sklear_ELASTIC_regression

Elastic net regression

import numpy as npfrom sklearn import linear_modelimport matplotlib.pyplot as pltX = np.c_[.5,1].Ty = [.5,1]test = np.c_[0,2].Tregr = linear_model.LinearRegression()plt.figure(1)np.random.seed(0)for _ in range(6):    this_x = .1 * np.random.normal(size=(2,1)) + X    regr.fit(this_x,y)    plt.plot(test,regr.predict(test))    plt.scatter(this_x,y,s=3)plt.show(1)# # if there few data points per dimension and high varianceregr2 = linear_model.Ridge(alpha=.1)plt.figure(2)for _ in range(6):    this_x = .1 * np.random.normal(size=(2,1)) + X    regr2.fit(this_x,y)    plt.plot(test,regr2.predict(test))    plt.scatter(this_x,y,s=3)plt.show(2)# choose fit alphaalphas = np.logspace(-4, -1, 6)print (alphas)# from __future__ import print_functionscores = [regr2.set_params(alpha=alpha).fit(this_x, y,).score(this_x, y) for alpha in alphas]print(scores)