def Threshold_1D(X, W=None, J=2, lam=0.1, optCoarse=1):
"""
Code that performs signal thresholding in the 1D isotropic undecimated wavelets
Inputs:
X : Input data (m x n_samp)
W : weights (m x n_coef)
J : number of scales
lam : threshold
optCoarse : if 0, set the coarse scale coefficients to 0
Output:
X_thrd : Output data (m x n_samp)
"""
CardS = None
nS = np.shape(X)
n = nS[0]
t = np.int(nS[1])
Wt_c = pys.forward1d(X, J=J)
S = Wt_c[:, :, 0:J].reshape(n, J * t)
for r in range(n):
if W == None:
if CardS is not None:
Kval = np.int(CardS[r])
I = np.argsort(abs(S[r, :]))[::-1]
th = abs(S[r, I[Kval]])
else:
th = lam
S[r, :] = (S[r, :] -
th * np.sign(S[r, :])) * (abs(S[r, :]) - th > 0)
else:
S[r, :] = (S[r, :] - lam * W[r, :] * np.sign(S[r, :])) * (
abs(S[r, :]) - lam * W[r, :] > 0)
Wt_c[:, :, 0:J] = S.reshape(n, t, J)
if optCoarse == 0:
Wt_c[:, :, J] = np.zeros(np.shape(Wt_c[:, :, J]))
rec = pys.backward1d(Wt_c)
return rec
def bGMCA_MainBlock_NMF_ondt_naif(X=0,
n=0,
A=0,
S=0,
kend=3,
nmax=100,
L0=0,
verb=0,
blocksize=2,
tol=1e-12,
optBlock=0,
J=2):
'''Usage:
S,A,exception = bGMCA_MainBlock_NMF_ondt_naif(X=0,n=0,A=0,S=0,kend=3,nmax=100,L0=0,verb=0,blocksize=2,tol=1e-12,optBlock=0,J=0)
Inputs:
X : m x t array (input data, each row corresponds to a single observation)
n : scalar (number of sources to be estimated)
A : m x n array (initialization of the mixing matrix - can be performed by the bGMCA function)
S : n x t array (initialization of the sources - can be performed by the bGMCA function)
kend : scalar (final value of the k-mad thresholding)
nmax : scalar (number of iterations)
L0 : boolean (0: use of the L1 norm, 1: use of the L0 norm)
verb : boolean (if set to 1, in verbose mode)
blocksize : int (size of the blocks)
tol : float (stopping criterion)
optBlock: int in {0,1,2,3} (way the block is constructed: 0 : Random creation of the block, 1 : Correlation, 2 : Loop over all the sources + take the sources that are the most correlated with the current one, 3 : We create the batchs sequentially)
J : int (number of wavelet scales. If J = 0, no wavelet transform)
Outputs:
S : n x t array (estimated sources)
A : m x n array (estimated mixing matrix)
exception : boolean (equals to 1 is a SVD did not converge during the iterations)
Description:
Computes the sources and the mixing matrix with bGMCA using blocks. Enforces non-negativity in the direct domain and sparsity in the wavelet domain. Does not perform the initialization of A and S
Example:
S,A,exception = bGMCA_MainBlock_NMF_ondt_naif(X,n=50,A,S,kend=3,nmax=10000,L0=0,verb=1,blocksize=5,tol=1e-12,optBlock=0,J=2)'''
Xdir = cp.deepcopy(X)
t = np.shape(Xdir)[1]
Xw = pys.forward1d(X, J=J)
n_Xw = np.shape(Xw)
X = Xw[:, :, 0:J].reshape(n_Xw[0], n_Xw[1] * J)
#--- Initialization
optPos = 1
powCor = 0.5
n_S = np.shape(S)
exception = 0
perc = 1. / nmax
thrdTab = np.zeros((nmax + 1, n_S[0]))
tabCard = -np.ones((n, 1))
if optPos in [1, 2, 3]:
for ii in range(
0, n_S[0]
): # Initialization to ensure that the given matrices A and S are non negative
Sl = S[ii, :]
Al = A[:, ii]
if optPos in [1, 2]:
indMax = np.argmax(abs(Sl))
if Sl[indMax] < 0:
Sl = -Sl
Al = -Al
Sl[Sl < 0] = 0
if optPos == 1:
Al[Al < 0] = 0
Al = Al / np.linalg.norm(Al)
elif optPos == 3:
indMax = np.argmax(abs(Al))
if Al[indMax] < 0:
Sl = -Sl
Al = -Al
Al[Al < 0] = 0
Al = Al / (1e-24 + np.linalg.norm(Al))
S[ii, :] = Sl
A[:, ii] = Al
# Stopping/iteration variables
Go_On = 1
it = 1
if verb:
print("Starting main loop ...")
