def main():
(X, Y) = preProcessing_Data("./data/semeion.data")
scaler = MinMaxScaler()
X_norm = scaler.fit_transform(X)
#Y_norm = scaler.fit_transform(Y)
#rbfSize = 64
#kmeans = KMeans(n_clusters=rbfSize).fit(X_norm)
#print(kmeans.cluster_centers_)
#print(np.shape(kmeans.cluster_centers_))
#rbf = RBF(X_norm, Y, kmeans.cluster_centers_, hL_size=rbfSize, oL_size=10)
#rbf.learningPhase_2()
kf = KFold(n_splits=5)
fold = 1
for train_index, test_index in kf.split(X_norm):
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
rbfSize = 16
print(fold, "º Fold")
while (rbfSize <= 72):
kmeans = KMeans(n_clusters=rbfSize).fit(X_train)
rbf = RBF(X_train,
Y_train,
kmeans.cluster_centers_,
hL_size=rbfSize,
oL_size=10)
rbf.learningPhase_2()
Y_pred = []
for i in X_test:
Y_pred.append(rbf.predict(i))
score = accuracy_score(Y_test, np.array(Y_pred))
print("\tRBF Size: ", rbfSize, "| Score: ", score)
rbfSize += 16
fold += 1
print("=======================")
def test_XOR(self):
X = np.array([[0, 0],
[0, 1],
[1, 0],
[1, 1]])
T = np.array([[0],
[1],
[1],
[0]])
rbf = RBF(centers=X) # centers are data itself
rbf.fit(X, T)
prediction = rbf.predict(X)
self.assertTrue(np.all( (prediction > 0.5) == T))
def test_sin(self):
n = 10000
X = np.random.rand(n).reshape(-1,1)
noise = 0.3
T = 0.5*np.sin(4*np.pi*X) + 0.5 + np.random.normal(size = n, scale = noise).reshape(-1,1)
rbf = RBF(n_centers=20, activation='gaussian', sigma = 0.05)
rbf.fit(X,T)
Tp = rbf.predict(X)
error = RMSE(Tp, T)
# Xp = np.linspace(0,1,1000).reshape(-1,1)
# Tp = rbf.predict(Xp)
# plt.scatter(X,T)
# plt.plot(Xp,Tp, c = 'y')
# plt.show()
epsilon = 0.005
self.assertTrue(error < noise + epsilon)
def test_reg(self):
# sinusoidal function
def f(x):
return 0.5*np.sin(4*np.pi*x) + 0.5
# train on data noisily following f
n = 80
X = np.random.rand(n).reshape(-1,1)
noise = 0.05
T = f(X) + np.random.normal(size = n, scale = noise).reshape(-1,1)
rbf = RBF(n_centers=20, activation='gaussian', sigma = 0.05, lambdaReg=20.)
rbf.fit(X,T)
xl = np.linspace(0,1,1000).reshape(-1,1)
yl = rbf.predict(xl)
# plt.scatter(X, T) # training data
# plt.plot(xl, f(xl)) # true curve
# plt.plot(xl,yl) # learned curve
# plt.show()
epsilon = 0.01
true_error = RMSE(yl, f(xl))
self.assertLess(true_error, noise + epsilon)
def test_sin_redundancy(self):
n = 1000
X1 = np.random.rand(n).reshape(-1,1)
X2 = np.random.rand(n).reshape(-1,1) # redundant dimension
X = np.concatenate([X1, X2], axis = 1)
noise = 0.05
T = 0.5*np.sin(4*np.pi*X1) + 0.5 + np.random.normal(size = n, scale = noise).reshape(-1,1)
# rbf train
rbf = RBF(n_centers=150, activation='gaussian', sigma = 0.3, lambdaReg=1e-6)
rbf.fit(X,T)
# predict
Tp = rbf.predict(X)
error = RMSE(Tp, T)
# Xp1 = np.linspace(0,1,1000).reshape(-1,1)
# Xp2 = np.random.rand(1000).reshape(-1,1) # random 2nd co-ordinate
# Xp = np.concatenate([Xp1,Xp2], axis = 1)
# Tp = rbf.predict(Xp)
# plt.scatter(X1,T)
# plt.plot(Xp1.reshape(-1,1) ,Tp, c = 'y')
# plt.show()
epsilon = 0.01
self.assertTrue(error < noise + epsilon)
for row in r:
if row:
test.append(row)
c = list(zip(input, target))
shuffle(c)
input = [x[0] for x in c]
target = [x[1] for x in c]
input = np.array(input, np.float64)
target = np.array(target, np.float64)
test = np.transpose(np.array(test, np.float64))
print(len(input))
net = RBF(len(input), args.n, args.sigma)
net.train(input, target, args.reg)
x_list = [x[0] for x in oinput]
y_list = [net.predict(x) for x in oinput]
yo_list = [x for x in otarget]
f = lambda x: 233.846 * (1 - np.exp(-0.00604 * x))
yf_list = [f(x) for x in x_list]
error = sum([(y_list[i] - yo_list[i])**2
for i in range(len(y_list))]) / len(y_list)
print("Error: ", error)
plt.plot(x_list, y_list, label='Predicción', c='b')
plt.plot(x_list, yo_list, label='Datos', c='r')
plt.plot(x_list, yf_list, label='Modelo proporcionado', c='y')
plt.scatter(test[0], test[1], label='Conjunto de prueba', c='g', marker='.')
