Merge a08fb546a9fcaddd214064292fc83c31c4644dcd into 11330e464ba3a7f08aaf73bc918281472c503b1d

Cargo.toml

@@ -14,8 +14,6 @@ crate-type = ["cdylib", "lib"]
 [dependencies]
 chrono = "^0.4.10"
 criterion = { version = "0.5", features = ["html_reports"], optional = true }
-
-[dev-dependencies]
 rand = "^0.9.1"
 
 [features]

src/compute/mod.rs

@@ -0,0 +1,4 @@
+
+pub mod models;
+
+pub mod stats;

src/compute/models/activations.rs

@@ -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);
+    }
+}

src/compute/models/dense_nn.rs

@@ -0,0 +1,340 @@
+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]
+    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() {
+                assert!(
+                    (before[(i, j)] - after[(i, j)]).abs() < 1e-12,
+                    "prediction changed despite 0 epochs"
+                );
+            }
+        }
+    }
+
+    #[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);
+
+        assert!(
+            after_loss < before_loss,
+            "MSE did not decrease (before: {}, after: {})",
+            before_loss,
+            after_loss
+        );
+    }
+}

src/compute/models/gaussian_nb.rs

@@ -0,0 +1,214 @@
+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);
+        }
+        if groups.is_empty() {
+            panic!("No class labels found in y");
+        }
+
+        // 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();
+            if count == 0 {
+                panic!("Class group for label {} is empty", c);
+            }
+            // 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);
+        clf.predict(&x);
+    }
+}

src/compute/models/k_means.rs

@@ -0,0 +1,113 @@
+use crate::matrix::Matrix;
+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: pick k distinct rows at random -----
+        let mut rng = rand::rng();
+        let mut indices: Vec<usize> = (0..m).collect();
+        indices.shuffle(&mut rng);
+        let mut centroids = Matrix::zeros(k, n);
+        for (c, &i) in indices[..k].iter().enumerate() {
+            for j in 0..n {
+                centroids[(c, j)] = x[(i, j)];
+            }
+        }
+
+        let mut labels = vec![0usize; m];
+        for _ in 0..max_iter {
+            // ----- assignment step -----
+            let mut changed = false;
+            for i in 0..m {
+                let mut best = 0usize;
+                let mut best_dist = f64::MAX;
+                for c in 0..k {
+                    let mut dist = 0.0;
+                    for j in 0..n {
+                        let d = x[(i, j)] - centroids[(c, j)];
+                        dist += d * d;
+                    }
+                    if dist < best_dist {
+                        best_dist = dist;
+                        best = c;
+                    }
+                }
+                if labels[i] != best {
+                    labels[i] = best;
+                    changed = true;
+                }
+            }
+
+            // ----- update step -----
+            let mut counts = vec![0usize; k];
+            let mut centroids = Matrix::zeros(k, n);
+            for i in 0..m {
+                let c = labels[i];
+                counts[c] += 1;
+                for j in 0..n {
+                    centroids[(c, j)] += x[(i, j)];
+                }
+            }
+            for c in 0..k {
+                if counts[c] > 0 {
+                    for j in 0..n {
+                        centroids[(c, j)] /= counts[c] as f64;
+                    }
+                }
+            }
+
+            // ----- convergence test -----
+            if !changed {
+                break; // assignments stable
+            }
+            if tol > 0.0 {
+                // optional centroid-shift tolerance
+                let mut shift: f64 = 0.0;
+                for c in 0..k {
+                    for j in 0..n {
+                        let d = centroids[(c, j)] - centroids[(c, j)]; // previous stored?
+                        shift += d * d;
+                    }
+                }
+                if shift.sqrt() < 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();
+        let n = x.cols();
+        let mut labels = vec![0usize; m];
+        for i in 0..m {
+            let mut best = 0usize;
+            let mut best_dist = f64::MAX;
+            for c in 0..k {
+                let mut dist = 0.0;
+                for j in 0..n {
+                    let d = x[(i, j)] - self.centroids[(c, j)];
+                    dist += d * d;
+                }
+                if dist < best_dist {
+                    best_dist = dist;
+                    best = c;
+                }
+            }
+            labels[i] = best;
+        }
+        labels
+    }
+}

src/compute/models/linreg.rs

@@ -0,0 +1,54 @@
+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);
+    }
+}

src/compute/models/logreg.rs

@@ -0,0 +1,55 @@
+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);
+    }
+}

