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Merge /home/ammar/qp2/plugins/qp_plugins_scemama

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Abdallah AMMAR 2021-04-08 16:49:30 +02:00
commit bf4bebacb5
7 changed files with 281 additions and 1 deletions
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2
devel/svdwf/Evar_TruncSVD.irp.f
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@ -389,7 +389,7 @@ subroutine run
! !!! ! !!!
!deallocate( Averif, Uverif, Dverif, Vtverif ) !deallocate( Averif, Uverif, Dverif, Vtverif )
! !!! ! !!!
low_rank = 12 low_rank = 10
! !!! ! !!!
err_verif = 0.d0 err_verif = 0.d0
do j = 1, n do j = 1, n

1
devel/svdwf/NEED
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@ -1 +1,2 @@
determinants determinants
davidson_undressed

10
devel/svdwf/QR.py Normal file
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@ -0,0 +1,10 @@
# !!!
import numpy as np
# !!!
def QR_fact(X):
Q, R = np.linalg.qr(X, mode="reduced")
D = np.diag( np.sign( np.diag(R) ) )
Qunique = np.dot(Q,D)
#Runique = np.dot(D,R)
return(Qunique)
# !!!

68
devel/svdwf/R3SVD_AMMAR.py Normal file
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@ -0,0 +1,68 @@
# !!!
import numpy as np
from QR import QR_fact
from RSVD import powit_RSVD
# !!!
def R3SVD_AMMAR(A, t, delta_t, npow, nb_oversamp, err_thr, maxit, tol):
# !!!
# build initial QB decomposition
# !!!
n = A.shape[1]
G = np.random.randn(n, t)
normA = np.linalg.norm(A, ord='fro')**2
i_it = 0
rank = 0
Y = np.dot(A,G)
# The power scheme
for j in range(npow):
Q = QR_fact(Y)
Q = QR_fact( np.dot(A.T,Q) )
Y = np.dot(A,Q)
# orthogonalization process
Q_old = QR_fact(Y)
B = np.dot(Q_old.T,A)
normB = np.linalg.norm(B, ord='fro')**2
# error percentage
errpr = abs( normA - normB ) / normA
rank += t
i_it += 1
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
# !!!
# incrementally build up QB decomposition
# !!!
while ( (errpr>err_thr) and (i_it<maxit) and (rank<=min(A.shape)-delta_t) ): #
G = np.random.randn(n, delta_t)
Y = np.dot(A,G)
#Y = Y - np.dot(Q_old, np.dot(Q_old.T,Y) ) # orthogonalization with Q
# power scheme
for j in range(npow):
Q = QR_fact(Y)
Q = QR_fact( np.dot(A.T,Q) )
Y = np.dot(A,Q)
Y = Y - np.dot(Q_old, np.dot(Q_old.T,Y) ) # orthogonalization with Q
Q_new = QR_fact(Y)
B_new = np.dot(Q_new.T,A)
# build up approximate basis
Q_old = np.append(Q_new, Q_old, axis=1)
#B = np.append(B_new, B, axis=0)
normB += np.linalg.norm(B_new, ord='fro')**2
errpr = abs( normA - normB ) / normA
rank += delta_t
i_it += 1
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
# !!!
#UL, SL, VLT = np.linalg.svd(B, full_matrices=0)
#UL = np.dot(Q_old,UL)
# !!!
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
UL, SL, VLT = powit_RSVD(A, rank, npow, nb_oversamp)
#return UL, SL, VLT
# !!!
rank = SL.shape[0]
new_r = rank
for i in range(rank):
if( SL[i] <= tol ):
new_r = i
break
return UL[:,:(new_r)], SL[:(new_r)], VLT[:(new_r),:]
# !!!

