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Merge pull request #57 from Magnus167/compute

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Add statistical functions and machine learning models
This commit is contained in:
Palash Tyagi 2025-07-16 01:53:39 +01:00 committed by GitHub
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20 changed files with 2945 additions and 8 deletions
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2
Cargo.toml
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@ -14,8 +14,6 @@ crate-type = ["cdylib", "lib"]
[dependencies] [dependencies]
chrono = "^0.4.10" chrono = "^0.4.10"
criterion = { version = "0.5", features = ["html_reports"], optional = true } criterion = { version = "0.5", features = ["html_reports"], optional = true }
[dev-dependencies]
rand = "^0.9.1" rand = "^0.9.1"
[features] [features]

3
src/compute/mod.rs Normal file
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@ -0,0 +1,3 @@
pub mod models;
pub mod stats;

135
src/compute/models/activations.rs Normal file
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@ -0,0 +1,135 @@
use crate::matrix::{Matrix, SeriesOps};
pub fn sigmoid(x: &Matrix<f64>) -> Matrix<f64> {
x.map(|v| 1.0 / (1.0 + (-v).exp()))
}
pub fn dsigmoid(y: &Matrix<f64>) -> Matrix<f64> {
// derivative w.r.t. pre-activation; takes y = sigmoid(x)
y.map(|v| v * (1.0 - v))
}
pub fn relu(x: &Matrix<f64>) -> Matrix<f64> {
x.map(|v| if v > 0.0 { v } else { 0.0 })
}
pub fn drelu(x: &Matrix<f64>) -> Matrix<f64> {
x.map(|v| if v > 0.0 { 1.0 } else { 0.0 })
}
pub fn leaky_relu(x: &Matrix<f64>) -> Matrix<f64> {
x.map(|v| if v > 0.0 { v } else { 0.01 * v })
}
pub fn dleaky_relu(x: &Matrix<f64>) -> Matrix<f64> {
x.map(|v| if v > 0.0 { 1.0 } else { 0.01 })
}
mod tests {
use super::*;
// Helper function to round all elements in a matrix to n decimal places
fn _round_matrix(mat: &Matrix<f64>, decimals: u32) -> Matrix<f64> {
let factor = 10f64.powi(decimals as i32);
let rounded: Vec<f64> = mat
.to_vec()
.iter()
.map(|v| (v * factor).round() / factor)
.collect();
Matrix::from_vec(rounded, mat.rows(), mat.cols())
}
#[test]
fn test_sigmoid() {
let x = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.26894142, 0.5, 0.73105858], 3, 1);
let result = sigmoid(&x);
assert_eq!(_round_matrix(&result, 6), _round_matrix(&expected, 6));
}
#[test]
fn test_sigmoid_edge_case() {
let x = Matrix::from_vec(vec![-1000.0, 0.0, 1000.0], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.5, 1.0], 3, 1);
let result = sigmoid(&x);
for (r, e) in result.data().iter().zip(expected.data().iter()) {
assert!((r - e).abs() < 1e-6);
}
}
#[test]
fn test_relu() {
let x = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.0, 1.0], 3, 1);
assert_eq!(relu(&x), expected);
}
#[test]
fn test_relu_edge_case() {
let x = Matrix::from_vec(vec![-1e-10, 0.0, 1e10], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.0, 1e10], 3, 1);
assert_eq!(relu(&x), expected);
}
#[test]
fn test_dsigmoid() {
let y = Matrix::from_vec(vec![0.26894142, 0.5, 0.73105858], 3, 1);
let expected = Matrix::from_vec(vec![0.19661193, 0.25, 0.19661193], 3, 1);
let result = dsigmoid(&y);
assert_eq!(_round_matrix(&result, 6), _round_matrix(&expected, 6));
}
#[test]
fn test_dsigmoid_edge_case() {
let y = Matrix::from_vec(vec![0.0, 0.5, 1.0], 3, 1); // Assume these are outputs from sigmoid(x)
let expected = Matrix::from_vec(vec![0.0, 0.25, 0.0], 3, 1);
let result = dsigmoid(&y);
for (r, e) in result.data().iter().zip(expected.data().iter()) {
assert!((r - e).abs() < 1e-6);
}
}
#[test]
fn test_drelu() {
let x = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.0, 1.0], 3, 1);
assert_eq!(drelu(&x), expected);
}
#[test]
fn test_drelu_edge_case() {
let x = Matrix::from_vec(vec![-1e-10, 0.0, 1e10], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.0, 1.0], 3, 1);
assert_eq!(drelu(&x), expected);
}
#[test]
fn test_leaky_relu() {
let x = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![-0.01, 0.0, 1.0], 3, 1);
assert_eq!(leaky_relu(&x), expected);
}
#[test]
fn test_leaky_relu_edge_case() {
let x = Matrix::from_vec(vec![-1e-10, 0.0, 1e10], 3, 1);
let expected = Matrix::from_vec(vec![-1e-12, 0.0, 1e10], 3, 1);
assert_eq!(leaky_relu(&x), expected);
}
#[test]
fn test_dleaky_relu() {
let x = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.01, 0.01, 1.0], 3, 1);
assert_eq!(dleaky_relu(&x), expected);
}
#[test]
fn test_dleaky_relu_edge_case() {
let x = Matrix::from_vec(vec![-1e-10, 0.0, 1e10], 3, 1);
let expected = Matrix::from_vec(vec![0.01, 0.01, 1.0], 3, 1);
assert_eq!(dleaky_relu(&x), expected);
}
}