print(" ")
print(" - Final k: ", kend)
print(" - Maximum number of iterations: ", nmax)
print(" - Batch size: ", blocksize)
print(" - Using support-based threshold estimation")
if L0:
print(" - Using L0 norm rather than L1")
print(" ")
print(" ... processing ...")
start_time = time.time()
# Defines the residual
Resi = cp.deepcopy(X)
#---
#--- Main loop
#---
while Go_On:
it += 1
if it == nmax:
Go_On = 0
#--- Estimate the sources (only when the corresponding column is non-zeros)
sigA = np.sqrt(np.sum(A * A, axis=0))
indS = np.where(sigA > 1e-24)[0]
if optBlock == 0: # Random
if blocksize < n_S[0]:
IndBatch = randperm(
len(indS)) #---- mini-batch amongst available sources
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
indS = indS[IndBatch[0:blocksize]]
elif optBlock == 1: # Correlation
if blocksize < n_S[0]:
currSrcInd = it % len(indS)
currSrcInd = indS[currSrcInd]
currSrc = np.power(S[currSrcInd, :].T, powCor)
matCor = np.dot(np.power(S, powCor), currSrc)
matCor[currSrcInd] = -1
IndBatch = np.argsort(matCor)
IndBatch = np.append(IndBatch, currSrcInd)
IndBatch = IndBatch[::-1]
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [currSrcInd]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
elif optBlock == 2: # Loop over all the sources + take the sources that are the most correlated with the current one
if blocksize < n_S[0]:
currSrcInd = it % len(indS)
currSrcInd = indS[currSrcInd]
angTab = np.dot(A[:, currSrcInd].T, A)
angTab[
currSrcInd] = -2 #0 : correspond a l'angle le plus grand, 90 degres
IndBatch = np.argsort(angTab)
IndBatch = np.append(IndBatch, currSrcInd)
IndBatch = IndBatch[::-1]
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [currSrcInd]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
elif optBlock == 4: # We create the batchs sequentially
indStt = blocksize * (it - 1) % np.shape(S)[0]
indEnd = blocksize * it % np.shape(S)[0]
if indStt < indEnd:
IndBatch = range(int(indStt), int(indEnd))
else:
ind1 = range(indStt, np.shape(S)[0])
if (indEnd > 0):
ind2 = range(0, indEnd)
IndBatch = np.concatenate((ind1, ind2))
else:
IndBatch = ind1
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [indStt]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
Resi = Resi + np.dot(A[:, indS],
S[indS, :]) # Putting back the sources
if np.size(indS) > 0:
if len(indS) > 1:
# Least_square estimate
Ra = np.dot(A[:, indS].T, A[:, indS])
excThisIt = 0
try:
Ua, Sa, Va = np.linalg.svd(Ra)
except np.linalg.linalg.LinAlgError:
print(indS)
print(np.sum(np.isnan(Ra)))
print(np.sum(np.isinf(Ra)))
print('SVD DID NOT CONVERGE')
exception += 1
excThisIt = 1
if excThisIt == 0:
cd_Ra = np.min(Sa) / np.max(Sa)
else:
cd_Ra = np.linalg.norm(Ra, ord=-2) / np.linalg.norm(Ra,
ord=2)
if (cd_Ra > 1e-12) and not (
excThisIt): # if the condition number is large enough
iRa = np.dot(Va.T, np.dot(np.diag(1. / Sa), Ua.T))
piA = np.dot(iRa, A[:, indS].T)
piA = np.real(piA)
S[indS, :] = np.dot(piA, Resi)
if (cd_Ra < 1e-12 or excThisIt
