def estimator(A_val, y_batch_val, hparams):
x_hat_batch = []
for i in range(hparams.batch_size):
y_val = copy.deepcopy(y_batch_val[i])
x_hat = utils.solve_lasso(A_val, y_val, hparams)
x_hat = np.maximum(np.minimum(x_hat, 1), 0)
x_hat_batch.append(x_hat)
x_hat_batch = np.asarray(x_hat_batch)
return x_hat_batch
def estimator(A_val, y_batch_val, hparams):
x_hat_batch = []
W = wavelet_basis()
WA = np.dot(W, A_val)
for j in range(hparams.batch_size):
y_val = y_batch_val[j]
z_hat = utils.solve_lasso(WA, y_val, hparams)
x_hat = np.dot(z_hat, W)
x_hat_max = np.abs(x_hat).max()
x_hat = x_hat / (1.0 * x_hat_max)
x_hat_batch.append(x_hat)
x_hat_batch = np.asarray(x_hat_batch)
return x_hat_batch
def estimator(A_val, y_batch_val, hparams):
# One can prove that taking 2D DCT of each row of A,
# then solving usual LASSO, and finally taking 2D ICT gives the correct answer.
A_new = copy.deepcopy(A_val)
for i in range(A_val.shape[1]):
A_new[:, i] = vec([dct2(channel) for channel in devec(A_new[:, i])])
x_hat_batch = []
for j in range(hparams.batch_size):
y_val = y_batch_val[j]
z_hat = utils.solve_lasso(A_new, y_val, hparams)
x_hat = vec([idct2(channel) for channel in devec(z_hat)]).T
x_hat = np.maximum(np.minimum(x_hat, 1), -1)
x_hat_batch.append(x_hat)
return x_hat_batch
def estimator(A_val, y_batch_val, hparams):
x_hat_batch = []
W = wavelet_basis()
# U, V = utils.RGB_matrix()
# V = (V/127.5) - 1.0
# U = U/127.5
def convert(W):
# convert W from YCbCr to RGB
W_ = W.copy()
V = np.zeros((12288, 1))
# R
V[0::3] = ((255.0 / 219.0) * (-16.0)) + ((255.0 * 0.701 / 112.0) *
(-128.0))
W_[:, 0::3] = (255.0 / 219.0) * W[:, 0::3] + (0.0) * W[:, 1::3] + (
255.0 * 0.701 / 112.0) * W[:, 2::3]
# G
V[1::3] = ((255.0 / 219.0) *
(-16.0)) - ((0.886 * 0.114 * 255.0 / (112.0 * 0.587)) *
(-128.0)) - ((255.0 * 0.701 * 0.299 /
(112.0 * 0.587)) * (-128.0))
W_[:, 1::3] = (255.0 / 219.0) * W[:, 0::3] - (
0.886 * 0.114 * 255.0 /
(112.0 * 0.587)) * W[:, 1::3] - (255.0 * 0.701 * 0.299 /
(112.0 * 0.587)) * W[:, 2::3]
# B
V[2::3] = ((255.0 / 219.0) * (-16.0)) + ((0.886 * 255.0 /
(112.0)) * (-128.0))
W_[:, 2::3] = (255.0 / 219.0) * W[:, 0::3] + (
0.886 * 255.0 / (112.0)) * W[:, 1::3] + 0.0 * W[:, 2::3]
return W_, V
# WU = np.dot(W, U.T)
WU, V = convert(W)
WU = WU / 127.5
V = (V / 127.5) - 1.0
WA = np.dot(WU, A_val)
y_batch_val_temp = y_batch_val - np.dot(V.T, A_val)
for j in range(hparams.batch_size):
y_val = y_batch_val_temp[j]
z_hat = utils.solve_lasso(WA, y_val, hparams)
x_hat = np.dot(z_hat, WU) + V.ravel()
x_hat_max = np.abs(x_hat).max()
x_hat = x_hat / (1.0 * x_hat_max)
x_hat_batch.append(x_hat)
x_hat_batch = np.asarray(x_hat_batch)
return x_hat_batch
def estimator(A_val, y_batch_val,
hparams): # (e.g. of estimator: lasso_wavelet)
x_hat_batch = []
W = wavelet_basis()
WA = np.dot(W, A_val)
for j in range(hparams.batch_size):
y_val = y_batch_val[j]
z_hat = utils.solve_lasso(