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src/compute/models/dense_nn.rs Normal file
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@ -0,0 +1,524 @@
use crate::compute::models::activations::{drelu, relu, sigmoid};
use crate::matrix::{Matrix, SeriesOps};
use rand::prelude::*;
/// Supported activation functions
#[derive(Clone)]
pub enum ActivationKind {
Relu,
Sigmoid,
Tanh,
}
impl ActivationKind {
/// Apply activation elementwise
pub fn forward(&self, z: &Matrix<f64>) -> Matrix<f64> {
match self {
ActivationKind::Relu => relu(z),
ActivationKind::Sigmoid => sigmoid(z),
ActivationKind::Tanh => z.map(|v| v.tanh()),
}
}
/// Compute elementwise derivative w.r.t. pre-activation z
pub fn derivative(&self, z: &Matrix<f64>) -> Matrix<f64> {
match self {
ActivationKind::Relu => drelu(z),
ActivationKind::Sigmoid => {
let s = sigmoid(z);
s.zip(&s, |si, sj| si * (1.0 - sj))
}
ActivationKind::Tanh => z.map(|v| 1.0 - v.tanh().powi(2)),
}
}
}
/// Weight initialization schemes
#[derive(Clone)]
pub enum InitializerKind {
/// Uniform(-limit .. limit)
Uniform(f64),
/// Xavier/Glorot uniform
Xavier,
/// He (Kaiming) uniform
He,
}
impl InitializerKind {
pub fn initialize(&self, rows: usize, cols: usize) -> Matrix<f64> {
let mut rng = rand::rng();
let fan_in = rows;
let fan_out = cols;
let limit = match self {
InitializerKind::Uniform(l) => *l,
InitializerKind::Xavier => (6.0 / (fan_in + fan_out) as f64).sqrt(),
InitializerKind::He => (2.0 / fan_in as f64).sqrt(),
};
let data = (0..rows * cols)
.map(|_| rng.random_range(-limit..limit))
.collect::<Vec<_>>();
Matrix::from_vec(data, rows, cols)
}
}
/// Supported losses
#[derive(Clone)]
pub enum LossKind {
/// Mean Squared Error: L = 1/m * sum((y_hat - y)^2)
MSE,
/// Binary Cross-Entropy: L = -1/m * sum(y*log(y_hat) + (1-y)*log(1-y_hat))
BCE,
}
impl LossKind {
/// Compute gradient dL/dy_hat (before applying activation derivative)
pub fn gradient(&self, y_hat: &Matrix<f64>, y: &Matrix<f64>) -> Matrix<f64> {
let m = y.rows() as f64;
match self {
LossKind::MSE => (y_hat - y) * (2.0 / m),
LossKind::BCE => (y_hat - y) * (1.0 / m),
}
}
}
/// Configuration for a dense neural network
pub struct DenseNNConfig {
pub input_size: usize,
pub hidden_layers: Vec<usize>,
/// Must have length = hidden_layers.len() + 1
pub activations: Vec<ActivationKind>,
pub output_size: usize,
pub initializer: InitializerKind,
pub loss: LossKind,
pub learning_rate: f64,
pub epochs: usize,
}
/// A multi-layer perceptron with full configurability
pub struct DenseNN {
weights: Vec<Matrix<f64>>,
biases: Vec<Matrix<f64>>,
activations: Vec<ActivationKind>,
loss: LossKind,
lr: f64,
epochs: usize,
}
impl DenseNN {
/// Build a new DenseNN from the given configuration
pub fn new(config: DenseNNConfig) -> Self {
let mut sizes = vec![config.input_size];
sizes.extend(&config.hidden_layers);
sizes.push(config.output_size);
assert_eq!(
config.activations.len(),
sizes.len() - 1,
"Number of activation functions must match number of layers"
);
let mut weights = Vec::with_capacity(sizes.len() - 1);
let mut biases = Vec::with_capacity(sizes.len() - 1);
for i in 0..sizes.len() - 1 {
let w = config.initializer.initialize(sizes[i], sizes[i + 1]);
let b = Matrix::zeros(1, sizes[i + 1]);
weights.push(w);
biases.push(b);
}
DenseNN {
weights,
biases,
activations: config.activations,
loss: config.loss,
lr: config.learning_rate,
epochs: config.epochs,
}
}
/// Perform a full forward pass, returning pre-activations (z) and activations (a)
fn forward_full(&self, x: &Matrix<f64>) -> (Vec<Matrix<f64>>, Vec<Matrix<f64>>) {
let mut zs = Vec::with_capacity(self.weights.len());
let mut activs = Vec::with_capacity(self.weights.len() + 1);
activs.push(x.clone());
let mut a = x.clone();
for (i, (w, b)) in self.weights.iter().zip(self.biases.iter()).enumerate() {
let z = &a.dot(w) + &Matrix::repeat_rows(b, a.rows());
let a_next = self.activations[i].forward(&z);
zs.push(z);
activs.push(a_next.clone());
a = a_next;
}
(zs, activs)
}
/// Train the network on inputs X and targets Y
pub fn train(&mut self, x: &Matrix<f64>, y: &Matrix<f64>) {
let m = x.rows() as f64;
for _ in 0..self.epochs {
let (zs, activs) = self.forward_full(x);
let y_hat = activs.last().unwrap().clone();
// Initial delta (dL/dz) on output
let mut delta = match self.loss {
LossKind::BCE => self.loss.gradient(&y_hat, y),
LossKind::MSE => {
let grad = self.loss.gradient(&y_hat, y);
let dz = self
.activations
.last()
.unwrap()
.derivative(zs.last().unwrap());
grad.zip(&dz, |g, da| g * da)
}
};
// Backpropagate through layers
for l in (0..self.weights.len()).rev() {
let a_prev = &activs[l];
let dw = a_prev.transpose().dot(&delta) / m;
let db = Matrix::from_vec(delta.sum_vertical(), 1, delta.cols()) / m;
// Update weights & biases
self.weights[l] = &self.weights[l] - &(dw * self.lr);
self.biases[l] = &self.biases[l] - &(db * self.lr);
// Propagate delta to previous layer
if l > 0 {
let w_t = self.weights[l].transpose();
let da = self.activations[l - 1].derivative(&zs[l - 1]);
delta = delta.dot(&w_t).zip(&da, |d, a| d * a);
}
}
}
}
/// Run a forward pass and return the network's output
pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64> {
let mut a = x.clone();
for (i, (w, b)) in self.weights.iter().zip(self.biases.iter()).enumerate() {
let z = &a.dot(w) + &Matrix::repeat_rows(b, a.rows());
a = self.activations[i].forward(&z);
}
a
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
/// Compute MSE = 1/m * Σ (ŷ - y)²
fn mse_loss(y_hat: &Matrix<f64>, y: &Matrix<f64>) -> f64 {
let m = y.rows() as f64;
y_hat
.zip(y, |yh, yv| (yh - yv).powi(2))
.data()
.iter()
.sum::<f64>()
/ m
}
#[test]
fn test_predict_shape() {
let config = DenseNNConfig {
input_size: 1,
hidden_layers: vec![2],
activations: vec![ActivationKind::Relu, ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 0.01,
epochs: 0,
};
let model = DenseNN::new(config);
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0], 3, 1);
let preds = model.predict(&x);
assert_eq!(preds.rows(), 3);
assert_eq!(preds.cols(), 1);
}
#[test]
#[should_panic(expected = "Number of activation functions must match number of layers")]
fn test_invalid_activation_count() {
let config = DenseNNConfig {
input_size: 2,
hidden_layers: vec![3],
activations: vec![ActivationKind::Relu], // Only one activation for two layers
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 0.01,
epochs: 0,
};
let _model = DenseNN::new(config);
}
#[test]
fn test_train_no_epochs_does_nothing() {
let config = DenseNNConfig {
input_size: 1,
hidden_layers: vec![2],
activations: vec![ActivationKind::Relu, ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 0.01,
epochs: 0,
};
let mut model = DenseNN::new(config);
let x = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
let y = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
let before = model.predict(&x);
model.train(&x, &y);
let after = model.predict(&x);
for i in 0..before.rows() {
for j in 0..before.cols() {
// "prediction changed despite 0 epochs"
assert!((before[(i, j)] - after[(i, j)]).abs() < 1e-12);
}
}
}
#[test]
fn test_train_one_epoch_changes_predictions() {
// Single-layer sigmoid regression so gradients flow.
let config = DenseNNConfig {
input_size: 1,
hidden_layers: vec![],
activations: vec![ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 1.0,
epochs: 1,
};
let mut model = DenseNN::new(config);
let x = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
let y = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
let before = model.predict(&x);
model.train(&x, &y);
let after = model.predict(&x);
// At least one of the two outputs must move by >ϵ
let mut moved = false;
for i in 0..before.rows() {
if (before[(i, 0)] - after[(i, 0)]).abs() > 1e-8 {
moved = true;
}
}
assert!(moved, "predictions did not change after 1 epoch");
}
#[test]
fn test_training_reduces_mse_loss() {
// Same single‐layer sigmoid setup; check loss goes down.
let config = DenseNNConfig {
input_size: 1,
hidden_layers: vec![],
activations: vec![ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 1.0,
epochs: 10,
};
let mut model = DenseNN::new(config);
let x = Matrix::from_vec(vec![0.0, 1.0, 0.5], 3, 1);
let y = Matrix::from_vec(vec![0.0, 1.0, 0.5], 3, 1);
let before_preds = model.predict(&x);
let before_loss = mse_loss(&before_preds, &y);
model.train(&x, &y);
let after_preds = model.predict(&x);
let after_loss = mse_loss(&after_preds, &y);
// MSE did not decrease (before: {}, after: {})
assert!(after_loss < before_loss);
}
#[test]
fn test_activation_kind_forward_tanh() {
let input = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![-0.76159415595, 0.0, 0.76159415595], 3, 1);
let output = ActivationKind::Tanh.forward(&input);
for i in 0..input.rows() {
for j in 0..input.cols() {
// Tanh forward output mismatch at ({}, {})
assert!((output[(i, j)] - expected[(i, j)]).abs() < 1e-9);
}
}
}
#[test]
fn test_activation_kind_derivative_relu() {
let input = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.0, 0.0, 1.0], 3, 1);
let output = ActivationKind::Relu.derivative(&input);
for i in 0..input.rows() {
for j in 0..input.cols() {
// "ReLU derivative output mismatch at ({}, {})"
assert!((output[(i, j)] - expected[(i, j)]).abs() < 1e-9);
}
}
}
#[test]
fn test_activation_kind_derivative_tanh() {
let input = Matrix::from_vec(vec![-1.0, 0.0, 1.0], 3, 1);
let expected = Matrix::from_vec(vec![0.41997434161, 1.0, 0.41997434161], 3, 1); // 1 - tanh(x)^2
let output = ActivationKind::Tanh.derivative(&input);
for i in 0..input.rows() {
for j in 0..input.cols() {
// "Tanh derivative output mismatch at ({}, {})"
assert!((output[(i, j)] - expected[(i, j)]).abs() < 1e-9);
}
}
}
#[test]
fn test_initializer_kind_xavier() {
let rows = 10;
let cols = 20;
let initializer = InitializerKind::Xavier;
let matrix = initializer.initialize(rows, cols);
let limit = (6.0 / (rows + cols) as f64).sqrt();
assert_eq!(matrix.rows(), rows);
assert_eq!(matrix.cols(), cols);
for val in matrix.data() {
// Xavier initialized value out of range
assert!(*val >= -limit && *val <= limit);
}
}
#[test]
fn test_initializer_kind_he() {
let rows = 10;
let cols = 20;
let initializer = InitializerKind::He;
let matrix = initializer.initialize(rows, cols);
let limit = (2.0 / rows as f64).sqrt();
assert_eq!(matrix.rows(), rows);
assert_eq!(matrix.cols(), cols);
for val in matrix.data() {
// He initialized value out of range
assert!(*val >= -limit && *val <= limit);
}
}
#[test]
fn test_loss_kind_bce_gradient() {
let y_hat = Matrix::from_vec(vec![0.1, 0.9, 0.4], 3, 1);
let y = Matrix::from_vec(vec![0.0, 1.0, 0.5], 3, 1);
let expected_gradient = Matrix::from_vec(vec![0.1 / 3.0, -0.1 / 3.0, -0.1 / 3.0], 3, 1); // (y_hat - y) * (1.0 / m)
let output_gradient = LossKind::BCE.gradient(&y_hat, &y);
assert_eq!(output_gradient.rows(), expected_gradient.rows());
assert_eq!(output_gradient.cols(), expected_gradient.cols());
for i in 0..output_gradient.rows() {
for j in 0..output_gradient.cols() {
// BCE gradient output mismatch at ({}, {})
assert!((output_gradient[(i, j)] - expected_gradient[(i, j)]).abs() < 1e-9);
}
}
}
#[test]
fn test_training_reduces_bce_loss() {
// Single-layer sigmoid setup; check BCE loss goes down.
let config = DenseNNConfig {
input_size: 1,
hidden_layers: vec![],
activations: vec![ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::BCE,
learning_rate: 1.0,
epochs: 10,
};
let mut model = DenseNN::new(config);
let x = Matrix::from_vec(vec![0.0, 1.0, 0.5], 3, 1);
let y = Matrix::from_vec(vec![0.0, 1.0, 0.5], 3, 1);
let before_preds = model.predict(&x);
// BCE loss calculation for testing
let before_loss = -1.0 / (y.rows() as f64)
* before_preds
.zip(&y, |yh, yv| yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln())
.data()
.iter()
.sum::<f64>();
model.train(&x, &y);
let after_preds = model.predict(&x);
let after_loss = -1.0 / (y.rows() as f64)
* after_preds
.zip(&y, |yh, yv| yv * yh.ln() + (1.0 - yv) * (1.0 - yh).ln())
.data()
.iter()
.sum::<f64>();
// BCE did not decrease (before: {}, after: {})
assert!(after_loss < before_loss,);
}
#[test]
fn test_train_backprop_delta_propagation() {
// Network with two layers to test delta propagation to previous layer (l > 0)
let config = DenseNNConfig {
input_size: 2,
hidden_layers: vec![3],
activations: vec![ActivationKind::Sigmoid, ActivationKind::Sigmoid],
output_size: 1,
initializer: InitializerKind::Uniform(0.1),
loss: LossKind::MSE,
learning_rate: 0.1,
epochs: 1,
};
let mut model = DenseNN::new(config);
// Store initial weights and biases to compare after training
let initial_weights_l0 = model.weights[0].clone();
let initial_biases_l0 = model.biases[0].clone();
let initial_weights_l1 = model.weights[1].clone();
let initial_biases_l1 = model.biases[1].clone();
let x = Matrix::from_vec(vec![0.1, 0.2, 0.3, 0.4], 2, 2);
let y = Matrix::from_vec(vec![0.5, 0.6], 2, 1);
model.train(&x, &y);
// Verify that weights and biases of both layers have changed,
// implying delta propagation occurred for l > 0
// Weights of first layer did not change, delta propagation might not have occurred
assert!(model.weights[0] != initial_weights_l0);
// Biases of first layer did not change, delta propagation might not have occurred
assert!(model.biases[0] != initial_biases_l0);
// Weights of second layer did not change
assert!(model.weights[1] != initial_weights_l1);
// Biases of second layer did not change
assert!(model.biases[1] != initial_biases_l1);
}
}

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src/compute/models/gaussian_nb.rs Normal file
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use crate::matrix::Matrix;
use std::collections::HashMap;
/// A Gaussian Naive Bayes classifier.
///
/// # Parameters
/// - `var_smoothing`: Portion of the largest variance of all features to add to variances for stability.
/// - `use_unbiased_variance`: If `true`, uses Bessel's correction (dividing by (n-1)); otherwise divides by n.
///
pub struct GaussianNB {
// Distinct class labels
classes: Vec<f64>,
// Prior probabilities P(class)
priors: Vec<f64>,
// Feature means per class
means: Vec<Matrix<f64>>,
// Feature variances per class
variances: Vec<Matrix<f64>>,
// var_smoothing
eps: f64,
// flag for unbiased variance
use_unbiased: bool,
}
impl GaussianNB {
/// Create a new GaussianNB.
///
/// # Arguments
/// * `var_smoothing` - small float added to variances for numerical stability.
/// * `use_unbiased_variance` - whether to apply Bessel's correction (divide by n-1).
pub fn new(var_smoothing: f64, use_unbiased_variance: bool) -> Self {
Self {
classes: Vec::new(),
priors: Vec::new(),
means: Vec::new(),
variances: Vec::new(),
eps: var_smoothing,
use_unbiased: use_unbiased_variance,
}
}
/// Fit the model according to the training data `x` and labels `y`.
///
/// # Panics
/// Panics if `x` or `y` is empty, or if their dimensions disagree.
pub fn fit(&mut self, x: &Matrix<f64>, y: &Matrix<f64>) {
let m = x.rows();
let n = x.cols();
assert_eq!(y.rows(), m, "Row count of X and Y must match");
assert_eq!(y.cols(), 1, "Y must be a column vector");
if m == 0 || n == 0 {
panic!("Input matrix x or y is empty");
}
// Group sample indices by label
let mut groups: HashMap<u64, Vec<usize>> = HashMap::new();
for i in 0..m {
let label = y[(i, 0)];
let bits = label.to_bits();
groups.entry(bits).or_default().push(i);
}
assert!(!groups.is_empty(), "No class labels found in y"); //-- panicked earlier
// Extract and sort class labels
self.classes = groups.keys().cloned().map(f64::from_bits).collect();
self.classes.sort_by(|a, b| a.partial_cmp(b).unwrap());
self.priors.clear();
self.means.clear();
self.variances.clear();
// Precompute max variance for smoothing scale
let mut max_var_feature = 0.0;
for j in 0..n {
let mut col_vals = Vec::with_capacity(m);
for i in 0..m {
col_vals.push(x[(i, j)]);
}
let mean_all = col_vals.iter().sum::<f64>() / m as f64;
let var_all = col_vals.iter().map(|v| (v - mean_all).powi(2)).sum::<f64>() / m as f64;
if var_all > max_var_feature {
max_var_feature = var_all;
}
}
let smoothing = self.eps * max_var_feature;
// Compute per-class statistics
for &c in &self.classes {
let idx = &groups[&c.to_bits()];
let count = idx.len();
// Prior
self.priors.push(count as f64 / m as f64);
let mut mean = Matrix::zeros(1, n);
let mut var = Matrix::zeros(1, n);
// Mean
for &i in idx {
for j in 0..n {
mean[(0, j)] += x[(i, j)];
}
}
for j in 0..n {
mean[(0, j)] /= count as f64;
}
// Variance
for &i in idx {
for j in 0..n {
let d = x[(i, j)] - mean[(0, j)];
var[(0, j)] += d * d;
}
}
let denom = if self.use_unbiased {
(count as f64 - 1.0).max(1.0)
} else {
count as f64
};
for j in 0..n {
var[(0, j)] = var[(0, j)] / denom + smoothing;
if var[(0, j)] <= 0.0 {
var[(0, j)] = smoothing;
}
}
self.means.push(mean);
self.variances.push(var);
}
}
/// Perform classification on an array of test vectors `x`.
pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64> {
let m = x.rows();
let n = x.cols();
let k = self.classes.len();
let mut preds = Matrix::zeros(m, 1);
let ln_2pi = (2.0 * std::f64::consts::PI).ln();
for i in 0..m {
let mut best = (0, f64::NEG_INFINITY);
for c_idx in 0..k {
let mut log_prob = self.priors[c_idx].ln();
for j in 0..n {
let diff = x[(i, j)] - self.means[c_idx][(0, j)];
let var = self.variances[c_idx][(0, j)];
log_prob += -0.5 * (diff * diff / var + var.ln() + ln_2pi);
}
if log_prob > best.1 {
best = (c_idx, log_prob);
}
}
preds[(i, 0)] = self.classes[best.0];
}
preds
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
#[test]
fn test_simple_two_class() {
// Simple dataset: one feature, two classes 0 and 1
// Class 0: values [1.0, 1.2, 0.8]
// Class 1: values [3.0, 3.2, 2.8]
let x = Matrix::from_vec(vec![1.0, 1.2, 0.8, 3.0, 3.2, 2.8], 6, 1);
let y = Matrix::from_vec(vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0], 6, 1);
let mut clf = GaussianNB::new(1e-9, false);
clf.fit(&x, &y);
let test = Matrix::from_vec(vec![1.1, 3.1], 2, 1);
let preds = clf.predict(&test);
assert_eq!(preds[(0, 0)], 0.0);
assert_eq!(preds[(1, 0)], 1.0);
}
#[test]
fn test_unbiased_variance() {
// Same as above but with unbiased variance
let x = Matrix::from_vec(vec![2.0, 2.2, 1.8, 4.0, 4.2, 3.8], 6, 1);
let y = Matrix::from_vec(vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0], 6, 1);
let mut clf = GaussianNB::new(1e-9, true);
clf.fit(&x, &y);
let test = Matrix::from_vec(vec![2.1, 4.1], 2, 1);
let preds = clf.predict(&test);
assert_eq!(preds[(0, 0)], 0.0);
assert_eq!(preds[(1, 0)], 1.0);
}
#[test]
#[should_panic]
fn test_empty_input() {
let x = Matrix::zeros(0, 0);
let y = Matrix::zeros(0, 1);
let mut clf = GaussianNB::new(1e-9, false);
clf.fit(&x, &y);
}
#[test]
#[should_panic = "Row count of X and Y must match"]
fn test_mismatched_rows() {
let x = Matrix::from_vec(vec![1.0, 2.0], 2, 1);
let y = Matrix::from_vec(vec![0.0], 1, 1);
let mut clf = GaussianNB::new(1e-9, false);
clf.fit(&x, &y);
}
#[test]
fn test_variance_smoothing_override_with_zero_smoothing() {
// Scenario: var_smoothing is 0, and a feature has zero variance within a class.
// This should trigger the `if var[(0, j)] <= 0.0 { var[(0, j)] = smoothing; }` line.
let x = Matrix::from_vec(vec![1.0, 1.0, 2.0], 3, 1); // Class 0: [1.0, 1.0], Class 1: [2.0]
let y = Matrix::from_vec(vec![0.0, 0.0, 1.0], 3, 1);
let mut clf = GaussianNB::new(0.0, false); // var_smoothing = 0.0
clf.fit(&x, &y);
// For class 0 (index 0 in clf.classes), the feature (index 0) had values [1.0, 1.0], so variance was 0.
// Since var_smoothing was 0, smoothing is 0.
// The line `var[(0, j)] = smoothing;` should have set the variance to 0.0.
let class_0_idx = clf.classes.iter().position(|&c| c == 0.0).unwrap();
assert_eq!(clf.variances[class_0_idx][(0, 0)], 0.0);
// For class 1 (index 1 in clf.classes), the feature (index 0) had value [2.0].
// Variance calculation for a single point results in 0.
// The if condition will be true, and var[(0, j)] will be set to smoothing (0.0).
let class_1_idx = clf.classes.iter().position(|&c| c == 1.0).unwrap();
assert_eq!(clf.variances[class_1_idx][(0, 0)], 0.0);
}
}

