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
# 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)