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283 lines
7.6 KiB
Markdown
283 lines
7.6 KiB
Markdown
# Machine Learning
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The `compute::models` module bundles several learning algorithms that operate on
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`Matrix` structures. These examples highlight the basic training and prediction
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APIs. For more end‑to‑end walkthroughs see the examples directory in the
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repository.
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Currently implemented models include:
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- Linear and logistic regression
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- K‑means clustering
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- Principal component analysis (PCA)
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- Gaussian Naive Bayes
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- Dense neural networks
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## Linear Regression
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::linreg::LinReg;
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use rustframe::matrix::Matrix;
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let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
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let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
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let mut model = LinReg::new(1);
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model.fit(&x, &y, 0.01, 100);
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let preds = model.predict(&x);
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assert_eq!(preds.rows(), 4);
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```
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## K-means Walkthrough
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::k_means::KMeans;
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use rustframe::matrix::Matrix;
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let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
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let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
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let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
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let cluster = model.predict(&new_point)[0];
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```
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## Logistic Regression
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::logreg::LogReg;
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use rustframe::matrix::Matrix;
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let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
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let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
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let mut model = LogReg::new(1);
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model.fit(&x, &y, 0.1, 200);
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let preds = model.predict_proba(&x);
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assert_eq!(preds.rows(), 4);
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```
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## Principal Component Analysis
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::pca::PCA;
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use rustframe::matrix::Matrix;
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let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
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let pca = PCA::fit(&data, 1, 0);
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let transformed = pca.transform(&data);
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assert_eq!(transformed.cols(), 1);
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```
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## Gaussian Naive Bayes
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Gaussian Naive Bayes classifier for continuous features:
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::gaussian_nb::GaussianNB;
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use rustframe::matrix::Matrix;
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// Training data with 2 features
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let x = Matrix::from_rows_vec(vec![
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1.0, 2.0,
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2.0, 3.0,
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3.0, 4.0,
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4.0, 5.0
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], 4, 2);
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// Class labels (0 or 1)
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let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
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// Train the model
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let mut model = GaussianNB::new(1e-9, true);
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model.fit(&x, &y);
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// Make predictions
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let predictions = model.predict(&x);
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assert_eq!(predictions.rows(), 4);
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```
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## Dense Neural Networks
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Simple fully connected neural network:
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::dense_nn::{DenseNN, DenseNNConfig, ActivationKind, InitializerKind, LossKind};
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use rustframe::matrix::Matrix;
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// Training data with 2 features
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let x = Matrix::from_rows_vec(vec![
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0.0, 0.0,
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0.0, 1.0,
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1.0, 0.0,
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1.0, 1.0
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], 4, 2);
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// XOR target outputs
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let y = Matrix::from_vec(vec![0.0, 1.0, 1.0, 0.0], 4, 1);
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// Create a neural network with 2 hidden layers
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let config = DenseNNConfig {
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input_size: 2,
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hidden_layers: vec![4, 4],
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output_size: 1,
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activations: vec![ActivationKind::Sigmoid, ActivationKind::Sigmoid, ActivationKind::Sigmoid],
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initializer: InitializerKind::Uniform(0.5),
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loss: LossKind::MSE,
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learning_rate: 0.1,
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epochs: 1000,
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};
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let mut model = DenseNN::new(config);
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// Train the model
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model.train(&x, &y);
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// Make predictions
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let predictions = model.predict(&x);
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assert_eq!(predictions.rows(), 4);
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```
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## Real-world Examples
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### Housing Price Prediction
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::linreg::LinReg;
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use rustframe::matrix::Matrix;
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// Features: square feet and bedrooms
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let features = Matrix::from_rows_vec(vec![
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2100.0, 3.0,
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1600.0, 2.0,
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2400.0, 4.0,
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1400.0, 2.0,
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], 4, 2);
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// Sale prices
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let target = Matrix::from_vec(vec![400_000.0, 330_000.0, 369_000.0, 232_000.0], 4, 1);
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let mut model = LinReg::new(2);
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model.fit(&features, &target, 1e-8, 10_000);
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// Predict price of a new home
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let new_home = Matrix::from_vec(vec![2000.0, 3.0], 1, 2);
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let predicted_price = model.predict(&new_home);
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println!("Predicted price: ${}", predicted_price.data()[0]);
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```
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### Spam Detection
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::logreg::LogReg;
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use rustframe::matrix::Matrix;
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// 20 e-mails × 5 features = 100 numbers (row-major, spam first)
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let x = Matrix::from_rows_vec(
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vec![
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// ─────────── spam examples ───────────
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2.0, 1.0, 1.0, 1.0, 1.0, // "You win a FREE offer - click for money-back bonus!"