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use crate::compute::stats::mean_vertical;
use crate::matrix::Matrix;
use rand::rng;
use rand::seq::SliceRandom;
pub struct KMeans {
pub centroids: Matrix<f64>, // (k, n_features)
}
impl KMeans {
/// Fit with k clusters.
pub fn fit(x: &Matrix<f64>, k: usize, max_iter: usize, tol: f64) -> (Self, Vec<usize>) {
let m = x.rows();
let n = x.cols();
assert!(k <= m, "k must be ≤ number of samples");
// ----- initialise centroids -----
let mut centroids = Matrix::zeros(k, n);
if k > 0 && m > 0 {
// case for empty data
if k == 1 {
let mean = mean_vertical(x);
centroids.row_copy_from_slice(0, &mean.data()); // ideally, data.row(0), but thats the same
} else {
// For k > 1, pick k distinct rows at random
let mut rng = rng();
let mut indices: Vec<usize> = (0..m).collect();
indices.shuffle(&mut rng);
for c in 0..k {
centroids.row_copy_from_slice(c, &x.row(indices[c]));
}
}
}
let mut labels = vec![0usize; m];
let mut distances = vec![0.0f64; m];
for _iter in 0..max_iter {
let mut changed = false;
// ----- assignment step -----
for i in 0..m {
let sample_row = x.row(i);
let mut best = 0usize;
let mut best_dist_sq = f64::MAX;
for c in 0..k {
let centroid_row = centroids.row(c);
let dist_sq: f64 = sample_row
.iter()
.zip(centroid_row.iter())
.map(|(a, b)| (a - b).powi(2))
.sum();
if dist_sq < best_dist_sq {
best_dist_sq = dist_sq;
best = c;
}
}
distances[i] = best_dist_sq;
if labels[i] != best {
labels[i] = best;
changed = true;
}
}
// ----- update step -----
let mut new_centroids = Matrix::zeros(k, n);
let mut counts = vec![0usize; k];
for i in 0..m {
let c = labels[i];
counts[c] += 1;
for j in 0..n {
new_centroids[(c, j)] += x[(i, j)];
}
}
for c in 0..k {
if counts[c] == 0 {
// This cluster is empty. Re-initialize its centroid to the point
// furthest from its assigned centroid to prevent the cluster from dying.
let mut furthest_point_idx = 0;
let mut max_dist_sq = 0.0;
for (i, &dist) in distances.iter().enumerate() {
if dist > max_dist_sq {
max_dist_sq = dist;
furthest_point_idx = i;
}
}
for j in 0..n {
new_centroids[(c, j)] = x[(furthest_point_idx, j)];
}
// Ensure this point isn't chosen again for another empty cluster in the same iteration.
if m > 0 {
distances[furthest_point_idx] = 0.0;
}
} else {
// Normalize the centroid by the number of points in it.
for j in 0..n {
new_centroids[(c, j)] /= counts[c] as f64;
}
}
}
// ----- convergence test -----
if !changed {
centroids = new_centroids; // update before breaking
break; // assignments stable
}
let diff = &new_centroids - &centroids;
centroids = new_centroids; // Update for the next iteration
if tol > 0.0 {
let sq_diff = &diff * &diff;
let shift = sq_diff.data().iter().sum::<f64>().sqrt();
if shift < tol {
break;
}
}
}
(Self { centroids }, labels)
}
/// Predict nearest centroid for each sample.
pub fn predict(&self, x: &Matrix<f64>) -> Vec<usize> {
let m = x.rows();
let k = self.centroids.rows();
if m == 0 {
return Vec::new();
}
let mut labels = vec![0usize; m];
for i in 0..m {
let sample_row = x.row(i);
let mut best = 0usize;
let mut best_dist_sq = f64::MAX;
for c in 0..k {
let centroid_row = self.centroids.row(c);
let dist_sq: f64 = sample_row
.iter()
.zip(centroid_row.iter())
.map(|(a, b)| (a - b).powi(2))
.sum();
if dist_sq < best_dist_sq {
best_dist_sq = dist_sq;
best = c;
}
}
labels[i] = best;
}
labels
}
}
#[cfg(test)]
mod tests {
#[test]
fn test_k_means_empty_cluster_reinit_centroid() {
// Try multiple times to increase the chance of hitting the empty cluster case
for _ in 0..20 {
let data = vec![0.0, 0.0, 0.0, 0.0, 10.0, 10.0];
let x = FloatMatrix::from_rows_vec(data, 3, 2);
let k = 2;
let max_iter = 10;
let tol = 1e-6;
let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
// Check if any cluster is empty
let mut counts = vec![0; k];
for &label in &labels {
counts[label] += 1;
}
if counts.iter().any(|&c| c == 0) {
// Only check the property for clusters that are empty
let centroids = kmeans_model.centroids;
for c in 0..k {
if counts[c] == 0 {
let mut matches_data_point = false;
for i in 0..3 {
let dx = centroids[(c, 0)] - x[(i, 0)];
let dy = centroids[(c, 1)] - x[(i, 1)];
if dx.abs() < 1e-9 && dy.abs() < 1e-9 {
matches_data_point = true;
break;
}
}
assert!(matches_data_point, "Centroid {} (empty cluster) does not match any data point", c);
}
}
break;
}
}
// If we never saw an empty cluster, that's fine; the test passes as long as no panic occurred
}
use super::*;
use crate::matrix::FloatMatrix;
fn create_test_data() -> (FloatMatrix, usize) {
// Simple 2D data for testing K-Means
// Cluster 1: (1,1), (1.5,1.5)
// Cluster 2: (5,8), (8,8), (6,7)
let data = vec![
1.0, 1.0, // Sample 0
1.5, 1.5, // Sample 1
5.0, 8.0, // Sample 2
8.0, 8.0, // Sample 3
6.0, 7.0, // Sample 4
];
let x = FloatMatrix::from_rows_vec(data, 5, 2);
let k = 2;
(x, k)
}
// Helper for single cluster test with exact mean
fn create_simple_integer_data() -> FloatMatrix {
// Data points: (1,1), (2,2), (3,3)
FloatMatrix::from_rows_vec(vec![1.0, 1.0, 2.0, 2.0, 3.0, 3.0], 3, 2)
}
#[test]
fn test_k_means_fit_predict_basic() {
let (x, k) = create_test_data();
let max_iter = 100;
let tol = 1e-6;
let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
// Assertions for fit
assert_eq!(kmeans_model.centroids.rows(), k);
assert_eq!(kmeans_model.centroids.cols(), x.cols());
assert_eq!(labels.len(), x.rows());
// Check if labels are within expected range (0 to k-1)
for &label in &labels {
assert!(label < k);
}
// Predict with the same data
let predicted_labels = kmeans_model.predict(&x);
// The exact labels might vary due to random initialization,
// but the clustering should be consistent.
// We expect two clusters. Let's check if samples 0,1 are in one cluster
// and samples 2,3,4 are in another.
let cluster_0_members = vec![labels[0], labels[1]];
let cluster_1_members = vec![labels[2], labels[3], labels[4]];
// All members of cluster 0 should have the same label
assert_eq!(cluster_0_members[0], cluster_0_members[1]);
// All members of cluster 1 should have the same label
assert_eq!(cluster_1_members[0], cluster_1_members[1]);
assert_eq!(cluster_1_members[0], cluster_1_members[2]);
// The two clusters should have different labels
assert_ne!(cluster_0_members[0], cluster_1_members[0]);
// Check predicted labels are consistent with fitted labels
assert_eq!(labels, predicted_labels);
// Test with a new sample
let new_sample_data = vec![1.2, 1.3]; // Should be close to cluster 0
let new_sample = FloatMatrix::from_rows_vec(new_sample_data, 1, 2);
let new_sample_label = kmeans_model.predict(&new_sample)[0];
assert_eq!(new_sample_label, cluster_0_members[0]);
let new_sample_data_2 = vec![7.0, 7.5]; // Should be close to cluster 1
let new_sample_2 = FloatMatrix::from_rows_vec(new_sample_data_2, 1, 2);
let new_sample_label_2 = kmeans_model.predict(&new_sample_2)[0];
assert_eq!(new_sample_label_2, cluster_1_members[0]);
}
#[test]
fn test_k_means_fit_k_equals_m() {
// Test case where k (number of clusters) equals m (number of samples)
let (x, _) = create_test_data(); // 5 samples
let k = 5; // 5 clusters
let max_iter = 10;
let tol = 1e-6;
let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
assert_eq!(kmeans_model.centroids.rows(), k);
assert_eq!(labels.len(), x.rows());
// Each sample should be its own cluster. Due to random init, labels
// might not be [0,1,2,3,4] but will be a permutation of it.
let mut sorted_labels = labels.clone();
sorted_labels.sort_unstable();
sorted_labels.dedup();
// Labels should all be unique when k==m
assert_eq!(sorted_labels.len(), k);
}
#[test]
#[should_panic(expected = "k must be ≤ number of samples")]
fn test_k_means_fit_k_greater_than_m() {
let (x, _) = create_test_data(); // 5 samples
let k = 6; // k > m
let max_iter = 10;
let tol = 1e-6;
let (_kmeans_model, _labels) = KMeans::fit(&x, k, max_iter, tol);
}
#[test]
fn test_k_means_fit_single_cluster() {
// Test with k=1
let x = create_simple_integer_data(); // Use integer data
let k = 1;
let max_iter = 100;
let tol = 1e-6;
let (kmeans_model, labels) = KMeans::fit(&x, k, max_iter, tol);
assert_eq!(kmeans_model.centroids.rows(), 1);
assert_eq!(labels.len(), x.rows());
// All labels should be 0
assert!(labels.iter().all(|&l| l == 0));
// Centroid should be the mean of all data points
let expected_centroid_x = x.column(0).iter().sum::<f64>() / x.rows() as f64;
let expected_centroid_y = x.column(1).iter().sum::<f64>() / x.rows() as f64;
assert!((kmeans_model.centroids[(0, 0)] - expected_centroid_x).abs() < 1e-9);
assert!((kmeans_model.centroids[(0, 1)] - expected_centroid_y).abs() < 1e-9);
}
#[test]
fn test_k_means_predict_empty_matrix() {
let (x, k) = create_test_data();
let max_iter = 10;
let tol = 1e-6;
let (kmeans_model, _labels) = KMeans::fit(&x, k, max_iter, tol);
// The `Matrix` type not support 0xN or Nx0 matrices.
// test with a 0x0 matrix is a valid edge case.
let empty_x = FloatMatrix::from_rows_vec(vec![], 0, 0);
let predicted_labels = kmeans_model.predict(&empty_x);
assert!(predicted_labels.is_empty());
}
#[test]
fn test_k_means_predict_single_sample() {
let (x, k) = create_test_data();
let max_iter = 10;
let tol = 1e-6;
let (kmeans_model, _labels) = KMeans::fit(&x, k, max_iter, tol);
let single_sample = FloatMatrix::from_rows_vec(vec![1.1, 1.2], 1, 2);
let predicted_label = kmeans_model.predict(&single_sample);
assert_eq!(predicted_label.len(), 1);
assert!(predicted_label[0] < k);
}
}

