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fixed incorrectly commited file
This commit is contained in:
Palash Tyagi
parent
96f434bf94
commit
4648800a09
@ -1,105 +1,113 @@
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use crate::matrix::Matrix;
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use crate::matrix::Matrix;
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use std::collections::HashMap;
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use rand::seq::SliceRandom;
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pub struct GaussianNB {
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pub struct KMeans {
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classes: Vec<f64>, // distinct labels
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pub centroids: Matrix<f64>, // (k, n_features)
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priors: Vec<f64>, // P(class)
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means: Vec<Matrix<f64>>,
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variances: Vec<Matrix<f64>>,
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eps: f64, // var-smoothing
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}
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}
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impl GaussianNB {
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impl KMeans {
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pub fn new(var_smoothing: f64) -> Self {
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/// Fit with k clusters.
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Self {
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pub fn fit(x: &Matrix<f64>, k: usize, max_iter: usize, tol: f64) -> (Self, Vec<usize>) {
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classes: vec![],
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priors: vec![],
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means: vec![],
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variances: vec![],
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eps: var_smoothing,
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}
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}
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pub fn fit(&mut self, x: &Matrix<f64>, y: &Matrix<f64>) {
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let m = x.rows();
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let m = x.rows();
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let n = x.cols();
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let n = x.cols();
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assert_eq!(y.rows(), m);
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assert!(k <= m, "k must be ≤ number of samples");
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assert_eq!(y.cols(), 1);
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// ----- group samples by label -----
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// ----- initialise centroids: pick k distinct rows at random -----
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let mut groups: HashMap<i64, Vec<usize>> = HashMap::new();
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let mut rng = rand::rng();
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for i in 0..m {
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let mut indices: Vec<usize> = (0..m).collect();
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groups.entry(y[(i, 0)] as i64).or_default().push(i);
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indices.shuffle(&mut rng);
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let mut centroids = Matrix::zeros(k, n);
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for (c, &i) in indices[..k].iter().enumerate() {
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for j in 0..n {
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centroids[(c, j)] = x[(i, j)];
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}
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}
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}
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self.classes = groups.keys().cloned().map(|v| v as f64).collect::<Vec<_>>();
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let mut labels = vec![0usize; m];
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self.classes.sort_by(|a, b| a.partial_cmp(b).unwrap());
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for _ in 0..max_iter {
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// ----- assignment step -----
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self.priors.clear();
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let mut changed = false;
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self.means.clear();
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for i in 0..m {
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self.variances.clear();
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let mut best = 0usize;
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let mut best_dist = f64::MAX;
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for &c in &self.classes {
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for c in 0..k {
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let idx = &groups[&(c as i64)];
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let mut dist = 0.0;
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let count = idx.len();
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for j in 0..n {
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self.priors.push(count as f64 / m as f64);
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let d = x[(i, j)] - centroids[(c, j)];
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dist += d * d;
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let mut mean = Matrix::zeros(1, n);
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}
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let mut var = Matrix::zeros(1, n);
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if dist < best_dist {
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best_dist = dist;
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// mean
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best = c;
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for &i in idx {
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}
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for j in 0..n {
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}
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mean[(0, j)] += x[(i, j)];
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if labels[i] != best {
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labels[i] = best;
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changed = true;
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}
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}
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}
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}
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for j in 0..n {
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mean[(0, j)] /= count as f64;
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}
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// variance
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// ----- update step -----
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for &i in idx {
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let mut counts = vec![0usize; k];
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let mut centroids = Matrix::zeros(k, n);
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for i in 0..m {
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let c = labels[i];
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counts[c] += 1;
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for j in 0..n {
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for j in 0..n {
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let d = x[(i, j)] - mean[(0, j)];
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centroids[(c, j)] += x[(i, j)];
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var[(0, j)] += d * d;
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}
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}
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}
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}
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for j in 0..n {
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var[(0, j)] = var[(0, j)] / count as f64 + self.eps;
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}
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self.means.push(mean);
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self.variances.push(var);
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}
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}
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/// Return class labels (shape m×1) for samples in X.
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pub fn predict(&self, x: &Matrix<f64>) -> Matrix<f64> {
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let m = x.rows();
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let k = self.classes.len();
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let n = x.cols();
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let mut preds = Matrix::zeros(m, 1);
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let ln_2pi = (2.0 * std::f64::consts::PI).ln();
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for i in 0..m {
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let mut best_class = 0usize;
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let mut best_log_prob = f64::NEG_INFINITY;
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for c in 0..k {
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for c in 0..k {
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// log P(y=c) + Σ log N(x_j | μ, σ²)
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if counts[c] > 0 {
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let mut log_prob = self.priors[c].ln();
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for j in 0..n {
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for j in 0..n {
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centroids[(c, j)] /= counts[c] as f64;
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let mean = self.means[c][(0, j)];
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}
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let var = self.variances[c][(0, j)];
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}
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let diff = x[(i, j)] - mean;
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}
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log_prob += -0.5 * (diff * diff / var + var.ln() + ln_2pi);
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}
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// ----- convergence test -----
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if log_prob > best_log_prob {
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if !changed {
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best_log_prob = log_prob;
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break; // assignments stable
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best_class = c;
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}
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if tol > 0.0 {
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// optional centroid-shift tolerance
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let mut shift: f64 = 0.0;
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for c in 0..k {
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for j in 0..n {
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let d = centroids[(c, j)] - centroids[(c, j)]; // previous stored?
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shift += d * d;
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}
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}
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if shift.sqrt() < tol {
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break;
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}
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}
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}
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}
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preds[(i, 0)] = self.classes[best_class];
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}
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}
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preds
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(Self { centroids }, labels)
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}
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/// Predict nearest centroid for each sample.
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pub fn predict(&self, x: &Matrix<f64>) -> Vec<usize> {
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let m = x.rows();
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let k = self.centroids.rows();
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let n = x.cols();
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let mut labels = vec![0usize; m];
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for i in 0..m {
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let mut best = 0usize;
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let mut best_dist = f64::MAX;
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for c in 0..k {
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let mut dist = 0.0;
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for j in 0..n {
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let d = x[(i, j)] - self.centroids[(c, j)];
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dist += d * d;
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}
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if dist < best_dist {
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best_dist = dist;
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best = c;
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}
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}
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labels[i] = best;
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}
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labels
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}
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}
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}
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}
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