logistic_regression.py 文件源码

def logistic_regression_cost_gradient(parameters, input, output):
    """
    Cost and gradient for logistic regression
    :param parameters: weight vector
    :param input: feature vector
    :param output: binary label (0 or 1)
    :return: cost and gradient for the input and output
    """
    prediction = expit(np.dot(input, parameters))
    if output:
        inside_log = prediction
    else:
        inside_log = 1.0 - prediction

    if inside_log != 0.0:
        cost = -np.log(inside_log)
    else:
        cost = np.finfo(float).min

    gradient = (prediction - output) * input
    return cost, gradient