def demo(index):
items = bayes.loadDataFile("/Users/aarti/ai_pa2/data/facedata/facedatatest",150,60,70)
labels = bayes.loadLabelsFile("/Users/aarti/ai_pa2/data/facedata/facedatatestlabels",150)
my_tree = training(100)
actual = testing_data[index][-1]
prediction = list(classify(testing_data[index], my_tree).keys())[0]
if actual == prediction: print "correct prediction"
else: print "incorrect prediction"
print "actual : ", actual, "| prediction : ", prediction
print items[index]
def training_data_formatter_face():
items = bayes.loadDataFile("/Users/aarti/ai_pa2/data/facedata/facedatatrain",451,60,70)
labels = bayes.loadLabelsFile("/Users/aarti/ai_pa2/data/facedata/facedatatrainlabels",451)
output = [[0]*101 for _ in range(451)]
for x in range(451):
for i in range(70):
for j in range(60):
if items[x].getPixel(j, i) != 0:
index = (10 * (i//7)) + (j//6)
output[x][index] += 1
output[x][100] = labels[x]
return output
def training_data_formatter_digit():
items = bayes.loadDataFile(
"/Users/aarti/ai_pa2/data/digitdata/trainingimages", 5000, 28, 28)
labels = bayes.loadLabelsFile(
"/Users/aarti/ai_pa2/data/digitdata/traininglabels", 5000)
output = [[0] * 50 for _ in range(5000)]
for x in range(5000):
for i in range(28):
for j in range(28):
if items[x].getPixel(j, i) != 0:
index = (7 * (i // 4)) + (j // 4)
output[x][index] += 1
output[x][49] = labels[x]
return output