def test(self, test_data, label):
n_pos = 0.0
for i in range(len(test_data)):
if sigmoid(self.W, test_data[i]) >= 0.5 and self.label[i] == 1:
n_pos += 1
elif sigmoid(self.W, test_data[i]) < 0.5 and self.label[i] == 0:
n_pos += 1
print n_pos, len(test_data), n_pos / len(test_data)
print self.W
def gradient(w, lst_data, lst_label):
g = np.zeros(len(w))
for index, data in enumerate(lst_data):
mu = sigmoid(w, data)
label = lst_label[index]
g += data * (mu - label)
return g
def gradient(self, w, lst_data, lst_label):
g = np.zeros(len(w))
for index, data in enumerate(lst_data):
mu = sigmoid(w, data)
label = lst_label[index]
g += data * (mu - label)
g += np.array(w) * self.beta
return g
def nll(self, w):
warnings.filterwarnings("always")
self.likelihood = 0.0
for index, sample in enumerate(self.data):
mu = sigmoid(w, sample)
label = self.label[index]
try:
self.likelihood -= np.log(mu ** label * (1 - mu) ** (1 - label))
except Warning:
print "NLL, w", w
return self.likelihood + self.beta / 2 * np.dot(w, w)
