Python linalg.eigh方法代碼示例

本文整理匯總了Python中numpy.linalg.eigh方法的典型用法代碼示例。如果您正苦於以下問題：Python linalg.eigh方法的具體用法？Python linalg.eigh怎麽用？Python linalg.eigh使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類numpy.linalg的用法示例。

在下文中一共展示了linalg.eigh方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: do

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def do(self, a, b, tags):
        # note that eigenvalue arrays returned by eig must be sorted since
        # their order isn't guaranteed.
        ev, evc = linalg.eigh(a)
        evalues, evectors = linalg.eig(a)
        evalues.sort(axis=-1)
        assert_almost_equal(ev, evalues)

        assert_allclose(dot_generalized(a, evc),
                        np.asarray(ev)[..., None, :] * np.asarray(evc),
                        rtol=get_rtol(ev.dtype))

        ev2, evc2 = linalg.eigh(a, 'U')
        assert_almost_equal(ev2, evalues)

        assert_allclose(dot_generalized(a, evc2),
                        np.asarray(ev2)[..., None, :] * np.asarray(evc2),
                        rtol=get_rtol(ev.dtype), err_msg=repr(a))

開發者ID:Frank-qlu，項目名稱:recruit，代碼行數:20，代碼來源:test_linalg.py

示例2: test_UPLO

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def test_UPLO(self):
        Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
        Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
        tgt = np.array([-1, 1], dtype=np.double)
        rtol = get_rtol(np.double)

        # Check default is 'L'
        w, v = np.linalg.eigh(Klo)
        assert_allclose(w, tgt, rtol=rtol)
        # Check 'L'
        w, v = np.linalg.eigh(Klo, UPLO='L')
        assert_allclose(w, tgt, rtol=rtol)
        # Check 'l'
        w, v = np.linalg.eigh(Klo, UPLO='l')
        assert_allclose(w, tgt, rtol=rtol)
        # Check 'U'
        w, v = np.linalg.eigh(Kup, UPLO='U')
        assert_allclose(w, tgt, rtol=rtol)
        # Check 'u'
        w, v = np.linalg.eigh(Kup, UPLO='u')
        assert_allclose(w, tgt, rtol=rtol)

開發者ID:Frank-qlu，項目名稱:recruit，代碼行數:23，代碼來源:test_linalg.py

示例3: test_0_size

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.float32)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

開發者ID:Frank-qlu，項目名稱:recruit，代碼行數:23，代碼來源:test_linalg.py

示例4: do

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def do(self, a, b):
        # note that eigenvalue arrays returned by eig must be sorted since
        # their order isn't guaranteed.
        ev, evc = linalg.eigh(a)
        evalues, evectors = linalg.eig(a)
        evalues.sort(axis=-1)
        assert_almost_equal(ev, evalues)

        assert_allclose(dot_generalized(a, evc),
                        np.asarray(ev)[..., None, :] * np.asarray(evc),
                        rtol=get_rtol(ev.dtype))

        ev2, evc2 = linalg.eigh(a, 'U')
        assert_almost_equal(ev2, evalues)

        assert_allclose(dot_generalized(a, evc2),
                        np.asarray(ev2)[..., None, :] * np.asarray(evc2),
                        rtol=get_rtol(ev.dtype), err_msg=repr(a))

開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:20，代碼來源:test_linalg.py

示例5: do

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def do(self, a, b):
        # note that eigenvalue arrays must be sorted since
        # their order isn't guaranteed.
        ev, evc = linalg.eigh(a)
        evalues, evectors = linalg.eig(a)
        ev.sort(axis=-1)
        evalues.sort(axis=-1)
        assert_almost_equal(ev, evalues)

        assert_allclose(dot_generalized(a, evc),
                        np.asarray(ev)[...,None,:] * np.asarray(evc),
                        rtol=get_rtol(ev.dtype))

        ev2, evc2 = linalg.eigh(a, 'U')
        ev2.sort(axis=-1)
        assert_almost_equal(ev2, evalues)

        assert_allclose(dot_generalized(a, evc2),
                        np.asarray(ev2)[...,None,:] * np.asarray(evc2),
                        rtol=get_rtol(ev.dtype), err_msg=repr(a))

開發者ID:ktraunmueller，項目名稱:Computable，代碼行數:22，代碼來源:test_linalg.py

示例6: test_UPLO

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def test_UPLO(self):
        Klo = np.array([[0, 0],[1, 0]], dtype=np.double)
        Kup = np.array([[0, 1],[0, 0]], dtype=np.double)
        tgt = np.array([-1, 1], dtype=np.double)
        rtol = get_rtol(np.double)

        # Check default is 'L'
        w, v = np.linalg.eigh(Klo)
        assert_allclose(np.sort(w), tgt, rtol=rtol)
        # Check 'L'
        w, v = np.linalg.eigh(Klo, UPLO='L')
        assert_allclose(np.sort(w), tgt, rtol=rtol)
        # Check 'l'
        w, v = np.linalg.eigh(Klo, UPLO='l')
        assert_allclose(np.sort(w), tgt, rtol=rtol)
        # Check 'U'
        w, v = np.linalg.eigh(Kup, UPLO='U')
        assert_allclose(np.sort(w), tgt, rtol=rtol)
        # Check 'u'
        w, v = np.linalg.eigh(Kup, UPLO='u')
        assert_allclose(np.sort(w), tgt, rtol=rtol)

開發者ID:ktraunmueller，項目名稱:Computable，代碼行數:23，代碼來源:test_linalg.py

示例7: spectral_decomposition

# 需要導入模塊: from numpy import linalg [as 別名]
# 或者: from numpy.linalg import eigh [as 別名]
def spectral_decomposition(A):
    r"""Spectral decomposition of a Hermitian matrix.

    Args:
        A (array): Hermitian matrix

    Returns:
        (vector[float], list[array[complex]]): (a, P): eigenvalues and hermitian projectors
            such that :math:`A = \sum_k a_k P_k`.
    """
    d, v = eigh(A)
    P = []
    for k in range(d.shape[0]):
        temp = v[:, k]
        P.append(np.outer(temp, temp.conj()))
    return d, P


# ========================================================
#  fixed gates/observables
# ========================================================

開發者ID:XanaduAI，項目名稱:pennylane，代碼行數:23，代碼來源:numpy_ops.py

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