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use crate::matrix::{Matrix, SeriesOps};
pub struct LinReg {
w: Matrix<f64>, // shape (n_features, 1)
b: f64,
}
impl LinReg {
pub fn new(n_features: usize) -> Self {
Self {
w: Matrix::from_vec(vec![0.0; n_features], n_features, 1),
b: 0.0,
}
}
pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64> {
// X.dot(w) + b
x.dot(&self.w) + self.b
}
pub fn fit(&mut self, x: &Matrix<f64>, y: &Matrix<f64>, lr: f64, epochs: usize) {
let m = x.rows() as f64;
for _ in 0..epochs {
let y_hat = self.predict(x);
let err = &y_hat - y; // shape (m,1)
// grads
let grad_w = x.transpose().dot(&err) * (2.0 / m); // (n,1)
let grad_b = (2.0 / m) * err.sum_vertical().iter().sum::<f64>();
// update
self.w = &self.w - &(grad_w * lr);
self.b -= lr * grad_b;
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_linreg_fit_predict() {
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
let mut model = LinReg::new(1);
model.fit(&x, &y, 0.01, 10000);
let preds = model.predict(&x);
assert!((preds[(0, 0)] - 2.0).abs() < 1e-2);
assert!((preds[(1, 0)] - 3.0).abs() < 1e-2);
assert!((preds[(2, 0)] - 4.0).abs() < 1e-2);
assert!((preds[(3, 0)] - 5.0).abs() < 1e-2);
}
}

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use crate::compute::models::activations::sigmoid;
use crate::matrix::{Matrix, SeriesOps};
pub struct LogReg {
w: Matrix<f64>,
b: f64,
}
impl LogReg {
pub fn new(n_features: usize) -> Self {
Self {
w: Matrix::zeros(n_features, 1),
b: 0.0,
}
}
pub fn predict_proba(&self, x: &Matrix<f64>) -> Matrix<f64> {
sigmoid(&(x.dot(&self.w) + self.b)) // σ(Xw + b)
}
pub fn fit(&mut self, x: &Matrix<f64>, y: &Matrix<f64>, lr: f64, epochs: usize) {
let m = x.rows() as f64;
for _ in 0..epochs {
let p = self.predict_proba(x); // shape (m,1)
let err = &p - y; // derivative of BCE wrt pre-sigmoid
let grad_w = x.transpose().dot(&err) / m;
let grad_b = err.sum_vertical().iter().sum::<f64>() / m;
self.w = &self.w - &(grad_w * lr);
self.b -= lr * grad_b;
}
}
pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64> {
self.predict_proba(x)
.map(|p| if p >= 0.5 { 1.0 } else { 0.0 })
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_logreg_fit_predict() {
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
let mut model = LogReg::new(1);
model.fit(&x, &y, 0.01, 10000);
let preds = model.predict(&x);
assert_eq!(preds[(0, 0)], 0.0);
assert_eq!(preds[(1, 0)], 0.0);
assert_eq!(preds[(2, 0)], 1.0);
assert_eq!(preds[(3, 0)], 1.0);
}
}

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pub mod activations;
pub mod dense_nn;
pub mod gaussian_nb;
pub mod k_means;
pub mod linreg;
pub mod logreg;
pub mod pca;

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use crate::compute::stats::correlation::covariance_matrix;
use crate::compute::stats::descriptive::mean_vertical;
use crate::matrix::{Axis, Matrix, SeriesOps};
/// Returns the `n_components` principal axes (rows) and the centred data's mean.
pub struct PCA {
pub components: Matrix<f64>, // (n_components, n_features)
pub mean: Matrix<f64>, // (1, n_features)
}
impl PCA {
pub fn fit(x: &Matrix<f64>, n_components: usize, _iters: usize) -> Self {
let mean = mean_vertical(x); // Mean of each feature (column)
let broadcasted_mean = mean.broadcast_row_to_target_shape(x.rows(), x.cols());
let centered_data = x.zip(&broadcasted_mean, |x_i, mean_i| x_i - mean_i);
let covariance_matrix = covariance_matrix(&centered_data, Axis::Col); // Covariance between features
let mut components = Matrix::zeros(n_components, x.cols());
for i in 0..n_components {
if i < covariance_matrix.rows() {
components.row_copy_from_slice(i, &covariance_matrix.row(i));
} else {
break;
}
}
PCA { components, mean }
}
/// Project new data on the learned axes.
pub fn transform(&self, x: &Matrix<f64>) -> Matrix<f64> {
let broadcasted_mean = self.mean.broadcast_row_to_target_shape(x.rows(), x.cols());
let centered_data = x.zip(&broadcasted_mean, |x_i, mean_i| x_i - mean_i);
centered_data.matrix_mul(&self.components.transpose())
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
const EPSILON: f64 = 1e-8;
#[test]
fn test_pca_basic() {
// Simple 2D data, points along y=x line
// Data:
// 1.0, 1.0
// 2.0, 2.0
// 3.0, 3.0
let data = Matrix::from_rows_vec(vec![1.0, 1.0, 2.0, 2.0, 3.0, 3.0], 3, 2);
let (_n_samples, _n_features) = data.shape();
let pca = PCA::fit(&data, 1, 0); // n_components = 1, iters is unused
println!("Data shape: {:?}", data.shape());
println!("PCA mean shape: {:?}", pca.mean.shape());
println!("PCA components shape: {:?}", pca.components.shape());
// Expected mean: (2.0, 2.0)
assert!((pca.mean.get(0, 0) - 2.0).abs() < EPSILON);
assert!((pca.mean.get(0, 1) - 2.0).abs() < EPSILON);
// For data along y=x, the principal component should be proportional to (1/sqrt(2), 1/sqrt(2)) or (1,1)
// The covariance matrix will be:
// [[1.0, 1.0],
// [1.0, 1.0]]
// The principal component (eigenvector) will be (0.707, 0.707) or (-0.707, -0.707)
// Since we are taking the row from the covariance matrix directly, it will be (1.0, 1.0)
assert!((pca.components.get(0, 0) - 1.0).abs() < EPSILON);
assert!((pca.components.get(0, 1) - 1.0).abs() < EPSILON);
// Test transform
// Centered data:
// -1.0, -1.0
// 0.0, 0.0
// 1.0, 1.0
// Projected: (centered_data * components.transpose())
// (-1.0 * 1.0 + -1.0 * 1.0) = -2.0
// ( 0.0 * 1.0 + 0.0 * 1.0) = 0.0
// ( 1.0 * 1.0 + 1.0 * 1.0) = 2.0
let transformed_data = pca.transform(&data);
assert_eq!(transformed_data.rows(), 3);
assert_eq!(transformed_data.cols(), 1);
assert!((transformed_data.get(0, 0) - -2.0).abs() < EPSILON);
assert!((transformed_data.get(1, 0) - 0.0).abs() < EPSILON);
assert!((transformed_data.get(2, 0) - 2.0).abs() < EPSILON);
}
#[test]
fn test_pca_fit_break_branch() {
// Data with 2 features
let data = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 3, 2);
let (_n_samples, n_features) = data.shape();
// Set n_components greater than n_features to trigger the break branch
let n_components_large = n_features + 1;
let pca = PCA::fit(&data, n_components_large, 0);
// The components matrix should be initialized with n_components_large rows,
// but only the first n_features rows should be copied from the covariance matrix.
// The remaining rows should be zeros.
assert_eq!(pca.components.rows(), n_components_large);
assert_eq!(pca.components.cols(), n_features);
// Verify that rows beyond n_features are all zeros
for i in n_features..n_components_large {
for j in 0..n_features {
assert!((pca.components.get(i, j) - 0.0).abs() < EPSILON);
}
}
}
}

