def rank(A):
if isinstance(A, ndarray):
F = Algebra()
r = F.rank(A._M)
return int(r)
else:
raise PyValueException, "Rank function can only be called on matrix objects"
def rank(A):
if isinstance(A,ndarray):
F = Algebra();
r=F.rank(A._M);
return int(r);
else:
raise PyValueException, "Rank function can only be called on matrix objects"
def __div__(A, B):
try:
F = Algebra();
R = F.solve(A._M, B._M);
return R;
except (java.lang.IllegalArgumentException), e:
# check the error class types according to the matrix class so we can intelligently report the error.
print e.getMessage()
return None
def norm(A, ntype=None):
F = Algebra()
if ntype == 'fro':
r = F.normF(A._M)
elif ntype == 2:
r = F.norm2(A._M)
else:
r = F.norm2(A._M)
return r
def norm(A,ntype=None):
F = Algebra();
if ntype=='fro':
r=F.normF(A._M);
elif ntype == 2:
r=F.norm2(A._M);
else:
r=F.norm2(A._M);
return r;
def __mul__(A, B):
# need to check types and multiple based on them..
try:
F = Algebra()
C = (F.mult(A._M, B._M))
except:
raise PyValueException, "Inner dimension mismatch in matrix multiply."
return None
return ndarray(C)
def __div__(A, B):
try:
F = Algebra()
R = F.solve(A._M, B._M)
return R
except (java.lang.IllegalArgumentException), e:
# check the error class types according to the matrix class so we can intelligently report the error.
print e.getMessage()
return None
def __mul__(A, B):
# need to check types and multiple based on them..
try:
F = Algebra();
C=(F.mult(A._M, B._M));
except:
raise PyValueException, "Inner dimension mismatch in matrix multiply.";
return None;
return ndarray(C)
def solve_transpose(A, B):
F = Algebra();
if isinstance(A, ndarray) and isinstance(B, float):
return F.solveTranspose(A._M,B);
elif isinstance(B, ndarray) and isinstance(A, float):
return F.solveTranspose(A, B._M);
elif isinstance(A, ndarray) and isinstance(B, ndarray):
return ndarray(F.solveTranspose(A._M, B._M));
else:
return A / B
def solve(A, B):
F = Algebra()
if isinstance(A, ndarray) and isinstance(B, float):
return F.solve(A._M, B)
elif isinstance(B, ndarray) and isinstance(A, float):
return F.solve(A, B._M)
elif isinstance(A, ndarray) and isinstance(B, ndarray):
return ndarray(F.solve(A._M, B._M))
else:
return A / B
def directlinearsolve():
from no.uib.cipr.matrix import Matrices, DenseMatrix, DenseVector
from cern.colt.matrix import DoubleMatrix2D, DoubleFactory2D
from cern.colt.matrix.linalg import Algebra
sz = 1000
# MTJ
x = DenseVector(sz)
t = time.time()
mat = Matrices.random(sz,sz)
b = Matrices.random(sz)
mat.solve(b,x)
print "Mtj :" + str(time.time() - t)
# Colt
alg = Algebra()
f = DoubleFactory2D.dense
t = time.time()
A = f.random(sz,sz)
b = f.random(sz,1)
x = alg.solve(A,b)
print "Colt :" + str(time.time() - t)
def inverse(A):
F = Algebra();
x=F.inverse(A._M);
return ndarray(x)
def transpose(A):
F = Algebra();
if isinstance(A,float):
return A;
else:
return ndarray(F.transpose(A._M));
def cond(A):
F = Algebra();
return F.cond(A._M);
def cond(A):
F = Algebra()
return F.cond(A._M)
def transpose(A):
F = Algebra()
if isinstance(A, float):
return A
else:
return ndarray(F.transpose(A._M))
def inverse(A):
F = Algebra()
x = F.inverse(A._M)
return ndarray(x)