def validateFace(self, dataFile, labelsFile, n, width, height, itemNo = -1):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
function = 0
guess = 0
correct = 0
if itemNo != -1:
image = items[itemNo]
features = getFeatures.getFeatures(image.pixels, width, height)
for j in range(len(features)):
function += (self.weightsFace[j] * features[j])
function += self.weightsFace[len(features)]
if function >= 0: return 1
else: return 0
for i in range(len(items)):
image = items[i]
features = getFeatures.getFeatures(image.pixels, width, height)
for j in range(len(features)):
function += (self.weightsFace[j] * features[j])
function += self.weightsFace[len(features)]
if function >= 0: guess = 1
else: guess = 0
if guess == labels[i]: correct += 1
function = 0
return correct/n
def validateDigit(self, dataFile, labelsFile, n, width, height, itemNo = -1):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
function = [0,0,0,0,0,0,0,0,0,0]
guess = 0
correct = 0
if itemNo != -1:
for k in range(10):
image = items[itemNo]
features = getFeatures.getFeaturesPixels(image.pixels, width, height)
for j in range(len(features)):
function[k] += (self.weightsDigit[j][k] * features[j])
function[k] += self.weightsDigit[len(features)][k]
return function.index(max(function))
for i in range(len(items)):
for k in range(10):
image = items[i]
features = getFeatures.getFeaturesPixels(image.pixels, width, height)
for j in range(len(features)):
function[k] += (self.weightsDigit[j][k] * features[j])
function[k] += self.weightsDigit[len(features)][k]
guess = function.index(max(function))
if guess == labels[i]: correct += 1
function = [0,0,0,0,0,0,0,0,0,0]
return correct/n
def trainFace(self, dataFile, labelsFile, n, width, height, randomList):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
self.featurepd = []
numberFaces = 0
# featureTimes is a list of tuples where the first number is the number of times the feature occurs when the image is not a face
# and the second number is the number of times the features occurs when the image is a face
featureTimes = []
numberFeatures = (math.ceil(height / getFeatures.DIM)) * math.ceil(
(width / getFeatures.DIM))
for i in range(numberFeatures):
featureTimes.append([0.0, 0.0])
for count in randomList:
image = items[count]
if labels[count] == 1:
numberFaces += 1.0
features = getFeatures.getFeatures(image.pixels, width, height)
for i in range(len(features)):
if features[i] > 0:
if labels[count] == 1:
featureTimes[i][1] += 1
else:
featureTimes[i][0] += 1
for i in range(len(featureTimes)):
self.featurepd.append([
(featureTimes[i][0] + self.k) /
(len(randomList) - numberFaces + self.k),
(featureTimes[i][1] + self.k) / (numberFaces + self.k)
])
self.faceProb = numberFaces / len(randomList)
def validateDigit(self, dataFile, labelsFile, n, width, height, itemNo=-1):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
like = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
guess = 0
correct = 0
if itemNo != -1:
image = items[itemNo]
features = getFeatures.getFeaturesPixels(image.pixels, width,
height)
productTrue = 1
productFalse = 1
for k in range(10):
for j in range(len(self.featurepdDig)):
if features[j] > 0:
productTrue *= self.featurepdDig[j][k][0]
productFalse *= self.featurepdDig[j][k][1]
else:
productTrue *= (1 - self.featurepdDig[j][k][0])
productFalse *= (1 - self.featurepdDig[j][k][1])
like[k] = (productTrue *
self.digitProb[k]) / (productFalse *
(1 - self.digitProb[k]))
productTrue = 1
productFalse = 1
guess = like.index(max(like))
return guess
for i in range(len(items)):
image = items[i]
features = getFeatures.getFeaturesPixels(image.pixels, width,
height)
productTrue = 1
productFalse = 1
for k in range(10):
for j in range(len(self.featurepdDig)):
if features[j] > 0:
productTrue *= self.featurepdDig[j][k][0]
productFalse *= self.featurepdDig[j][k][1]
else:
productTrue *= (1 - self.featurepdDig[j][k][0])
productFalse *= (1 - self.featurepdDig[j][k][1])
like[k] = (productTrue *
self.digitProb[k]) / (productFalse *
(1 - self.digitProb[k]))
productTrue = 1
productFalse = 1
guess = like.index(max(like))
if guess == labels[i]:
correct += 1
return correct / n
def validateFace(self, dataFile, labelsFile, n, width, height, itemNo=-1):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
like = 0.0
guess = 0
correct = 0
if itemNo != -1:
image = items[itemNo]
features = getFeatures.getFeatures(image.pixels, width, height)
productTrue = pow(10, 300.0)
productFalse = pow(10, 300.0)
for j in range(len(self.featurepd)):
if features[j] > 0:
productTrue *= self.featurepd[j][1]
productFalse *= self.featurepd[j][0]
else:
productTrue *= (1 - self.featurepd[j][1])
productFalse *= (1 - self.featurepd[j][0])
