def main(): for count in range(5): output1 = [] output1.append(testCarData()[1]) print "accuracy1", testCarData()[1] print "max", max(output1) print "stddev", stDeviation(output1) print "average", average(output1) output2 = [] output2.append(testPenData()[1]) print "accuracy2", testPenData()[1] print "max", max(output2) print "stddev", stDeviation(output2) print "average", average(output2)
def varyPtronsTestCarData():
ptrons = 0
testAccuracy = []
while ptrons <= 40:
nnet, ta = testCarData([ptrons])
testAccuracy.append(ta)
ptrons += 5
return testAccuracy
def trainCarDataWithRestart(n, hiddenLayerPerceptrons=None):
accuracyList = []
with open(OUTPUT_FILENAME, 'a+') as fh:
fh.write("-------------------- Training with " +
str(hiddenLayerPerceptrons) +
" Perceptron(s) --------------------\n")
fh.write("Start training car data for " + str(n) + " round(s) ...\n")
for run in range(n):
if hiddenLayerPerceptrons is not None:
_, accuracy = testCarData(hiddenLayers=[hiddenLayerPerceptrons])
else:
_, accuracy = testCarData()
accuracyList.append(accuracy)
with open(OUTPUT_FILENAME, 'a+') as fh:
fh.write("Round " + str(run + 1) + ": " + str(accuracy) + "\n")
with open(OUTPUT_FILENAME, 'a+') as fh:
fh.write("All rounds finished.\n")
fh.write("---------- Accuracy Statistics ----------\n")
fh.write("Max : " + str(max(accuracyList)) + "\n")
fh.write("Average : " + str(average(accuracyList)) + "\n")
fh.write("Standard Deviation: " + str(stDeviation(accuracyList)) +
"\n\n")
def main(): count = 0 while count <= 40: output1 = [] output2 = [] for num in range(5): output1.append(testCarData([count])[1]) output2.append(testPenData([count])[1]) print "max1", max(output1) print "stddev1", stDeviation(output1) print "average1", average(output1) print "max2", max(output2) print "stddev2", stDeviation(output2) print "average2", average(output2) count += 5
from NeuralNetUtil import buildExamplesFromCarData, buildExamplesFromPenData
from NeuralNet import buildNeuralNet
from Testing import testPenData, testCarData, average, stDeviation
accuracyPenData = []
accuracyCarData = []
for i in range(5):
nnet, accuracy = testPenData()
accuracyPenData.append(accuracy)
nnet, accuracyCar = testCarData()
accuracyCarData.append(accuracyCar)
print("Pen Data Average Accuracy: ", average(accuracyPenData))
print("Pen Data Max Accuracy: ", max(accuracyPenData))
print("Pen Data St Dev Accuracy: ", stDeviation(accuracyPenData))
print(accuracyPenData)
print("Car Data Average Accuracy: ", average(accuracyCarData))
print("Car Data Max Accuracy: ", max(accuracyCarData))
print("Car Data St Dev Accuracy: ", stDeviation(accuracyCarData))
print(accuracyCarData)
from Testing import testPenData, testCarData, average, stDeviation
penList = []
carList = []
for i in range(5):
penResults = testPenData()
penList.append(penResults[1])
carResults = testCarData()
carList.append(carResults[1])
print 'Pen', penList
print 'Car', carList
penAverage = average(penList)
penStDev = stDeviation(penList)
penMax = max(penList)
carAverage = average(carList)
carStDev = stDeviation(carList)
carMax = max(carList)
print 'Pen:', penAverage, penStDev, penMax
print 'Car:', carAverage, carStDev, carMax
with open('Pen0.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile,
delimiter=',',
quotechar='|',
quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow([
'Data Type', 'Perceptrons', 'Average Accuracy', 'Standard Deviation',
'Maximum Accuracy'
])
for i in range(0, 9):
penAccuracyList = []
carAccuracyList = []
for k in range(5):
penAccuracyList.append(testPenData(hiddenLayers=[i * 5])[1])
carAccuracyList.append(testCarData(hiddenLayers=[i * 5])[1])
avgPen = average(penAccuracyList)
stDevPen = stDeviation(penAccuracyList)
maxPen = max(penAccuracyList)
avgCar = average(carAccuracyList)
stDevCar = stDeviation(carAccuracyList)
maxCar = max(carAccuracyList)
print "writing"
spamwriter.writerow(
['PenData',
