Python linalg.get_blas_funcs方法代码示例

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def _matvec(v, alpha, cs, ds):
        axpy, scal, dotc = get_blas_funcs(['axpy', 'scal', 'dotc'],
                                          cs[:1] + [v])
        w = alpha * v
        for c, d in zip(cs, ds):
            a = dotc(d, v)
            w = axpy(c, w, w.size, a)
        return w 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def _solve(v, alpha, cs, ds):
        """Evaluate w = M^-1 v"""
        if len(cs) == 0:
            return v/alpha

        # (B + C D^H)^-1 = B^-1 - B^-1 C (I + D^H B^-1 C)^-1 D^H B^-1

        axpy, dotc = get_blas_funcs(['axpy', 'dotc'], cs[:1] + [v])

        c0 = cs[0]
        A = alpha * np.identity(len(cs), dtype=c0.dtype)
        for i, d in enumerate(ds):
            for j, c in enumerate(cs):
                A[i,j] += dotc(d, c)

        q = np.zeros(len(cs), dtype=c0.dtype)
        for j, d in enumerate(ds):
            q[j] = dotc(d, v)
        q /= alpha
        q = solve(A, q)

        w = v/alpha
        for c, qc in zip(cs, q):
            w = axpy(c, w, w.size, -qc)

        return w 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def norm2(q):
    q = np.asarray(q)
    nrm2 = get_blas_funcs('nrm2', dtype=q.dtype)
    return nrm2(q) 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def test_get_blas_funcs():
    # check that it returns Fortran code for arrays that are
    # fortran-ordered
    f1, f2, f3 = get_blas_funcs(
        ('axpy', 'axpy', 'axpy'),
        (np.empty((2,2), dtype=np.complex64, order='F'),
         np.empty((2,2), dtype=np.complex128, order='C'))
        )

    # get_blas_funcs will choose libraries depending on most generic
    # array
    assert_equal(f1.typecode, 'z')
    assert_equal(f2.typecode, 'z')
    if cblas is not None:
        assert_equal(f1.module_name, 'cblas')
        assert_equal(f2.module_name, 'cblas')

    # check defaults.
    f1 = get_blas_funcs('rotg')
    assert_equal(f1.typecode, 'd')

    # check also dtype interface
    f1 = get_blas_funcs('gemm', dtype=np.complex64)
    assert_equal(f1.typecode, 'c')
    f1 = get_blas_funcs('gemm', dtype='F')
    assert_equal(f1.typecode, 'c')

    # extended precision complex
    f1 = get_blas_funcs('gemm', dtype=np.longcomplex)
    assert_equal(f1.typecode, 'z')

    # check safe complex upcasting
    f1 = get_blas_funcs('axpy',
                        (np.empty((2,2), dtype=np.float64),
                         np.empty((2,2), dtype=np.complex64))
                        )
    assert_equal(f1.typecode, 'z') 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def test_get_blas_funcs_alias():
    # check alias for get_blas_funcs
    f, g = get_blas_funcs(('nrm2', 'dot'), dtype=np.complex64)
    assert f.typecode == 'c'
    assert g.typecode == 'c'

    f, g, h = get_blas_funcs(('dot', 'dotc', 'dotu'), dtype=np.float64)
    assert f is g
    assert f is h 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def norm(x):
    nrm2 = get_blas_funcs(['nrm2'], [x])[0]
    return nrm2(x) 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def norm(x):
    """Compute the Euclidean or Frobenius norm of x.

    Returns the Euclidean norm when x is a vector, the Frobenius norm when x
    is a matrix (2-d array). More precise than sqrt(squared_norm(x)).
    """
    x = np.asarray(x)

    if np.any(np.iscomplex(x)):
        return np.sqrt(squared_norm(x))
    else:
        nrm2, = linalg.get_blas_funcs(['nrm2'], [x])
        return nrm2(x) 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def test_inplace_swap_row():
    X = np.array([[0, 3, 0],
                  [2, 4, 0],
                  [0, 0, 0],
                  [9, 8, 7],
                  [4, 0, 5]], dtype=np.float64)
    X_csr = sp.csr_matrix(X)
    X_csc = sp.csc_matrix(X)

    swap = linalg.get_blas_funcs(('swap',), (X,))
    swap = swap[0]
    X[0], X[-1] = swap(X[0], X[-1])
    inplace_swap_row(X_csr, 0, -1)
    inplace_swap_row(X_csc, 0, -1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())

    X[2], X[3] = swap(X[2], X[3])
    inplace_swap_row(X_csr, 2, 3)
    inplace_swap_row(X_csc, 2, 3)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    assert_raises(TypeError, inplace_swap_row, X_csr.tolil())

    X = np.array([[0, 3, 0],
                  [2, 4, 0],
                  [0, 0, 0],
                  [9, 8, 7],
                  [4, 0, 5]], dtype=np.float32)
    X_csr = sp.csr_matrix(X)
    X_csc = sp.csc_matrix(X)
    swap = linalg.get_blas_funcs(('swap',), (X,))
    swap = swap[0]
    X[0], X[-1] = swap(X[0], X[-1])
    inplace_swap_row(X_csr, 0, -1)
    inplace_swap_row(X_csc, 0, -1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    X[2], X[3] = swap(X[2], X[3])
    inplace_swap_row(X_csr, 2, 3)
    inplace_swap_row(X_csc, 2, 3)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    assert_raises(TypeError, inplace_swap_row, X_csr.tolil()) 

# 需要导入模块: from scipy import linalg [as 别名]
# 或者: from scipy.linalg import get_blas_funcs [as 别名]
def test_inplace_swap_column():
    X = np.array([[0, 3, 0],
                  [2, 4, 0],
                  [0, 0, 0],
                  [9, 8, 7],
                  [4, 0, 5]], dtype=np.float64)
    X_csr = sp.csr_matrix(X)
    X_csc = sp.csc_matrix(X)

    swap = linalg.get_blas_funcs(('swap',), (X,))
    swap = swap[0]
    X[:, 0], X[:, -1] = swap(X[:, 0], X[:, -1])
    inplace_swap_column(X_csr, 0, -1)
    inplace_swap_column(X_csc, 0, -1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())

    X[:, 0], X[:, 1] = swap(X[:, 0], X[:, 1])
    inplace_swap_column(X_csr, 0, 1)
    inplace_swap_column(X_csc, 0, 1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    assert_raises(TypeError, inplace_swap_column, X_csr.tolil())

    X = np.array([[0, 3, 0],
                  [2, 4, 0],
                  [0, 0, 0],
                  [9, 8, 7],
                  [4, 0, 5]], dtype=np.float32)
    X_csr = sp.csr_matrix(X)
    X_csc = sp.csc_matrix(X)
    swap = linalg.get_blas_funcs(('swap',), (X,))
    swap = swap[0]
    X[:, 0], X[:, -1] = swap(X[:, 0], X[:, -1])
    inplace_swap_column(X_csr, 0, -1)
    inplace_swap_column(X_csc, 0, -1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    X[:, 0], X[:, 1] = swap(X[:, 0], X[:, 1])
    inplace_swap_column(X_csr, 0, 1)
    inplace_swap_column(X_csc, 0, 1)
    assert_array_equal(X_csr.toarray(), X_csc.toarray())
    assert_array_equal(X, X_csc.toarray())
    assert_array_equal(X, X_csr.toarray())
    assert_raises(TypeError, inplace_swap_column, X_csr.tolil())