def costFunctionReg(theta, X, y, lmbda):
# Initialize some useful values
m = y.shape[0] # number of training examples
# You need to return the following variables correctly
J = 0
grad = np.zeros(theta.shape)
# ====================== YOUR CODE HERE ======================
def h(X, theta):
return X.dot(theta)
J = np.float(-y.T * np.nan_to_num(np.log(sigmoid(h(X, theta))).T) -
(1 - y).T * np.nan_to_num(np.log(1 - sigmoid(h(X, theta))).T)) / m
reg_cost = theta.copy()
reg_cost[0] = 0
J += (lmbda * reg_cost.T.dot(reg_cost)) / (2 * m)
grad = (sigmoid(h(X, theta)) - y.T).dot(X) / m
reg_grad = theta * (float(lmbda) / m)
reg_grad[0] = 0
grad = grad.A1 + reg_grad
# =============================================================
return (J, grad)
def costFunctionReg(theta, X, y, lmbda):
# Initialize some useful values
m = y.shape[0] # number of training examples
# You need to return the following variables correctly
J = 0
grad = np.zeros(theta.shape)
# ====================== YOUR CODE HERE ======================
def h(X, theta):
return X.dot(theta)
J = np.float(-y.T * np.nan_to_num(np.log(sigmoid(h(X, theta))).T) -
(1 - y).T *
np.nan_to_num(np.log(1 - sigmoid(h(X, theta))).T)) / m
reg_cost = theta.copy()
reg_cost[0] = 0
J += (lmbda * reg_cost.T.dot(reg_cost)) / (2 * m)
grad = (sigmoid(h(X, theta)) - y.T).dot(X) / m
reg_grad = theta * (float(lmbda) / m)
reg_grad[0] = 0
grad = grad.A1 + reg_grad
# =============================================================
return (J, grad)
def costFunction(theta, X, y):
# Initialize some useful values
m = y.shape[0] # number of training examples
# You need to return the following variables correctly
J = 0
grad = np.zeros(theta.shape)
# ====================== YOUR CODE HERE ======================
def h(X, theta):
return X.dot(theta)
J = np.float(-y.T * np.nan_to_num(np.log(sigmoid(h(X, theta))).T) -
(1 - y).T * np.nan_to_num(np.log(1 - sigmoid(h(X, theta))).T)) / m
grad = (sigmoid(h(X, theta)) - y.T).dot(X) / m
# =============================================================
return (J, grad.A1)
def costFunction(theta, X, y):
# Initialize some useful values
m = y.shape[0] # number of training examples
# You need to return the following variables correctly
J = 0
grad = np.zeros(theta.shape)
# ====================== YOUR CODE HERE ======================
def h(X, theta):
return X.dot(theta)
J = np.float(-y.T * np.nan_to_num(np.log(sigmoid(h(X, theta))).T) -
(1 - y).T *
np.nan_to_num(np.log(1 - sigmoid(h(X, theta))).T)) / m
grad = (sigmoid(h(X, theta)) - y.T).dot(X) / m
# =============================================================
return (J, grad.A1)
def predict(theta, X):
m = X.shape[0] # Number of training examples
# You need to return the following variables correctly
p = np.zeros(m, )
# ====================== YOUR CODE HERE ======================
# Instructions: Complete the following code to make predictions using
# your learned logistic regression parameters.
# You should set p to a vector of 0's and 1's
#
p = (sigmoid(X.dot(theta)) >= 0.5) * 1
# =========================================================================
return p