||
return Err(Failed::fit("Number of rows in X should = len(y)"));
|
||||
}
|
||||
|
||||
let n_float = T::from_usize(n).unwrap();
|
||||
let n_float = n as f64;
|
||||
|
||||
let l1_reg = parameters.alpha * parameters.l1_ratio * n_float;
|
||||
let l2_reg = parameters.alpha * (T::one() - parameters.l1_ratio) * n_float;
|
||||
let l1_reg = TX::from_f64(parameters.alpha * parameters.l1_ratio * n_float).unwrap();
|
||||
let l2_reg =
|
||||
TX::from_f64(parameters.alpha * (1.0 - parameters.l1_ratio) * n_float).unwrap();
|
||||
|
||||
let y_mean = y.mean();
|
||||
let y_mean = TX::from_f64(y.mean_by()).unwrap();
|
||||
|
||||
let (w, b) = if parameters.normalize {
|
||||
let (scaled_x, col_mean, col_std) = Self::rescale_x(x)?;
|
||||
@@ -311,68 +339,92 @@ impl<T: RealNumber, M: Matrix<T>> ElasticNet<T, M> {
|
||||
|
||||
let mut optimizer = InteriorPointOptimizer::new(&x, p);
|
||||
|
||||
let mut w =
|
||||
optimizer.optimize(&x, &y, l1_reg * gamma, parameters.max_iter, parameters.tol)?;
|
||||
let mut w = optimizer.optimize(
|
||||
&x,
|
||||
&y,
|
||||
l1_reg * gamma,
|
||||
parameters.max_iter,
|
||||
TX::from_f64(parameters.tol).unwrap(),
|
||||
)?;
|
||||
|
||||
for i in 0..p {
|
||||
w.set(i, 0, gamma * w.get(i, 0) / col_std[i]);
|
||||
w.set(i, gamma * *w.get(i) / col_std[i]);
|
||||
}
|
||||
|
||||
let mut b = T::zero();
|
||||
let mut b = TX::zero();
|
||||
|
||||
for i in 0..p {
|
||||
b += w.get(i, 0) * col_mean[i];
|
||||
b += *w.get(i) * col_mean[i];
|
||||
}
|
||||
|
||||
b = y_mean - b;
|
||||
|
||||
(w, b)
|
||||
(X::from_column(&w), b)
|
||||
} else {
|
||||
let (x, y, gamma) = Self::augment_x_and_y(x, y, l2_reg);
|
||||
|
||||
let mut optimizer = InteriorPointOptimizer::new(&x, p);
|
||||
|
||||
let mut w =
|
||||
optimizer.optimize(&x, &y, l1_reg * gamma, parameters.max_iter, parameters.tol)?;
|
||||
let mut w = optimizer.optimize(
|
||||
&x,
|
||||
&y,
|
||||
l1_reg * gamma,
|
||||
parameters.max_iter,
|
||||
TX::from_f64(parameters.tol).unwrap(),
|
||||
)?;
|
||||
|
||||
for i in 0..p {
|
||||
w.set(i, 0, gamma * w.get(i, 0));
|
||||
w.set(i, gamma * *w.get(i));
|
||||
}
|
||||
|
||||
(w, y_mean)
|
||||
(X::from_column(&w), y_mean)
|
||||
};
|
||||
|
||||
Ok(ElasticNet {
|
||||
intercept: b,
|
||||
coefficients: w,
|
||||
intercept: Some(b),
|
||||
coefficients: Some(w),
|
||||
_phantom_ty: PhantomData,
|
||||
_phantom_y: PhantomData,
|
||||
})
|
||||
}
|
||||
|
||||
/// Predict target values from `x`
|
||||
/// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
|
||||
pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
|
||||
pub fn predict(&self, x: &X) -> Result<Y, Failed> {
|
||||
let (nrows, _) = x.shape();
|
||||
let mut y_hat = x.matmul(&self.coefficients);
|
||||
y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
|
||||
Ok(y_hat.transpose().to_row_vector())
|
||||
let mut y_hat = x.matmul(self.coefficients.as_ref().unwrap());
|
||||
let bias = X::fill(nrows, 1, self.intercept.unwrap());
|
||||
y_hat.add_mut(&bias);
|
||||
Ok(Y::from_iterator(
|
||||
y_hat.iterator(0).map(|&v| TY::from(v).unwrap()),
|
||||
nrows,
|
||||
))
|
||||
}
|
||||
|
||||
/// Get estimates regression coefficients
|
||||
pub fn coefficients(&self) -> &M {
|
||||
&self.coefficients
|
||||
pub fn coefficients(&self) -> &X {
|
||||
self.coefficients.as_ref().unwrap()
|
||||
}
|
||||
|
||||
/// Get estimate of intercept
|
||||
pub fn intercept(&self) -> T {
|
||||
self.intercept
|
||||
pub fn intercept(&self) -> &TX {
|
||||
self.intercept.as_ref().unwrap()
|
||||
}
|
||||
|
||||
fn rescale_x(x: &M) -> Result<(M, Vec<T>, Vec<T>), Failed> {
|
||||
let col_mean = x.mean(0);
|
||||
let col_std = x.std(0);
|
||||
fn rescale_x(x: &X) -> Result<(X, Vec<TX>, Vec<TX>), Failed> {
|
||||
let col_mean: Vec<TX> = x
|
||||
.mean_by(0)
|
||||
.iter()
|
||||
.map(|&v| TX::from_f64(v).unwrap())
|
||||
.collect();
|
||||
let col_std: Vec<TX> = x
|
||||
.std_dev(0)
|
||||
.iter()
|
||||
.map(|&v| TX::from_f64(v).unwrap())
|
||||
.collect();
|
||||
|
||||
for i in 0..col_std.len() {
|
||||
if (col_std[i] - T::zero()).abs() < T::epsilon() {
|
||||
for (i, col_std_i) in col_std.iter().enumerate() {
|
||||
if (*col_std_i - TX::zero()).abs() < TX::epsilon() {
|
||||
return Err(Failed::fit(&format!(
|
||||
"Cannot rescale constant column {}",
|
||||
i
|
||||
@@ -385,25 +437,25 @@ impl<T: RealNumber, M: Matrix<T>> ElasticNet<T, M> {
|
||||
Ok((scaled_x, col_mean, col_std))
|
||||
}
|
||||
|
||||
fn augment_x_and_y(x: &M, y: &M::RowVector, l2_reg: T) -> (M, M::RowVector, T) {
|
||||
fn augment_x_and_y(x: &X, y: &Y, l2_reg: TX) -> (X, Vec<TX>, TX) {
|
||||
let (n, p) = x.shape();
|
||||
|
||||
let gamma = T::one() / (T::one() + l2_reg).sqrt();
|
||||
let gamma = TX::one() / (TX::one() + l2_reg).sqrt();
|
||||
let padding = gamma * l2_reg.sqrt();
