def main():
iris = datasets.load_iris()
x = iris.data[50:150]
t = iris.target[50:150]
for i in range(100):
if t[i] == 2:
t[i] = -1
skf = model_selection.StratifiedKFold(n_splits=10)
avg_accuracy = 0
for train, test in skf.split(x, t):
classifier = PerceptronClassifier(4, 0.1)
classifier.learn(t[train], x[train])
accuracy = 0.0
for test_x, test_t in zip(x[test], t[test]):
test_y = classifier.classify(test_x)
if test_y == test_t:
accuracy += 1
accuracy /= len(test)
avg_accuracy += accuracy
avg_accuracy /= 10
print 'average accuracy:', avg_accuracy * 100, '%'
def testing(num):
trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60, 70)
trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels", num)
testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70)
testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels", 151)
validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70)
validLabels = samples.loadLabelsFile("data/facedata/facedatavalidationlabels", 301)
perceptron=PerceptronClassifier(trainData, trainLabels,0)
perceptron.train(trainData, trainLabels,10)
print "==================================="
print "Test Data"
guess=perceptron.classify(testData)
samples.verify(perceptron, guess, testLabels)
print "==================================="
print "Validation Data"
guess=perceptron.classify(validData)
samples.verify(perceptron,guess,validLabels)
def testing(num):
trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28)
trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num)
testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28)
testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000)
validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28)
validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000)
perceptron=PerceptronClassifier(trainData, trainLabels,0)
perceptron.train(trainData, trainLabels,10)
print "***********************************"
print "*************Test Data*************"
guess=perceptron.classify(testData)
samples.verify(perceptron, guess, testLabels)
print "***********************************"
print "************Valid Data*************"
guess=perceptron.classify(validData)
samples.verify(perceptron,guess,validLabels)
def testing(num):
trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28)
trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num)
testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28)
testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000)
validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28)
validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000)
perceptron=PerceptronClassifier(trainData, trainLabels,0)
perceptron.train(trainData, trainLabels,10)
print "==================================="
print "Test Data"
guess=perceptron.classify(testData)
samples.verify(perceptron, guess, testLabels)
print "==================================="
print "Validation Data"
guess=perceptron.classify(validData)
samples.verify(perceptron,guess,validLabels)
def testing(num):
trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60,
70)
trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels",
num)
testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60,
70)
testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels",
151)
validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301,
60, 70)
validLabels = samples.loadLabelsFile(
"data/facedata/facedatavalidationlabels", 301)
perceptron = PerceptronClassifier(trainData, trainLabels, 0)
perceptron.train(trainData, trainLabels, 10)
print "==================================="
print "Test Data"
guess = perceptron.classify(testData)
samples.verify(perceptron, guess, testLabels)
print "==================================="
print "Validation Data"
guess = perceptron.classify(validData)
samples.verify(perceptron, guess, validLabels)
class Perceptron:
def __init__(self):
iris = datasets.load_iris()
x = iris.data[0:150]
t = iris.target[0:150]
for i in range(150):
if t[i] == 0:
t[i] = -1
if t[i] == 2:
t[i] = 1
self.classifier = PerceptronClassifier(4, 0.1)
self.classifier.learn(t, x)
def predict(self, x):
y = self.classifier.classify(x)
if y == -1:
return 'setosa'
else:
return None
FaceData.FACE_DATUM_HEIGHT)
# Conduct training and testing
percentages, runtimes = ([], [])
for n, n_samples in enumerate(
range(n_train // 10, n_train + 1, n_train // 10)):
start_time = time.time()
print('Training with {}% of data'.format((n + 1) * 10))
idx = np.random.choice(n_train, n_samples, replace=False)
train_img_sample = np.array(trainingDataList)[idx].tolist()
train_labels_sample = np.array(
face_data.face_train_labels)[idx].tolist()
errors = classifier.train(train_img_sample, train_labels_sample)
print('errors over 3 iterations', errors)
print('Validating...')
validation_guesses = classifier.classify(validationDataList)
correct = [
validation_guesses[i] == face_data.face_validation_labels[i]
for i in range(len(face_data.face_validation_labels))
].count(True)
print(str(correct), 'correct out of ',
str(len(face_data.face_validation_labels)))
print('Testing...')
test_guesses = classifier.classify(testDataList)
correct = [
test_guesses[i] == face_data.face_test_labels[i]
for i in range(len(face_data.face_test_labels))
].count(True)
percentage = (100.0 * correct / len(face_data.face_test_labels))
print(str(correct), 'correct out of ',
str(len(face_data.face_test_labels)), 'percentage ', percentage)