== 1): # otherwise a few gradient descent steps
print('GRADIENT STEP')
if excThisIt == 0:
La = np.max(Sa)
else:
La = np.linalg.norm(Ra, ord=2)
print('SVD DID NOT CONVERGE')
for it_A in range(0, 10):
S[indS, :] = S[indS, :] + 1 / La * np.dot(
A[:, indS].T, X - np.dot(A[:, indS], S[indS, :]))
else: # only a single element in the selection / the update is straightforward
S[indS[0], :] = np.dot(
1. / np.linalg.norm(A[:, indS[0]]) * A[:, indS[0]], Resi)
# Non-negativity
if optPos in [
1, 2
]: # If the non-negativity is enforced on the sources
StempMat = np.zeros((len(indS), t, J + 1))
StempMat[:, :, 0:J] = S[indS, :].reshape(len(indS), t, J)
StempMat[:, :, J] = np.dot(np.linalg.pinv(A),
Xw[:, :,
J])[indS, :] # coarse scale
StempDir = pys.backward1d(
StempMat) # Go back to the direct domain
StempDir[
StempDir < 0] = 0 # Projection on the positive orthant
StempMat = pys.forward1d(
StempDir, J=J) # Go again the the wavelet domain
S[indS, :] = StempMat[:, :, 0:J].reshape(len(indS), J * t)
Stemp = S[indS, :]
# Thresholding
for r in range(len(indS)):
St = Stemp[r, :]
#Computation of the effective support
indNZ = np.where(abs(St) > kend * mad(St))[0]
thrd = mad(St[indNZ])
# Computation of the threshold
Kval = np.min([
np.floor(np.max([0.01, perc * it]) * len(indNZ)),
n_S[1] - 1.
]) # Includes an increasing percentage of available entries
I = abs(St[indNZ]).argsort()[::-1]
Kval = np.int(np.min([np.max([Kval, n]), len(I) - 1.]))
tabCard[indS[r]] = Kval
IndIX = np.int(indNZ[I[Kval]])
thrd = abs(St[IndIX])
thrdTab[it, indS[r]] = thrd
St[(abs(St) < thrd)] = 0
indNZ = np.where(abs(St) > thrd)[0]
if L0 == 0:
St[indNZ] = St[indNZ] - thrd * np.sign(St[indNZ])
S[indS, :] = Stemp
# --- Updating the mixing matrix
if len(indS) > 1:
Atemp = unmix_cgsolve(S[indS, :].T, Resi.T,
maxiter=n) #---- Using CG
A[:, indS] = Atemp.T
if optPos in [1, 3]: # Enforcing non-negativity on A
for ii in range(len(indS)):
Al = A[:, indS[ii]]
Al[Al < 0] = 0
A[:, indS[ii]] = Al
A[:, indS] = np.dot(
A[:, indS],
np.diag(1. /
(1e-24 + np.sqrt(np.sum(A[:, indS]**2, axis=0))))
) #--- Renormalization
else: # Case where the update is explicit
Atemp = np.dot(Resi, S[indS, :].T)
A[:, indS] = Atemp / (1e-24 + np.linalg.norm(Atemp))
Resi = Resi - np.dot(A[:, indS],
S[indS, :]) #--- Removing the sources
if (np.mod(it, 500) == 1) & (verb == 1):
print("It #", it)
if len(indS) == 1:
print("Deflation")
elif len(indS) == n:
print("GMCA")
else:
print("minibatch - ratio : ", len(indS) / n)
if verb:
elapsed_time = time.time() - start_time
print("Stopped after ", it, " iterations, in ", elapsed_time,
" seconds")
SFinW = np.zeros((n, t, J + 1))
SFinW[:, :, 0:J] = S.reshape(n, t, J)
SFinW[:, :, J] = np.dot(np.linalg.pinv(A), Xw[:, :, J])
S = pys.backward1d(SFinW)
return S, A, exception
def PALM_NMF_MainBlock(X=0,
n=0,
A=0,
S=0,
kend=3,
nmax=100,
L0=0,
blocksize=2,
tol=1e-12,
optPos=0,
J=0):
'''Usage:
S,A = PALM_NMF_MainBlock(X=0,n=0,A=0,S=0,kend=3,nmax=100,L0=0,blocksize=2,tol=1e-12,optPos = 0,J=0)