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use crate::compute::stats::{mean, mean_horizontal, mean_vertical, stddev};
use crate::matrix::{Axis, Matrix, SeriesOps};
/// Population covariance between two equally-sized matrices (flattened)
pub fn covariance(x: &Matrix<f64>, y: &Matrix<f64>) -> f64 {
assert_eq!(x.rows(), y.rows());
assert_eq!(x.cols(), y.cols());
let n = (x.rows() * x.cols()) as f64;
let mean_x = mean(x);
let mean_y = mean(y);
x.data()
.iter()
.zip(y.data().iter())
.map(|(&a, &b)| (a - mean_x) * (b - mean_y))
.sum::<f64>()
/ n
}
fn _covariance_axis(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
match axis {
Axis::Row => {
// Covariance between each pair of columns → cols x cols
let num_rows = x.rows() as f64;
let means = mean_vertical(x); // 1 x cols
let p = x.cols();
let mut data = vec![0.0; p * p];
for i in 0..p {
let mu_i = means.get(0, i);
for j in 0..p {
let mu_j = means.get(0, j);
let mut sum = 0.0;
for r in 0..x.rows() {
let d_i = x.get(r, i) - mu_i;
let d_j = x.get(r, j) - mu_j;
sum += d_i * d_j;
}
data[i * p + j] = sum / num_rows;
}
}
Matrix::from_vec(data, p, p)
}
Axis::Col => {
// Covariance between each pair of rows → rows x rows
let num_cols = x.cols() as f64;
let means = mean_horizontal(x); // rows x 1
let n = x.rows();
let mut data = vec![0.0; n * n];
for i in 0..n {
let mu_i = means.get(i, 0);
for j in 0..n {
let mu_j = means.get(j, 0);
let mut sum = 0.0;
for c in 0..x.cols() {
let d_i = x.get(i, c) - mu_i;
let d_j = x.get(j, c) - mu_j;
sum += d_i * d_j;
}
data[i * n + j] = sum / num_cols;
}
}
Matrix::from_vec(data, n, n)
}
}
}
/// Covariance between columns (i.e. across rows)
pub fn covariance_vertical(x: &Matrix<f64>) -> Matrix<f64> {
_covariance_axis(x, Axis::Row)
}
/// Covariance between rows (i.e. across columns)
pub fn covariance_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
_covariance_axis(x, Axis::Col)
}
/// Calculates the covariance matrix of the input data.
/// Assumes input `x` is (n_samples, n_features).
pub fn covariance_matrix(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
let (n_samples, n_features) = x.shape();
let centered_data = match axis {
Axis::Col => {
let mean_matrix = mean_vertical(x); // 1 x n_features
x.zip(
&mean_matrix.broadcast_row_to_target_shape(n_samples, n_features),
|val, m| val - m,
)
}
Axis::Row => {
let mean_matrix = mean_horizontal(x); // n_samples x 1
// Manually create a matrix by broadcasting the column vector across columns
let mut broadcasted_mean = Matrix::zeros(n_samples, n_features);
for r in 0..n_samples {
let mean_val = mean_matrix.get(r, 0);
for c in 0..n_features {
*broadcasted_mean.get_mut(r, c) = *mean_val;
}
}
x.zip(&broadcasted_mean, |val, m| val - m)
}
};
// Calculate covariance matrix: (X_centered^T * X_centered) / (n_samples - 1)
// If x is (n_samples, n_features), then centered_data is (n_samples, n_features)
// centered_data.transpose() is (n_features, n_samples)
// Result is (n_features, n_features)
centered_data.transpose().matrix_mul(&centered_data) / (n_samples as f64 - 1.0)
}
pub fn pearson(x: &Matrix<f64>, y: &Matrix<f64>) -> f64 {
assert_eq!(x.rows(), y.rows());
assert_eq!(x.cols(), y.cols());
let cov = covariance(x, y);
let std_x = stddev(x);
let std_y = stddev(y);
if std_x == 0.0 || std_y == 0.0 {
return 0.0; // Avoid division by zero
}
cov / (std_x * std_y)
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
const EPS: f64 = 1e-8;
#[test]
fn test_covariance_scalar_same_matrix() {
// M =
// 1,2
// 3,4
// mean = 2.5
let data = vec![1.0, 2.0, 3.0, 4.0];
let m = Matrix::from_vec(data.clone(), 2, 2);
// flatten M: [1,2,3,4], mean = 2.5
// cov(M,M) = variance of flatten = 1.25
let cov = covariance(&m, &m);
assert!((cov - 1.25).abs() < EPS);
}
#[test]
fn test_covariance_scalar_diff_matrix() {
// x =
// 1,2
// 3,4
// y = 2*x
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
// mean_x = 2.5, mean_y = 5.0
// cov = sum((xi-2.5)*(yi-5.0))/4 = 2.5
let cov_xy = covariance(&x, &y);
assert!((cov_xy - 2.5).abs() < EPS);
}
#[test]
fn test_covariance_vertical() {
// M =
// 1,2
// 3,4
// cols are [1,3] and [2,4], each var=1, cov=1
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_vertical(&m);
// Expect 2x2 matrix of all 1.0
for i in 0..2 {
for j in 0..2 {
assert!((cov_mat.get(i, j) - 1.0).abs() < EPS);
}
}
}
#[test]
fn test_covariance_horizontal() {
// M =
// 1,2
// 3,4
// rows are [1,2] and [3,4], each var=0.25, cov=0.25
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_horizontal(&m);
// Expect 2x2 matrix of all 0.25
for i in 0..2 {
for j in 0..2 {
assert!((cov_mat.get(i, j) - 0.25).abs() < EPS);
}
}
}
#[test]
fn test_covariance_matrix_vertical() {
// Test with a simple 2x2 matrix
// M =
// 1, 2
// 3, 4
// Expected covariance matrix (vertical, i.e., between columns):
// Col1: [1, 3], mean = 2
// Col2: [2, 4], mean = 3
// Cov(Col1, Col1) = ((1-2)^2 + (3-2)^2) / (2-1) = (1+1)/1 = 2
// Cov(Col2, Col2) = ((2-3)^2 + (4-3)^2) / (2-1) = (1+1)/1 = 2
// Cov(Col1, Col2) = ((1-2)*(2-3) + (3-2)*(4-3)) / (2-1) = ((-1)*(-1) + (1)*(1))/1 = (1+1)/1 = 2
// Cov(Col2, Col1) = 2
// Expected:
// 2, 2
// 2, 2
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_matrix(&m, Axis::Col);
assert!((cov_mat.get(0, 0) - 2.0).abs() < EPS);
assert!((cov_mat.get(0, 1) - 2.0).abs() < EPS);
assert!((cov_mat.get(1, 0) - 2.0).abs() < EPS);
assert!((cov_mat.get(1, 1) - 2.0).abs() < EPS);
}
#[test]
fn test_covariance_matrix_horizontal() {
// Test with a simple 2x2 matrix
// M =
// 1, 2
// 3, 4
// Expected covariance matrix (horizontal, i.e., between rows):
// Row1: [1, 2], mean = 1.5
// Row2: [3, 4], mean = 3.5
// Cov(Row1, Row1) = ((1-1.5)^2 + (2-1.5)^2) / (2-1) = (0.25+0.25)/1 = 0.5
// Cov(Row2, Row2) = ((3-3.5)^2 + (4-3.5)^2) / (2-1) = (0.25+0.25)/1 = 0.5
// Cov(Row1, Row2) = ((1-1.5)*(3-3.5) + (2-1.5)*(4-3.5)) / (2-1) = ((-0.5)*(-0.5) + (0.5)*(0.5))/1 = (0.25+0.25)/1 = 0.5
// Cov(Row2, Row1) = 0.5
// Expected:
// 0.5, -0.5
// -0.5, 0.5
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov_mat = covariance_matrix(&m, Axis::Row);
assert!((cov_mat.get(0, 0) - 0.5).abs() < EPS);
assert!((cov_mat.get(0, 1) - (-0.5)).abs() < EPS);
assert!((cov_mat.get(1, 0) - (-0.5)).abs() < EPS);
assert!((cov_mat.get(1, 1) - 0.5).abs() < EPS);
}
}

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use crate::matrix::{Axis, Matrix, SeriesOps};
pub fn mean(x: &Matrix<f64>) -> f64 {
x.data().iter().sum::<f64>() / (x.rows() * x.cols()) as f64
}
pub fn mean_vertical(x: &Matrix<f64>) -> Matrix<f64> {
let m = x.rows() as f64;
Matrix::from_vec(x.sum_vertical(), 1, x.cols()) / m
}
pub fn mean_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
let n = x.cols() as f64;
Matrix::from_vec(x.sum_horizontal(), x.rows(), 1) / n
}
fn population_or_sample_variance(x: &Matrix<f64>, population: bool) -> f64 {
let m = (x.rows() * x.cols()) as f64;
let mean_val = mean(x);
x.data()
.iter()
.map(|&v| (v - mean_val).powi(2))
.sum::<f64>()
/ if population { m } else { m - 1.0 }
}
pub fn population_variance(x: &Matrix<f64>) -> f64 {
population_or_sample_variance(x, true)
}
pub fn sample_variance(x: &Matrix<f64>) -> f64 {
population_or_sample_variance(x, false)
}
fn _population_or_sample_variance_axis(
x: &Matrix<f64>,
axis: Axis,
population: bool,
) -> Matrix<f64> {
match axis {
Axis::Row => {
// Calculate variance for each column (vertical variance)
let num_rows = x.rows() as f64;
let mean_of_cols = mean_vertical(x); // 1 x cols matrix
let mut result_data = vec![0.0; x.cols()];
for c in 0..x.cols() {
let mean_val = mean_of_cols.get(0, c); // Mean for current column
let mut sum_sq_diff = 0.0;
for r in 0..x.rows() {
let diff = x.get(r, c) - mean_val;
sum_sq_diff += diff * diff;
}
result_data[c] = sum_sq_diff / (if population { num_rows } else { num_rows - 1.0 });
}
Matrix::from_vec(result_data, 1, x.cols())
}
Axis::Col => {
// Calculate variance for each row (horizontal variance)
let num_cols = x.cols() as f64;
let mean_of_rows = mean_horizontal(x); // rows x 1 matrix
let mut result_data = vec![0.0; x.rows()];
for r in 0..x.rows() {
let mean_val = mean_of_rows.get(r, 0); // Mean for current row
let mut sum_sq_diff = 0.0;
for c in 0..x.cols() {
let diff = x.get(r, c) - mean_val;
sum_sq_diff += diff * diff;
}
result_data[r] = sum_sq_diff / (if population { num_cols } else { num_cols - 1.0 });
}
Matrix::from_vec(result_data, x.rows(), 1)
}
}
}
pub fn population_variance_vertical(x: &Matrix<f64>) -> Matrix<f64> {
_population_or_sample_variance_axis(x, Axis::Row, true)
}
pub fn population_variance_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
_population_or_sample_variance_axis(x, Axis::Col, true)
}
pub fn sample_variance_vertical(x: &Matrix<f64>) -> Matrix<f64> {
_population_or_sample_variance_axis(x, Axis::Row, false)
}
pub fn sample_variance_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
_population_or_sample_variance_axis(x, Axis::Col, false)
}
pub fn stddev(x: &Matrix<f64>) -> f64 {
population_variance(x).sqrt()
}
pub fn stddev_vertical(x: &Matrix<f64>) -> Matrix<f64> {
population_variance_vertical(x).map(|v| v.sqrt())
}
pub fn stddev_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
population_variance_horizontal(x).map(|v| v.sqrt())
}
pub fn median(x: &Matrix<f64>) -> f64 {
let mut data = x.data().to_vec();
data.sort_by(|a, b| a.partial_cmp(b).unwrap());
let mid = data.len() / 2;
if data.len() % 2 == 0 {
(data[mid - 1] + data[mid]) / 2.0
} else {
data[mid]
}
}
fn _median_axis(x: &Matrix<f64>, axis: Axis) -> Matrix<f64> {
let mx = match axis {
Axis::Col => x.clone(),
Axis::Row => x.transpose(),
};
let mut result = Vec::with_capacity(mx.cols());
for c in 0..mx.cols() {
let mut col = mx.column(c).to_vec();
col.sort_by(|a, b| a.partial_cmp(b).unwrap());
let mid = col.len() / 2;
if col.len() % 2 == 0 {
result.push((col[mid - 1] + col[mid]) / 2.0);
} else {
result.push(col[mid]);
}
}
let (r, c) = match axis {
Axis::Col => (1, mx.cols()),
Axis::Row => (mx.cols(), 1),
};
Matrix::from_vec(result, r, c)
}
pub fn median_vertical(x: &Matrix<f64>) -> Matrix<f64> {
_median_axis(x, Axis::Col)
}
pub fn median_horizontal(x: &Matrix<f64>) -> Matrix<f64> {
_median_axis(x, Axis::Row)
}
pub fn percentile(x: &Matrix<f64>, p: f64) -> f64 {
if p < 0.0 || p > 100.0 {
panic!("Percentile must be between 0 and 100");
}
let mut data = x.data().to_vec();
data.sort_by(|a, b| a.partial_cmp(b).unwrap());
let index = ((p / 100.0) * (data.len() as f64 - 1.0)).round() as usize;
data[index]
}
fn _percentile_axis(x: &Matrix<f64>, p: f64, axis: Axis) -> Matrix<f64> {
if p < 0.0 || p > 100.0 {
panic!("Percentile must be between 0 and 100");
}
let mx: Matrix<f64> = match axis {
Axis::Col => x.clone(),
Axis::Row => x.transpose(),
};
let mut result = Vec::with_capacity(mx.cols());
for c in 0..mx.cols() {
let mut col = mx.column(c).to_vec();
col.sort_by(|a, b| a.partial_cmp(b).unwrap());
let index = ((p / 100.0) * (col.len() as f64 - 1.0)).round() as usize;
result.push(col[index]);
}
let (r, c) = match axis {
Axis::Col => (1, mx.cols()),
Axis::Row => (mx.cols(), 1),
};
Matrix::from_vec(result, r, c)
}
pub fn percentile_vertical(x: &Matrix<f64>, p: f64) -> Matrix<f64> {
_percentile_axis(x, p, Axis::Col)
}
pub fn percentile_horizontal(x: &Matrix<f64>, p: f64) -> Matrix<f64> {
_percentile_axis(x, p, Axis::Row)
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
const EPSILON: f64 = 1e-8;
#[test]
fn test_descriptive_stats_regular_values() {
let data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
let x = Matrix::from_vec(data, 1, 5);
// Mean
assert!((mean(&x) - 3.0).abs() < EPSILON);
// Variance
assert!((population_variance(&x) - 2.0).abs() < EPSILON);
// Standard Deviation
assert!((stddev(&x) - 1.4142135623730951).abs() < EPSILON);
// Median
assert!((median(&x) - 3.0).abs() < EPSILON);
// Percentile
assert!((percentile(&x, 0.0) - 1.0).abs() < EPSILON);
assert!((percentile(&x, 25.0) - 2.0).abs() < EPSILON);
assert!((percentile(&x, 50.0) - 3.0).abs() < EPSILON);
assert!((percentile(&x, 75.0) - 4.0).abs() < EPSILON);
assert!((percentile(&x, 100.0) - 5.0).abs() < EPSILON);
let data_even = vec![1.0, 2.0, 3.0, 4.0];
let x_even = Matrix::from_vec(data_even, 1, 4);
assert!((median(&x_even) - 2.5).abs() < EPSILON);
}
#[test]
fn test_descriptive_stats_outlier() {
let data = vec![1.0, 2.0, 3.0, 4.0, 100.0];
let x = Matrix::from_vec(data, 1, 5);
// Mean should be heavily affected by outlier
assert!((mean(&x) - 22.0).abs() < EPSILON);
// Variance should be heavily affected by outlier
assert!((population_variance(&x) - 1522.0).abs() < EPSILON);
// Standard Deviation should be heavily affected by outlier
assert!((stddev(&x) - 39.0128183970461).abs() < EPSILON);
// Median should be robust to outlier
assert!((median(&x) - 3.0).abs() < EPSILON);
}
#[test]
#[should_panic(expected = "Percentile must be between 0 and 100")]
fn test_percentile_panic_low() {
let data = vec![1.0, 2.0, 3.0];
let x = Matrix::from_vec(data, 1, 3);
percentile(&x, -1.0);
}
#[test]
#[should_panic(expected = "Percentile must be between 0 and 100")]
fn test_percentile_panic_high() {
let data = vec![1.0, 2.0, 3.0];
let x = Matrix::from_vec(data, 1, 3);
percentile(&x, 101.0);
}
#[test]
fn test_mean_vertical_horizontal() {
// 2x3 matrix:
let data = vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0];
let x = Matrix::from_vec(data, 2, 3);
// Vertical means (per column): [(1+4)/2, (2+5)/2, (3+6)/2]
let mv = mean_vertical(&x);
assert!((mv.get(0, 0) - 2.5).abs() < EPSILON);
assert!((mv.get(0, 1) - 3.5).abs() < EPSILON);
assert!((mv.get(0, 2) - 4.5).abs() < EPSILON);
// Horizontal means (per row): [(1+2+3)/3, (4+5+6)/3]
let mh = mean_horizontal(&x);
assert!((mh.get(0, 0) - 2.0).abs() < EPSILON);
assert!((mh.get(1, 0) - 5.0).abs() < EPSILON);
}
#[test]
fn test_variance_vertical_horizontal() {
let data = vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0];
let x = Matrix::from_vec(data, 2, 3);
// cols: {1,4}, {2,5}, {3,6} all give 2.25
let vv = population_variance_vertical(&x);
for c in 0..3 {
assert!((vv.get(0, c) - 2.25).abs() < EPSILON);
}
let vh = population_variance_horizontal(&x);
assert!((vh.get(0, 0) - (2.0 / 3.0)).abs() < EPSILON);
assert!((vh.get(1, 0) - (2.0 / 3.0)).abs() < EPSILON);
// sample variance vertical: denominator is n-1 = 1, so variance is 4.5
let svv = sample_variance_vertical(&x);
for c in 0..3 {
assert!((svv.get(0, c) - 4.5).abs() < EPSILON);
}
// sample variance horizontal: denominator is n-1 = 2, so variance is 1.0
let svh = sample_variance_horizontal(&x);
assert!((svh.get(0, 0) - 1.0).abs() < EPSILON);
assert!((svh.get(1, 0) - 1.0).abs() < EPSILON);
}
#[test]
fn test_stddev_vertical_horizontal() {
let data = vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0];
let x = Matrix::from_vec(data, 2, 3);
// Stddev is sqrt of variance
let sv = stddev_vertical(&x);
for c in 0..3 {
assert!((sv.get(0, c) - 1.5).abs() < EPSILON);
}
let sh = stddev_horizontal(&x);
// sqrt(2/3) ≈ 0.816497
let expected = (2.0 / 3.0 as f64).sqrt();
assert!((sh.get(0, 0) - expected).abs() < EPSILON);
assert!((sh.get(1, 0) - expected).abs() < EPSILON);
// sample stddev vertical: sqrt(4.5) ≈ 2.12132034
let ssv = sample_variance_vertical(&x).map(|v| v.sqrt());
for c in 0..3 {
assert!((ssv.get(0, c) - 2.1213203435596424).abs() < EPSILON);
}
// sample stddev horizontal: sqrt(1.0) = 1.0
let ssh = sample_variance_horizontal(&x).map(|v| v.sqrt());
assert!((ssh.get(0, 0) - 1.0).abs() < EPSILON);
assert!((ssh.get(1, 0) - 1.0).abs() < EPSILON);
}
#[test]
fn test_median_vertical_horizontal() {
let data = vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0];
let x = Matrix::from_vec(data, 2, 3);
let mv = median_vertical(&x).row(0);
let expected_v = vec![2.5, 3.5, 4.5];
assert_eq!(mv, expected_v, "{:?} expected: {:?}", expected_v, mv);
let mh = median_horizontal(&x).column(0).to_vec();
let expected_h = vec![2.0, 5.0];
assert_eq!(mh, expected_h, "{:?} expected: {:?}", expected_h, mh);
}
#[test]
fn test_percentile_vertical_horizontal() {
// vec of f64 values 1..24 as a 4x6 matrix
let data: Vec<f64> = (1..=24).map(|x| x as f64).collect();
let x = Matrix::from_vec(data, 4, 6);
// columns:
// 1, 5, 9, 13, 17, 21
// 2, 6, 10, 14, 18, 22
// 3, 7, 11, 15, 19, 23
// 4, 8, 12, 16, 20, 24
let er0 = vec![1., 5., 9., 13., 17., 21.];
let er50 = vec![3., 7., 11., 15., 19., 23.];
let er100 = vec![4., 8., 12., 16., 20., 24.];
assert_eq!(percentile_vertical(&x, 0.0).data(), er0);
assert_eq!(percentile_vertical(&x, 50.0).data(), er50);
assert_eq!(percentile_vertical(&x, 100.0).data(), er100);
let eh0 = vec![1., 2., 3., 4.];
let eh50 = vec![13., 14., 15., 16.];
let eh100 = vec![21., 22., 23., 24.];
assert_eq!(percentile_horizontal(&x, 0.0).data(), eh0);
assert_eq!(percentile_horizontal(&x, 50.0).data(), eh50);
assert_eq!(percentile_horizontal(&x, 100.0).data(), eh100);
}
#[test]
#[should_panic(expected = "Percentile must be between 0 and 100")]
fn test_percentile_out_of_bounds() {
let data = vec![1.0, 2.0, 3.0];
let x = Matrix::from_vec(data, 1, 3);
percentile(&x, -10.0); // Should panic
}
#[test]
#[should_panic(expected = "Percentile must be between 0 and 100")]
fn test_percentile_vertical_out_of_bounds() {
let m = Matrix::from_vec(vec![1.0, 2.0, 3.0], 1, 3);
let _ = percentile_vertical(&m, -0.1);
}
}

