コード例 #1
0
    def __mul__(self, other):
        """
        Multiply a sparse matrix by another sparse matrix

            >>> L1 = _PysparseMatrixFromShape(rows=3, cols=3)
            >>> L1.put([3.,10.,numerix.pi,2.5], [0,0,1,2], [2,1,1,0])
            >>> L2 = _PysparseIdentityMatrix(size=3)
            >>> L2.put([4.38,12357.2,1.1], [2,1,0], [1,0,2])

            >>> tmp = numerix.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)))

            >>> numerix.allclose((L1 * L2).numpyArray, tmp)
            1

        or a sparse matrix by a vector

            >>> tmp = numerix.array((29., 6.28318531, 2.5))
            >>> numerix.allclose(L1 * numerix.array((1,2,3),'d'), tmp)
            1

        or a vector by a sparse matrix

            >>> tmp = numerix.array((7.5, 16.28318531,  3.))
            >>> numerix.allclose(numerix.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


        """
        N = self.matrix.shape[1]

        if isinstance(other, _PysparseMatrix):
            return _PysparseMatrix(
                matrix=spmatrix.matrixmultiply(self.matrix, other.matrix))
        else:
            shape = numerix.shape(other)
            if shape == ():
                L = spmatrix.ll_mat(N, N, N)
                L.put(other * numerix.ones(N, 'l'))
                return _PysparseMatrix(
                    matrix=spmatrix.matrixmultiply(self.matrix, L))
            elif shape == (N, ):
                y = numerix.empty((self.matrix.shape[0], ))
                self.matrix.matvec(other, y)
                return y
            else:
                raise TypeError
コード例 #2
0
    def jac(self, x, *args, **kwargs):
        """
        Evaluate sparse Jacobian of constraints at x.
        Returns a sparse matrix in format self.mformat
        (0 = compressed sparse row, 1 = linked list).

        The constraints appear in the following order:

        1. equalities
        2. lower bound only
        3. upper bound only
        4. range constraints.
        """
        store_zeros = kwargs.get('store_zeros', False)
        store_zeros = 1 if store_zeros else 0
        if len(args) > 0:
            if type(args[0]).__name__ == 'll_mat':
                J = self.model.eval_J(x, self.mformat, store_zeros, args[0])
            else:
                return None
        else:
            J = self.model.eval_J(x, self.mformat, store_zeros)
        self.Jeval += 1

        # Warning!! This next line doesn't work for the coordinate format option.
        if self.scale_con is not None:
            J = spmatrix.matrixmultiply(self.scale_con_diag, J)
        return J  #[self.permC,:]
コード例 #3
0
ファイル: amplpy.py プロジェクト: joeywen/nlpy
    def jac(self, x, *args, **kwargs):
        """
        Evaluate sparse Jacobian of constraints at x.
        Returns a sparse matrix in format self.mformat
        (0 = compressed sparse row, 1 = linked list).

        The constraints appear in the following order:

        1. equalities
        2. lower bound only
        3. upper bound only
        4. range constraints.
        """
        store_zeros = kwargs.get('store_zeros', False)
        store_zeros = 1 if store_zeros else 0
        if len(args) > 0:
            if type(args[0]).__name__ == 'll_mat':
                J = self.model.eval_J(x, self.mformat, store_zeros, args[0])
            else:
                return None
        else:
            J = self.model.eval_J(x, self.mformat, store_zeros)
        self.Jeval += 1

        # Warning!! This next line doesn't work for the coordinate format option.
        if self.scale_con is not None:
            J = spmatrix.matrixmultiply(self.scale_con_diag, J)
        return J #[self.permC,:]
コード例 #4
0
 def __mul__(self, other):
     if isinstance(other, _PysparseMeshMatrix):
         return _PysparseMeshMatrix(mesh=self.mesh,
                                    matrix=spmatrix.matrixmultiply(
                                        self.matrix, other.matrix))
     else:
         return _PysparseMatrixFromShape.__mul__(self, other)
コード例 #5
0
ファイル: pysparseMatrix.py プロジェクト: regmi/fipy
    def __mul__(self, other):
        """
        Multiply a sparse matrix by another sparse matrix
        