Inputs:
X : m x t array (input data, each row corresponds to a single observation)
n : scalar (number of sources to be estimated)
A : m x n array (initialization of the mixing matrix - can be performed by the bGMCA function)
S : n x t array (initialization of the sources - can be performed by the bGMCA function)
kend : scalar (final value of the k-mad thresholding)
nmax : scalar (number of iterations)
L0 : boolean (0: use of the L1 norm, 1: use of the L0 norm)
blocksize : int (size of the blocks)
tol : float (stopping criterion)
optPos : int in {0,1,2,3} option for the non-negativity: 0 no constraint, 1 : non-negativity on A and S, 2: non-negativity on S, 3: non-negativity on A
J : int (number of wavelet scales. If J = 0, no wavelet transform)
Outputs:
S : n x t array (estimated sources)
A : m x n array (estimated mixing matrix)
Description:
Computes the sources and the mixing matrix with a PALM algorithm using blocks. Allows to enforce the non-negativity in the direct domain but not to use sparsity in the wavelet domain at the same time. Does not perform the initialization of A and S
Example:
S,A = PALM_NMF_MainBlock(X,n=50,A,S,kend=3,nmax=100000,L0=0,blocksize=10,tol=1e-12,optPos = 0,J=0)'''
# Stopping/iteration variables
#optPos : 1 : tout, 2 : S, 3 : A
if J > 0: # Wavelet transform
t = np.shape(X)[1]
Xw = pys.forward1d(X, J=J)
n_Xw = np.shape(Xw)
X = Xw[:, :, 0:J].reshape(n_Xw[0], n_Xw[1] * J)
# Initializations
Go_On = 1
it = 1
Aold = deepcopy(A)
deltaTabAngle = np.zeros(nmax + 1)
n_X = np.shape(X)
thrdTab = -np.ones((n, n_X[1]))
# Initialization to ensure that the given matrices A and S are non negative
n_S = np.shape(S)
for ii in range(0, n_S[0]):
Sl = S[ii, :]
Al = A[:, ii]
if optPos in [1, 2]:
indMax = np.argmax(abs(Sl))
if Sl[indMax] < 0:
Sl = -Sl
Al = -Al
Sl[Sl < 0] = 0
if optPos == 1:
Al[Al < 0] = 0
if np.linalg.norm(Al) > 1:
Al = Al / np.linalg.norm(Al)
elif optPos == 3:
indMax = np.argmax(abs(Al))
if Al[indMax] < 0:
Sl = -Sl
Al = -Al
Al[Al < 0] = 0
if np.linalg.norm(Al) > 1:
Al = Al / np.linalg.norm(Al)
S[ii, :] = Sl
A[:, ii] = Al
#---
#--- Main loop
#---
while Go_On:
it += 1
if it == nmax: # If stopping criterion met
Go_On = 0
# We create the blocks deterministically
indStt = blocksize * (it - 1) % np.shape(S)[0]
indEnd = blocksize * it % np.shape(S)[0]
if indStt < indEnd:
indS = range(int(indStt), int(indEnd))
else:
ind1 = range(indStt, np.shape(S)[0])
if (indEnd > 0):
ind2 = range(0, indEnd)
indS = np.concatenate((ind1, ind2))
else:
indS = ind1
if np.size(indS) > 0:
# Computation of a gradient step for S
Stemp = S[indS, :].copy()
Atemp = A[:, indS].copy()
specNormAtemp = np.linalg.norm(np.dot(Atemp.T, Atemp), ord=2)
deltaF = np.dot(Atemp.T, np.dot(A, S) - X)
Stemp = Stemp - 1 / specNormAtemp * deltaF
# Thresholding
for r in range(0, np.shape(Stemp)[0]):
gradSt = Stemp[r, :]
if thrdTab[
indS[r],
0] < 0: # We fix the threshold at the first iteration and keep the same for the following ones.
