Python csr_scale_columns Examples

Example #1

File: mcl.py Project: atiasnir/mypy

def _csr_normalize_columns(m):
    factor = m.sum(0)
    nnzeros = np.where(factor > 0)
    factor[nnzeros] = 1.0 / factor[nnzeros]
    factor = np.array(factor)[0]
    spt.csr_scale_columns(m.shape[0], m.shape[1], m.indptr, m.indices, m.data, factor)

    return m

Example #2

File: mcl.py Project: coheneyl/mypy

def _csr_normalize_columns(m):
    factor = m.sum(0)
    nnzeros = np.where(factor > 0)
    factor[nnzeros] = 1.0 / factor[nnzeros]
    factor = np.array(factor)[0]
    spt.csr_scale_columns(m.shape[0], m.shape[1], m.indptr, m.indices, m.data,
                          factor)

    return m

Example #3

File: main.py Project: abhi-shek/Comment-Classification

def normalize_sets_sparse (train, test):
    norm = train.copy()
    norm.data **= 2 # Square every value
    norm = norm.sum(axis=0) # Sum every column
    n_nonzeros = np.where(norm > 0)
    norm[n_nonzeros] = 1.0 / np.sqrt(norm[n_nonzeros])
    norm = np.array(norm).T[0]
    
    sparsetools.csr_scale_columns(train.shape[0], train.shape[1], train.indptr, train.indices, train.data, norm)
    sparsetools.csr_scale_columns(test.shape[0], test.shape[1], test.indptr, test.indices, test.data, norm)
    
    return train, test

Example #4

def normalize_sets_sparse(train, test):
    norm = train.copy()
    norm.data **= 2  # Square every value
    norm = norm.sum(axis=0)  # Sum every column
    n_nonzeros = np.where(norm > 0)
    norm[n_nonzeros] = 1.0 / np.sqrt(norm[n_nonzeros])
    norm = np.array(norm).T[0]

    sparsetools.csr_scale_columns(train.shape[0], train.shape[1], train.indptr,
                                  train.indices, train.data, norm)
    sparsetools.csr_scale_columns(test.shape[0], test.shape[1], test.indptr,
                                  test.indices, test.data, norm)

    return train, test

Example #5

File: test_spfuncs.py Project: sprevrha/scipy-refactor

    def test_scale_rows_and_cols(self):
        D = matrix([[1,0,0,2,3],
                    [0,4,0,5,0],
                    [0,0,6,7,0]])


        #TODO expose through function
        S = csr_matrix(D)
        v = array([1,2,3])
        csr_scale_rows(3,5,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), diag(v)*D )

        S = csr_matrix(D)
        v = array([1,2,3,4,5])
        csr_scale_columns(3,5,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), D*diag(v) )

        # blocks
        E = kron(D,[[1,2],[3,4]])
        S = bsr_matrix(E,blocksize=(2,2))
        v = array([1,2,3,4,5,6])
        bsr_scale_rows(3,5,2,2,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), diag(v)*E )

        S = bsr_matrix(E,blocksize=(2,2))
        v = array([1,2,3,4,5,6,7,8,9,10])
        bsr_scale_columns(3,5,2,2,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), E*diag(v) )

        E = kron(D,[[1,2,3],[4,5,6]])
        S = bsr_matrix(E,blocksize=(2,3))
        v = array([1,2,3,4,5,6])
        bsr_scale_rows(3,5,2,3,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), diag(v)*E )

        S = bsr_matrix(E,blocksize=(2,3))
        v = array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])
        bsr_scale_columns(3,5,2,3,S.indptr,S.indices,S.data,v)
        assert_equal(S.todense(), E*diag(v) )

Example #6

File: lib_deconvolve_em.py Project: yztxwd/cplate

def csr_scale_columns(A, x):
    sparsetools.csr_scale_columns(A.shape[0], A.shape[1], A.indptr, A.indices,
                                  A.data, x)

Example #7

File: mcl.py Project: atiasnir/mypy

def _csr_scale_columns(m, scale):
    spt.csr_scale_columns(m.shape[0], m.shape[1], m.indptr, m.indices, m.data, scale)

Example #8

File: mcl.py Project: coheneyl/mypy

def _csr_scale_columns(m, scale):
    spt.csr_scale_columns(m.shape[0], m.shape[1], m.indptr, m.indices, m.data,
                          scale)

Example #9

File: lib_deconvolve_em.py Project: airoldilab/cplate

def csr_scale_columns(A, x):
    sparsetools.csr_scale_columns(A.shape[0], A.shape[1],
                                  A.indptr, A.indices, A.data,
                                  x)