def test_hinge_loss_backward():
"""
Tests the backward pass of the hinge loss function
"""
from your_code import HingeLoss
X = np.array([[-1, 2, 1], [-3, 4, 1]])
w = np.array([1, 2, 3])
y = np.array([1, -1])
loss = HingeLoss(regularization=None)
_true = np.array([-1.5, 2, 0.5])
_est = loss.backward(X, w, y)
print(_est)
def test_hinge_loss_forward():
"""
Tests the forward pass of the hinge loss function
"""
from your_code import HingeLoss
X = np.array([[-1, 2, 1], [-3, 4, 1]])
w = np.array([1, 2, 3])
y = np.array([1, -1])
loss = HingeLoss(regularization=None)
_true = 4.5
_est = loss.forward(X, w, y)
assert np.allclose(_true, _est)
def __init__(self,
loss,
regularization=None,
learning_rate=0.01,
reg_param=0.05):
self.learning_rate = learning_rate
# Select regularizer
if regularization == 'l1':
regularizer = L1Regularization(reg_param) #?
elif regularization == 'l2':
regularizer = L2Regularization(reg_param)
elif regularization is None:
regularizer = None
else:
raise ValueError(
'Regularizer {} is not defined'.format(regularization))
# Select loss function
if loss == 'hinge':
self.loss = HingeLoss(regularizer)
elif loss == 'squared':
self.loss = SquaredLoss(regularizer)
else:
raise ValueError('Loss function {} is not defined'.format(loss))
self.model = None