def processResults(network,results):
stepf = lambda x: 0 if x < .5 else 1
test_data = [(t[0], t[1], stepf(t[2])) for t in results]
percHits = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 1]) # Percentage right hits
falseAlarm = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 0]) # Percentage false positives
dPrime = funcs.dprime(percHits, falseAlarm)
out = (percHits, falseAlarm, dPrime, network.weights)
return out
def processResults(network,results):
stepf = lambda x: [0 if i < .5 else 1 for i in x]
test_data = [(t[0], t[1], stepf(t[2])) for t in results]
outnum = len(test_data[1][1])
percHits = [np.mean(k) for k in [[1 if t[2][i] == 1 else 0 for t in test_data if t[1][i] == 1] for i in range(outnum)]] # Percentage right hits per element
falseAlarm = [np.mean(k) for k in [[1 if t[2][i] == 1 else 0 for t in test_data if t[1][i] == 0] for i in range(outnum)]] # Percentage false positives per element
dPrime = funcs.dprime(percHits, falseAlarm)
out = (percHits,falseAlarm, dPrime, network.weights)
#print 'Hit % = {}, but false alarm % = {}, d\' = {}'.format(percHits,falseAlarm, dPrime)
return out
def main(datafile = '../data_by_cookie_slim.json', outputFolder = '.', iterations = 10, epochmult = 4):
filename = 'runPerceptronReal'
outputFile = '{}/{}.p'.format(outputFolder,filename)
#plt.close('all')
#Load data
print 'Perceptron quit after 20 from 10 games with Perceptron'
data = funcs.loadData(datafile)
#Generate artificial training data
#mu = np.mean([k[:10] for k in data if len(k) >= 10],axis=0)
#signalf = lambda x: 0 if x[2] < 18000 else 1
#training_data = funcs.generateSamples(500000, signalf)
#Set up perceptron
s = 10 # games
eta = 0.2 # learning rate
dPrimes = [0]*iterations #
endweights = []
out = []
training_data = [(np.array(k[:s]),0 if len(k) < 2*s else 1) for k in data if len(k) >= s]
n = len(training_data) * epochmult
print 'iterations: {}\nMultiplier Samplesize Epochs: {}\noutput file: {}\n'.format(iterations,epochmult,outputFile)
print 'Overall plays over 20 plays: {}'.format(np.mean([t[1] for t in training_data]))
print 'Learning from {} samples...'.format(len(training_data))
for i in xrange(iterations):
#print 'Preparing training data'
w = 2 * np.random.rand(s) - 1
stepf = lambda x: 0 if x < 0 else 1
#print 'Training perceptron - n = {} and s = {}'.format(n,s)
for j in xrange(n):
x, expected = choice(training_data)
result = np.dot(w,x)
error = expected - stepf(result)
w += eta * error * funcs.normalize(x)
#print 'Training completed'
#print 'Testing performance'
#test_data consists of rows (x, expected, result) or (x, signal, response)
#print w
test_data = [(t[0], t[1], stepf(np.dot(w,t[0]))) for t in training_data]
percHits = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 1]) # Percentage right hits
falseAlarm = np.mean([1 if t[2] == 1 else 0 for t in test_data if t[1] == 0]) # Percentage false positives
dPrime = funcs.dprime(percHits, falseAlarm)
#if percHits> .65:
endweights.append(funcs.normalize(w))
dPrimes[i] = dPrime
#print 'Hit % = {}, but false alarm % = {}, d\' = {}'.format(percHits,falseAlarm, dPrime)
out.append((percHits, falseAlarm, dPrime, w))
#print w # print the weights
pickle.dump(out,open(outputFile, 'wb'))
#print out
#results = network.test(samples)
dprimes = pickle.load(open(outputFile,'rb'))
#set nan to 0
print
print 'Results:'
print 'Mean d\' score for each quit opportunity: {}'.format([np.mean([k[i] for k in dprimes]) for i in xrange(1)])
print 'Std : {}'.format([np.std([k[i] for k in dprimes]) for i in xrange(1)])
print 'Max : {}'.format([np.max([k[i] for k in dprimes]) for i in xrange(1)])
print
print
