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basic_kde
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| import numpy as np | |
| from scipy.stats import norm | |
| def silverman_bandwidth(x_data: np.ndarray) -> float: | |
| return (4/(3*x_data.shape[0]))**0.2 * np.std(x_data) | |
| def basic_kde(x_data: np.ndarray, x_prediction: np.ndarray) -> np.ndarray: | |
| """Perform Gaussian Kernel Density Estimation. | |
| Args: | |
| x_data: Sample points drawn from the distribution to estimate. | |
| Numpy array of shape (N,) | |
| x_prediction: Points at which to evaluate the density estimate. | |
| Numpy array of shape (N,) | |
| Returns: | |
| Densities evaluated at `x_prediction` by averaging gaussian kernels | |
| around `x_data`. Numpy array of shape (N,) | |
| """ | |
| h = silverman_bandwidth(x_data) | |
| pdf_values = norm.pdf(x_prediction.reshape(1, -1), | |
| loc=x_data.reshape(-1, 1), | |
| scale=h) | |
| densities = np.mean(pdf_values, axis=0) | |
| return densities |
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