thrd = mad(
gradSt
) # We have a single value for the threshold per line
thrd = kend * thrd
thrd = thrd * np.ones((1, len(gradSt)))[0, :]
thrdTab[indS[r], :] = thrd
thrd = thrdTab[indS[r], :] / specNormAtemp
if optPos in [
1, 2
]: # if the non-negativity is enforced on the sources
gradSt[gradSt < thrd] = 0
indNZ = np.where(gradSt > thrd)[0]
if L0 == 0:
gradSt[indNZ] = gradSt[indNZ] - thrd[indNZ] * np.sign(
gradSt[indNZ]
) # We only have the positive part => we don't need to use the sign
else:
gradSt[(abs(gradSt) < thrd)] = 0
indNZ = np.where(abs(gradSt) > thrd)[0]
if L0 == 0:
gradSt[indNZ] = gradSt[indNZ] - thrd[indNZ] * np.sign(
gradSt[indNZ])
Stemp[r, :] = gradSt.copy()
A[:, indS] = Atemp.copy()
S[indS, :] = Stemp.copy()
# Computation of the gradient step for A
Stemp = S[indS, :].copy()
Atemp = A[:, indS].copy()
specNormStemp = np.linalg.norm(np.dot(Stemp, Stemp.T), ord=2)
Atemp = Atemp - 1 / specNormStemp * np.dot(
(np.dot(A, S) - X), Stemp.T)
if optPos in [
1, 3
]: # If non-negativity is enforced on the mixing matrix
for r in range(
0, Atemp.shape[1]
): # Le fait de multiplier par D ne change rien, vu que ses coeffs sont positifs.
Ac = Atemp[:, r]
indMax = np.argmax(abs(Ac))
if Ac[indMax] < 0:
print('Warning, max < 0')
Ac[Ac < 0] = 0
Atemp[:, r] = Ac
# Projection of the column of gradA on the L1 ball
for r in range(0, Atemp.shape[1]):
if (np.linalg.norm(Atemp[:, r]) > 1):
Atemp[:, r] = Atemp[:, r] / np.linalg.norm(
Atemp[:, r]) # Normalisation validee
A[:, indS] = Atemp.copy()
S[indS, :] = Stemp.copy()
# Computation of the stopping criterion
DeltaAngle = np.sum(A * Aold, axis=0)
deltaTabAngle[it] = np.min(
DeltaAngle
) # min car cos decroissant: on veut que le plus grand angle soit faible, donc le plus petit cos grand
if np.mod(it, 500) == 0:
print(it)
Delta = np.linalg.norm(A - Aold) / (1e-24 +
np.linalg.norm(Aold))
print('Delta angle: %s' % (np.arccos(deltaTabAngle[it])))
print('Delta: %s' % (Delta))
if it > 10000 and deltaTabAngle[it] > np.cos(9 * 1e-8):
Go_On = 0
Aold = deepcopy(A)
if J > 0: # Giong back into the direct domain
SFinW = np.zeros((n, t, J + 1))
SFinW[:, :, 0:J] = S.reshape(n, t, J)
SFinW[:, :, J] = np.dot(np.linalg.pinv(A), Xw[:, :, J])
S = pys.backward1d(SFinW)
return S, A
def bGMCA_MainBlock(X=0,
n=0,
A=0,
S=0,
kend=3,
nmax=100,
L0=1,
verb=0,
blocksize=2,
tol=1e-12,
optBlock=0,
J=0,
optPos=0):
'''Usage:
S,A,exception,Sw = bGMCA_MainBlock(X=0,n=0,A=0,S=0,kend=3,nmax=100,L0=1,verb=0,blocksize=2,tol=1e-12,optBlock=0,J=0,optPos = 0)
Inputs:
X : m x t array (input data, each row corresponds to a single observation)
n : scalar (number of sources to be estimated)
A : m x n array (initialization of the mixing matrix)
S : n x t array (initialization of the sources)
kend : scalar (final value of the k-mad thresholding)
nmax : scalar (number of iterations)
L0 : boolean (0: use of the L1 norm, 1: use of the L0 norm)
verb : boolean (if set to 1, in verbose mode)
blocksize : int (size of the blocks)
tol : float (stopping criterion)
