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b193952 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import numpy as np
class LogisticRegressionGradientDescent:
def __init__(self, n_iterations=1000, learning_rate=0.01):
self.weights = None
self.intercept = None
self.n_iterations = n_iterations
self.lr = learning_rate
self.errors = []
def _sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def _loss(self, y_true, y_pred):
epsilon = 1e-15
return -np.mean(y_true * np.log(y_pred + epsilon) + (1 - y_true) * np.log(1 - y_pred + epsilon))
def _gradient_descent(self, X, y_true, y_pred):
n_samples = X.shape[0]
dw = np.dot(X.T, (y_pred - y_true)) / n_samples
db = np.sum(y_pred - y_true) / n_samples
self.weights -= self.lr * dw
self.intercept -= self.lr * db
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.intercept = 0
for _ in range(self.n_iterations):
linear_output = np.dot(X, self.weights) + self.intercept
y_pred = self._sigmoid(linear_output)
loss = self._loss(y, y_pred)
self.errors.append(loss)
self._gradient_descent(X, y, y_pred)
def predict_proba(self, X):
return self._sigmoid(np.dot(X, self.weights) + self.intercept)
def predict(self, X):
proba = self.predict_proba(X)
return (proba >= 0.5).astype(int) |