src/compute/models/mod.rs

@@ -0,0 +1,7 @@
+pub mod linreg;
+pub mod logreg;
+pub mod dense_nn;
+pub mod k_means;    
+pub mod pca;
+pub mod gaussian_nb;
+pub mod activations;

src/compute/models/pca.rs

@@ -0,0 +1,85 @@
+use crate::matrix::{Matrix, SeriesOps};
+use rand;
+
+/// 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 m = x.rows();
+        let n = x.cols();
+        assert!(n_components <= n);
+
+        // ----- centre data -----
+        let mean_vec = {
+            let mut v = Matrix::zeros(1, n);
+            for j in 0..n {
+                let mut s = 0.0;
+                for i in 0..m {
+                    s += x[(i, j)];
+                }
+                v[(0, j)] = s / m as f64;
+            }
+            v
+        };
+        let x_centered = x - &mean_vec;
+
+        // ----- covariance matrix C = Xᵀ·X / (m-1) -----
+        let cov = x_centered.transpose().dot(&x_centered) * (1.0 / (m as f64 - 1.0));
+
+        // ----- power iteration to find top eigenvectors -----
+        let mut comp = Matrix::zeros(n_components, n);
+        let mut b = Matrix::zeros(1, n);            // current vector
+        for c in 0..n_components {
+            // random initial vector
+            for j in 0..n {
+                b[(0, j)] = rand::random::<f64>() - 0.5;
+            }
+            // subtract projections on previously found components
+            for prev in 0..c {
+                // let proj = b.dot(Matrix::from_vec(data, rows, cols).transpose())[(0, 0)];
+                // let proj = b.dot(&comp.row(prev).transpose())[(0, 0)];
+                let proj = b.dot(&Matrix::from_vec(comp.row(prev).to_vec(), 1, n).transpose())[(0, 0)];
+                // subtract projection to maintain orthogonality
+                for j in 0..n {
+                    b[(0, j)] -= proj * comp[(prev, j)];
+                }
+            }
+            // iterate
+            for _ in 0..iters {
+                // b = C·bᵀ
+                let mut nb = cov.dot(&b.transpose()).transpose();
+                // subtract projections again to maintain orthogonality
+                for prev in 0..c {
+                    let proj = nb.dot(&Matrix::from_vec(comp.row(prev).to_vec(), 1, n).transpose())[(0, 0)];
+                    for j in 0..n {
+                        nb[(0, j)] -= proj * comp[(prev, j)];
+                    }
+                }
+                // normalise
+                let norm = nb.data().iter().map(|v| v * v).sum::<f64>().sqrt();
+                for j in 0..n {
+                    nb[(0, j)] /= norm;
+                }
+                b = nb;
+            }
+            // store component
+            for j in 0..n {
+                comp[(c, j)] = b[(0, j)];
+            }
+        }
+        Self {
+            components: comp,
+            mean: mean_vec,
+        }
+    }
+
+    /// Project new data on the learned axes.
+    pub fn transform(&self, x: &Matrix<f64>) -> Matrix<f64> {
+        let x_centered = x - &self.mean;
+        x_centered.dot(&self.components.transpose())
+    }
+}

src/lib.rs

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

src/matrix/mat.rs

@@ -89,6 +89,10 @@ impl<T: Clone> Matrix<T> {
         self.cols
     }
 
+    pub fn shape(&self) -> (usize, usize) {
+        (self.rows, self.cols)
+    }
+
     /// Get element reference (immutable). Panics on out-of-bounds.
     pub fn get(&self, r: usize, c: usize) -> &T {
         &self[(r, c)]
@@ -179,6 +183,40 @@ impl<T: Clone> Matrix<T> {
         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.
     /// 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) {
@@ -308,6 +346,41 @@ impl<T: Clone> Matrix<T> {
         self.data = new_data;
         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)
+    }
+}
+
+impl Matrix<f64> {
+    /// Creates a new matrix filled with a specific value of the specified size.
+    pub fn filled(rows: usize, cols: usize, value: f64) -> Self {
+        Matrix {
+            rows,
+            cols,
+            data: vec![value; rows * cols], // Fill with the specified value
+        }
+    }
+    /// 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)
+    }
 }
 
 impl<T> Index<(usize, usize)> for Matrix<T> {
@@ -1110,6 +1183,48 @@ mod tests {
         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]
+    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]
     fn test_column() {
         let matrix = static_test_matrix_2x4();
@@ -1794,4 +1909,25 @@ 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]);
+    }
 }