58
devel/svdwf/R3SVD_LiYu.py Normal file
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@ -0,0 +1,58 @@
# !!!
import numpy as np
from QR import QR_fact
# !!!
def R3SVD_LiYu(A, t, delta_t, npow, err_thr, maxit):
# !!!
# build initial QB decomposition
# !!!
n = A.shape[1]
G = np.random.randn(n, t) # n x t Gaussian random matrix
normA = np.linalg.norm(A, ord='fro')**2
i_it = 0
rank = 0
Y = np.dot(A,G)
# The power scheme
for j in range(npow):
Q = QR_fact(Y)
Q = QR_fact( np.dot(A.T,Q) )
Y = np.dot(A,Q)
# orthogonalization process
Q_old = QR_fact(Y)
B = np.dot(Q_old.T,A)
normB = np.linalg.norm(B, ord='fro')**2
# error percentage
errpr = abs( normA - normB ) / normA
rank += t
i_it += 1
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
# !!!
# incrementally build up QB decomposition
# !!!
while ( (errpr>err_thr) and (i_it<maxit) and (rank<=min(A.shape)-delta_t) ): #
G = np.random.randn(n, delta_t) # n x delta_t Gaussian random matrix
Y = np.dot(A,G)
Y = Y - np.dot(Q_old, np.dot(Q_old.T,Y) ) # orthogonalization with Q
# power scheme
for j in range(npow):
Q = QR_fact(Y)
Q = QR_fact( np.dot(A.T,Q) )
Y = np.dot(A,Q)
Y = Y - np.dot(Q_old, np.dot(Q_old.T,Y) ) # orthogonalization with Q
Q_new = QR_fact(Y)
B_new = np.dot(Q_new.T,A)
# build up approximate basis
Q_old = np.append(Q_new, Q_old, axis=1)
B = np.append(B_new, B, axis=0)
rank += delta_t
i_it += 1
normB += np.linalg.norm(B_new, ord='fro')**2
errpr = abs( normA - normB ) / normA
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
# !!!
print("iteration = {}, rank = {}, error = {}".format(i_it, rank, errpr))
UL, SL, VLT = np.linalg.svd(B, full_matrices=0)
UL = np.dot(Q_old,UL)
# !!!
return UL, SL, VLT
# !!!

20
devel/svdwf/RSVD.py Normal file
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@ -0,0 +1,20 @@
# !!!
import numpy as np
from QR import QR_fact
# !!!
def powit_RSVD(X, new_r, nb_powit, nb_oversamp):
# !!!
G = np.random.randn(X.shape[1], new_r+nb_oversamp)
Q = QR_fact( np.dot(X,G) )
# !!!
for _ in range(nb_powit):
Q = QR_fact( np.dot(X.T,Q) )
Q = QR_fact( np.dot(X,Q) )
# !!!
Y = np.dot(Q.T,X)
# !!!
U, S, VT = np.linalg.svd(Y, full_matrices=0)
U = np.dot(Q,U)
return U[:,:(new_r)], S[:(new_r)], VT[:(new_r),:]
# !!!
# !!!

