python类eig()的实例源码

test_regression.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
test_regression.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
gauss.py 文件源码 项目:astrology 作者: mattsgithub 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def get_dimensions(self):
        # Calculate within class scatter
        Sw = np.sum(self._scatter_matrix_by_class.values(),
                    axis=0)

        # Calculate between class scatter
        s = (self._p, self._p)
        Sb = np.zeros(s)
        for k in self._classes:
            a = self._mean_vector_by_class[k] - self._global_mean_vector
            Sb += self._N_by_class[k] * a.dot(a.T)

        # Compute eigenvectors
        Sw_inv = inv(Sw)
        A = Sw_inv.dot(Sb)
        eigen_values, eigen_vectors = eig(A)
        idx = np.argsort(eigen_values)
        eigen_vectors = eigen_vectors[idx][::-1]
        return eigen_vectors
PolyMesh.py 文件源码 项目:laplacian-meshes 作者: bmershon 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def getPrincipalAxes(self):
        X = self.VPos - self.getCentroid()
        XTX = (X.T).dot(X)
        (lambdas, axes) = linalg.eig(XTX)
        #Put the eigenvalues in decreasing order
        idx = lambdas.argsort()[::-1]
        lambdas = lambdas[idx]
        axes = axes[:, idx]
        T = X.dot(axes)
        maxProj = T.max(0)
        minProj = T.min(0)
        axes = axes.T #Put each axis on each row to be consistent with everything else
        return (axes, maxProj, minProj)        

    #Delete the parts of the mesh below "plane".  If fillHoles
    #is true, plug up the holes that result from the cut
test_regression.py 文件源码 项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda 作者: SignalMedia 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
test_regression.py 文件源码 项目:aws-lambda-numpy 作者: vitolimandibhrata 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
sst.py 文件源码 项目:anompy 作者: takuti 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def __compute_lanczos(self, H, G):
        """Compute change-point score using the Lanczos method.

        """
        # assuming m = w
        self.q, _, _ = power1(G, self.q, n_iter=1)

        k = 2 * self.r if self.r % 2 == 0 else 2 * self.r - 1
        T = lanczos(np.dot(H, H.T), self.q, k)

        # find eigenvectors and eigenvalues of T
        # eigvals, eigvecs = ln.eig(T)
        eigvals, eigvecs = tridiag_eig(T, n_iter=1)

        # `eig()` returns unordered eigenvalues,
        # so the top-r eigenvectors should be picked carefully
        return 1 - np.sqrt(np.sum(eigvecs[0, np.argsort(eigvals)[::-1][:self.r]] ** 2))
test_regression.py 文件源码 项目:lambda-numba 作者: rlhotovy 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
PolyMesh.py 文件源码 项目:procrustes 作者: bmershon 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def getPrincipalAxes(self):
        X = self.VPos - self.getCentroid()
        XTX = (X.T).dot(X)
        (lambdas, axes) = linalg.eig(XTX)
        #Put the eigenvalues in decreasing order
        idx = lambdas.argsort()[::-1]
        lambdas = lambdas[idx]
        axes = axes[:, idx]
        T = X.dot(axes)
        maxProj = T.max(0)
        minProj = T.min(0)
        axes = axes.T #Put each axis on each row to be consistent with everything else
        return (axes, maxProj, minProj)        

    #Delete the parts of the mesh below "plane".  If fillHoles
    #is true, plug up the holes that result from the cut
test_regression.py 文件源码 项目:deliver 作者: orchestor 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
sst.py 文件源码 项目:datadog-anomaly-detector 作者: takuti 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def __compute_lanczos(self, H, G):
        """Compute change-point score using the Lanczos method.

