for i,l in enumerate(labels):
plt.scatter(xtPCA[labelsTrain==l,0],xtPCA[labelsTrain==l,1],alpha=0.5,c=classColors[i,:])
plt.title('Projected data over the components')
plt.xlim([-4,4])
plt.ylim([-4,4])
plt.show()
if (0):
#%% PARTIAL LEAST SQUARES
#%% PLS SVD
nComponents = np.arange(1,nClasses+1)
plsSvdScores = np.zeros((5,np.alen(nComponents)))
for i,n in enumerate(nComponents):
plssvd = PLSSVD(n_components=n)
plssvd.fit(Xtrain,Ytrain)
XtrainT = plssvd.transform(Xtrain)
XtestT = plssvd.transform(Xtest)
plsSvdScores[:,i] = util.classify(XtrainT,XtestT,labelsTrain,labelsTest)
plssvd = PLSSVD(n_components=2)
xt,yt = plssvd.fit_transform(Xtrain,Ytrain)
fig = plt.figure()
util.plotData(fig,xt,labelsTrain,classColors)
plt.title('First 2 components of projected data')
#%% Plot accuracies for PLSSVD
plt.figure()
for i in range (5):
plt.plot(nComponents,plsSvdScores[i,:],lw=3)
