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1git clone <repository-url>
2cd chg_package
3pip install -e .pip install -e ".[dev]"1from chg_algorithm import CHG
2import numpy as np
3
4# Initialize model
5model = CHG(input_dim=3, hidden_dim=24, num_heads=4)
6
7# Generate sample data
8X_train = np.random.randn(100, 3)
9y_train = np.sum(X_train**2, axis=1) + 0.1 * np.random.randn(100)
10X_test = np.random.randn(20, 3)
11
12# Make predictions
13pred_mean, pred_var = model.fit_predict(X_train, y_train, X_test)
14
15print(f"Predictions: {pred_mean}")
16print(f"Uncertainties: {np.sqrt(pred_var)}")1from chg_algorithm import run_chg_experiment
2
3# Run complete demonstration
4model, predictions, variances = run_chg_experiment()1from chg_algorithm import CHG, CHGOptimizer
2
3# Initialize model and optimizer
4model = CHG(input_dim=3, hidden_dim=24, num_heads=4)
5optimizer = CHGOptimizer(model, learning_rate=0.001)
6
7# Optimize model parameters
8for epoch in range(10):
9 optimizer.step(X_train, y_train)
10 lml = model.log_marginal_likelihood(X_train, y_train)
11 print(f"Epoch {epoch}: Log Marginal Likelihood = {lml:.4f}")input_dim (int): Dimensionality of input featureshidden_dim (int): Hidden dimension for feature transformationnum_heads (int): Number of attention headsfit_predict(X_train, y_train, X_test, noise_var=1e-6): Fit model and predictlog_marginal_likelihood(X, y, noise_var=1e-6): Compute log marginal likelihoodget_covariance_matrix(X): Get covariance matrix for inputsmodel (CHG): CHG model instance to optimizelearning_rate (float): Learning rate for parameter updatesstep(X, y, noise_var=1e-6): Perform one optimization stepcompute_gradients(X, y, noise_var=1e-6): Compute parameter gradientsgit checkout -b feature/amazing-feature)git commit -m 'Add some amazing feature')git push origin feature/amazing-feature)1@software{chg_algorithm,
2 title={CHG Algorithm: Covariance-based Hilbert Geometry for Gaussian Processes},
3 author={CHG Algorithm Team},
4 year={2024},
5 url={https://github.com/your-username/chg-algorithm}
6}