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psf/black code formatting (#1277)

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This commit is contained in:
William Zhang
2019-10-05 01:14:13 -04:00
committed by Christian Clauss
parent 07f04a2e55
commit 9eac17a408
291 changed files with 6014 additions and 4571 deletions
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73
neural_network/perceptron.py
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@ -1,4 +1,4 @@
'''
"""
Perceptron
w = w + N * (d(k) - y) * x(k)
@ -8,7 +8,7 @@
p1 = -1
p2 = 1
'''
"""
import random
@ -28,7 +28,7 @@ class Perceptron:
sample.insert(0, self.bias)
for i in range(self.col_sample):
self.weight.append(random.random())
self.weight.append(random.random())
self.weight.insert(0, self.bias)
@ -45,15 +45,18 @@ class Perceptron:
for j in range(self.col_sample + 1):
self.weight[j] = self.weight[j] + self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
self.weight[j] = (
self.weight[j]
+ self.learn_rate * (self.exit[i] - y) * self.sample[i][j]
)
erro = True
#print('Epoch: \n',epoch_count)
# print('Epoch: \n',epoch_count)
epoch_count = epoch_count + 1
# if you want controle the epoch or just by erro
if erro == False:
print(('\nEpoch:\n',epoch_count))
print('------------------------\n')
#if epoch_count > self.epoch_number or not erro:
print(("\nEpoch:\n", epoch_count))
print("------------------------\n")
# if epoch_count > self.epoch_number or not erro:
break
def sort(self, sample):
@ -64,12 +67,12 @@ class Perceptron:
y = self.sign(u)
if y == -1:
print(('Sample: ', sample))
print('classification: P1')
if y == -1:
print(("Sample: ", sample))
print("classification: P1")
else:
print(('Sample: ', sample))
print('classification: P2')
print(("Sample: ", sample))
print("classification: P2")
def sign(self, u):
return 1 if u >= 0 else -1
@ -105,19 +108,51 @@ samples = [
[-0.1013, 0.5989, 7.1812],
[2.4482, 0.9455, 11.2095],
[2.0149, 0.6192, 10.9263],
[0.2012, 0.2611, 5.4631]
[0.2012, 0.2611, 5.4631],
]
exit = [-1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1]
exit = [
-1,
-1,
-1,
1,
1,
-1,
1,
-1,
1,
1,
-1,
1,
-1,
-1,
-1,
-1,
1,
1,
1,
1,
-1,
1,
1,
1,
1,
-1,
-1,
1,
-1,
1,
]
network = Perceptron(sample=samples, exit = exit, learn_rate=0.01, epoch_number=1000, bias=-1)
network = Perceptron(
sample=samples, exit=exit, learn_rate=0.01, epoch_number=1000, bias=-1
)
network.training()
if __name__ == '__main__':
if __name__ == "__main__":
while True:
sample = []
for i in range(3):
sample.insert(i, float(input('value: ')))
sample.insert(i, float(input("value: ")))
network.sort(sample)