optBlock: int in {0,1,2,3} (way the block is constructed: 0 : Random creation of the block, 1 : Correlation, 2 : Loop over all the sources + take the sources that are the most correlated with the current one, 3 : We create the blocks sequentially)
J : int (number of wavelet scales. If J = 0, no wavelet transform)
optPos : int in {0,1,2,3} option for the non-negativity: 0 no constraint, 1 : non-negativity on A and S, 2: non-negativity on S, 3: non-negativity on A
Outputs:
S : n x t array (estimated sources)
A : m x n array (estimated mixing matrix)
exception : boolean (equals to 1 is a SVD did not converge during the iterations)
Sw : n x (t x j) array (wavelet transform of the sources)
Description:
Computes the sources and the mixing matrix with bGMCA using blocks. Does not perform the initialization of A and S
Example:
S,A,exception,Sw = bGMCA_MainBlock(Xw,50,A,S,kend = 3,nmax=10000,L0=0,verb=1,blocksize=10,optBlock=0,J=2,optPos=0)'''
if J > 0: # Use of wavelets
Xdir = cp.deepcopy(X)
t = np.shape(Xdir)[1]
Xw = pys.forward1d(X, J=J)
n_Xw = np.shape(Xw)
X = Xw[:, :, 0:J].reshape(n_Xw[0], n_Xw[1] * J)
#--- Initialization
powCor = 0.5
n_S = np.shape(S)
exception = 0
comptPasGrad = 0
perc = 1. / nmax
Aold = deepcopy(A)
deltaTabAngle = np.zeros(nmax + 1)
if optPos in [1, 2, 3]:
for ii in range(
0, n_S[0]
): # Initialization to ensure that the given matrices A and S are non negative
Sl = S[ii, :]
Al = A[:, ii]
if optPos in [1, 2]:
indMax = np.argmax(abs(Sl))
if Sl[indMax] < 0:
Sl = -Sl
Al = -Al
Sl[Sl < 0] = 0
if optPos == 1:
Al[Al < 0] = 0
Al = Al / np.linalg.norm(Al)
elif optPos == 3:
indMax = np.argmax(abs(Al))
if Al[indMax] < 0:
Sl = -Sl
Al = -Al
Al[Al < 0] = 0
Al = Al / (1e-24 + np.linalg.norm(Al))
S[ii, :] = Sl
A[:, ii] = Al
# Stopping/iteration variables
Go_On = 1
it = 1
if verb:
print("Starting main loop ...")
print(" ")
print(" - Final k: ", kend)
print(" - Maximum number of iterations: ", nmax)
print(" - Batch size: ", blocksize)
print(" - Using support-based threshold estimation")
if L0:
print(" - Using L0 norm rather than L1")
print(" ")
print(" ... processing ...")
start_time = time.time()
# Defines the residual
Resi = cp.deepcopy(X)
#---
#--- Main loop
#---
while Go_On:
it += 1
if it == nmax:
Go_On = 0
#--- Estimate the sources (only when the corresponding column is non-zeros)
sigA = np.sqrt(np.sum(A * A, axis=0))
indS = np.where(sigA > 1e-24)[0]
if optBlock == 0: # Random creation of the block
if blocksize < n_S[0]:
IndBatch = randperm(
len(indS)) #---- mini-batch amongst available sources
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
indS = indS[IndBatch[0:blocksize]]
elif optBlock == 1: # Correlation
if blocksize < n_S[0]:
currSrcInd = it % len(indS)
currSrcInd = indS[currSrcInd]
currSrc = np.power(S[currSrcInd, :].T, powCor)
matCor = np.dot(np.power(S, powCor), currSrc)
matCor[currSrcInd] = -1
IndBatch = np.argsort(matCor)
IndBatch = np.append(IndBatch, currSrcInd)
IndBatch = IndBatch[::-1]
