def test_preceptorn(traindata, testdata):
print("Initializing Perceptron")
times = int(input("max trian time for one data set: "))
ratio = float(input("learning ratio: "))
totalnumber = traindata.number
#first - try with ordered datas
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
plt.plot(il, al, label="size=%d" % (p * 0.01 * totalnumber))
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("trainning time")
plt.ylabel("accuracy")
plt.show()
def ops():
testdata = ImageDataSet(28, 28)
testdata.loadImageData("digitdata/testimages", -1)
testdata.loadLabelData("digitdata/testlabels", testdata.number)
data = ImageDataSet(28, 28)
data.loadImageData("digitdata/trainingimages", -1)
data.loadLabelData("digitdata/traininglabels", data.number)
for t in range(20, 101, 20):
images, labels = data.shuffleout(t)
al = []
il = []
pc = Perceptron(28 * 28,
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
for i in range(100):
pc.train(images, labels, 1, 0.8)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
plt.plot(il, al, label="size=%d" % (t * 0.01 * data.number))
leg = plt.legend(ncol=2, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlim([1, 100])
plt.xlabel("trainning time")
plt.ylabel("accuracy")
plt.show()
def stdmean():
limit = 0.7
ratio = 0.8
times = 5
print("digit")
traindata, testdata = dataloader_digit()
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
pc.train(images, labels, 3, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
plt.plot(pal, sal, label="digitdata Perceptron std")
plt.plot(pal, mal, label="digitdata Perceptron mean")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
for i in range(3):
images, labels = traindata.shuffleout(p)
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="digitdata NaiveBayes std")
plt.plot(pal, mal, label="digitdata NaiveBayes mean")
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)),
traindata.labeldomain)
pc.train(images, labels, 50, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="digitdata NeuralNetwork std")
plt.plot(pal, mal, label="digitdata NeuralNetwork mean")
print("face")
traindata, testdata = dataloader_face()
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
pc.train(images, labels, 3, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
plt.plot(pal, sal, label="facedata Perceptron std")
plt.plot(pal, mal, label="facedata Perceptron mean")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
for i in range(3):
images, labels = traindata.shuffleout(p)
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="facedata NaiveBayes std")
plt.plot(pal, mal, label="facedata NaiveBayes mean")
sal = []
mal = []
pal = []
for p in range(10, 101, 10):
al = []
il = []
for i in range(times):
images, labels = traindata.shuffleout(p)
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)),
traindata.labeldomain)
pc.train(images, labels, 50, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
sal.append(np.std(al))
mal.append(np.mean(al))
pal.append(p)
print(a)
plt.plot(pal, sal, label="facedata NeuralNetwork std")
plt.plot(pal, mal, label="facedata NeuralNetwork mean")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("time(in second)")
plt.show()
def timeana():
import time
limit = 0.7
ratio = 1
times = 200
print("digit")
traindata, testdata = dataloader_digit()
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="digitdata Perceptron")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
fal = []
pal = []
for p in range(20, 101, 10):
start = time.time()
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
images, labels = traindata.orderedout(p)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
end = time.time()
fal.append(end - start)
pal.append(p)
print(a)
plt.plot(pal, fal, label="digitdata NaiveBayes")
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="digitdata NeuralNetwork")
print("face")
traindata, testdata = dataloader_face()
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="facedata Perceptron")
feature_domians = [[i for i in np.arange(0, 1.1, 0.5)]
for _ in range(traindata.width * traindata.height)]
fal = []
pal = []
for p in range(20, 101, 10):
start = time.time()
nb = NaiveBayes(feature_domians, traindata.labeldomain, 1)
images, labels = traindata.orderedout(p)
nb.train(images, labels)
x = nb.classify(testdata.images)
a = Accuracy(x, testdata.labels)
end = time.time()
fal.append(end - start)
pal.append(p)
print(a)
plt.plot(pal, fal, label="facedata NaiveBayes")