            >>> L1 = _PysparseMatrix(size = 3)
            >>> L1.put([3.,10.,numerix.pi,2.5], [0,0,1,2], [2,1,1,0])
            >>> L2 = _PysparseIdentityMatrix(size = 3)
            >>> L2.put([4.38,12357.2,1.1], [2,1,0], [1,0,2])
            
            >>> tmp = numerix.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)))

            >>> numerix.allclose((L1 * L2).getNumpyArray(), tmp)
            1

        or a sparse matrix by a vector

            >>> tmp = numerix.array((29., 6.28318531, 2.5))       
            >>> numerix.allclose(L1 * numerix.array((1,2,3),'d'), tmp)
            1
            
        or a vector by a sparse matrix

            >>> tmp = numerix.array((7.5, 16.28318531,  3.))  
            >>> numerix.allclose(numerix.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

            
        """
        N = self.matrix.shape[0]

        if isinstance(other, _PysparseMatrix):
            return _PysparseMatrix(matrix = spmatrix.matrixmultiply(self.matrix, other._getMatrix()))
        else:
            shape = numerix.shape(other)
            if shape == ():
                L = spmatrix.ll_mat(N, N, N)
                L.put(other * numerix.ones(N))
                return _PysparseMatrix(matrix = spmatrix.matrixmultiply(self.matrix, L))
            elif shape == (N,):
                y = other.copy()
                self.matrix.matvec(other, y)
                return y
            else:
                raise TypeError
コード例 #6
0
    def outDegreeSequence(self):
        """
        Return a vector of the (out)degree for each vertex.
        """
        A = self.nativeAdjacencyMatrix()
        j = spmatrix.ll_mat(self.vList.getNumVertices(), 1)
        j[:, 0] = 1 

        degrees = spmatrix.matrixmultiply(A, j)
        degrees = PysparseMatrix(matrix=degrees)
        degrees = numpy.array(degrees.getNumpyArray().ravel(), numpy.int)
        return degrees
コード例 #7
0
    def outDegreeSequence(self):
        """
        Return a vector of the (out)degree for each vertex.
        """
        A = self.nativeAdjacencyMatrix()
        j = spmatrix.ll_mat(self.vList.getNumVertices(), 1)
        j[:, 0] = 1

        degrees = spmatrix.matrixmultiply(A, j)
        degrees = PysparseMatrix(matrix=degrees)
        degrees = numpy.array(degrees.getNumpyArray().ravel(), numpy.int)
        return degrees
コード例 #8
0
ファイル: pysparseMatrix.py プロジェクト: regmi/pysparse
    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'
コード例 #9
0
ファイル: test_spmatrix.py プロジェクト: regmi/pysparse
      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)
コード例 #10
0
ファイル: folkRank.py プロジェクト: Awais22000/folkrank
    def normalizeA(self):
        '''
        Normalize matrix A on rows
        '''
        # compute sum of rows in sum vector
        v = np.ones(self.n)
        sum = np.zeros(self.n)
        self.A.matvec(v, sum)           # sum = A*v

        D = spmatrix.ll_mat(self.n, self.n)

        for i in range(self.n):
            if sum[i] != 0:
                D[i,i] = 1.0/sum[i]
            else:
                D[i,i] = 1

        self.A = spmatrix.matrixmultiply(self.A, D)
コード例 #11
0
    def A(self, *args, **kwargs):
        """
        Evaluate sparse Jacobian of the linear part of the
        constraints. Useful to obtain constraint matrix
        when problem is a linear programming problem.
        """
        store_zeros = kwargs.get('store_zeros', False)
        store_zeros = 1 if store_zeros else 0
        if len(args) == 1:
            if type(args[0]).__name__ == 'll_mat':
                Amat = self.model.eval_A(store_zeros, args[0])
            else:
                return None
        else:
            Amat = self.model.eval_A(store_zeros)

        if self.scale_con is not None:
            Amat = spmatrix.matrixmultiply(Amat, self.scale_con_diag)
        return Amat
コード例 #12
0
ファイル: amplpy.py プロジェクト: joeywen/nlpy
    def A(self, *args, **kwargs):
        """
        Evaluate sparse Jacobian of the linear part of the
        constraints. Useful to obtain constraint matrix
        when problem is a linear programming problem.
        """
        store_zeros = kwargs.get('store_zeros', False)
        store_zeros = 1 if store_zeros else 0
        if len(args) == 1:
            if type(args[0]).__name__ == 'll_mat':
                Amat = self.model.eval_A(store_zeros,args[0])
            else:
                return None
        else:
            Amat = self.model.eval_A(store_zeros)

        if self.scale_con is not None:
            Amat = spmatrix.matrixmultiply(Amat, self.scale_con_diag)
        return Amat
コード例 #13
0
ファイル: dst.py プロジェクト: pmadhyastha/distrib
def spmatrixmul(matrix_a, matrix_b):
    """
    Sparse Matrix Multiplication using pysparse matrix

