def test_BasisfunctionRegression_simple():
x = np.arange(10.).reshape((10, 1))
y = np.arange(10.) + 1
dy = 1
mu = np.arange(11.)[:, None]
sigma = 1.0
clf = BasisFunctionRegression(mu=mu, sigma=sigma).fit(x, y, dy)
y_true = clf.predict(x)
assert_allclose(y, y_true, atol=1E-10)
def test_BasisfunctionRegression_simple():
x = np.arange(10.).reshape((10, 1))
y = np.arange(10.) + 1
dy = 1
mu = np.arange(11.)[:, None]
sigma = 1.0
clf = BasisFunctionRegression(mu=mu, sigma=sigma).fit(x, y, dy)
y_true = clf.predict(x)
assert_allclose(y, y_true, atol=1E-10)
z_sample, mu_sample, dmu = generate_mu_z(100, random_state=0)
cosmo = Cosmology()
z = np.linspace(0.01, 2, 1000)
mu_true = np.asarray(map(cosmo.mu, z))
#------------------------------------------------------------
# Define our classifiers
basis_mu = np.linspace(0, 2, 15)[:, None]
basis_sigma = 3 * (basis_mu[1] - basis_mu[0])
subplots = [221, 222, 223, 224]
classifiers = [
LinearRegression(),
PolynomialRegression(4),
BasisFunctionRegression('gaussian', mu=basis_mu, sigma=basis_sigma),
NadarayaWatson('gaussian', h=0.1)
]
text = [
'Straight-line Regression', '4th degree Polynomial\n Regression',
'Gaussian Basis Function\n Regression', 'Gaussian Kernel\n Regression'
]
# number of constraints of the model. Because
# Nadaraya-watson is just a weighted mean, it has only one constraint
n_constraints = [2, 5, len(basis_mu) + 1, 1]
#------------------------------------------------------------
# Plot the results
fig = plt.figure(figsize=(8, 8))
fig.subplots_adjust(left=0.1,
def fit_BasisFunction(features_train, labels_train, features_pred, kernel='gaussian', mu=mu0, sigma=0.1): model = BasisFunctionRegression(kernel, mu=mu, sigma=sigma) model.fit(features_train, labels_train) labels_pred = model.predict(features_pred) return labels_pred