Пример #1
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 def __init__(self, cords = [0, 0], life=10, visRange=1, visDim=1, symbols=20):
     self.cords = np.array(cords) # (y, x).. but we are symmetric under 90deg rotation
     self.life = np.float32(life)
     
     # nodeNet w/ input for each visDim inside range, symbols nodes and 10 outputs
     # visDim is the number of traits associated with a square, to start its plant life and herbivore
     self.Ninputs = visDim*np.power((1+2*visRange),2) + 1 # visDim x squares in vision range + life
     self.brainNet = nodeNet(self.Ninputs, symbols, 10) # one output for each square plus reproduce
     self.brainNet.mutate(1) # diversify initial population
Пример #2
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# the call to setFitness below breaks because of python's unusual scope rules
# see scope_test in Reed's sandbox for an explanation
# we need to go through and adjust things accordingly but it doesn't sounds like there is an easy way to make a copy ever  

import numpy as np
from nueralnet_classes import nodeNet

#create simple nodeNet with 3 inputs 5 nodes and 2 outputs
simpleNet = nodeNet(3, 5, 2)

# print the results of some simple inputs
print "---should give 5 very activated nodes time weight 1 so both outputs should be almost five:"
test_list = np.array([3, 3, 3])
print simpleNet.processInputs(test_list)

print "---set fitness by above metric, fitness should be very low (low fitness is good):"
answer = np.array([[5, 5]])
simpleNet.setFitness(test_list,answer)
print simpleNet.fitness
 
print "---should give 5 very un-activated nodes time weight 1 so both outputs should be almost 0:"
test_list = np.array([-3, -3, -3])
print simpleNet.processInputs(test_list)
 
print "---see the input weights:"
print simpleNet.inputWeights
print "---see the output weights:"
print simpleNet.outputWeights 

print "---vary them by 50% and then print again:"
simpleNet.mutate(0.5)