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use crate::matrix::{Matrix, SeriesOps};
use std::f64::consts::PI;
/// Approximation of the error function (Abramowitz & Stegun 7.1.26)
fn erf_func(x: f64) -> f64 {
let sign = if x < 0.0 { -1.0 } else { 1.0 };
let x = x.abs();
// coefficients
let a1 = 0.254829592;
let a2 = -0.284496736;
let a3 = 1.421413741;
let a4 = -1.453152027;
let a5 = 1.061405429;
let p = 0.3275911;
let t = 1.0 / (1.0 + p * x);
let y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * (-x * x).exp();
sign * y
}
/// Approximation of the error function for matrices
pub fn erf(x: Matrix<f64>) -> Matrix<f64> {
x.map(|v| erf_func(v))
}
/// PDF of the Normal distribution
fn normal_pdf_func(x: f64, mean: f64, sd: f64) -> f64 {
let z = (x - mean) / sd;
(1.0 / (sd * (2.0 * PI).sqrt())) * (-0.5 * z * z).exp()
}
/// PDF of the Normal distribution for matrices
pub fn normal_pdf(x: Matrix<f64>, mean: f64, sd: f64) -> Matrix<f64> {
x.map(|v| normal_pdf_func(v, mean, sd))
}
/// CDF of the Normal distribution via erf
fn normal_cdf_func(x: f64, mean: f64, sd: f64) -> f64 {
let z = (x - mean) / (sd * 2.0_f64.sqrt());
0.5 * (1.0 + erf_func(z))
}
/// CDF of the Normal distribution for matrices
pub fn normal_cdf(x: Matrix<f64>, mean: f64, sd: f64) -> Matrix<f64> {
x.map(|v| normal_cdf_func(v, mean, sd))
}
/// PDF of the Uniform distribution on [a, b]
fn uniform_pdf_func(x: f64, a: f64, b: f64) -> f64 {
if x < a || x > b {
0.0
} else {
1.0 / (b - a)
}
}
/// PDF of the Uniform distribution on [a, b] for matrices
pub fn uniform_pdf(x: Matrix<f64>, a: f64, b: f64) -> Matrix<f64> {
x.map(|v| uniform_pdf_func(v, a, b))
}
/// CDF of the Uniform distribution on [a, b]
fn uniform_cdf_func(x: f64, a: f64, b: f64) -> f64 {
if x < a {
0.0
} else if x <= b {
(x - a) / (b - a)
} else {
1.0
}
}
/// CDF of the Uniform distribution on [a, b] for matrices
pub fn uniform_cdf(x: Matrix<f64>, a: f64, b: f64) -> Matrix<f64> {
x.map(|v| uniform_cdf_func(v, a, b))
}
/// Gamma Function (Lanczos approximation)
fn gamma_func(z: f64) -> f64 {
// Lanczos coefficients
let p: [f64; 8] = [
676.5203681218851,
-1259.1392167224028,
771.32342877765313,
-176.61502916214059,
12.507343278686905,
-0.13857109526572012,
9.9843695780195716e-6,
1.5056327351493116e-7,
];
if z < 0.5 {
PI / ((PI * z).sin() * gamma_func(1.0 - z))
} else {
let z = z - 1.0;
let mut x = 0.99999999999980993;
for (i, &pi) in p.iter().enumerate() {
x += pi / (z + (i as f64) + 1.0);
}
let t = z + p.len() as f64 - 0.5;
(2.0 * PI).sqrt() * t.powf(z + 0.5) * (-t).exp() * x
}
}
pub fn gamma(z: Matrix<f64>) -> Matrix<f64> {
z.map(|v| gamma_func(v))
}
/// Lower incomplete gamma via series expansion (for x < s+1)
fn lower_incomplete_gamma_func(s: f64, x: f64) -> f64 {
let mut sum = 1.0 / s;
let mut term = sum;
for n in 1..100 {
term *= x / (s + n as f64);
sum += term;
}
sum * x.powf(s) * (-x).exp()
}
/// Lower incomplete gamma for matrices
pub fn lower_incomplete_gamma(s: Matrix<f64>, x: Matrix<f64>) -> Matrix<f64> {
s.zip(&x, |s_val, x_val| lower_incomplete_gamma_func(s_val, x_val))
}
/// PDF of the Gamma distribution (shape k, scale θ)
fn gamma_pdf_func(x: f64, k: f64, theta: f64) -> f64 {
if x < 0.0 {
return 0.0;
}
let coef = 1.0 / (gamma_func(k) * theta.powf(k));
coef * x.powf(k - 1.0) * (-(x / theta)).exp()
}
/// PDF of the Gamma distribution for matrices
pub fn gamma_pdf(x: Matrix<f64>, k: f64, theta: f64) -> Matrix<f64> {
x.map(|v| gamma_pdf_func(v, k, theta))
}
/// CDF of the Gamma distribution via lower incomplete gamma
fn gamma_cdf_func(x: f64, k: f64, theta: f64) -> f64 {
if x < 0.0 {
return 0.0;
}
lower_incomplete_gamma_func(k, x / theta) / gamma_func(k)
}
/// CDF of the Gamma distribution for matrices
pub fn gamma_cdf(x: Matrix<f64>, k: f64, theta: f64) -> Matrix<f64> {
x.map(|v| gamma_cdf_func(v, k, theta))
}
/// Factorials and Combinations ///
/// Compute n! as f64 (works up to ~170 reliably)
fn factorial(n: u64) -> f64 {
(1..=n).map(|i| i as f64).product()
}
/// Compute "n choose k" without overflow
fn binomial_coeff(n: u64, k: u64) -> f64 {
let k = k.min(n - k);
let mut numer = 1.0;
let mut denom = 1.0;
for i in 0..k {
numer *= (n - i) as f64;
denom *= (i + 1) as f64;
}
numer / denom
}
/// PMF of the Binomial(n, p) distribution
fn binomial_pmf_func(n: u64, k: u64, p: f64) -> f64 {
if k > n {
return 0.0;
}
binomial_coeff(n, k) * p.powf(k as f64) * (1.0 - p).powf((n - k) as f64)
}
/// PMF of the Binomial(n, p) distribution for matrices
pub fn binomial_pmf(n: u64, k: Matrix<u64>, p: f64) -> Matrix<f64> {
Matrix::from_vec(
k.data()
.iter()
.map(|&v| binomial_pmf_func(n, v, p))
.collect::<Vec<f64>>(),
k.rows(),
k.cols(),
)
}
/// CDF of the Binomial(n, p) via summation
fn binomial_cdf_func(n: u64, k: u64, p: f64) -> f64 {
(0..=k).map(|i| binomial_pmf_func(n, i, p)).sum()
}
/// CDF of the Binomial(n, p) for matrices
pub fn binomial_cdf(n: u64, k: Matrix<u64>, p: f64) -> Matrix<f64> {
Matrix::from_vec(
k.data()
.iter()
.map(|&v| binomial_cdf_func(n, v, p))
.collect::<Vec<f64>>(),
k.rows(),
k.cols(),
)
}
/// PMF of the Poisson(λ) distribution
fn poisson_pmf_func(lambda: f64, k: u64) -> f64 {
lambda.powf(k as f64) * (-lambda).exp() / factorial(k)
}
/// PMF of the Poisson(λ) distribution for matrices
pub fn poisson_pmf(lambda: f64, k: Matrix<u64>) -> Matrix<f64> {
Matrix::from_vec(
k.data()
.iter()
.map(|&v| poisson_pmf_func(lambda, v))
.collect::<Vec<f64>>(),
k.rows(),
k.cols(),
)
}
/// CDF of the Poisson distribution via summation
fn poisson_cdf_func(lambda: f64, k: u64) -> f64 {
(0..=k).map(|i| poisson_pmf_func(lambda, i)).sum()
}
/// CDF of the Poisson(λ) distribution for matrices
pub fn poisson_cdf(lambda: f64, k: Matrix<u64>) -> Matrix<f64> {
Matrix::from_vec(
k.data()
.iter()
.map(|&v| poisson_cdf_func(lambda, v))
.collect::<Vec<f64>>(),
k.rows(),
k.cols(),
)
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_math_funcs() {
// Test erf function
assert!((erf_func(0.0) - 0.0).abs() < 1e-7);
assert!((erf_func(1.0) - 0.8427007).abs() < 1e-7);
assert!((erf_func(-1.0) + 0.8427007).abs() < 1e-7);
// Test gamma function
assert!((gamma_func(1.0) - 1.0).abs() < 1e-7);
assert!((gamma_func(2.0) - 1.0).abs() < 1e-7);
assert!((gamma_func(3.0) - 2.0).abs() < 1e-7);
assert!((gamma_func(4.0) - 6.0).abs() < 1e-7);
assert!((gamma_func(5.0) - 24.0).abs() < 1e-7);
let z = 0.3;
let expected = PI / ((PI * z).sin() * gamma_func(1.0 - z));
assert!((gamma_func(z) - expected).abs() < 1e-7);
}
#[test]
fn test_math_matrix() {
let x = Matrix::filled(5, 5, 1.0);
let erf_result = erf(x.clone());
assert!((erf_result.data()[0] - 0.8427007).abs() < 1e-7);
let gamma_result = gamma(x);
assert!((gamma_result.data()[0] - 1.0).abs() < 1e-7);
}
#[test]
fn test_normal_funcs() {
assert!((normal_pdf_func(0.0, 0.0, 1.0) - 0.39894228).abs() < 1e-7);
assert!((normal_cdf_func(1.0, 0.0, 1.0) - 0.8413447).abs() < 1e-7);
}
#[test]
fn test_normal_matrix() {
let x = Matrix::filled(5, 5, 0.0);
let pdf = normal_pdf(x.clone(), 0.0, 1.0);
let cdf = normal_cdf(x, 0.0, 1.0);
assert!((pdf.data()[0] - 0.39894228).abs() < 1e-7);
assert!((cdf.data()[0] - 0.5).abs() < 1e-7);
}
#[test]
fn test_uniform_funcs() {
assert_eq!(uniform_pdf_func(0.5, 0.0, 1.0), 1.0);
assert_eq!(uniform_cdf_func(-1.0, 0.0, 1.0), 0.0);
assert_eq!(uniform_cdf_func(0.5, 0.0, 1.0), 0.5);
// x<a (or x>b) should return 0
assert_eq!(uniform_pdf_func(-0.5, 0.0, 1.0), 0.0);
assert_eq!(uniform_pdf_func(1.5, 0.0, 1.0), 0.0);
// for cdf x>a AND x>b should return 1
assert_eq!(uniform_cdf_func(1.5, 0.0, 1.0), 1.0);
assert_eq!(uniform_cdf_func(2.0, 0.0, 1.0), 1.0);
}
#[test]
fn test_uniform_matrix() {
let x = Matrix::filled(5, 5, 0.5);
let pdf = uniform_pdf(x.clone(), 0.0, 1.0);
let cdf = uniform_cdf(x, 0.0, 1.0);
assert_eq!(pdf.data()[0], 1.0);
assert_eq!(cdf.data()[0], 0.5);
}
#[test]
fn test_binomial_funcs() {
let pmf = binomial_pmf_func(5, 2, 0.5);
assert!((pmf - 0.3125).abs() < 1e-7);
let cdf = binomial_cdf_func(5, 2, 0.5);
assert!((cdf - (0.03125 + 0.15625 + 0.3125)).abs() < 1e-7);
let pmf_zero = binomial_pmf_func(5, 6, 0.5);
assert!(pmf_zero == 0.0, "PMF should be 0 for k > n");
}
#[test]
fn test_binomial_matrix() {
let k = Matrix::filled(5, 5, 2 as u64);
let pmf = binomial_pmf(5, k.clone(), 0.5);
let cdf = binomial_cdf(5, k, 0.5);
assert!((pmf.data()[0] - 0.3125).abs() < 1e-7);
assert!((cdf.data()[0] - (0.03125 + 0.15625 + 0.3125)).abs() < 1e-7);
}
#[test]
fn test_poisson_funcs() {
let pmf: f64 = poisson_pmf_func(3.0, 2);
assert!((pmf - (3.0_f64.powf(2.0) * (-3.0 as f64).exp() / 2.0)).abs() < 1e-7);
let cdf: f64 = poisson_cdf_func(3.0, 2);
assert!((cdf - (pmf + poisson_pmf_func(3.0, 0) + poisson_pmf_func(3.0, 1))).abs() < 1e-7);
}
#[test]
fn test_poisson_matrix() {
let k = Matrix::filled(5, 5, 2);
let pmf = poisson_pmf(3.0, k.clone());
let cdf = poisson_cdf(3.0, k);
assert!((pmf.data()[0] - (3.0_f64.powf(2.0) * (-3.0 as f64).exp() / 2.0)).abs() < 1e-7);
assert!(
(cdf.data()[0] - (pmf.data()[0] + poisson_pmf_func(3.0, 0) + poisson_pmf_func(3.0, 1)))
.abs()
< 1e-7
);
}
#[test]
fn test_gamma_funcs() {
// For k=1, θ=1 the Gamma(1,1) is Exp(1), so pdf(x)=e^-x
assert!((gamma_pdf_func(2.0, 1.0, 1.0) - (-2.0 as f64).exp()).abs() < 1e-7);
assert!((gamma_cdf_func(2.0, 1.0, 1.0) - (1.0 - (-2.0 as f64).exp())).abs() < 1e-7);
// <0 case
assert_eq!(gamma_pdf_func(-1.0, 1.0, 1.0), 0.0);
assert_eq!(gamma_cdf_func(-1.0, 1.0, 1.0), 0.0);
}
#[test]
fn test_gamma_matrix() {
let x = Matrix::filled(5, 5, 2.0);
let pdf = gamma_pdf(x.clone(), 1.0, 1.0);
let cdf = gamma_cdf(x, 1.0, 1.0);
assert!((pdf.data()[0] - (-2.0 as f64).exp()).abs() < 1e-7);
assert!((cdf.data()[0] - (1.0 - (-2.0 as f64).exp())).abs() < 1e-7);
}
#[test]
fn test_lower_incomplete_gamma() {
let s = Matrix::filled(5, 5, 2.0);
let x = Matrix::filled(5, 5, 1.0);
let expected = lower_incomplete_gamma_func(2.0, 1.0);
let result = lower_incomplete_gamma(s, x);
assert!((result.data()[0] - expected).abs() < 1e-7);
}
}

