def test_simple_perseptron_predict():
sp = SimplePerceptron(3,1,threshold=1.0)
assert sp.predict([0,0,0]) == [0]
assert sp.predict([0,0,1]) == [1]
assert sp.predict([0,1,0]) == [1]
assert sp.predict([1,0,0]) == [1]
assert sp.predict([0,1,1]) == [1]
assert sp.predict([1,0,1]) == [1]
assert sp.predict([1,1,0]) == [1]
assert sp.predict([1,1,1]) == [1]
def test_simple_perseptron_train():
sp = SimplePerceptron(3,1, threshold=0.5, learning_coefficient=0.5)
train_data = [ [[0,0,0],[1]],]
sp.train(train_data)
assert sp.weight_mat[0].tolist() == [0, 1, 1, 1]
train_data = [ [[0,0,1],[0]], ]
sp.train(train_data)
assert sp.weight_mat[0].tolist() == [-0.5, 1, 1, 0.5]
train_data = [ [[0,1,0],[0]], ]
sp.train(train_data)
assert sp.weight_mat[0].tolist() == [-1, 1, 0.5, 0.5]
train_data = [ [[1,0,0],[0]], ]
sp.train(train_data)
assert sp.weight_mat[0].tolist() == [-1.5,0.5,0.5, 0.5]
def simple_perceptron():
# simple perceptron
sp = SimplePerceptron( input_neuron_num = 2,
output_neuron_num = 1,
threshold = 1.0,
learning_coefficient = 0.5,
output_function_type = 1)
# train data
x_range = [0,30]
y_range = [0,30]
liner_data = TrainingData.liner_training_data(x_range, y_range)
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' )
# 学習前分離線
data = get_predict_list(x_range,y_range, sp)
plot(fig, data)
# 学習
sample_border = len(train_data) / 3
random.shuffle(train_data)
for i in range(100):
sp.train(train_data[:sample_border])
# 学習後分離線
data = get_predict_list(x_range,y_range, sp)
plot(fig, data)
plt.show()
def simple_sample():
sp = SimplePerceptron( input_neuron_num = 2,
output_neuron_num = 1,
threshold = 1.0,
learning_coefficient = 0.5,
output_function_type = 0)
train_data = [
[[0,0,],[0]],
[[0,1,],[1]],
[[1,0,],[0]],
[[1,1,],[1]],
]
print '---- before'
print sp.predict([0,0,])
print sp.predict([0,1,])
print sp.predict([1,0,])
print sp.predict([1,1,])
for i in range(100):
sp.train(train_data)
print '---- after'
print sp.predict([0,0,])
print sp.predict([0,1,])
print sp.predict([1,0,])
print sp.predict([1,1,])