mirror of
https://gitlab.com/scemama/qp_plugins_scemama.git
synced 2024-08-30 07:53:39 +02:00
Merge /home/ammar/qp2/plugins/qp_plugins_scemama
This commit is contained in:
commit
bf4bebacb5
@ -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 +1,2 @@
|
|||||||
determinants
|
determinants
|
||||||
|
davidson_undressed
|
||||||
|
10
devel/svdwf/QR.py
Normal file
10
devel/svdwf/QR.py
Normal file
@ -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
68
devel/svdwf/R3SVD_AMMAR.py
Normal file
@ -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
58
devel/svdwf/R3SVD_LiYu.py
Normal file
@ -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
20
devel/svdwf/RSVD.py
Normal file
@ -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
123
devel/svdwf/pyth_RSVD.py
Normal file
@ -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=' ')
|
||||||
|
# !!!
|
||||||
|
# !!!
|
Loading…
Reference in New Issue
Block a user