# Non Linear Methods for Regression

## Kernel Ridge Regression

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()

## Support Vector Regression

See Support Vector Machines (SVMs)