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Implement training sequence

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George Lacey 2017-02-10 15:32:06 +00:00
parent 6a3253df56
commit f274146d58
1 changed files with 20 additions and 7 deletions
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27
Main.py
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@ -4,13 +4,26 @@ from Dataset import Dataset
rand = Random()
data = Dataset(open("input.txt").read().split('\n'), open("target.txt").read().split('\n'))
learn_rate = 0.01
for key in data.inputs:
i = data.inputs
print("v1: ", i[key].value1, "v2: ", i[key].value2, "target: ", i[key].target)
p = Perceptron()
p.add_input("1", i[key].value1, rand.uniform(-1, 1))
p.add_input("2", i[key].value2, rand.uniform(-1, 1))
i = data.inputs
p.add_input("1", 1, rand.uniform(-1, 1))
p.add_input("2", 1, rand.uniform(-1, 1))
p.add_input("bias", 1, rand.uniform(-1, 1))
print("%f" % p.guess(i[key].target))
# train
for ind in range(0, 500):
i = data.inputs
for key in data.inputs:
p.input(i[key].value1, i[key].value2)
print("error: %f" % p.guess(i[key].target, learn_rate))
for i in range(0, 1000):
x = input("Arg1")
y = input("Arg2")
p.input(x, y)
if p.activation() == 1:
print("TRUE")
else:
print("FALSE")