        """
        # assuming m = w
        self.q, _, _ = power1(G, self.q, n_iter=1)

        k = 2 * self.r if self.r % 2 == 0 else 2 * self.r - 1
        T = lanczos(np.dot(H, H.T), self.q, k)

        # find eigenvectors and eigenvalues of T
        # eigvals, eigvecs = ln.eig(T)
        eigvals, eigvecs = tridiag_eig(T, n_iter=1)

        # `eig()` returns unordered eigenvalues,
        # so the top-r eigenvectors should be picked carefully
        return 1 - np.sqrt(np.sum(eigvecs[0, np.argsort(eigvals)[::-1][:self.r]] ** 2))
test_regression.py 文件源码 项目:Alfred 作者: jkachhadia 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def test_eig_build(self, level=rlevel):
        # Ticket #652
        rva = array([1.03221168e+02 + 0.j,
                     -1.91843603e+01 + 0.j,
                     -6.04004526e-01 + 15.84422474j,
                     -6.04004526e-01 - 15.84422474j,
                     -1.13692929e+01 + 0.j,
                     -6.57612485e-01 + 10.41755503j,
                     -6.57612485e-01 - 10.41755503j,
                     1.82126812e+01 + 0.j,
                     1.06011014e+01 + 0.j,
                     7.80732773e+00 + 0.j,
                     -7.65390898e-01 + 0.j,
                     1.51971555e-15 + 0.j,
                     -1.51308713e-15 + 0.j])
        a = arange(13 * 13, dtype=float64)
        a.shape = (13, 13)
        a = a % 17
        va, ve = linalg.eig(a)
        va.sort()
        rva.sort()
        assert_array_almost_equal(va, rva)
qcqp.py 文件源码 项目:qcqp 作者: cvxgrp 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
def solve_spectral(prob, *args, **kwargs):
    """Solve the spectral relaxation with lambda = 1.
    """

    # TODO: do this efficiently without SDP lifting

    # lifted variables and semidefinite constraint
    X = cvx.Semidef(prob.n + 1)

    W = prob.f0.homogeneous_form()
    rel_obj = cvx.Minimize(cvx.sum_entries(cvx.mul_elemwise(W, X)))

    W1 = sum([f.homogeneous_form() for f in prob.fs if f.relop == '<='])
    W2 = sum([f.homogeneous_form() for f in prob.fs if f.relop == '=='])

    rel_prob = cvx.Problem(
        rel_obj,
        [
            cvx.sum_entries(cvx.mul_elemwise(W1, X)) <= 0,
            cvx.sum_entries(cvx.mul_elemwise(W2, X)) == 0,
            X[-1, -1] == 1
        ]
    )
    rel_prob.solve(*args, **kwargs)

    if rel_prob.status not in [cvx.OPTIMAL, cvx.OPTIMAL_INACCURATE]:
        raise Exception("Relaxation problem status: %s" % rel_prob.status)

    (w, v) = LA.eig(X.value)
    return np.sqrt(np.max(w))*np.asarray(v[:-1, np.argmax(w)]).flatten(), rel_prob.value
ellipse.py 文件源码 项目:pyrsss 作者: butala 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
def fit_ellipse(x,y):
    x = x[:,NP.newaxis]
    y = y[:,NP.newaxis]
    D =  NP.hstack((x*x, x*y, y*y, x, y, NP.ones_like(x)))
    S = NP.dot(D.T,D)
    C = NP.zeros([6,6])
    C[0,2] = C[2,0] = 2; C[1,1] = -1
    E, V =  eig(NP.dot(inv(S), C))
    n = NP.argmax(NP.abs(E))
    a = V[:,n]
    return a
bbox_regressor.py 文件源码 项目:PyMDNet 作者: HungWei-Andy 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def train_bbox_regressor(X, bbox, gt):
  config = Data()
  config.min_overlap = 0.6
  config.delta = 1000
  config.method = 'ridge_reg_chol'

  # get training groundtruth
  Y, O = get_examples(bbox, gt)
  X = X[O>config.min_overlap]
  Y = Y[O>config.min_overlap]

  # add bias
  X = np.c_[X, np.ones([X.shape[0], 1])]

  # center and decorrelate targets
  mu = np.mean(Y, axis=0).reshape(1, -1)
  Y = Y - mu
  S = dot(Y.T, Y) / Y.shape[0]
  D, V = eig(S)
  T = dot(dot(V, diag(1.0/sqrt(D+0.001))), V.T)
  T_inv = dot(dot(V, diag(sqrt(D+0.001))), V.T)
  Y = dot(Y, T)

  model = Data()
  model.mu = mu
  model.T = T
  model.T_inv = T_inv
  model.Beta = np.c_[solve(X, Y[:, 0], config.delta, config.method),
                     solve(X, Y[:, 1], config.delta, config.method),
                     solve(X, Y[:, 2], config.delta, config.method),
                     solve(X, Y[:, 3], config.delta, config.method)]

