Python trainDL_Memory Examples

Example #1

File: test_dictLearn.py Project: humancontext/WSI_Preprocessing

def test_trainDL_Memory():
    img_file = 'lena.png'
    try:
        img = Image.open(img_file)
    except:
        print("Cannot load image %s : skipping test" %img_file)
        return None
    I = np.array(img) / 255.
    if I.ndim == 3:
        A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])))
        rgb = True
    else:
        A = np.asfortranarray(I)
        rgb = False

    m = 8
    n = 8
    X = spams.im2col_sliding(A,m,n,rgb)

    X = X - np.tile(np.mean(X,0),(X.shape[0],1))
    X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
    X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)

    param = { 'K' : 200, # learns a dictionary with 100 elements
          'lambda1' : 0.15, 'numThreads' : 4,
          'iter' : 100}

    ############# FIRST EXPERIMENT  ##################
    tic = time.time()
    D = spams.trainDL_Memory(X,**param)
    tac = time.time()
    t = tac - tic
    print('time of computation for Dictionary Learning: %f' %t)

    print('Evaluating cost function...')
    lparam = _extract_lasso_param(param)
    alpha = spams.lasso(X,D = D,**lparam)
    xd = X - D * alpha
    R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
    print("objective function: %f" %R)
    #* ? DISPLAY

    ############# SECOND EXPERIMENT  ##################
    tic = time.time()
    D = spams.trainDL(X,**param)
    tac = time.time()
    t = tac - tic
    print('time of computation for Dictionary Learning: %f' %t)
    print('Evaluating cost function...')
    alpha = spams.lasso(X,D = D,**lparam)
    xd = X - D * alpha
    R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
    print("objective function: %f" %R)

    #* ? DISPLAY

    return None

Example #2

File: test_dictLearn.py Project: samuelstjean/spams-python

def test_trainDL_Memory():
    img_file = 'lena.png'
    try:
        img = Image.open(img_file)
    except:
        print("Cannot load image %s : skipping test" %img_file)
        return None
    I = np.array(img) / 255.
    if I.ndim == 3:
        A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])))
        rgb = True
    else:
        A = np.asfortranarray(I)
        rgb = False

    m = 8;n = 8;
    X = spams.im2col_sliding(A,m,n,rgb)

    X = X - np.tile(np.mean(X,0),(X.shape[0],1))
    X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
    X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)

    param = { 'K' : 200, # learns a dictionary with 100 elements
          'lambda1' : 0.15, 'numThreads' : 4,
          'iter' : 100}

    ############# FIRST EXPERIMENT  ##################
    tic = time.time()
    D = spams.trainDL_Memory(X,**param)
    tac = time.time()
    t = tac - tic
    print('time of computation for Dictionary Learning: %f' %t)

    print('Evaluating cost function...')
    lparam = _extract_lasso_param(param)
    alpha = spams.lasso(X,D = D,**lparam)
    xd = X - D * alpha
    R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
    print("objective function: %f" %R)
    #* ? DISPLAY

    ############# SECOND EXPERIMENT  ##################
    tic = time.time()
    D = spams.trainDL(X,**param)
    tac = time.time()
    t = tac - tic
    print('time of computation for Dictionary Learning: %f' %t)
    print('Evaluating cost function...')
    alpha = spams.lasso(X,D = D,**lparam)
    xd = X - D * alpha
    R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
    print("objective function: %f" %R)

    #* ? DISPLAY

    return None

Example #3

File: clusterNets.py Project: ppuliu/MineDenseCode

def sparseCoding(X):
    X = np.asfortranarray(X)
    param = { 'K' : NCLUSTER,	# size of the dictionary 
          'lambda1' : 0.15, 
          #'posD' : True,	# dictionary positive constrain
          #'modeD' : 1,	# L1 regulization regularization on D
          'iter' : ITER} # runtime limit 15mins
    
    D = spams.trainDL_Memory(X,**param)
    lparam = _extract_lasso_param(param)
    print 'genrating codes...'
    alpha = spams.lasso(X,D = D,**lparam)
    return D, alpha

Example #4

File: tsttraindl.py Project: gbkedar/spams_toolbox_with_cmake_cpp_wrap

R = np.mean(0.5 * (xd * xd).sum(axis=0) + param['lambda1'] * np.abs(alpha).sum(axis=0))
print "objective function: %f" %R

############
img_file = '../extdata/portrait.png'
try:
    img = Image.open(img_file)
except:
    print "Cannot load image %s : skipping test" %img_file
    exit()
I = np.array(img) / 255.
if I.ndim == 3:
    A = np.asfortranarray(I.reshape((I.shape[0],I.shape[1] * I.shape[2])),dtype = myfloat)
    rgb = True
else:
    A = np.asfortranarray(I,dtype = myfloat)
    rgb = False
    
m = 8;n = 8;
X = spams.im2col_sliding(A,m,n,rgb)

X = X - np.tile(np.mean(X,0),(X.shape[0],1))
X = np.asfortranarray(X / np.tile(np.sqrt((X * X).sum(axis=0)),(X.shape[0],1)))
X = np.asfortranarray(X[:,np.arange(0,X.shape[1],10)],dtype = myfloat)

param = { 'K' : 200, # learns a dictionary with 100 elements
          'lambda1' : 0.15, 'numThreads' : 4,
          'iter' : 100}
D = spams.trainDL_Memory(X,param)
print "DTYPE %s" %str(D.dtype)