plt.xlabel("Edad")
plt.ylabel("Peso")
plt.legend(loc='upper left')
#plt.savefig("graphs/rabbit_"+str(args.n)+"_neurons_"+str(args.eta)+".png",bbox_inches='tight')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
from rbf import RBF
# создание тестовых данных
x = np.linspace(0, 10, 100)
y = np.sin(x)
# предсказание с помощью RBF-сети
model = RBF(hidden_shape=10, sigma=1.)
model.fit(x, y)
y_pred = model.predict(x)
# отображение на графие
plt.plot(x, y, 'b-', label='тест')
plt.plot(x, y_pred, 'r-', label='RBF')
plt.legend(loc='upper right')
plt.title('Интерполяция при использовании RBF-сети')
plt.show()
hit_rates = []
no_of_attributes = dataset.shape[1] - 1
no_of_classes = len(dataset[0, no_of_attributes])
# insert bias
no_rows = dataset.shape[0]
dataset = np.c_[-1 * np.ones(no_rows), dataset]
# perceptron = Perceptron(no_of_classes, no_of_attributes, 5, 'logistic')
for j in range(0, 20):
print("realization %d" % j)
train_X, train_y, test_X, test_y = Classifier.train_test_split(dataset)
train_X = np.array(train_X, dtype=float)
test_X = np.array(test_X, dtype=float)
sigma, center = RBF.model_training(no_of_classes, no_of_attributes,
train_X, train_y)
rbf = RBF(no_of_classes, no_of_attributes, center, sigma)
rbf.train(train_X, train_y)
predictions = rbf.predict(test_X)
hit_rates.append(rbf.evaluate(test_y, predictions))
print(rbf.confusion_matrix(test_y, predictions))
# rbf.plot_decision_boundaries(train_X, train_y, test_X, test_y, rbf, j)
print('hit rates: {}'.format(hit_rates))
print('accuracy: {}'.format(np.mean(hit_rates)))
print('std: {}'.format(np.std(hit_rates)))
# RBF.show_plot_decision_boundaries()
import numpy as np
import matplotlib.pyplot as plt
from rbf import RBF
# Gerando 100 dados aleatórios
X = np.random.uniform(0., 1., 100)
X = np.sort(X, axis=0)
# Gerando a saída baseado nos dados aleatórios
# Seno de (2 * PI * X) + noise
noise = np.random.uniform(-0.1, 0.1, 100)
Y = np.sin(2 * np.pi * X) + noise
# Criando e treinando a rede
learning_rate = 0.01 # Taxa de aprendizado
epochs = 100 # Epocas
k = 2 # Número de neurônios
rbf = RBF(k=k, learning_rate=learning_rate, epochs=epochs)
rbf.train(X, Y)
# Previsão
predictions = rbf.predict(X)
# Ploantdo dados de treinamento vs previsão
plt.plot(X, Y, '-o', label='train')
plt.plot(X, predictions, '-o', label='predict')
plt.legend()
plt.tight_layout()
plt.savefig('train-and-predict.png')
ys = h(xs)
noise = np.random.normal(0, 1, ys.shape)
ys_noise = ys + noise
input = [[x] for x in xs]
shuffle(input)
input = np.array(input, np.float64)
ns = [10, 20, 25, 50, 60, 70, 80]
sigmas = [0.1, 0.25, 0.5, 0.75, 1, 100, 200, 300, 400, 500]
regs = [0, 1, 0.5, 0.1, 0.01, 0.001]
for n in ns:
for sigma in sigmas:
for reg in regs:
net = RBF(len(input), n, sigma)
net.train(input, ys_noise, reg)
approx = [net.predict(x) for x in xs]
plt.plot(xs, ys, label='Función', c='b')
plt.plot(xs, ys_noise, label='Función con ruido', c='g')
plt.plot(xs, approx, label='Aproximación', c='r')
plt.legend(loc='upper left')
error = sum([(ys_noise[i] - approx[i])**2
for i in range(len(ys_noise))]) / len(ys_noise)
print("Centros: {} | Sigma: {} | Lambda: {} | Error: {}".format(
n, sigma, reg, error))
plt.savefig("graphs/centers_" + str(n) + "_sigma_" + str(sigma) +
"_lambda_" + str(reg) + ".png",
bbox_inches='tight')
plt.clf()
