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")