import matplotlib.pyplot as pyplot import matplotlib.mlab as mlab import matplotlib.axes import math q = 2 Y, lbls = datasets.lvmLoadData('oil') #Y = Y[0:10, :] numData = Y.shape[0] d = Y.shape[1] #v, X = netlab.pca(Y.transpose(), q) X = numpy.random.normal(0.0, 1e-1, (numData, 2)) Xstore = X; Xstor = numpy.empty(Xstore.shape) Ystore = Y; Y = ndlwrap.fromarray(Y) X = ndlwrap.fromarray(X) # Set up kernel function kern = nl.cmpndKern() kern1 = nl.rbfKern(X) kern2 = nl.biasKern(X) kern3 = nl.whiteKern(X) kern3.setVariance(1e-3) kern.addKern(kern1) kern.addKern(kern2) kern.addKern(kern3)
q = 2 Y, lbls = datasets.lvmLoadData('oil') #Y = Y[0:10,:] numData = Y.shape[0] d = Y.shape[1] v, u = netlab.pca(Y, 2) v[np.nonzero(v<0)]=0 Ymean = Y.mean(axis=0) Ycentre = Y - Ymean X = np.mat(Ycentre)*np.mat(u)*np.mat(np.diag(1/v)) Xstore = X; Xstor = np.empty(Xstore.shape) Ystore = Y; Y = nw.fromarray(Y) X = nw.fromarray(X) # Set up kernel function kern = nl.cmpndKern() kern1 = nl.rbfKern(X) kern2 = nl.biasKern(X) kern3 = nl.whiteKern(X) kern3.setVariance(1e-4) kern.addKern(kern1) kern.addKern(kern2) kern.addKern(kern3) noise = nl.gaussianNoise(Y)
import netlab import matplotlib.pyplot as pyplot import matplotlib.mlab as mlab import matplotlib.axes q = 2 Y, lbls = datasets.lvmLoadData('oil100') d = Y.shape[0] numData = Y.shape[1] v, X = netlab.pca(Y.transpose(), q) X = numpy.random.normal(0.0, 1e-6, (Y.shape[0], 2)) Xstore = X; Xstor = numpy.empty(Xstore.shape) Ystore = Y; Y = ndlwrap.fromarray(Y) X = ndlwrap.fromarray(X) # Set up kernel function kern = ndlml.cmpndKern() kern1 = ndlml.rbfKern(X) kern2 = ndlml.biasKern(X) kern3 = ndlml.whiteKern(X) kern.addKern(kern1) kern.addKern(kern2) kern.addKern(kern3) noise = ndlml.gaussianNoise(Y)
q = 2 Y, lbls = datasets.lvmLoadData('oil') #Y = Y[0:10,:] numData = Y.shape[0] d = Y.shape[1] v, u = netlab.pca(Y, 2) v[np.nonzero(v < 0)] = 0 Ymean = Y.mean(axis=0) Ycentre = Y - Ymean X = np.mat(Ycentre) * np.mat(u) * np.mat(np.diag(1 / v)) Xstore = X Xstor = np.empty(Xstore.shape) Ystore = Y Y = nw.fromarray(Y) X = nw.fromarray(X) # Set up kernel function kern = nl.cmpndKern() kern1 = nl.rbfKern(X) kern2 = nl.biasKern(X) kern3 = nl.whiteKern(X) kern3.setVariance(1e-4) kern.addKern(kern1) kern.addKern(kern2) kern.addKern(kern3) noise = nl.gaussianNoise(Y)