def __mul__(self, other):
"""
Multiply a sparse matrix by another sparse matrix
>>> L1 = PysparseMatrix(size = 3)
>>> L1.put([3.,10.,numpy.pi,2.5], [0,0,1,2], [2,1,1,0])
>>> L2 = PysparseMatrix(size = 3)
>>> L2.put(numpy.ones(3), numpy.arange(3), numpy.arange(3))
>>> L2.put([4.38,12357.2,1.1], [2,1,0], [1,0,2])
>>> tmp = numpy.array(((1.23572000e+05, 2.31400000e+01, 3.00000000e+00),
... (3.88212887e+04, 3.14159265e+00, 0.00000000e+00),
... (2.50000000e+00, 0.00000000e+00, 2.75000000e+00)))
>>> numpy.allclose((L1 * L2).getNumpyArray(), tmp)
1
or a sparse matrix by a vector
>>> tmp = numpy.array((29., 6.28318531, 2.5))
>>> numpy.allclose(L1 * numpy.array((1,2,3),'d'), tmp)
1
or a vector by a sparse matrix
>>> tmp = numpy.array((7.5, 16.28318531, 3.))
>>> numpy.allclose(numpy.array((1,2,3),'d') * L1, tmp) ## The multiplication is broken. Numpy is calling __rmul__ for every element instead of with the whole array.
1
"""
M, N = self.getShape()
if isinstance(other, PysparseMatrix):
if N != other.getShape()[0]:
raise TypeError, 'Matrices dimensions do not match for product'
p = spmatrix.matrixmultiply(self.matrix, other.getMatrix())
return PysparseMatrix(matrix=p)
else:
shape = numpy.shape(other)
if shape == (): # other is a scalar
p = self.matrix.copy()
p.scale(other)
return PysparseMatrix(matrix=p)
elif shape == (N, ):
y = numpy.empty(M)
self.matrix.matvec(other, y)
return y
else:
raise TypeError, 'Cannot multiply objects'
def __mul__(self, other):
"""
Multiply a sparse matrix by another sparse matrix
>>> L1 = PysparseMatrix(size = 3)
>>> L1.put([3.,10.,numpy.pi,2.5], [0,0,1,2], [2,1,1,0])
>>> L2 = PysparseMatrix(size = 3)
>>> L2.put(numpy.ones(3), numpy.arange(3), numpy.arange(3))
>>> L2.put([4.38,12357.2,1.1], [2,1,0], [1,0,2])
>>> tmp = numpy.array(((1.23572000e+05, 2.31400000e+01, 3.00000000e+00),
... (3.88212887e+04, 3.14159265e+00, 0.00000000e+00),
... (2.50000000e+00, 0.00000000e+00, 2.75000000e+00)))
>>> numpy.allclose((L1 * L2).getNumpyArray(), tmp)
1
or a sparse matrix by a vector
>>> tmp = numpy.array((29., 6.28318531, 2.5))
>>> numpy.allclose(L1 * numpy.array((1,2,3),'d'), tmp)
1
or a vector by a sparse matrix
>>> tmp = numpy.array((7.5, 16.28318531, 3.))
>>> numpy.allclose(numpy.array((1,2,3),'d') * L1, tmp) ## The multiplication is broken. Numpy is calling __rmul__ for every element instead of with the whole array.
1
"""
M, N = self.getShape()
if isinstance(other, PysparseMatrix):
if N != other.getShape()[0]:
raise TypeError, 'Matrices dimensions do not match for product'
p = spmatrix.matrixmultiply(self.matrix, other.getMatrix())
return PysparseMatrix(matrix=p)
else:
shape = numpy.shape(other)
if shape == (): # other is a scalar
p = self.matrix.copy()
p.scale(other)
return PysparseMatrix(matrix=p)
elif shape == (N,):
y = numpy.empty(M)
self.matrix.matvec(other, y)
return y
else:
raise TypeError, 'Cannot multiply objects'
def testRandomMat(self):
eps = 2.2204460492503131e-16
n = 30
m = 60
k = 30
for i in range(100):
A = spmatrix_util.ll_mat_rand(n, k, 0.9)
B = spmatrix_util.ll_mat_rand(k, m, 0.4)
C = spmatrix.matrixmultiply(A, B)
t = numpy.zeros(k, "d")
y1 = numpy.zeros(n, "d")
y2 = numpy.zeros(n, "d")
for s in range(10):
x = RandomArray.random((m,))
C.matvec(x, y1)
B.matvec(x, t)
A.matvec(t, y2)
self.failUnless(math.sqrt(numpy.dot(y1 - y2, y1 - y2)) < eps * n * m * k)
def testRandomMat(self):
eps = 2.2204460492503131E-16
n = 30
m = 60
k = 30
for i in range(100):
A = spmatrix_util.ll_mat_rand(n, k, 0.9)
B = spmatrix_util.ll_mat_rand(k, m, 0.4)
C = spmatrix.matrixmultiply(A, B)
t = numpy.zeros(k, 'd')
y1 = numpy.zeros(n, 'd')
y2 = numpy.zeros(n, 'd')
for s in range(10):
x = RandomArray.random((m, ))
C.matvec(x, y1)
B.matvec(x, t)
A.matvec(t, y2)
self.failUnless(
math.sqrt(numpy.dot(y1 - y2, y1 - y2)) < eps * n * m * k)
T[4:8,1:3] = As[4:8,1:3]
printMatrix(T)
print 'this should raise execptions...\n'
try:
T[6:9,6:9] = A[6:9,6:9]
except:
traceback.print_exc()
try:
T[5:9, 4:10] = A[5:9, 4:10]
except:
traceback.print_exc()
print 'Matrix multiplications'
printMatrix(spmatrix.matrixmultiply(I, A))
printMatrix(spmatrix.matrixmultiply(Is, A))
printMatrix(spmatrix.matrixmultiply(O, O))
printMatrix(spmatrix.matrixmultiply(Os, O))
print 'Dot product'
printMatrix(spmatrix.dot(I, A))
print 'Matrix export'
A[:4,:4].export_mtx('A.mtx', 3)
As[:4,:4].export_mtx('As.mtx', 3)
print open('A.mtx').read()
print open('As.mtx').read()
import time
from pysparse.sparse import spmatrix
n = 1000000
# create 2 nxn tridiag matrices
A = spmatrix.ll_mat(n, n)
B = spmatrix.ll_mat(n, n)
for i in xrange(n):
A[i,i] = i
B[i,i] = i
if i > 0:
A[i,i-1] = 1
B[i,i-1] = 1
if i < n-1:
A[i,i+1] = 1
B[i,i-1] = 1
t1 = time.clock()
C = spmatrix.matrixmultiply(A, B)
t_mult = time.clock() - t1
print 'time for multiplying %dx%d matrices: %.2f sec' % (n, n, t_mult)
print C[:10,:10]
import time
from pysparse.sparse import spmatrix
n = 1000000
# create 2 nxn tridiag matrices
A = spmatrix.ll_mat(n, n)
B = spmatrix.ll_mat(n, n)
for i in xrange(n):
A[i, i] = i
B[i, i] = i
if i > 0:
A[i, i - 1] = 1
B[i, i - 1] = 1
if i < n - 1:
A[i, i + 1] = 1
B[i, i - 1] = 1
t1 = time.clock()
C = spmatrix.matrixmultiply(A, B)
t_mult = time.clock() - t1
print 'time for multiplying %dx%d matrices: %.2f sec' % (n, n, t_mult)
print C[:10, :10]