like = (productTrue * self.faceProb) / (productFalse *
(1 - self.faceProb))
if like >= 1:
return 1
else:
return 0
for i in range(len(items)):
image = items[i]
features = getFeatures.getFeatures(image.pixels, width, height)
productTrue = pow(10, 300.0)
productFalse = pow(10, 300.0)
for j in range(len(self.featurepd)):
if features[j] > 0:
productTrue *= self.featurepd[j][1]
productFalse *= self.featurepd[j][0]
else:
productTrue *= (1 - self.featurepd[j][1])
productFalse *= (1 - self.featurepd[j][0])
like = (productTrue * self.faceProb) / (productFalse *
(1 - self.faceProb))
if like >= 1:
guess = 1
else:
guess = 0
if guess == labels[i]:
correct += 1
return correct / n
def trainFace(self, dataFile, labelsFile, n, width, height, rList):
items = readData.loadDataFile(dataFile, n, width, height)
old_labels = readData.loadLabelsFile(labelsFile, n)
self.weightsFace = []
numberFeatures = (math.ceil(height/getFeatures.DIM)) * math.ceil((width/getFeatures.DIM))
for i in range(numberFeatures + 1):
self.weightsFace.append(0)
labels = []
for r in rList:
labels.append(old_labels[r])
# this array contains the features for every image
features = []
for i in rList:
features.append(getFeatures.getFeatures(items[i].pixels, width, height))
loop = 0
index = 0
# traverses all the items and updates the weights accordingly
# stops when a full loop is completed without updates
iterations = 0
while loop < len(rList) and iterations < 10 *len(rList):
iterations +=1
function = 0
for i in range(numberFeatures):
function += self.weightsFace[i] * features[index][i]
function += self.weightsFace[numberFeatures]
if (function >= 0 and labels[index] == 1) or (function < 0 and labels[index] == 0):
loop += 1
elif function < 0 and labels[index] == 1:
for i in range(numberFeatures):
self.weightsFace[i] += features[index][i]
self.weightsFace[numberFeatures] += 1
loop = 0
else:
for i in range(numberFeatures):
self.weightsFace[i] -= features[index][i]
self.weightsFace[numberFeatures] -= 1
loop = 0
if index == len(rList)-1: index = 0
else: index += 1
def trainDigit(self, dataFile, labelsFile, n, width, height, rList):
items = readData.loadDataFile(dataFile, n, width, height)
old_labels = readData.loadLabelsFile(labelsFile, n)
self.weightsDigit = []
numberFeatures = height * width
for i in range(numberFeatures + 1):
self.weightsDigit.append([0,0,0,0,0,0,0,0,0,0])
# this array contains the features for every image
features = []
labels = []
for i in rList:
features.append(getFeatures.getFeaturesPixels(items[i].pixels, width, height))
labels.append(old_labels[i])
loop = 0
index = 0
# traverses all the items and updates the weights accordingly
# stops when a full loop is completed without updates
guess = 0
iterations = 0
while loop < len(rList) and iterations < 10 * len(rList):
function = [0,0,0,0,0,0,0,0,0,0]
iterations += 1
for k in range(10):
for i in range(numberFeatures):
function[k] += self.weightsDigit[i][k] * features[index][i]
function[k] += self.weightsDigit[numberFeatures][k]
guess = function.index(max(function))
if guess == labels[index]:
loop += 1
else:
for i in range(numberFeatures):
self.weightsDigit[i][labels[index]] += features[index][i]
self.weightsDigit[numberFeatures][labels[index]] += 1
for i in range(numberFeatures):
self.weightsDigit[i][guess] -= features[index][i]
self.weightsDigit[numberFeatures][guess] -= 1
loop = 0
if index == len(rList)-1: index = 0
else: index += 1
def trainDigit(self, dataFile, labelsFile, n, width, height, randomList):
items = readData.loadDataFile(dataFile, n, width, height)
labels = readData.loadLabelsFile(labelsFile, n)
self.featurepdDig = []
numberDigits = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# featureTimes is a list of tuples where the first number is the number of times the feature occurs when the image is not a face
# and the second number if the number of times the features occurs when the image is a face
featureTimes = []
numberFeatures = height * width
for i in range(numberFeatures):
featureTimes.append([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
for count in randomList:
image = items[count]
numberDigits[labels[count]] += 1
features = getFeatures.getFeaturesPixels(image.pixels, width,
height)
for i in range(len(features)):
if features[i] > 0:
featureTimes[i][labels[count]] += 1
pd = []
totalF = 0
for i in range(len(featureTimes)):
for m in range(10):
totalF += featureTimes[i][m]
for j in range(10):
pd.append([
(featureTimes[i][j] + self.k) / (numberDigits[j] + self.k),
(totalF - featureTimes[i][j] + self.k) /
(len(randomList) - numberDigits[j] + self.k)
])
self.featurepdDig.append(pd)
pd = []
totalF = 0
for k in range(10):
self.digitProb[k] = numberDigits[k] / len(randomList)