WA, y_val, hparams
) # min l1-norm(z_hat) s.t. z_hat*w*A = y; z_hat is sparse
x_hat = np.dot(z_hat, W) # to compute img
x_hat_max = np.abs(x_hat).max()
x_hat = x_hat / (1.0 * x_hat_max) # normalize
x_hat_batch.append(x_hat)
x_hat_batch = np.asarray(x_hat_batch)
return x_hat_batch
def estimator(A_val, y_batch_val, hparams):
if A_val is None:
A = np.eye(y_batch_val.shape[1]) #TODO remove idle
else:
A = A_val.T
mode = hparams.tv_or_lasso_mode
y = utils_lasso.get_y(mode,y_batch_val,A)
W = utils_lasso.load_W(hparams, A, y)
x_hat_batch = []
if W is None:
AW = A
else:
if W is None or 'tv-norm' in hparams.model_types:
AW = None
else:
AW = A.dot(W)
if 'fista' in mode:
clf1 = FistaRegressor(penalty='l1')
gs = GridSearchCV(clf1, {'alpha': np.logspace(-3, 3, 10)})
gs.fit(AW, y)
x_hat_batch = gs.best_estimator_.coef_.dot(W)#.T
elif 'cvxpy' in mode:
maxit = utils_lasso.get_key('reweight',mode,int)
weight = np.array([])
errors = []
for it in range(maxit):
if maxit >1:
print('Computing reweighting iteration: {}'.format(it+1))
x_hat_batch,weight = utils_lasso.cvxpy_problem(mode,W,A,AW,y,weight)
current_error = np.mean(utils.get_l2_loss(np.array(hparams.x),utils_lasso.get_x(mode,x_hat_batch)))#np.mean(np.mean((np.array(hparams.x)-x_hat_batch)**2,axis=1))
errors.append(current_error)
print('Current error: {}'.format(current_error))
if maxit>1:
errors = np.array(errors)
np.save(utils.get_checkpoint_dir(hparams, hparams.model_types[0])+'error_course',errors)
elif 'cvxopt' in mode:
for i in range(hparams.batch_size):
y_val = y_batch_val[i]
x_hat = utils_lasso.tv_cvxopt_partial(A_val, y_val,hparams)
x_hat_batch.append(x_hat)
print('Processed {} percent of all images'.format(float(i+1)/hparams.batch_size*100))
x_hat_batch = np.asarray(x_hat_batch)
elif 'sklearn' in mode:
for i in range(hparams.batch_size):
y_val = y_batch_val[i]
x_hat = utils.solve_lasso(A_val, y_val, hparams)
x_hat = np.maximum(np.minimum(x_hat, 1), 0)
x_hat_batch.append(x_hat)
print('Processed {} percent of all images'.format(float(i+1)/hparams.batch_size*100))
x_hat_batch = np.asarray(x_hat_batch)
elif 'bestkTermApprox' in mode:
for i in range(hparams.batch_size):
y_val = y_batch_val[i]
x_hat = utils_lasso.check_bestkTermApprox(mode, W, y_val)
x_hat = np.maximum(np.minimum(x_hat, 1), 0)
x_hat_batch.append(x_hat)
print('Processed {} percent of all images'.format(float(i+1)/hparams.batch_size*100))
x_hat_batch = np.asarray(x_hat_batch)
elif 'bruteForce' in mode: #slithly redundant to above
for i in range(hparams.batch_size):
y_val = y_batch_val[i]
x_hat = utils_lasso.brute_force(mode, A_val,W,y_val)
x_hat = np.maximum(np.minimum(x_hat, 1), 0)
x_hat_batch.append(x_hat)
print('Processed {} percent of all images'.format(float(i+1)/hparams.batch_size*100))
x_hat_batch = np.asarray(x_hat_batch)
else:
raise NotImplementedError('Mode {} does not exist!'.format(mode))
return utils_lasso.get_x(mode,x_hat_batch)
