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Perceptron: Difference between revisions
added python language section
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end procedure
main()</lang>
=={{header|Python 3}}==
<lang python>import random
TRAINING_LENGTH = 2000
class Perceptron:
'''Simple one neuron simulated neural network'''
def __init__(self,n):
self.c = .01
self.weights = [random.uniform(-1.0, 1.0) for _ in range(n)]
def feed_forward(self, inputs):
vars = []
for i in range(len(inputs)):
vars.append(inputs[i] * self.weights[i])
return self.activate(sum(vars))
def activate(self, value):
return 1 if value > 0 else -1
def train(self, inputs, desired):
guess = self.feed_forward(inputs)
error = desired - guess
for i in range(len(inputs)):
self.weights[i] += self.c * error * inputs[i]
class Trainer():
''' '''
def __init__(self, x, y, a):
self.inputs = [x, y, 1]
self.answer = a
def F(x):
return 2 * x + 1
if __name__ == "__main__":
ptron = Perceptron(3)
training = []
for i in range(TRAINING_LENGTH):
x = random.uniform(-10,10)
y = random.uniform(-10,10)
answer = 1
if y < F(x): answer = -1
training.append(Trainer(x,y,answer))
result = []
for y in range(-10,10):
temp = []
for x in range(-10,10):
if ptron.feed_forward([x,y,1]) == 1:
temp.append('^')
else:
temp.append('.')
result.append(temp)
print('Untrained')
for row in result:
print(''.join(v for v in row))
for t in training:
ptron.train(t.inputs, t.answer)
result = []
for y in range(-10,10):
temp = []
for x in range(-10,10):
if ptron.feed_forward([x,y,1]) == 1:
temp.append('^')
else:
temp.append('.')
result.append(temp)
print('Trained')
for row in result:
print(''.join(v for v in row))</lang>
{{out}}
<pre>
Untrained
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Trained
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^^^^^^^^^^^^^^^.....</pre>
=={{header|Racket}}==
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