str(i * 5),
str(avgPen),
str(stDevPen),
str(maxPen)])
spamwriter.writerow(
maxRound = round( maximumPen, 3) maximumPenList.append( maxRound ) avg = round( average( accuracyListPen ) , 3) avgPenList.append( avg ) stdev = round( stDeviation( accuracyListPen ) , 3) stdevPenList.append( stdev ) # do hidden layer size = 0, for Car maximumCar = 0 accuracyListCar = [] for i in xrange(5): nnet, accuracy = testCarData( [] ) # accuracyList.append(accuracy) accuracyListCar.append(accuracy) if maximumCar < accuracy: maximumCar = accuracy maxRound = round( maximumCar, 3) maximumCarList.append( maxRound ) avg = round( average( accuracyListCar ) , 3) avgCarList.append( avg ) stdev = round( stDeviation( accuracyListCar ) , 3) stdevCarList.append( stdev ) # do hidden layer size = 5, 10, ..., 40
import Testing
from Testing import testCarData
from Testing import testPenData
from Testing import average
from Testing import stDeviation
from NeuralNetUtil import buildExamplesFromCarData, buildExamplesFromPenData
from NeuralNet import buildNeuralNet
import cPickle
from math import pow, sqrt
if __name__ == "__main__":
pAccuracy = []
cAccuracy = []
for i in range(5):
nnet, p = testPenData()
nnet, c = testCarData()
pAccuracy.append(p)
cAccuracy.append(c)
print "PenData:"
print "Max = " + str(max(pAccuracy))
print "Average = " + str(average(pAccuracy))
print "SD = " + str(stDeviation(pAccuracy))
print "CarData:"
print "Max = " + str(max(cAccuracy))
print "Average = " + str(average(cAccuracy))
print "SD = " + str(stDeviation(cAccuracy))
avg = 0
stdev = 0
accuracyList = [] # both pen and data
accuracyListCar = []
for i in xrange(5):
nnet, accuracy = testPenData()
accuracyList.append(accuracy)
if maximum < accuracy:
maximum = accuracy
avg = average(accuracyList)
stdev = stDeviation(accuracyList)
for i in xrange(5):
nnet, accuracy = testCarData()
accuracyList.append(accuracy)
accuracyListCar.append(accuracy)
if maximumCar < accuracy:
maximumCar = accuracy
avgCar = average(accuracyListCar)
stdevCar = stDeviation(accuracyListCar)
avgBoth = average(accuracyList)
stdevBoth = stDeviation(accuracyList)
print "maximum: ", maximum
print "avg: ", avg
print "stdev: ", stdev
print "\n"
def randResTestCarData(randRestarts=1):
testAccuracy = []
for testNum in range(0, randRestarts):
nnet, ta = testCarData()
testAccuracy.append(ta)
return testAccuracy
from Testing import testPenData, testCarData, average, stDeviation
import csv
with open('Pen0.csv', 'wb') as csvfile:
spamwriter = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
spamwriter.writerow(['Data Type', 'Perceptrons', 'Average Accuracy', 'Standard Deviation', 'Maximum Accuracy'])
for i in range(0, 9):
penAccuracyList = []
carAccuracyList = []
for k in range(5):
penAccuracyList.append(testPenData(hiddenLayers=[i * 5])[1])
carAccuracyList.append(testCarData(hiddenLayers=[i * 5])[1])
avgPen = average(penAccuracyList)
stDevPen = stDeviation(penAccuracyList)
maxPen = max(penAccuracyList)
avgCar = average(carAccuracyList)
stDevCar = stDeviation(carAccuracyList)
maxCar = max(carAccuracyList)
print "writing"
spamwriter.writerow(['PenData', str(i * 5), str(avgPen), str(stDevPen), str(maxPen)])
spamwriter.writerow(['Car Data', str(i * 5), str(avgCar), str(stDevCar), str(maxCar)])
if len(sys.argv) == 1 or sys.argv[1] == "5":
print("Q5")
f = open("q5results.txt", "w")
f.write("\n>>> QUESTION 5 Pen \n")
ans = []
for x in range(5):
nnet, accuracy = testPenData(hiddenLayers = [24])
ans.append(accuracy)
results(ans, f)
f.write("\n\n\n>>> QUESTION 5 Car \n")
ans = []
for x in range(5):
nnet, accuracy = testCarData(hiddenLayers = [24])
ans.append(accuracy)
results(ans, f)
f.close()
if len(sys.argv) == 1 or sys.argv[1] == "6":
print("Q6")
pans = []
for p in range(0, 41, 5):
perp = []
for x in range(5):
print("P=" + str(p) + " I=" + str(x))
nnet, accuracy = testPenData(hiddenLayers = [p])
perp.append(accuracy)