z = self.__softmax(f)
z_k = z - miniT
dh1 = np.sum(z_k, axis=0)
self.intercept_ = self.intercept_ - self.eta * dh1
dh2 = np.dot(z_k.T, miniX)
self.coef_ = self.coef_ - self.eta * dh2
if __name__ == '__main__':
import time
mnist = MNIST(pathMNIST='./mnist')
k_class = 10
# 学習データを作成
y_train = mnist.getLabel('L')
X_train = mnist.getImage('L')
X_train /= 255
X_test = mnist.getImage('T')
X_test /= 255
y_test = mnist.getLabel('T')
gradient_methods = ['GD', 'SGD', 'MSGD']
for gradient_method in gradient_methods:
clf = Logistic_regression(gradient_method=gradient_method, seed=1)
print(clf.eta)
print(clf.gradient_method)
start = time.time()
clf.fit(X_train, y_train)
print('学習にかかった時間:', time.time() - start)
clf.score(X_test, y_test)