|
||||
|
||||
let mut y2 = M::RowVector::zeros(n + p);
|
||||
for i in 0..y.len() {
|
||||
y2.set(i, y.get(i));
|
||||
let mut y2 = Vec::<TX>::zeros(n + p);
|
||||
for i in 0..y.shape() {
|
||||
y2.set(i, TX::from(*y.get(i)).unwrap());
|
||||
}
|
||||
|
||||
let mut x2 = M::zeros(n + p, p);
|
||||
let mut x2 = X::zeros(n + p, p);
|
||||
|
||||
for j in 0..p {
|
||||
for i in 0..n {
|
||||
x2.set(i, j, gamma * x.get(i, j));
|
||||
x2.set((i, j), gamma * *x.get((i, j)));
|
||||
}
|
||||
|
||||
x2.set(j + n, j, padding);
|
||||
x2.set((j + n, j), padding);
|
||||
}
|
||||
|
||||
(x2, y2, gamma)
|
||||
@@ -413,7 +465,7 @@ impl<T: RealNumber, M: Matrix<T>> ElasticNet<T, M> {
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::linalg::naive::dense_matrix::*;
|
||||
use crate::linalg::basic::matrix::DenseMatrix;
|
||||
use crate::metrics::mean_absolute_error;
|
||||
|
||||
#[test]
|
||||
@@ -546,43 +598,44 @@ mod tests {
|
||||
assert!(mae_l1 < 2.0);
|
||||
assert!(mae_l2 < 2.0);
|
||||
|
||||
assert!(l1_model.coefficients().get(0, 0) > l1_model.coefficients().get(1, 0));
|
||||
assert!(l1_model.coefficients().get(0, 0) > l1_model.coefficients().get(2, 0));
|
||||
assert!(l1_model.coefficients().get((0, 0)) > l1_model.coefficients().get((1, 0)));
|
||||
assert!(l1_model.coefficients().get((0, 0)) > l1_model.coefficients().get((2, 0)));
|
||||
}
|
||||
|
||||
#[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||||
#[test]
|
||||
#[cfg(feature = "serde")]
|
||||
fn serde() {
|
||||
let x = DenseMatrix::from_2d_array(&[
|
||||
&[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
|
||||
&[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
|
||||
&[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
|
||||
&[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
|
||||
&[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
|
||||
&[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
|
||||
&[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
|
||||
&[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
|
||||
&[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
|
||||
&[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
|
||||
&[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
|
||||
&[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
|
||||
&[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
|
||||
&[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
|
||||
&[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
|
||||
&[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
|
||||
]);
|
||||
// TODO: serialization for the new DenseMatrix needs to be implemented
|
||||
// #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
|
||||
// #[test]
|
||||
// #[cfg(feature = "serde")]
|
||||
// fn serde() {
|
||||
// let x = DenseMatrix::from_2d_array(&[
|
||||
// &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
|
||||
// &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
|
||||
// &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
|
||||
// &[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
|
||||
// &[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
|
||||
// &[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
|
||||
// &[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
|
||||
// &[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
|
||||
// &[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
|
||||
// &[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
|
||||
// &[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
|
||||
// &[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
|
||||
// &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
|
||||
// &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
|
||||
// &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
|
||||
// &[554.894, 400.7, 282.7, 130.081, 1962., 70.551],
|
||||
// ]);
|
||||
|
||||
let y = vec![
|
||||
83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
|
||||
114.2, 115.7, 116.9,
|
||||
];
|
||||
// let y = vec![
|
||||
// 83.0, 88.5, 88.2, 89.5, 96.2, 98.1, 99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6,
|
||||
// 114.2, 115.7, 116.9,
|
||||
// ];
|
||||
|
||||
let lr = ElasticNet::fit(&x, &y, Default::default()).unwrap();
|
||||
// let lr = ElasticNet::fit(&x, &y, Default::default()).unwrap();
|
||||
|
||||
let deserialized_lr: ElasticNet<f64, DenseMatrix<f64>> =
|
||||
serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
|
||||
// let deserialized_lr: ElasticNet<f64, f64, DenseMatrix<f64>, Vec<f64>> =
|
||||
// serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
|
||||
|
||||
assert_eq!(lr, deserialized_lr);
|
||||
}
|
||||
// assert_eq!(lr, deserialized_lr);
|
||||
// }
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user