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1.0, 0.0, 1.0, 1.0, 0.0, // "FREE offer! Click now!"
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0.0, 2.0, 0.0, 1.0, 1.0, // "Win win win - money inside, click…"
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1.0, 1.0, 0.0, 0.0, 1.0, // "Limited offer to win easy money…"
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1.0, 0.0, 1.0, 0.0, 1.0, // ...
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0.0, 1.0, 1.0, 1.0, 0.0, // ...
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2.0, 0.0, 0.0, 1.0, 1.0, // ...
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0.0, 1.0, 1.0, 0.0, 1.0, // ...
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1.0, 1.0, 1.0, 1.0, 0.0, // ...
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1.0, 0.0, 0.0, 1.0, 1.0, // ...
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// ─────────── ham examples ───────────
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0.0, 0.0, 0.0, 0.0, 0.0, // "See you at the meeting tomorrow."
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0.0, 0.0, 0.0, 1.0, 0.0, // "Here's the Zoom click-link."
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0.0, 0.0, 0.0, 0.0, 1.0, // "Expense report: money attached."
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0.0, 0.0, 0.0, 1.0, 1.0, // ...
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0.0, 1.0, 0.0, 0.0, 0.0, // "Did we win the bid?"
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0.0, 0.0, 0.0, 0.0, 0.0, // ...
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0.0, 0.0, 0.0, 1.0, 0.0, // ...
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1.0, 0.0, 0.0, 0.0, 0.0, // "Special offer for staff lunch."
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0.0, 0.0, 0.0, 0.0, 0.0, // ...
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0.0, 0.0, 0.0, 1.0, 0.0,
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],
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20,
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5,
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);
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// Labels: 1 = spam, 0 = ham
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let y = Matrix::from_vec(
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vec![
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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, // 10 spam
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, // 10 ham
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],
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20,
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1,
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);
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// Train
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let mut model = LogReg::new(5);
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model.fit(&x, &y, 0.01, 5000);
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// Predict
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// e.g. "free money offer"
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let email_data = vec![1.0, 0.0, 1.0, 0.0, 1.0];
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let email = Matrix::from_vec(email_data, 1, 5);
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let prob_spam = model.predict_proba(&email);
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println!("Probability of spam: {:.4}", prob_spam.data()[0]);
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```
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### Iris Flower Classification
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::gaussian_nb::GaussianNB;
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use rustframe::matrix::Matrix;
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// Features: sepal length and petal length
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let x = Matrix::from_rows_vec(vec![
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5.1, 1.4, // setosa
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4.9, 1.4, // setosa
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6.2, 4.5, // versicolor
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5.9, 5.1, // virginica
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], 4, 2);
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let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 2.0], 4, 1);
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let names = vec!["setosa", "versicolor", "virginica"];
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let mut model = GaussianNB::new(1e-9, true);
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model.fit(&x, &y);
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let sample = Matrix::from_vec(vec![5.0, 1.5], 1, 2);
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let predicted_class = model.predict(&sample);
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let class_name = names[predicted_class.data()[0] as usize];
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println!("Predicted class: {} ({:?})", class_name, predicted_class.data()[0]);
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```
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### Customer Segmentation
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```rust
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# extern crate rustframe;
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use rustframe::compute::models::k_means::KMeans;
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use rustframe::matrix::Matrix;
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// Each row: [age, annual_income]
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let customers = Matrix::from_rows_vec(
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vec![
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25.0, 40_000.0, 34.0, 52_000.0, 58.0, 95_000.0, 45.0, 70_000.0,
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],
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4,
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2,
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);
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let (model, labels) = KMeans::fit(&customers, 2, 20, 1e-4);
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let new_customer = Matrix::from_vec(vec![30.0, 50_000.0], 1, 2);
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let cluster = model.predict(&new_customer)[0];
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println!("New customer belongs to cluster: {}", cluster);
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println!("Cluster labels: {:?}", labels);
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```
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For helper functions and upcoming modules, visit the
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[utilities](./utilities.md) section.
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