recImgP = join(path, "reconstructionImages")
htmlPath = join(path, "objects")
html = Control.HTMLObjectsView(
Reducer,
path,
createOriginalViewer(origImgP, htmlPath, Reducer, QLSA),
createRepresentationViewer(repImgP, htmlPath, Reducer, np.ceil(np.sqrt(r))),
createReconstructionViewer(recImgP, htmlPath, Reducer, QLSA),
)
html.generate()
del html
del Reducer
I = "/home/jecamargom/tmp/datasets/faces"
M, Docs = Control.imagesMatrix(I, 361)
DocsP = [join(I, f) for f in Docs]
print "[DEBUG] Max number in ORL faces matrix", M.max()
print "[DEBUG] Matrix Dimensions : ", M.shape
p = "/home/jecamargom/tmp/experiments/faces1"
r = 50
# generateFactorization("QLSA",Control.QLSA,M,Docs,DocsP,p,r)
generateFactorization("QLSA2", Control.QLSA2, M, Docs, DocsP, p, r, True)
# generateFactorization("NMF",Control.NMF,M,Docs,DocsP,p,r)
# generateFactorization("VQ",Control.VQ,M,Docs,DocsP,p,r)
# generateFactorization("PCA",Control.PCA,M,Docs,DocsP,p,r)
p = "/home/jecamargom/tmp/experiments/faces2"
r = 25
import Control I = '/home/tuareg/Documents/UNAL/7mo semestre/Machine Learning 2/TMP/datasets/ORLFull' M,L = Control.imagesMatrix(I) M = Control.quantumNormalize(M) quantumImagesDirectory = '/home/tuareg/Documents/UNAL/7mo semestre/Machine Learning 2/TMP/datasets/ORLFullQuantumNormalized/' imageGenerator = Control.imageViewGenerator(M.max(),M.min(),quantumImagesDirectory,quantumImagesDirectory,112,True) for i in range(M.shape[1]): imageGenerator.toImage(M[:,i],L[i])