    Objective:
    ----------
    To multiply two sparse matrices - relatively dense

    Reason:
    -------
    Scipy.sparse unfortunately has matrix indices with datatype
    int32. While pysparse is more robust and more efficient.

    Process:
    --------
    It saves the scipy matrices to disk in the standard matrix market
    format to the disk. Then reads it to a pysparse format and uses
    Pysparse's inbuilt matrixmultipy operation. The result is
    converted back to a scipy csr matrix.

    This function takes two scipy matrices as input.

    """
    sp_matrix_a = spmatrix.ll_mat(matrix_a.shape[0], matrix_a.shape[1])
    sp_matrix_b = spmatrix.ll_mat(matrix_b.shape[0], matrix_b.shape[1])
    # read it to form a pysparse spmatrix.
    sp_matrix_a.update_add_at(matrix_a.tocoo().data,
                              matrix_a.tocoo().row,
                              matrix_a.tocoo().col)
    sp_matrix_b.update_add_at(matrix_b.tocoo().data,
                              matrix_b.tocoo().row,
                              matrix_b.tocoo().col)
    # multiply the matrices.
    sp_result = spmatrix.matrixmultiply(sp_matrix_a, sp_matrix_b)
    #conversion to scipy sparse matrix
    data, row, col = sp_result.find()
    result = ss.csr_matrix((data, (row, col)), shape=sp_result.shape)
    #deleting files and refreshing memory
    del sp_result, sp_matrix_a, sp_matrix_b, matrix_a, matrix_b

    return result
コード例 #14
0
ファイル: dst.py プロジェクト: f00barin/distrib
def spmatrixmul(matrix_a, matrix_b):
    """
    Sparse Matrix Multiplication using pysparse matrix

    Objective:
    ----------
    To multiply two sparse matrices - relatively dense

    Reason:
    -------
    Scipy.sparse unfortunately has matrix indices with datatype
    int32. While pysparse is more robust and more efficient.

    Process:
    --------
    It saves the scipy matrices to disk in the standard matrix market
    format to the disk. Then reads it to a pysparse format and uses
    Pysparse's inbuilt matrixmultipy operation. The result is
    converted back to a scipy csr matrix.

    This function takes two scipy matrices as input.

    """
    sp_matrix_a = spmatrix.ll_mat(matrix_a.shape[0], matrix_a.shape[1])
    sp_matrix_b = spmatrix.ll_mat(matrix_b.shape[0], matrix_b.shape[1])
    # read it to form a pysparse spmatrix.
    sp_matrix_a.update_add_at(matrix_a.tocoo().data, matrix_a.tocoo().row,
            matrix_a.tocoo().col)
    sp_matrix_b.update_add_at(matrix_b.tocoo().data, matrix_b.tocoo().row,
            matrix_b.tocoo().col)
    # multiply the matrices.
    sp_result = spmatrix.matrixmultiply(sp_matrix_a, sp_matrix_b)
    #conversion to scipy sparse matrix
    data, row, col = sp_result.find()
    result = ss.csr_matrix((data, (row, col)), shape=sp_result.shape)
    #deleting files and refreshing memory
    del sp_result, sp_matrix_a, sp_matrix_b, matrix_a, matrix_b

    return result
コード例 #15
0
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()
コード例 #16
0
import time
from pysparse 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]
コード例 #17
0
ファイル: pysparseMatrix.py プロジェクト: usnistgov/fipy
 def __mul__(self, other):
     if isinstance(other, _PysparseMeshMatrix):
         return _PysparseMeshMatrix(mesh=self.mesh,
                                    matrix=spmatrix.matrixmultiply(self.matrix, other.matrix))
     else:
         return _PysparseMatrixFromShape.__mul__(self, other)
コード例 #18
0
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()
コード例 #19
0
import time
from pysparse 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]