RegularizedLogisticRegression.py

import numpy as np
from gradientForClassification import compute_gradient


def compute_cost_reg(X, y, w, b, lambda_=1):
    """
    Computes the cost over all examples
    Args:
      X : (ndarray Shape (m,n)) data, m examples by n features
      y : (ndarray Shape (m,))  target value
      w : (ndarray Shape (n,))  values of parameters of the model
      b : (scalar)              value of bias parameter of the model
      lambda_ : (scalar, float) Controls amount of regularization
    Returns:
      total_cost : (scalar)     cost
    """

    m, n = X.shape

    # Calls the compute_cost function that you implemented above
    cost_without_reg = compute_cost(X, y, w, b)

    # You need to calculate this value

    ### START CODE HERE ###

    reg_cost = (lambda_ / (2 * m)) * (np.sum(w ** 2))

    ### END CODE HERE ###

    # Add the regularization cost to get the total cost
    total_cost = cost_without_reg + reg_cost

    return total_cost


def compute_gradient_reg(X, y, w, b, lambda_=1):
    """
    Computes the gradient for logistic regression with regularization

    Args:
      X : (ndarray Shape (m,n)) data, m examples by n features
      y : (ndarray Shape (m,))  target value
      w : (ndarray Shape (n,))  values of parameters of the model
      b : (scalar)              value of bias parameter of the model
      lambda_ : (scalar,float)  regularization constant
    Returns
      dj_db : (scalar)             The gradient of the cost w.r.t. the parameter b.
      dj_dw : (ndarray Shape (n,)) The gradient of the cost w.r.t. the parameters w.

    """
    m, n = X.shape

    dj_db, dj_dw = compute_gradient(X, y, w, b)

    ### START CODE HERE ###
    dj_dw = dj_dw + (lambda_ / m) * w

    ### END CODE HERE ###

    return dj_db, dj_dw