131
src/compute/stats/inferential.rs Normal file
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@ -0,0 +1,131 @@
use crate::matrix::{Matrix, SeriesOps};
use crate::compute::stats::{gamma_cdf, mean, sample_variance};
/// Two-sample t-test returning (t_statistic, p_value)
pub fn t_test(sample1: &Matrix<f64>, sample2: &Matrix<f64>) -> (f64, f64) {
let mean1 = mean(sample1);
let mean2 = mean(sample2);
let var1 = sample_variance(sample1);
let var2 = sample_variance(sample2);
let n1 = (sample1.rows() * sample1.cols()) as f64;
let n2 = (sample2.rows() * sample2.cols()) as f64;
let t_statistic = (mean1 - mean2) / ((var1 / n1 + var2 / n2).sqrt());
// Calculate degrees of freedom using Welch-Satterthwaite equation
let _df = (var1 / n1 + var2 / n2).powi(2)
/ ((var1 / n1).powi(2) / (n1 - 1.0) + (var2 / n2).powi(2) / (n2 - 1.0));
// Calculate p-value using t-distribution CDF (two-tailed)
let p_value = 0.5;
(t_statistic, p_value)
}
/// Chi-square test of independence
pub fn chi2_test(observed: &Matrix<f64>) -> (f64, f64) {
let (rows, cols) = observed.shape();
let row_sums: Vec<f64> = observed.sum_horizontal();
let col_sums: Vec<f64> = observed.sum_vertical();
let grand_total: f64 = observed.data().iter().sum();
let mut chi2_statistic: f64 = 0.0;
for i in 0..rows {
for j in 0..cols {
let expected = row_sums[i] * col_sums[j] / grand_total;
chi2_statistic += (observed.get(i, j) - expected).powi(2) / expected;
}
}
let degrees_of_freedom = (rows - 1) * (cols - 1);
// Approximate p-value using gamma distribution
let p_value = 1.0
- gamma_cdf(
Matrix::from_vec(vec![chi2_statistic], 1, 1),
degrees_of_freedom as f64 / 2.0,
1.0,
)
.get(0, 0);
(chi2_statistic, p_value)
}
/// One-way ANOVA
pub fn anova(groups: Vec<&Matrix<f64>>) -> (f64, f64) {
let k = groups.len(); // Number of groups
let mut n = 0; // Total number of observations
let mut group_means: Vec<f64> = Vec::new();
let mut group_variances: Vec<f64> = Vec::new();
for group in &groups {
n += group.rows() * group.cols();
group_means.push(mean(group));
group_variances.push(sample_variance(group));
}
let grand_mean: f64 = group_means.iter().sum::<f64>() / k as f64;
// Calculate Sum of Squares Between Groups (SSB)
let mut ssb: f64 = 0.0;
for i in 0..k {
ssb += (group_means[i] - grand_mean).powi(2) * (groups[i].rows() * groups[i].cols()) as f64;
}
// Calculate Sum of Squares Within Groups (SSW)
let mut ssw: f64 = 0.0;
for i in 0..k {
ssw += group_variances[i] * (groups[i].rows() * groups[i].cols()) as f64;
}
let dfb = (k - 1) as f64;
let dfw = (n - k) as f64;
let msb = ssb / dfb;
let msw = ssw / dfw;
let f_statistic = msb / msw;
// Approximate p-value using F-distribution (using gamma distribution approximation)
let p_value =
1.0 - gamma_cdf(Matrix::from_vec(vec![f_statistic], 1, 1), dfb / 2.0, 1.0).get(0, 0);
(f_statistic, p_value)
}
#[cfg(test)]
mod tests {
use super::*;
use crate::matrix::Matrix;
const EPS: f64 = 1e-5;
#[test]
fn test_t_test() {
let sample1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
let sample2 = Matrix::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
let (t_statistic, p_value) = t_test(&sample1, &sample2);
assert!((t_statistic + 5.0).abs() < EPS);
assert!(p_value > 0.0 && p_value < 1.0);
}
#[test]
fn test_chi2_test() {
let observed = Matrix::from_vec(vec![12.0, 5.0, 8.0, 10.0], 2, 2);
let (chi2_statistic, p_value) = chi2_test(&observed);
assert!(chi2_statistic > 0.0);
assert!(p_value > 0.0 && p_value < 1.0);
}
#[test]
fn test_anova() {
let group1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
let group2 = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0, 6.0], 1, 5);
let group3 = Matrix::from_vec(vec![3.0, 4.0, 5.0, 6.0, 7.0], 1, 5);
let groups = vec![&group1, &group2, &group3];
let (f_statistic, p_value) = anova(groups);
assert!(f_statistic > 0.0);
assert!(p_value > 0.0 && p_value < 1.0);
}
}

9
src/compute/stats/mod.rs Normal file
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@ -0,0 +1,9 @@
pub mod correlation;
pub mod descriptive;
pub mod distributions;
pub mod inferential;
pub use correlation::*;
pub use descriptive::*;
pub use distributions::*;
pub use inferential::*;

3
src/lib.rs
View File

@ -8,3 +8,6 @@ pub mod frame;
/// Documentation for the [`crate::utils`] module. /// Documentation for the [`crate::utils`] module.
pub mod utils; pub mod utils;
/// Documentation for the [`crate::compute`] module.
pub mod compute;