  # pack 
  bbox_reg = Data()
  bbox_reg.model = model
  bbox_reg.config = config
  return bbox_reg
test_linalg.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def do(self, a, b):
        ev = linalg.eigvals(a)
        evalues, evectors = linalg.eig(a)
        assert_almost_equal(ev, evalues)
test_linalg.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def do(self, a, b):
        evalues, evectors = linalg.eig(a)
        assert_allclose(dot_generalized(a, evectors),
                        np.asarray(evectors) * np.asarray(evalues)[..., None, :],
                        rtol=get_rtol(evalues.dtype))
        assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix)))
test_linalg.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, dtype)
            assert_equal(v.dtype, dtype)

            x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, get_complex_dtype(dtype))
            assert_equal(v.dtype, get_complex_dtype(dtype))

        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype
test_linalg.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def do(self, a, b):
        # note that eigenvalue arrays returned by eig must be sorted since
        # their order isn't guaranteed.
        ev = linalg.eigvalsh(a, 'L')
        evalues, evectors = linalg.eig(a)
        evalues.sort(axis=-1)
        assert_allclose(ev, evalues, rtol=get_rtol(ev.dtype))

        ev2 = linalg.eigvalsh(a, 'U')
        assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype))
foundation.py 文件源码 项目:Virtual-Makeup 作者: badarsh2 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def fitEllipse(x,y):
    x = x[:,np.newaxis]
    y = y[:,np.newaxis]
    D =  np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))
    S = np.dot(D.T,D)
    C = np.zeros([6,6])
    C[0,2] = C[2,0] = 2; C[1,1] = -1
    E, V =  eig(np.dot(inv(S), C))
    n = np.argmax(np.abs(E))
    a = V[:,n]
    return a
ergodic.py 文件源码 项目:nelpy 作者: nelpy 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def steady_state(P):
    """
    Calculates the steady state probability vector for a regular Markov
    transition matrix P
    Parameters
    ----------
    P        : matrix (kxk)
               an ergodic Markov transition probability matrix
    Returns
    -------
    implicit : matrix (kx1)
               steady state distribution
    Examples
    --------
    Taken from Kemeny and Snell. [1]_ Land of Oz example where the states are
    Rain, Nice and Snow - so there is 25 percent chance that if it
    rained in Oz today, it will snow tomorrow, while if it snowed today in
    Oz there is a 50 percent chance of snow again tomorrow and a 25
    percent chance of a nice day (nice, like when the witch with the monkeys
    is melting).
    >>> import numpy as np
    >>> p=np.matrix([[.5, .25, .25],[.5,0,.5],[.25,.25,.5]])
    >>> steady_state(p)
    matrix([[ 0.4],
            [ 0.2],
            [ 0.4]])
    Thus, the long run distribution for Oz is to have 40 percent of the
    days classified as Rain, 20 percent as Nice, and 40 percent as Snow
    (states are mutually exclusive).
    """

    v,d=la.eig(np.transpose(P))

    # for a regular P maximum eigenvalue will be 1
    mv=max(v)
    # find its position
    i=v.tolist().index(mv)

    # normalize eigenvector corresponding to the eigenvalue 1
    return d[:,i]/sum(d[:,i])
test_linalg.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 19 收藏 0 点赞 0 评论 0
def do(self, a, b):
        ev = linalg.eigvals(a)
        evalues, evectors = linalg.eig(a)
        assert_almost_equal(ev, evalues)
test_linalg.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def do(self, a, b):
        evalues, evectors = linalg.eig(a)
        assert_allclose(dot_generalized(a, evectors),
                        np.asarray(evectors) * np.asarray(evalues)[..., None, :],
                        rtol=get_rtol(evalues.dtype))
        assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix)))
test_linalg.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def test_types(self):
        def check(dtype):
            x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, dtype)
            assert_equal(v.dtype, dtype)

