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https://github.com/TheAlgorithms/Python.git
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Sanskar ModiandChristian Clauss
a23264cca9
* added mini batch gradient descent algo in ml dir * updated code * updated code quality * updated docs * Refactor parameter documentation in mini_batch_gradient_descent.py Updated parameter documentation format in mini_batch_gradient_descent.py. --------- Co-authored-by: Christian Clauss <cclauss@me.com>
77 lines
2.1 KiB
Python
77 lines
2.1 KiB
Python
"""
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Mini-Batch Gradient Descent : https://en.wikipedia.org/wiki/Stochastic_gradient_descent
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Mini-batch gradient descent is an optimization method for training models
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by splitting the data into small batches.
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"""
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from __future__ import annotations
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import numpy as np
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def mini_batch_gradient_descent(
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feature_matrix: np.ndarray,
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target_values: np.ndarray,
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learning_rate: float = 0.01,
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batch_size: int = 16,
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n_epochs: int = 50,
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random_seed: int | None = None,
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) -> tuple[np.ndarray, float]:
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"""
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Mini-Batch Gradient Descent for linear regression.
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Parameters
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----------
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feature_matrix: Feature matrix.
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target_values: Target values.
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learning_rate: Learning rate.
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batch_size: Size of mini-batches.
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n_epochs: Number of training epochs.
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random_seed: Random seed for reproducibility.
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Returns
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-------
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weights: Learned weights.
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bias: Learned bias.
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Example
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-------
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>>> import numpy as np
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>>> X = np.array([[1], [2], [3], [4]])
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>>> y = np.array([2, 4, 6, 8])
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>>> w, b = mini_batch_gradient_descent(
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... X, y, learning_rate=0.1, batch_size=2, n_epochs=100, random_seed=42
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... )
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>>> round(float(w[0]), 1) # slope close to 2
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2.0
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"""
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n_samples, n_features = feature_matrix.shape
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weights = np.zeros(n_features)
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bias = 0.0
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rng = np.random.default_rng(random_seed)
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for _ in range(n_epochs):
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indices = rng.permutation(n_samples)
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feature_matrix_shuffled = feature_matrix[indices]
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target_values_shuffled = target_values[indices]
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for start_idx in range(0, n_samples, batch_size):
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end_idx = start_idx + batch_size
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feature_batch = feature_matrix_shuffled[start_idx:end_idx]
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target_batch = target_values_shuffled[start_idx:end_idx]
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predictions = np.dot(feature_batch, weights) + bias
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errors = predictions - target_batch
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weights -= learning_rate * (feature_batch.T @ errors) / len(target_batch)
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bias -= learning_rate * np.mean(errors)
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return weights, bias
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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