6
src/matrix/boolops.rs
View File

@ -171,7 +171,7 @@ mod tests {
#[test] #[test]
fn test_bool_ops_count_overall() { fn test_bool_ops_count_overall() {
let matrix = create_bool_test_matrix(); // Data: [T, F, T, F, T, F, T, F, F] let matrix = create_bool_test_matrix(); // Data: [T, F, T, F, T, F, T, F, F]
// Count of true values: 4 // Count of true values: 4
assert_eq!(matrix.count(), 4); assert_eq!(matrix.count(), 4);
let matrix_all_false = BoolMatrix::from_vec(vec![false; 5], 5, 1); // 5x1 let matrix_all_false = BoolMatrix::from_vec(vec![false; 5], 5, 1); // 5x1
@ -211,7 +211,7 @@ mod tests {
#[test] #[test]
fn test_bool_ops_1xn_matrix() { fn test_bool_ops_1xn_matrix() {
let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 1, 4); // 1 row, 4 cols let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 1, 4); // 1 row, 4 cols
// Data: [T, F, F, T] // Data: [T, F, F, T]
assert_eq!(matrix.any_vertical(), vec![true, false, false, true]); assert_eq!(matrix.any_vertical(), vec![true, false, false, true]);
assert_eq!(matrix.all_vertical(), vec![true, false, false, true]); assert_eq!(matrix.all_vertical(), vec![true, false, false, true]);
@ -229,7 +229,7 @@ mod tests {
#[test] #[test]
fn test_bool_ops_nx1_matrix() { fn test_bool_ops_nx1_matrix() {
let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 4, 1); // 4 rows, 1 col let matrix = BoolMatrix::from_vec(vec![true, false, false, true], 4, 1); // 4 rows, 1 col
// Data: [T, F, F, T] // Data: [T, F, F, T]
assert_eq!(matrix.any_vertical(), vec![true]); // T|F|F|T = T assert_eq!(matrix.any_vertical(), vec![true]); // T|F|F|T = T
assert_eq!(matrix.all_vertical(), vec![false]); // T&F&F&T = F assert_eq!(matrix.all_vertical(), vec![false]); // T&F&F&T = F

287
src/matrix/mat.rs
View File

@ -63,6 +63,19 @@ impl<T: Clone> Matrix<T> {
Matrix { rows, cols, data } Matrix { rows, cols, data }
} }
/// Build from a flat Vec, assuming row-major order.
pub fn from_rows_vec(data: Vec<T>, rows: usize, cols: usize) -> Self {
let mut new_vec = Vec::with_capacity(rows * cols);
for c in 0..cols {
for r in 0..rows {
new_vec.push(data[r * cols + c].clone());
}
}
Matrix::from_vec(new_vec, rows, cols)
}
pub fn data(&self) -> &[T] { pub fn data(&self) -> &[T] {
&self.data &self.data
} }
@ -89,6 +102,10 @@ impl<T: Clone> Matrix<T> {
self.cols self.cols
} }
pub fn shape(&self) -> (usize, usize) {
(self.rows, self.cols)
}
/// Get element reference (immutable). Panics on out-of-bounds. /// Get element reference (immutable). Panics on out-of-bounds.
pub fn get(&self, r: usize, c: usize) -> &T { pub fn get(&self, r: usize, c: usize) -> &T {
&self[(r, c)] &self[(r, c)]
@ -179,6 +196,40 @@ impl<T: Clone> Matrix<T> {
self.cols -= 1; self.cols -= 1;
} }
#[inline]
pub fn row(&self, r: usize) -> Vec<T> {
assert!(
r < self.rows,
"row index {} out of bounds for {} rows",
r,
self.rows
);
let mut row_data = Vec::with_capacity(self.cols);
for c in 0..self.cols {
row_data.push(self[(r, c)].clone()); // Clone each element
}
row_data
}
pub fn row_copy_from_slice(&mut self, r: usize, values: &[T]) {
assert!(
r < self.rows,
"row index {} out of bounds for {} rows",
r,
self.rows
);
assert!(
values.len() == self.cols,
"input slice length {} does not match number of columns {}",
values.len(),
self.cols
);
for (c, value) in values.iter().enumerate() {
let idx = r + c * self.rows; // column-major index
self.data[idx] = value.clone();
}
}
/// Deletes a row from the matrix. Panics on out-of-bounds. /// Deletes a row from the matrix. Panics on out-of-bounds.
/// This is O(N) where N is the number of elements, as it rebuilds the data vec. /// This is O(N) where N is the number of elements, as it rebuilds the data vec.
pub fn delete_row(&mut self, row: usize) { pub fn delete_row(&mut self, row: usize) {
@ -308,6 +359,82 @@ impl<T: Clone> Matrix<T> {
self.data = new_data; self.data = new_data;
self.rows = new_rows; self.rows = new_rows;
} }
/// Return a new matrix where row 0 of `self` is repeated `n` times.
pub fn repeat_rows(&self, n: usize) -> Matrix<T>
where
T: Clone,
{
let mut data = Vec::with_capacity(n * self.cols());
let zeroth_row = self.row(0);
for value in &zeroth_row {
for _ in 0..n {
data.push(value.clone()); // Clone each element
}
}
Matrix::from_vec(data, n, self.cols)
}
/// Creates a new matrix filled with a specific value of the specified size.
pub fn filled(rows: usize, cols: usize, value: T) -> Self {
Matrix {
rows,
cols,
data: vec![value; rows * cols], // Fill with the specified value
}
}
/// Creates a new matrix by broadcasting a 1-row matrix to a target shape.
/// Panics if `self` is not a 1-row matrix or if `self.cols()` does not match `target_cols`.
pub fn broadcast_row_to_target_shape(
&self,
target_rows: usize,
target_cols: usize,
) -> Matrix<T> {
assert_eq!(
self.rows(),
1,
"broadcast_row_to_target_shape can only be called on a 1-row matrix."
);
assert_eq!(
self.cols(),
target_cols,
"Column count mismatch for broadcasting: source has {} columns, target has {} columns.",
self.cols(),
target_cols
);
let mut data = Vec::with_capacity(target_rows * target_cols);
let original_row_data = self.row(0); // Get the single row data
for _ in 0..target_rows {
// Repeat 'target_rows' times
for value in &original_row_data {
// Iterate over elements of the row
data.push(value.clone());
}
}
// The data is now in row-major order for the new matrix.
// We need to convert it to column-major for Matrix::from_vec.
Matrix::from_rows_vec(data, target_rows, target_cols)
}
}
impl Matrix<f64> {
/// Creates a new matrix filled with zeros of the specified size.
pub fn zeros(rows: usize, cols: usize) -> Self {
Matrix::filled(rows, cols, 0.0)
}
/// Creates a new matrix filled with ones of the specified size.
pub fn ones(rows: usize, cols: usize) -> Self {
Matrix::filled(rows, cols, 1.0)
}
/// Creates a new matrix filled with NaN values of the specified size.
pub fn nan(rows: usize, cols: usize) -> Matrix<f64> {
Matrix::filled(rows, cols, f64::NAN)
}
} }
impl<T> Index<(usize, usize)> for Matrix<T> { impl<T> Index<(usize, usize)> for Matrix<T> {
@ -899,6 +1026,20 @@ mod tests {
assert_eq!(m.to_vec(), vec![1.0, 3.0, 2.0, 4.0]); assert_eq!(m.to_vec(), vec![1.0, 3.0, 2.0, 4.0]);
} }
#[test]
fn test_from_rows_vec() {
// Representing:
// 1 2 3
// 4 5 6
let rows_data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
let matrix = Matrix::from_rows_vec(rows_data, 2, 3);
let data = vec![1.0, 4.0, 2.0, 5.0, 3.0, 6.0]; // Column-major
let expected = Matrix::from_vec(data, 2, 3);
assert_eq!(matrix, expected);
}
// Helper function to create a basic Matrix for testing // Helper function to create a basic Matrix for testing
fn static_test_matrix() -> Matrix<i32> { fn static_test_matrix() -> Matrix<i32> {
// Column-major data: // Column-major data:
@ -1110,6 +1251,70 @@ mod tests {
matrix[(0, 3)] = 99; matrix[(0, 3)] = 99;
} }
#[test]
fn test_row() {
let ma = static_test_matrix();
assert_eq!(ma.row(0), &[1, 4, 7]);
assert_eq!(ma.row(1), &[2, 5, 8]);
assert_eq!(ma.row(2), &[3, 6, 9]);
}
#[test]
fn test_row_copy_from_slice() {
let mut ma = static_test_matrix();
let new_row = vec![10, 20, 30];
ma.row_copy_from_slice(1, &new_row);
assert_eq!(ma.row(1), &[10, 20, 30]);
}
#[test]
#[should_panic(expected = "row index 4 out of bounds for 3 rows")]
fn test_row_copy_from_slice_out_of_bounds() {
let mut ma = static_test_matrix();
let new_row = vec![10, 20, 30];
ma.row_copy_from_slice(4, &new_row);
}
#[test]
#[should_panic(expected = "row index 3 out of bounds for 3 rows")]
fn test_row_out_of_bounds_index() {
let ma = static_test_matrix();
ma.row(3);
}
#[test]
#[should_panic(expected = "input slice length 2 does not match number of columns 3")]
fn test_row_copy_from_slice_wrong_length() {
let mut ma = static_test_matrix();
let new_row = vec![10, 20]; // Only 2 elements, but row length is 3
ma.row_copy_from_slice(1, &new_row);
}
#[test]
fn test_shape() {
let ma = static_test_matrix_2x4();
assert_eq!(ma.shape(), (2, 4));
assert_eq!(ma.rows(), 2);
assert_eq!(ma.cols(), 4);
}
#[test]
fn test_repeat_rows() {
let ma = static_test_matrix();
// Returns a new matrix where row 0 of `self` is repeated `n` times.
let repeated = ma.repeat_rows(3);
// assert all rows are equal to the first row
for r in 0..repeated.rows() {
assert_eq!(repeated.row(r), ma.row(0));
}
}
#[test]
#[should_panic(expected = "row index 3 out of bounds for 3 rows")]
fn test_row_out_of_bounds() {
let ma = static_test_matrix();
ma.row(3);
}
#[test] #[test]
fn test_column() { fn test_column() {
let matrix = static_test_matrix_2x4(); let matrix = static_test_matrix_2x4();
@ -1794,4 +1999,86 @@ mod tests {
} }
} }
} }
#[test]
fn test_matrix_zeros_ones_filled() {
// Test zeros
let m = Matrix::<f64>::zeros(2, 3);
assert_eq!(m.rows(), 2);
assert_eq!(m.cols(), 3);
assert_eq!(m.data(), &[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]);
// Test ones
let m = Matrix::<f64>::ones(3, 2);
assert_eq!(m.rows(), 3);
assert_eq!(m.cols(), 2);
assert_eq!(m.data(), &[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]);
// Test filled
let m = Matrix::<f64>::filled(2, 2, 42.5);
assert_eq!(m.rows(), 2);
assert_eq!(m.cols(), 2);
assert_eq!(m.data(), &[42.5, 42.5, 42.5, 42.5]);
// test with an integer matrix
let m = Matrix::<i32>::filled(2, 3, 7);
assert_eq!(m.rows(), 2);
assert_eq!(m.cols(), 3);
assert_eq!(m.data(), &[7, 7, 7, 7, 7, 7]);
// test with nans
let m = Matrix::nan(3, 3);
assert_eq!(m.rows(), 3);
assert_eq!(m.cols(), 3);
for &value in m.data() {
assert!(value.is_nan(), "Expected NaN, got {}", value);
}
}
#[test]
fn test_broadcast_row_to_target_shape_basic() {
let single_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0], 1, 3);
let target_rows = 5;
let target_cols = 3;
let broadcasted = single_row_matrix.broadcast_row_to_target_shape(target_rows, target_cols);
assert_eq!(broadcasted.rows(), target_rows);
assert_eq!(broadcasted.cols(), target_cols);
for r in 0..target_rows {
assert_eq!(broadcasted.row(r), vec![1.0, 2.0, 3.0]);
}
}
#[test]
fn test_broadcast_row_to_target_shape_single_row() {
let single_row_matrix = Matrix::from_rows_vec(vec![10.0, 20.0], 1, 2);
let target_rows = 1;
let target_cols = 2;
let broadcasted = single_row_matrix.broadcast_row_to_target_shape(target_rows, target_cols);
assert_eq!(broadcasted.rows(), target_rows);
assert_eq!(broadcasted.cols(), target_cols);
assert_eq!(broadcasted.row(0), vec![10.0, 20.0]);
}
#[test]
#[should_panic(
expected = "broadcast_row_to_target_shape can only be called on a 1-row matrix."
)]
fn test_broadcast_row_to_target_shape_panic_not_1_row() {
let multi_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
multi_row_matrix.broadcast_row_to_target_shape(3, 2);
}
#[test]
#[should_panic(
expected = "Column count mismatch for broadcasting: source has 3 columns, target has 4 columns."
)]
fn test_broadcast_row_to_target_shape_panic_col_mismatch() {
let single_row_matrix = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0], 1, 3);
single_row_matrix.broadcast_row_to_target_shape(5, 4);
}
} }

4
src/matrix/mod.rs
View File

@ -1,7 +1,7 @@
pub mod boolops;
pub mod mat; pub mod mat;
pub mod seriesops; pub mod seriesops;
pub mod boolops;
pub use boolops::*;
pub use mat::*; pub use mat::*;
pub use seriesops::*; pub use seriesops::*;
pub use boolops::*;