def multilayer_perceptron():
mlnn = MultiLayerNeuralNetwork( [2, 4, 1],
threshold=0.5,
learning_coefficient=0.5,
sigmoid_alpha=5)
x_range = [0,10]
y_range = [0,10]
# liner_data = TrainingData.liner_training_data(x_range, y_range)
#liner_data = TrainingData.quadratic_function_data(x_range, y_range)
liner_data = TrainingData.sin_function_data(x_range, y_range, 5)
train_data = TrainingData.change_format(liner_data)
# 教師データのプロット
fig = plt.figure()
scat(fig, [key for key, value in liner_data.items() if value == 0], color='g' )
scat(fig, [key for key, value in liner_data.items() if value == 1], color='b' )
# 学習
sample_border = len(train_data)
random.shuffle(train_data)
#mlnn.train(train_data[:20])
mlnn.train(train_data[:sample_border])
# xに対応するyを算出
data = get_predict_list(x_range,y_range, mlnn)
# 学習後分離線
plot(fig, data)
plt.show()
def test_sin_function_data(self):
x_range = [0, int(math.pi * 2)]
y_range = [0, 15]
height = 10
liner_training_data = get_training_data.sin_function_data(x_range, y_range, height)
self.assertEquals(liner_training_data[(0, 1)], 1)
self.assertEquals(liner_training_data[(int(math.pi/2), 6)], 0)
self.assertEquals(liner_training_data[(int(math.pi), 1)], 0)
self.assertEquals(liner_training_data[(int(math.pi*3/2), 1)], 1)
