def rq(a,overwrite_a=0,lwork=None):
"""RQ decomposition of an M x N matrix a.
Description:
Find an upper-triangular matrix r and a unitary (orthogonal)
matrix q such that r * q = a
Inputs:
a -- the matrix
overwrite_a=0 -- if non-zero then discard the contents of a,
i.e. a is used as a work array if possible.
lwork=None -- >= shape(a)[1]. If None (or -1) compute optimal
work array size.
Outputs:
r, q -- matrices such that r * q = a
"""
# TODO: implement support for non-square and complex arrays
a1 = asarray_chkfinite(a)
if len(a1.shape) != 2:
raise ValueError, 'expected matrix'
M,N = a1.shape
if M != N:
raise ValueError, 'expected square matrix'
if issubclass(a1.dtype.type,complexfloating):
raise ValueError, 'expected real (non-complex) matrix'
overwrite_a = overwrite_a or (_datanotshared(a1,a))
gerqf, = get_lapack_funcs(('gerqf',),(a1,))
if lwork is None or lwork == -1:
# get optimal work array
rq,tau,work,info = gerqf(a1,lwork=-1,overwrite_a=1)
lwork = work[0]
rq,tau,work,info = gerqf(a1,lwork=lwork,overwrite_a=overwrite_a)
if info<0: raise ValueError, \
'illegal value in %-th argument of internal geqrf'%(-info)
gemm, = get_blas_funcs(('gemm',),(rq,))
t = rq.dtype.char
R = basic.triu(rq)
Q = numpy.identity(M,dtype=t)
ident = numpy.identity(M,dtype=t)
zeros = numpy.zeros
k = min(M,N)
for i in range(k):
v = zeros((M,),t)
v[N-k+i] = 1
v[0:N-k+i] = rq[M-k+i,0:N-k+i]
H = gemm(-tau[i],v,v,1+0j,ident,trans_b=2)
Q = gemm(1,Q,H)
return R, Q
def funm(A,func,disp=1):
"""matrix function for arbitrary callable object func.
"""
# func should take a vector of arguments (see vectorize if
# it needs wrapping.
# Perform Shur decomposition (lapack ?gees)
A = asarray(A)
if len(A.shape)!=2:
raise ValueError, "Non-matrix input to matrix function."
if A.dtype.char in ['F', 'D', 'G']:
cmplx_type = 1
else:
cmplx_type = 0
T, Z = schur(A)
T, Z = rsf2csf(T,Z)
n,n = T.shape
F = diag(func(diag(T))) # apply function to diagonal elements
F = F.astype(T.dtype.char) # e.g. when F is real but T is complex
minden = abs(T[0,0])
# implement Algorithm 11.1.1 from Golub and Van Loan
# "matrix Computations."
for p in range(1,n):
for i in range(1,n-p+1):
j = i + p
s = T[i-1,j-1] * (F[j-1,j-1] - F[i-1,i-1])
ksl = slice(i,j-1)
val = dot(T[i-1,ksl],F[ksl,j-1]) - dot(F[i-1,ksl],T[ksl,j-1])
s = s + val
den = T[j-1,j-1] - T[i-1,i-1]
if den != 0.0:
s = s / den
F[i-1,j-1] = s
minden = min(minden,abs(den))
F = dot(dot(Z, F),transpose(conjugate(Z)))
if not cmplx_type:
F = toreal(F)
tol = {0:feps, 1:eps}[_array_precision[F.dtype.char]]
if minden == 0.0:
minden = tol
err = min(1, max(tol,(tol/minden)*norm(triu(T,1),1)))
if product(ravel(logical_not(isfinite(F))),axis=0):
err = Inf
if disp:
if err > 1000*tol:
print "Result may be inaccurate, approximate err =", err
return F
else:
return F, err
def qr_old(a,overwrite_a=0,lwork=None):
"""QR decomposition of an M x N matrix a.
Description:
Find a unitary (orthogonal) matrix, q, and an upper-triangular
matrix r such that q * r = a
Inputs:
a -- the matrix
overwrite_a=0 -- if non-zero then discard the contents of a,
i.e. a is used as a work array if possible.
lwork=None -- >= shape(a)[1]. If None (or -1) compute optimal
work array size.
Outputs:
q, r -- matrices such that q * r = a
"""
a1 = asarray_chkfinite(a)
if len(a1.shape) != 2:
raise ValueError, 'expected matrix'
M,N = a1.shape
overwrite_a = overwrite_a or (_datanotshared(a1,a))
geqrf, = get_lapack_funcs(('geqrf',),(a1,))
if lwork is None or lwork == -1:
# get optimal work array
qr,tau,work,info = geqrf(a1,lwork=-1,overwrite_a=1)
lwork = work[0]
qr,tau,work,info = geqrf(a1,lwork=lwork,overwrite_a=overwrite_a)
if info<0: raise ValueError,\
'illegal value in %-th argument of internal geqrf'%(-info)
gemm, = get_blas_funcs(('gemm',),(qr,))
t = qr.dtype.char
R = basic.triu(qr)
Q = numpy.identity(M,dtype=t)
ident = numpy.identity(M,dtype=t)
zeros = numpy.zeros
for i in range(min(M,N)):
v = zeros((M,),t)
v[i] = 1
v[i+1:M] = qr[i+1:M,i]
H = gemm(-tau[i],v,v,1+0j,ident,trans_b=2)
Q = gemm(1,Q,H)
return Q, R
def qr(a,overwrite_a=0,lwork=None,econ=False,mode='qr'):
"""QR decomposition of an M x N matrix a.