fal = []
pal = []
for p in range(20, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
start = time.time()
pc = NeuralNetwork((traindata.width * traindata.height, 15, 15,
len(traindata.labeldomain)), traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
if a > limit:
end = time.time()
break
fal.append(end - start)
pal.append(p)
plt.plot(pal, fal, label="facedata NeuralNetwork")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("time(in second)")
plt.show()
def test_preceptorn_argmax_all():
print("Initializing Perceptron")
times = 30
ratio = 1
traindata, testdata = dataloader_digit()
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="digitdata ordered")
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.shuffleout(p)
al = []
il = []
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="digitdata random")
traindata, testdata = dataloader_face()
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.orderedout(p)
al = []
il = []
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="facedata ordered")
traindata, testdata = dataloader_face()
#first - try with ordered datas
fal = []
pal = []
for p in range(10, 101, 10):
images, labels = traindata.shuffleout(p)
al = []
il = []
pc = Perceptron(traindata.width * traindata.height,
traindata.labeldomain)
for i in range(times):
pc.train(images, labels, 1, ratio)
x = pc.classify(testdata.images)
a = Accuracy(x, testdata.labels)
al.append(a * 100)
il.append(i + 1)
print(a * 100)
fal.append(max(al))
pal.append(p)
plt.plot(pal, fal, label="facedata random")
leg = plt.legend(ncol=1, shadow=True, fancybox=True)
leg.get_frame().set_alpha(0.5)
plt.xlabel("data size precentage")
plt.ylabel("accuracy")
plt.show()
#Gerando pontos aleatorios com base na funcao
X = generatePoints(100)
X_with_x0 = [ [1] + x for x in X] ##adicionando bias
y = generateY(func, X)
w = linear_regression(X_with_x0, y)
# print(w)
perc = Perceptron()
perc._w = w
#gerando pontos fora da amostra
X = generatePoints(1000)
X_with_x0 = [ [1] + x for x in X] ##adicionando bias
y = generateY(func, X)
#Calculando Erro dentro da amostra
errorCount = 0
for i in range(0,len(y)):
x = X[i]
if perc.classify(x) != y[i]:
errorCount += 1
error = errorCount/len(y)
errors.append(error)
errors = np.array(errors)
print ('Erro fora da amostra: ', errors.mean())
y0 = uniform(-1, 1)
x1 = uniform(-1, 1)
y1 = uniform(-1, 1)
func.buildFromPoints(x0, y0, x1, y1)
# func._print()
#Gerando pontos aleatorios com base na funcao
X = generatePoints(10)
y = generateY(func, X)
perc = Perceptron()
perc.train(X, y)
#gerando novos pontos fora da amostra
X = generatePoints(1000)
y = generateY(func, X)
h = [perc.classify(x) for x in X]
# print('Target ',y)
# print('Predicted ',h)
errorCount = 0
for i in range(0, len(y)):
if y[i] != h[i]:
errorCount += 1
errProb = float(errorCount) / len(y)
errors.append(errProb)
print('P(f(x) != g(x)) = ', np.array(errors).mean())
return np.concatenate([x0, x1, x2]), np.concatenate(
[np.zeros(25), np.ones(25), 2 + np.zeros(25)]).astype(np.int)
return np.concatenate([x0,
x1]), np.concatenate([-np.ones(25),
np.ones(25)]).astype(np.int)
x_train, y_train = create_toy_data()
x1_test, x2_test = np.meshgrid(np.linspace(-5, 5, 100),
np.linspace(-5, 5, 100))
x_test = np.array([x1_test, x2_test]).reshape(2, -1).T
X_train = polynomialFeaturesTransform(x_train, 1)
X_test = polynomialFeaturesTransform(x_test, 1)
model = Perceptron()
model.fit(X_train, y_train)
y = model.classify(X_test)
y[y == -1] = 0
plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)
plt.contourf(x1_test,
x2_test,
y.reshape(100, 100),
alpha=0.2,
levels=np.linspace(0, 1, 3))
plt.xlim(-5, 5)
plt.ylim(-5, 5)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("demo/perceptron.png")
with open(training_file) as ftrain, open(testing_file) as ftest:
training = dp.prepare_data(ftrain)
testing = dp.prepare_data(ftest)
labels = dp.prepare_labels(training)
dp.label_data(training, labels[1])
dp.label_data(testing, labels[1])
print(labels)
print('Training data:')
for t in training:
print(t)
print('Testing data:')
for t in testing:
print(t)
alfa = float(input('Please input learning rate:\n'))
perceptron = Perceptron(af.step, alfa, len(training[0]) - 1)
print(perceptron.learn(0.99, training))
classify_testing_data()
while True:
observation = dp.prompt_vector()
decision = perceptron.classify(observation)
decision_class = labels[decision]
print('Decision output: {}\nDecision class: {}'.format(
decision, decision_class))
