Python scipy.linalg.get_blas_funcs() Examples

The following are 17 code examples of scipy.linalg.get_blas_funcs(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy.linalg , or try the search function .
Example #1
Source File: nonlin.py    From lambda-packs with MIT License 5 votes vote down vote up 
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
Example #2
Source File: lgmres.py    From Computable with MIT License 5 votes vote down vote up 
def norm2(q):
    q = np.asarray(q)
    nrm2 = get_blas_funcs('nrm2', dtype=q.dtype)
    return nrm2(q)
Example #3
Source File: nonlin.py    From Computable with MIT License 5 votes vote down vote up 
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
Example #4
Source File: nonlin.py    From Computable with MIT License 5 votes vote down vote up 
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
Example #5
Source File: test_blas.py    From Computable with MIT License 5 votes vote down vote up 
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')
Example #6
Source File: test_blas.py    From Computable with MIT License 5 votes vote down vote up 
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
Example #7
Source File: nonlin.py    From lambda-packs with MIT License 5 votes vote down vote up 
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
Example #8
Source File: nonlin.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
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
Example #9
Source File: nonlin.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
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
Example #10
Source File: math.py    From Lyssandra with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def norm(x):
    nrm2 = get_blas_funcs(['nrm2'], [x])[0]
    return nrm2(x)
Example #11
Source File: nonlin.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
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
Example #12
Source File: nonlin.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
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
Example #13
Source File: maths.py    From pactools with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
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)
Example #14
Source File: test_sparsefuncs.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
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())
Example #15
Source File: test_sparsefuncs.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
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())
Example #16
Source File: test_sparsefuncs.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
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())
Example #17
Source File: test_sparsefuncs.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
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())