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [currSrcInd]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
elif optBlock == 2: # Loop over all the sources + take the sources that are the most correlated with the current one
if blocksize < n_S[0]:
currSrcInd = it % len(indS)
currSrcInd = indS[currSrcInd]
angTab = np.dot(A[:, currSrcInd].T, A)
angTab[
currSrcInd] = -2 #0 : correspond a l'angle le plus grand, 90 degres
IndBatch = np.argsort(angTab)
IndBatch = np.append(IndBatch, currSrcInd)
IndBatch = IndBatch[::-1]
else:
IndBatch = range(len(indS))
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [currSrcInd]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
elif optBlock == 3: # We create the batchs deterministically
indStt = blocksize * (it - 1) % np.shape(S)[0]
indEnd = blocksize * it % np.shape(S)[0]
if indStt < indEnd:
IndBatch = range(int(indStt), int(indEnd))
else:
ind1 = range(indStt, np.shape(S)[0])
if (indEnd > 0):
ind2 = range(0, indEnd)
IndBatch = np.concatenate((ind1, ind2))
else:
IndBatch = ind1
if blocksize < len(indS):
if len(indS) == n_S[0]:
indS = indS[IndBatch[0:blocksize]]
else:
print('Attention, cas special')
indSTemp = [indStt]
ii = 0
while len(indSTemp) < blocksize:
ii = ii + 1
if IndBatch[ii] in indS:
indSTemp = indSTemp + [IndBatch[ii]]
Resi = Resi + np.dot(A[:, indS],
S[indS, :]) # Putting back the sources
if np.size(indS) > 0:
if len(indS) > 1:
# Least_square estimate
Ra = np.dot(A[:, indS].T, A[:, indS])
excThisIt = 0
try:
Ua, Sa, Va = np.linalg.svd(Ra)
except np.linalg.linalg.LinAlgError:
print(indS)
print(np.sum(np.isnan(Ra)))
print(np.sum(np.isinf(Ra)))
print('ATTENTION PAS DE CONVERGENCE DE LA SVD')
exception += 1
excThisIt = 1
if excThisIt == 0:
cd_Ra = np.min(Sa) / np.max(Sa)
else:
cd_Ra = np.linalg.norm(Sa, ord=-2) / np.linalg.norm(Sa,
ord=2)
if (cd_Ra > 1e-12) and not (
excThisIt): # if the condition number is large enough
iRa = np.dot(Va.T, np.dot(np.diag(1. / Sa), Ua.T))
piA = np.dot(iRa, A[:, indS].T)
S[indS, :] = np.dot(piA, Resi)
if (cd_Ra < 1e-12 or excThisIt
== 1): # otherwise a few gradient descent steps
comptPasGrad += 1
print('GRADIENT STEP INSTEAD OF PSEUDO-INVERSE NUMBER %s' %
comptPasGrad)
if excThisIt == 0:
La = np.max(Sa)
else:
La = np.linalg.norm(Ra, ord=2)
print('WARNING SVD DID NOT CONVERGE AT THIS ITERATION')
for it_A in range(0, 10):
S[indS, :] = S[indS, :] + 1 / La * np.dot(
A[:, indS].T, X - np.dot(A[:, indS], S[indS, :]))
else: # only a single element in the selection / the update is straightforward
S[indS[0], :] = np.dot(
1. / np.linalg.norm(A[:, indS[0]]) * A[:, indS[0]], Resi)
# Thresholding
Stemp = S[indS, :]
for r in range(len(indS)): # Thresholding
St = Stemp[r, :]
indNZ = np.where(abs(St) > kend * mad(St))[0]
thrd = mad(St[indNZ])
Kval = np.min([
np.floor(np.max([0.01, perc * it]) * len(indNZ)),
n_S[1] - 1.
]) # Includes an increasing percentage of available entries
I = abs(St[indNZ]).argsort()[::-1]
Kval = np.int(np.min([np.max([Kval, n]), len(I) - 1.]))
