def main():
## generate data and define inputs
mu = np.arange(0, 2 * math.pi, 0.1)
sigma = 0.1
x_train = np.arange(0, 2 * math.pi, 0.1)
x_test = np.arange(0.05, 2 * math.pi, 0.1)
# #! DELTA RULE
#! LEAST SQUARE
## init rbf class
dim = mu.shape[0]
rbf_LS = RBF(dim)
## Generate data
sinus, square = rbf_LS.generateData(x_train)
sinus_test, square_test = rbf_LS.generateData(x_test)
## Init and train.
weights = rbf_LS.initWeights()
weights, error = rbf_LS.train_DELTA(x_train, square, weights, mu, sigma)
## Evaluation
print(rbf_LS)
y_test = rbf_LS.evaluation_DELTA(x_test, weights, mu, sigma)
# y_test=threshold(y_test)
re = residualError(y_test, square_test)
plt.figure('Least Square Error')
plt.plot(x_test, y_test, label='Approximation')
plt.plot(x_test, square_test, label='True value')
plt.title('Least Square Error')
plt.legend()
plt.show()
def square_delta(x_test, x_train, mu, sigma):
dim = mu.shape[0]
rbf = RBF(dim)
_, square = rbf.generateData(x_train, noise=True)
_, square_test = rbf.generateData(x_test, noise=True)
square_test = square_test.reshape((square_test.shape[0], 1))
## Init and train.
weights = rbf.initWeights()
weights, _, _ = rbf.train_DELTA(x_train, weights, mu, sigma)
## Evaluation
y_test = rbf.evaluation_DELTA(x_test, weights, mu, sigma)
residual_error = np.sum(abs(y_test - square_test)) / y_test.shape[0]
return residual_error, y_test, square_test
