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net.py
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net.py
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import itertools
import numpy as np
import random
import time
import math
class Net(object):
def __init__(self):
self.time_step = 0
self.num_inputs = 198
self.num_hidden_units = 40
self.num_output_units = 2
self.cycles = 100
self.learning_rate = 0.1
self.discount_rate = 0.9
self.l = 0.7
self.features = [
[np.random.randn() for x in range(self.num_inputs)]
for y in range(self.cycles)
]
self.hidden_layer = [np.random.randn() for x in range(self.num_hidden_units)]
self.output_layer = [np.random.randn() for x in range(self.num_output_units)]
self.input_weights = [
[np.random.randn() for x in range(self.num_hidden_units)]
for y in range(self.num_inputs)
]
self.hidden_weights = [
[np.random.randn() for x in range(self.num_output_units)]
for y in range(self.num_hidden_units)
]
self.old_output = [0 for x in range(self.num_output_units)]
self.input_eligibility_trace = [
[
[0 for x in range(self.num_output_units)]
for y in range(self.num_hidden_units)
]
for z in range(self.num_inputs)
]
self.hidden_eligibility_trace = [
[0 for x in range(self.num_output_units)]
for y in range(self.num_hidden_units)
]
self.reward = [[0 for x in range(self.num_output_units)] for y in range(self.cycles)]
self.error = [0 for x in range(self.num_output_units)]
def sigmoid(self, z):
return 1.0 / (1.0 + np.exp(-z))
def getValue(self, features):
out = [np.random.randn() for x in range(self.num_output_units)]
h_l = [np.random.randn() for x in range(self.num_hidden_units)]
i_w = [
[np.random.randn() for x in range(self.num_hidden_units)]
for y in range(self.num_inputs)
]
h_w = [
[np.random.randn() for x in range(self.num_output_units)]
for y in range(self.num_hidden_units)
]
for j in range(0, 40):
for i in range(0, 198):
h_l[j] += features[i] * self.input_weights[i][j]
h_l[j] = self.sigmoid(h_l[j])
for k in range(0, 2):
out[k] = h_l[j] * self.hidden_weights[j][k]
out[k] = self.sigmoid(out[k])
return out
def feedforward(self, features):
for j in range(0, 40):
for i in range(0, 198):
self.hidden_layer[j] += features[i] * self.input_weights[i][j]
self.hidden_layer[j] = self.sigmoid(self.hidden_layer[j])
for k in range(0, 2):
self.output_layer[k] = self.hidden_layer[j] * self.hidden_weights[j][k]
self.output_layer[k] = self.sigmoid(self.output_layer[k])
self.time_step += 1
def do_td(self, features, out, error):
gradient = []
for k in range(0, 2):
gradient.append(out[k] * (1 - out[k]))
for j in range(0, self.num_hidden_units):
for k in range(0, 2):
self.hidden_eligibility_trace[j][k] = (
self.l * self.hidden_eligibility_trace[j][k]
+ gradient[k] * self.hidden_layer[j]
)
for i in range(0, 198):
self.input_eligibility_trace[i][j][k] = (
self.l * self.input_eligibility_trace[i][j][k]
+ gradient[k]
* self.hidden_weights[j][k]
* self.hidden_layer[j]
* (1 - self.hidden_layer[j])
* features[i]
)
for k in range(0, 2):
for j in range(0, 40):
self.hidden_weights[j][k] += (
self.learning_rate * error * self.hidden_eligibility_trace[j][k]
)
for i in range(0, 198):
self.input_weights[i][j] += (
self.learning_rate
* error
* self.input_eligibility_trace[i][j][k]
)