# \$Id: __init__.py,v 1.2 2011-10-06 19:14:49 wirawan Exp \$ # # wpylib.math.linalg main module # Created: 20110714 # Wirawan Purwanto # """ wpylib.math.linalg Provides convenience functions for linear algebra things beyond what's already provided by numpy. """ import numpy import numpy.linalg # My favorites: from numpy import dot, trace from numpy.linalg import det, inv from .gram_schmidt import modgs MATMUL_USE_BLAS = False def matmul(*Mats, **opts): """Do successive matrix product. For example, matmul(A,B,C,D) will evaluate a matrix multiplication ((A*B)*C)*D . The matrices must be of matching sizes.""" from numpy import asarray, dot, iscomplexobj use_blas = opts.get('use_blas', MATMUL_USE_BLAS) debug = opts.get('debug', True) if debug: def dbg(msg): print msg, else: def dbg(msg): pass if use_blas: try: from scipy.linalg.blas import zgemm, dgemm except: # Older scipy (<= 0.10?) from scipy.linalg.blas import fblas zgemm = fblas.zgemm dgemm = fblas.dgemm if not use_blas: p = dot(Mats[0], Mats[1]) for M in Mats[2:]: p = dot(p, M) else: dbg("Using BLAS\n") # FIXME: Right now only supporting double precision arithmetic. M0 = asarray(Mats[0]) M1 = asarray(Mats[1]) if iscomplexobj(M0) or iscomplexobj(M1): p = zgemm(alpha=1.0, a=M0, b=M1) Cplx = True dbg("- zgemm ") else: p = dgemm(alpha=1.0, a=M0, b=M1) Cplx = False dbg("- dgemm ") for M in Mats[2:]: M2 = asarray(M) if Cplx or iscomplexobj(M2): p = zgemm(alpha=1.0, a=p, b=M2) Cplx = True dbg(" zgemm") else: p = dgemm(alpha=1.0, a=p, b=M2) dbg(" dgemm") dbg("\n") return p