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 varyPtronsTestPenData():
ptrons = 0
testAccuracy = []
while ptrons <= 40:
nnet, ta = testPenData([ptrons])
testAccuracy.append(ta)
ptrons += 5
return testAccuracy
def trainPenDataWithRestart(n, hiddenLayerPerceptrons=None):
accuracyList = []
with open(OUTPUT_FILENAME, 'a+') as fh:
fh.write("-------------------- Training with " +
str(hiddenLayerPerceptrons) +
" Perceptron(s) --------------------\n")
fh.write("Start training pen data for " + str(n) + " round(s) ...\n")
for run in range(n):
if hiddenLayerPerceptrons is not None:
_, accuracy = testPenData(hiddenLayers=[hiddenLayerPerceptrons])
else:
_, accuracy = testPenData()
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
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)])
f = open('q6_results.txt', 'w')
# for each hidden layer size
maximumPenList = []
maximumCarList = []
avgPenList = []
avgCarList = []
stdevPenList = []
stdevCarList = []
# do hidden layer size = 0
maximumPen = 0
accuracyListPen = [] # used to calculate max, avg, and stdev of accuracy for all runs for 1 hidden layer size
for i in xrange(5): # 5 runs for each hidden layer size
nnet, accuracy = testPenData( [] )
accuracyListPen.append(accuracy)
if maximumPen < accuracy:
maximumPen = accuracy
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
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))
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'])
penAccuracyList = []
carAccuracyList = []
for k in range(5):
penAccuracyList.append(testPenData(hiddenLayers=[])[1])
#carAccuracyList.append(testCarData(hiddenLayers=[])[1])
avgPen = average(penAccuracyList)
stDevPen = stDeviation(penAccuracyList)
maxPen = max(penAccuracyList)
spamwriter.writerow(['PenData', '0', str(avgPen), str(stDevPen), str(maxPen)])
"""
avgCar = average(carAccuracyList)
stDevCar = stDeviation(carAccuracyList)
maxCar = max(carAccuracyList)
print "writing"
"""
#spamwriter.writerow(['Car Data', '0', str(avgCar), str(stDevCar), str(maxCar)])
from Testing import testPenData, testCarData, average, stDeviation
from NeuralNetUtil import buildExamplesFromCarData, buildExamplesFromPenData
# accuracy
maximum = 0
maximumCar = 0
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)
def randResTestPenData(randRestarts=1):
testAccuracy = []
for testNum in range(0, randRestarts):
nnet, ta = testPenData()
testAccuracy.append(ta)
return testAccuracy
f.write(str(a))
f.write("\n")
f.write('\nMax : ' + str(max(a)))
f.write('\nStDev : ' + str(stDeviation(a)))
f.write('\nMean : ' + str(average(a)))
if __name__ == '__main__':
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")