123
devel/svdwf/pyth_RSVD.py Normal file
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@ -0,0 +1,123 @@
#!/usr/bin/env python3
# !!!
import os, sys
# !!!
#QP_PATH=os.environ["QMCCHEM_PATH"]
#sys.path.insert(0,QMCCHEM_PATH+"/EZFIO/Python/")
# !!!
from ezfio import ezfio
from datetime import datetime
import numpy as np
from scipy.sparse.linalg import svds
from R3SVD_LiYu import R3SVD_LiYu
from RSVD import powit_RSVD
from R3SVD_AMMAR import R3SVD_AMMAR
import time
# !!!
fmt = '%5d' + 2 * ' %e'
# !!!
if __name__ == "__main__":
# !!!
if len(sys.argv) != 2:
print("Usage: %s <EZFIO_DIRECTORY>"%sys.argv[0])
sys.exit(1)
filename = sys.argv[1]
ezfio.set_file(filename)
# !!!
N_det = ezfio.get_spindeterminants_n_det()
A_rows = np.array(ezfio.get_spindeterminants_psi_coef_matrix_rows())
A_cols = np.array(ezfio.get_spindeterminants_psi_coef_matrix_columns())
A_vals = np.array(ezfio.get_spindeterminants_psi_coef_matrix_values())
nrows, ncols = ezfio.get_spindeterminants_n_det_alpha(), ezfio.get_spindeterminants_n_det_beta()
Y = np.zeros( (nrows, ncols) )
for k in range(N_det):
i = A_rows[k] - 1
j = A_cols[k] - 1
Y[i,j] = A_vals[0][k]
print("# # # # # # # # # # # # # # # # # # # # # #")
print('matrix dimensions = {} x {}'.format(nrows, ncols))
print("# # # # # # # # # # # # # # # # # # # # # # \n")
normY = np.linalg.norm(Y, ord='fro')
print( normY )
# !!!
print('Full SVD:')
t_beg = time.time()
U, S_FSVD, VT = np.linalg.svd(Y, full_matrices=0)
t_end = time.time()
rank = S_FSVD.shape[0]
energy = np.sum(np.square(S_FSVD)) / normY**2
err_SVD = 100. * np.linalg.norm(Y - np.dot(U,np.dot(np.diag(S_FSVD),VT)), ord='fro') / normY
print('rank = {}, energy = {}, error = {}%, CPU time = {} \n'.format(rank, energy, err_SVD, t_end-t_beg))
# !!!
np.savetxt('results_python/h2o_pseudo/SingValues_FullSVD.txt', np.transpose([ np.array(range(rank))+1, S_FSVD ]), fmt='%5d' + ' %e', delimiter=' ')
# !!!
t = 50
delta_t = 10
npow = 15
err_thr = 1e-3
maxit = 10
# !!!
print('RRR SVD Li & Yu:')
t_beg = time.time()
U, S_R3SVD, VT = R3SVD_LiYu(Y, t, delta_t, npow, err_thr, maxit)
t_end = time.time()
rank = S_R3SVD.shape[0]
energy = np.sum( np.square(S_R3SVD) ) / normY**2
err_SVD = 100. * np.linalg.norm(Y - np.dot(U,np.dot(np.diag(S_R3SVD),VT)), ord='fro') / normY
print('nb Pow It = {}, rank = {}, energy = {}, error = {} %, CPU time = {}\n'.format(npow, rank, energy, err_SVD, t_end-t_beg))
# !!!
err_R3SVD = np.zeros( (rank) )
for i in range(rank):
err_R3SVD[i] = 100.0 * abs( S_FSVD[i] - S_R3SVD[i] ) / S_FSVD[i]
np.savetxt('results_python/h2o_pseudo/SingValues_R3SVD.txt', np.transpose([ np.array(range(rank))+1, S_R3SVD, err_R3SVD ]), fmt=fmt, delimiter=' ')
# !!!
nb_oversamp = 10
tol_SVD = 1e-10
print('RRR SVD my version:')
t_beg = time.time()
U, S_MRSVD, VT = R3SVD_AMMAR(Y, t, delta_t, npow, nb_oversamp, err_thr, maxit, tol_SVD)
t_end = time.time()
rank = S_MRSVD.shape[0]
energy = np.sum( np.square(S_MRSVD) ) / normY**2
err_SVD = 100. * np.linalg.norm(Y - np.dot(U,np.dot(np.diag(S_MRSVD),VT)), ord='fro') / normY
print('nb Pow It = {}, rank = {}, energy = {}, error = {} %, CPU time = {}\n'.format(npow, rank, energy, err_SVD, t_end-t_beg))
# !!!
err_MRSVD = np.zeros( (rank) )
for i in range(rank):
err_MRSVD[i] = 100.0 * abs( S_FSVD[i] - S_MRSVD[i] ) / S_FSVD[i]
np.savetxt('results_python/h2o_pseudo/SingValues_MRSVD.txt', np.transpose([ np.array(range(rank))+1, S_MRSVD, err_MRSVD ]), fmt=fmt, delimiter=' ')
# !!!
trank = rank
print("Truncated RSVD (pre-fixed rank = {} & oversampling parameter = {}):".format(trank,nb_oversamp))
t_beg = time.time()
U, S_RSVD, VT = powit_RSVD(Y, trank, npow, nb_oversamp)
t_end = time.time()
rank = S_RSVD.shape[0]
energy = np.sum( np.square(S_RSVD) ) / normY**2
err_SVD = 100. * np.linalg.norm( Y - np.dot(U,np.dot(np.diag(S_RSVD),VT)), ord="fro") / normY
print('nb Pow It = {}, rank = {}, energy = {}, error = {} %, CPU time = {}\n'.format(npow, rank, energy, err_SVD, t_end-t_beg))
# !!!
err_RSVD = np.zeros( (rank) )
for i in range(rank):
err_RSVD[i] = 100.0 * abs( S_FSVD[i] - S_RSVD[i] ) / S_FSVD[i]
np.savetxt('results_python/h2o_pseudo/SingValues_RSVD.txt', np.transpose([ np.array(range(rank))+1, S_RSVD, err_RSVD ]), fmt=fmt, delimiter=' ')
# !!!
print("Truncated SVD (scipy):")
t_beg = time.time()
U, S_TSVD, VT = svds(Y, min(trank, min(Y.shape[0],Y.shape[1])-1 ), which='LM')
t_end = time.time()
rank = S_TSVD.shape[0]
energy = np.sum( np.square(S_TSVD) ) / normY**2
err_SVD = 100. * np.linalg.norm( Y - np.dot(U, np.dot(np.diag(S_TSVD),VT) ), ord="fro") / normY
print('rank = {}, energy = {}, error = {} %, CPU time = {}\n'.format(rank, energy, err_SVD, t_end-t_beg))
# !!!
err_TSVD = np.zeros( (rank) )
for i in range(rank-1):
for j in range(i+1,rank):
if( S_TSVD[j] > S_TSVD[i]):
S_TSVD[i], S_TSVD[j] = S_TSVD[j], S_TSVD[i]
for i in range(rank):
err_TSVD[i] = 100.0 * abs( S_FSVD[i] - S_TSVD[i] ) / S_FSVD[i]
np.savetxt('results_python/h2o_pseudo/SingValues_TSVD.txt', np.transpose([ np.array(range(rank))+1, S_TSVD, err_TSVD ]), fmt=fmt, delimiter=' ')
# !!!
# !!!