.. currentmodule:: mlpy Non Linear Methods for Regression ================================= Kernel Ridge Regression ----------------------- .. autoclass:: KernelRidge :members: Example: >>> import numpy as np >>> import matplotlib.pyplot as plt >>> import mlpy >>> np.random.seed(0) >>> x = np.arange(0, 2, 0.05).reshape(-1, 1) # training points >>> y = np.ravel(np.exp(x)) + np.random.normal(1, 0.2, x.shape[0]) # target values >>> xt = np.arange(0, 2, 0.01).reshape(-1, 1) # testing points >>> K = mlpy.kernel_gaussian(x, x, sigma=1) # training kernel matrix >>> Kt = mlpy.kernel_gaussian(xt, x, sigma=1) # testing kernel matrix >>> krr = KernelRidge(lmb=0.01) >>> krr.learn(K, y) >>> yt = krr.pred(Kt) >>> fig = plt.figure(1) >>> plot1 = plt.plot(x[:, 0], y, 'o') >>> plot2 = plt.plot(xt[:, 0], yt) >>> plt.show() .. image:: images/kernel_ridge.png Support Vector Regression ------------------------- See :doc:`svm`