3logistic_regression_from_scratch.py 文件源码

def train_logistic_regression(X_train, y_train, max_iter, learning_rate, fit_intercept=False):
    """ Train a logistic regression model
    Args:
        X_train, y_train (numpy.ndarray, training data set)
        max_iter (int, number of iterations)
        learning_rate (float)
        fit_intercept (bool, with an intercept w0 or not)
    Returns:
        numpy.ndarray, learned weights
    """
    if fit_intercept:
        intercept = np.ones((X_train.shape[0], 1))
        X_train = np.hstack((intercept, X_train))
    weights = np.zeros(X_train.shape[1])
    for iteration in range(max_iter):
        weights = update_weights_sgd(X_train, y_train, weights, learning_rate)
        # Check the cost for every 2 (for example) iterations
        if iteration % 2 == 0:
            print(compute_cost(X_train, y_train, weights))
    return weights


# Train the SGD model based on 10000 samples