def test_lwlr(self):
# python -m unittest tests_regression.Tests_Regression.test_lwlr
import locally_weighted_linear_regression as lwlr1
from discomll.regression import locally_weighted_linear_regression as lwlr2
x_train, y_train, x_test, y_test = datasets.regression_data()
train_data, test_data = datasets.regression_data_discomll()
lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])
for tau in taus:
thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(
estimation1)[sorted_indices]
results = lwlr2.fit_predict(train_data, test_data, tau=tau)
thetas2, estimation2 = [], []
for x_id, (est, thetas) in result_iterator(results):
estimation2.append(est)
thetas2.append(thetas)
self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3))
def test_lwlr(self):
# python -m unittest tests_regression.Tests_Regression.test_lwlr
import locally_weighted_linear_regression as lwlr1
from discomll.regression import locally_weighted_linear_regression as lwlr2
x_train, y_train, x_test, y_test = datasets.regression_data()
train_data, test_data = datasets.regression_data_discomll()
lwlr1 = lwlr1.Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
sorted_indices = np.argsort([str(el) for el in x_test[:, 1].tolist()])
for tau in taus:
thetas1, estimation1 = lwlr1.fit(x_train, y_train, x_test, tau=tau)
thetas1, estimation1 = np.array(thetas1)[sorted_indices], np.array(estimation1)[sorted_indices]
results = lwlr2.fit_predict(train_data, test_data, tau=tau)
thetas2, estimation2 = [], []
for x_id, (est, thetas) in result_iterator(results):
estimation2.append(est)
thetas2.append(thetas)
self.assertTrue(np.allclose(thetas1, thetas2, atol=1e-8))
self.assertTrue(np.allclose(estimation1, estimation2, atol=1e-3))
W = np.diag(weights) # diagonal matrix with weights
x_W = np.dot(X.T, W)
A = np.dot(x_W, X)
b = np.dot(x_W, y)
thetas.append(np.linalg.lstsq(A, b)[0]) # calculate thetas for given x with: A^-1 * b
estimation.append(np.dot(x, thetas[-1])) # calculate estimation for given x and thetas
return thetas, estimation
if __name__ == '__main__':
"""
Example was taken from:
http://www.dsplog.com/2012/02/05/weighted-least-squares-and-locally-weighted-linear-regression/
"""
import datasets
import matplotlib.pyplot as plt
X, y = datasets.regression_data()
lwlr = Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
plt.scatter(X[:, 1], y) # Plot train data
color = ["r", "g", "b"]
for i, tau in enumerate(taus):
thetas, estimation = lwlr.fit(X, y, X, tau=tau)
plt.plot(X[:, 1], estimation, c=color[i]) # Plot prediction
plt.show()
A = np.dot(x_W, X)
b = np.dot(x_W, y)
thetas.append(np.linalg.lstsq(
A, b)[0]) # calculate thetas for given x with: A^-1 * b
estimation.append(np.dot(
x, thetas[-1])) # calculate estimation for given x and thetas
return thetas, estimation
if __name__ == '__main__':
"""
Example was taken from:
http://www.dsplog.com/2012/02/05/weighted-least-squares-and-locally-weighted-linear-regression/
"""
import datasets
import matplotlib.pyplot as plt
X, y = datasets.regression_data()
lwlr = Locally_Weighted_Linear_Regression()
taus = [1, 10, 25]
plt.scatter(X[:, 1], y) # Plot train data
color = ["r", "g", "b"]
for i, tau in enumerate(taus):
thetas, estimation = lwlr.fit(X, y, X, tau=tau)
plt.plot(X[:, 1], estimation, c=color[i]) # Plot prediction
plt.show()
