Non Linear Methods for Regression¶
Kernel Ridge Regression¶
- class mlpy.KernelRidge(lmb=1.0, kernel=None)¶
Kernel Ridge Regression (dual).
Initialization.
Parameters : - lmb : float (>= 0.0)
regularization parameter
- kernel : None or mlpy.Kernel object.
if kernel is None, K and Kt in .learn() and in .pred() methods must be precomputed kernel matricies, else K and Kt must be training (resp. test) data in input space.
- alpha()¶
Return alpha.
- b()¶
Return b.
- learn(K, y)¶
Compute the regression coefficients.
- Parameters:
- K: 2d array_like object
- precomputed training kernel matrix (if kernel=None); training data in input space (if kernel is a Kernel object)
- y : 1d array_like object (N)
- target values
- pred(Kt)¶
Compute the predicted response.
Parameters : - Kt : 1d or 2d array_like object
precomputed test kernel matrix. (if kernel=None); test data in input space (if kernel is a Kernel object).
Returns : - p : integer or 1d numpy darray
predicted response
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()