Description:
Find a unitary (orthogonal) matrix, q, and an upper-triangular
matrix r such that q * r = a
Inputs:
a -- the matrix
overwrite_a=0 -- if non-zero then discard the contents of a,
i.e. a is used as a work array if possible.
lwork=None -- >= shape(a)[1]. If None (or -1) compute optimal
work array size.
econ=False -- computes the skinny or economy-size QR decomposition
only useful when M>N
mode='qr' -- if 'qr' then return both q and r; if 'r' then just return r
Outputs:
q,r - if mode=='qr'
r - if mode=='r'
"""
a1 = asarray_chkfinite(a)
if len(a1.shape) != 2:
raise ValueError("expected 2D array")
M, N = a1.shape
overwrite_a = overwrite_a or (_datanotshared(a1,a))
geqrf, = get_lapack_funcs(('geqrf',),(a1,))
if lwork is None or lwork == -1:
# get optimal work array
qr,tau,work,info = geqrf(a1,lwork=-1,overwrite_a=1)
lwork = work[0]
qr,tau,work,info = geqrf(a1,lwork=lwork,overwrite_a=overwrite_a)
if info<0:
raise ValueError("illegal value in %-th argument of internal geqrf"
% -info)
if not econ or M<N:
R = basic.triu(qr)
else:
R = basic.triu(qr[0:N,0:N])
if mode=='r':
return R
if find_best_lapack_type((a1,))[0]=='s' or find_best_lapack_type((a1,))[0]=='d':
gor_un_gqr, = get_lapack_funcs(('orgqr',),(qr,))
else:
gor_un_gqr, = get_lapack_funcs(('ungqr',),(qr,))
if M<N:
# get optimal work array
Q,work,info = gor_un_gqr(qr[:,0:M],tau,lwork=-1,overwrite_a=1)
lwork = work[0]
Q,work,info = gor_un_gqr(qr[:,0:M],tau,lwork=lwork,overwrite_a=1)
elif econ:
# get optimal work array
Q,work,info = gor_un_gqr(qr,tau,lwork=-1,overwrite_a=1)
lwork = work[0]
Q,work,info = gor_un_gqr(qr,tau,lwork=lwork,overwrite_a=1)
else:
t = qr.dtype.char
qqr = numpy.empty((M,M),dtype=t)
qqr[:,0:N]=qr
# get optimal work array
Q,work,info = gor_un_gqr(qqr,tau,lwork=-1,overwrite_a=1)
lwork = work[0]
Q,work,info = gor_un_gqr(qqr,tau,lwork=lwork,overwrite_a=1)
if info < 0:
raise ValueError("illegal value in %-th argument of internal gorgqr"
% -info)
return Q, R
def funm(A,func,disp=1):
"""Evaluate a matrix function specified by a callable.
Returns the value of matrix-valued function f at A. The function f
is an extension of the scalar-valued function func to matrices.
Parameters
----------
A : array, shape(M,M)
Matrix at which to evaluate the function
func : callable
Callable object that evaluates a scalar function f.
Must be vectorized (eg. using vectorize).
disp : boolean
Print warning if error in the result is estimated large
instead of returning estimated error. (Default: True)
Returns
-------
fA : array, shape(M,M)
Value of the matrix function specified by func evaluated at A
(if disp == False)
errest : float
1-norm of the estimated error, ||err||_1 / ||A||_1
"""
# Perform Shur decomposition (lapack ?gees)
A = asarray(A)
if len(A.shape)!=2:
raise ValueError, "Non-matrix input to matrix function."
if A.dtype.char in ['F', 'D', 'G']:
cmplx_type = 1
else:
cmplx_type = 0
T, Z = schur(A)
T, Z = rsf2csf(T,Z)
n,n = T.shape
F = diag(func(diag(T))) # apply function to diagonal elements
F = F.astype(T.dtype.char) # e.g. when F is real but T is complex
minden = abs(T[0,0])
# implement Algorithm 11.1.1 from Golub and Van Loan
# "matrix Computations."
for p in range(1,n):
for i in range(1,n-p+1):
j = i + p
s = T[i-1,j-1] * (F[j-1,j-1] - F[i-1,i-1])
ksl = slice(i,j-1)
val = dot(T[i-1,ksl],F[ksl,j-1]) - dot(F[i-1,ksl],T[ksl,j-1])
s = s + val
den = T[j-1,j-1] - T[i-1,i-1]
if den != 0.0:
s = s / den
F[i-1,j-1] = s
minden = min(minden,abs(den))
F = dot(dot(Z, F),transpose(conjugate(Z)))
if not cmplx_type:
F = toreal(F)
tol = {0:feps, 1:eps}[_array_precision[F.dtype.char]]
if minden == 0.0:
minden = tol
err = min(1, max(tol,(tol/minden)*norm(triu(T,1),1)))
if product(ravel(logical_not(isfinite(F))),axis=0):
err = Inf
if disp:
if err > 1000*tol:
print "Result may be inaccurate, approximate err =", err
return F
else:
return F, err
