from sklearn import linear_model, datasets
import pylab as pl
import numpy as np

diabetes = datasets.load_diabetes()

'''

# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis]
diabetes_X_temp = diabetes_X[:, :, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X_temp[:-20]
diabetes_X_test = diabetes_X_temp[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
pl.scatter(diabetes_X_test, diabetes_y_test,  color='black')
pl.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
        linewidth=3)

pl.xticks(())
pl.yticks(())

pl.show()
'''

diabetes_X = diabetes.data[:, np.newaxis]
diabetes_X_temp = diabetes_X[:, :, 2]

clf = linear_model.Ridge (alpha = .5)
clf.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
print clf.coef_
