def skip_cosine_fit(self):
x = np.cos(np.linspace(0, np.pi * 2, 99))
noise = (np.random.random((99, )) - .5)
for num_splines in range(5, 20, 2):
splines = sb.spline_base1d(99, num_splines, spline_order=5)
model = linear_model.BayesianRidge()
model.fit(splines, x + noise)
y = model.predict(splines)
self.assertTrue(np.corrcoef(x + noise, y)[0, 1]**2 > 0.7)
def skip_2Dcosine_fit(self):
x = np.cos(np.linspace(0,np.pi*2, 99))
X = np.dot(x[:,np.newaxis],x[:,np.newaxis].T)
noise = (np.random.random(X.shape)-.5)*0.1
for num_splines in range(2,20,2):
splines = sb.spline_base2d(99,99, num_splines, spline_order = 3)
model = linear_model.BayesianRidge()
model.fit(splines.T, (X+noise).flat)
y = model.predict(splines.T)
self.assertTrue( np.corrcoef(X+noise, y.reshape(X.shape))[0,1]**2 > 0.7)
"""
A comparison of different methods in linear_model methods.
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from scikits.learn import linear_model
if __name__ == '__main__':
n_iter = 20
time_ridge = np.empty(n_iter)
time_ols = np.empty(n_iter)
time_lasso = np.empty(n_iter)
dimensions = 10 * np.arange(n_iter)
n_samples, n_features = 100, 100
X = np.random.randn(n_samples, n_features)
y = np.random.randn(n_samples)
start = datetime.now()
ridge = linear_model.BayesianRidge()
ridge.fit(X, y)