IndIX = np.int(indNZ[I[Kval]])
thrd = abs(St[IndIX])
if optPos in [1, 2]:
St[St < thrd] = 0
indNZ = np.where(St > thrd)[0]
if L0 == 0:
St[indNZ] = St[indNZ] - thrd * np.sign(St[indNZ])
else:
St[(abs(St) < thrd)] = 0
indNZ = np.where(abs(St) > thrd)[0]
if L0 == 0:
St[indNZ] = St[indNZ] - thrd * np.sign(St[indNZ])
S[indS, :] = Stemp
# --- Updating the mixing matrix
if len(indS) > 1:
Atemp = unmix_cgsolve(S[indS, :].T, Resi.T,
maxiter=n) #---- Using CG
A[:, indS] = Atemp.T
A[:, indS] = np.dot(
A[:, indS],
np.diag(1. /
(1e-24 + np.sqrt(np.sum(A[:, indS]**2, axis=0))))
) #--- Renormalization
else:
Atemp = np.dot(Resi, S[indS, :].T)
A[:, indS] = Atemp / (1e-24 + np.linalg.norm(Atemp))
if optPos in [1, 3]:
for ii in range(len(indS)):
Al = A[:, indS[ii]]
Al[Al < 0] = 0
A[:, indS[ii]] = Al
Resi = Resi - np.dot(A[:, indS],
S[indS, :]) #--- Removing the sources
Delta = np.linalg.norm(A - Aold) / (1e-24 + np.linalg.norm(Aold))
if (np.mod(it, 500) == 1) & (verb == 1):
print("It #", it, " - Delta = ", Delta)
if len(indS) == 1:
print("Deflation")
elif len(indS) == n:
print("GMCA")
else:
print("minibatch - ratio : ", len(indS) / n)
if verb:
DeltaAngle = np.sum(A * Aold, axis=0)
deltaTabAngle[it] = np.min(
DeltaAngle
) # min car cos decroissant: on veut que le plus grand angle soit faible, donc le plus petit cos grand
if np.mod(it, 500) == 1:
print('Delta angle: %s' % (np.arccos(deltaTabAngle[it])))
Aold = deepcopy(A)
if verb:
elapsed_time = time.time() - start_time
print("Stopped after ", it, " iterations, in ", elapsed_time,
" seconds")
if J > 0:
Sw = cp.deepcopy(S)
SFinW = np.zeros((n, t, J + 1))
SFinW[:, :, 0:J] = S.reshape(n, t, J)
SFinW[:, :, J] = np.dot(np.linalg.pinv(A), Xw[:, :, J])
S = pys.backward1d(SFinW)
if J > 0:
return S, A, exception, Sw
else:
return S, A, exception
def GFB_WSparse_Positivity_1D(X,
W=None,
CardS=None,
J=2,
lam=1,
niter=250,
tol=1e-6,
Analysis=False,
verb=0,
XisOndt=0,
coarseScale=0,
A=0):
"""
Solves : Argmin_(x>=0) lam *||W o (S Phi^T) ||_1 using 1D wavelets
Inputs:
X : Input data (m x n_samp)
W : weights (m x n_coef)
niter : number of iterations
J : number of scales
tol : limit on the stopping criterion
lam : threshold
Analysis : if set, rather uses a synthesis prior
verb : enables verbose mode
XisOndt: set to 1 if X is already in the wavelet domain
coarseScale : contains the coarse scale of X if X is already in the wavelet domain
Output:
X_thrd : Output data (m x n_samp)
if XisOndt = 1, additional outputs:
coarseScale : coarse scale of X
"""
if XisOndt == 1:
n_X = np.shape(X)
m = n_X[0]
t = np.shape(coarseScale)[1]
Wt_c = np.zeros((m, t, J + 1))
Wt_c[:, :, 0:J] = X.reshape(m, t, J)
Wt_c[:, :, J] = np.dot(np.linalg.pinv(A), coarseScale) #coarseScale
X = pys.backward1d(Wt_c)
u = dp(X)
v = dp(X)
Xout = dp(X)
Xold = dp(Xout)
w_u = 0.8
w_v = 0.2
gamma = 0.9
mu = 1.5
it = 0
Go_On = 1
tk = 1
while Go_On:
it += 1
dG = Xout - X
# Update positivity
Xt = 2. * Xout - u - gamma * dG
Xt = Xt * (Xt > 0) - Xout
u = u + mu * Xt
# Update sparsity
Xt = 2. * Xout - v - gamma * dG
if Analysis:
Xt = Analysis_prox(Xt, W=W, J=J, lam=lam) - Xout
else:
Xt = Threshold_1D(Xt, W=W, J=J, lam=lam, CardS=CardS) - Xout
v = v + mu * Xt
# Update Sout
tkp = 0.5 * (1 + np.sqrt(1. + 4. * (tk**2)))
Xt = w_u * u + w_v * v
Xout = Xt + (tk - 1.) / tkp * (Xt - Xold)
diffX = np.sum(abs(Xold - Xout)) / (1e-24 + np.sum(abs(Xold)))
Xold = dp(Xout)
if verb:
print("It. #", it, " - relative variation : ", diffX)
if diffX < tol:
Go_On = 0
if it > niter:
Go_On = 0
if XisOndt == 1:
Xout = pys.forward1d(Xout, J=J)
coarseScale = Xout[:, :, J]
Xout = Wt_c[:, :, 0:J].reshape(m, J * t)
return Xout, coarseScale
else:
return Xout