            x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
            w, v = np.linalg.eig(x)
            assert_equal(w.dtype, get_complex_dtype(dtype))
            assert_equal(v.dtype, get_complex_dtype(dtype))

        for dtype in [single, double, csingle, cdouble]:
            yield check, dtype
test_linalg.py 文件源码 项目:krpcScripts 作者: jwvanderbeck 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def do(self, a, b):
        # note that eigenvalue arrays returned by eig must be sorted since
        # their order isn't guaranteed.
        ev = linalg.eigvalsh(a, 'L')
        evalues, evectors = linalg.eig(a)
        evalues.sort(axis=-1)
        assert_allclose(ev, evalues, rtol=get_rtol(ev.dtype))

        ev2 = linalg.eigvalsh(a, 'U')
        assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype))
models_reservoirs.py 文件源码 项目:smp_base 作者: x75 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def normalize_spectral_radius(M, g):
    """reservoirs.normalize_spectral_radius

    Normalize the spectral radius of a given matrix M to scale to g
    """
    # logger.debug('normalize_spectral_radius: M = %s, g = %s', M, g)
    # eig_success = False
    # eig_cnt = 0
    # compute eigenvalues
    # while not eig_success and eig_cnt < 100:
    try:
        [w,v] = LA.eig(M)
        eig_success = True
    except LA.linalg.LinAlgError as e:
        # logger.error('normalize_spectral_radius LA.eig(M) failed for N = %d with e = %s', M.shape[0], e)
        # eig_cnt += 1
        pass
    # get maximum absolute eigenvalue
    lae = np.max(np.abs(w))
    # if lae < 1e-3:
    #     lae = 1
    assert lae > 1e-3, "Largest eigenvalue is close to zero with lae = %f" % (lae, )
    # normalize matrix by max ev
    M /= lae
    # scale normalized matrix to desired spectral radius
    M *= g
    # logger.debug('normalize_spectral_radius: M = %s, g = %s, lae = %s', M, g, lae)
    # check for scaling
    [w,v] = LA.eig(M)
    lae = np.max(np.abs(w))
    # print "normalize_spectral_radius: lae post/desired = %f / %f" % (lae, g)
    assert np.abs(g - lae) < 0.1

################################################################################
# input matrix creation
gauss.py 文件源码 项目:spmpython 作者: mikaem 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def gauss(N):
    beta = 0.5/sqrt(1.-(2.*arange(1,N))**(-2))
    T = diag(beta, 1) + diag(beta, -1)
    x, V = eig(T)
    i = argsort(x)
    x = x[i]
    w = 2*V[0,i]**2
    return x, w
Modularity.py 文件源码 项目:Networks 作者: dencesun 项目源码 文件源码 阅读 28 收藏 0 点赞 0 评论 0
def modularity(graph):
    # convert to numpy adjacency matrix
    A = nx.to_numpy_matrix(graph)

    # compute adjacency matrix A's degree centrality
    degree_centrality = np.sum(A, axis=0, dtype=int)
    m = np.sum(degree_centrality, dtype=int) / 2

    # compute matrix B
    B = np.zeros(A.shape, dtype=float)

    for i in range(len(A)):
        for j in range(len(A)):
            B[i, j] = A[i, j] - (degree_centrality[0, i] * degree_centrality[0, j]) / float(2 * m)

    # compute A's eigenvector
    w, v = LA.eig(B)
    wmax = np.argmax(w)
    s = np.zeros((len(A), 1), dtype=float)

    for i in range(len(A)):
        if v[i, wmax] < 0:
            s[i, 0] = -1
        else:
            s[i, 0] = 1

    Q = s.T.dot(B.dot(s)) / float(4 * m)
    return Q[0, 0], s
test_linalg.py 文件源码 项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda 作者: SignalMedia 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def do(self, a, b):
        ev = linalg.eigvals(a)
        evalues, evectors = linalg.eig(a)
        assert_almost_equal(ev, evalues)
test_linalg.py 文件源码 项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda 作者: SignalMedia 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
def do(self, a, b):
        evalues, evectors = linalg.eig(a)
        assert_allclose(dot_generalized(a, evectors),
                        np.asarray(evectors) * np.asarray(evalues)[..., None, :],
                        rtol=get_rtol(evalues.dtype))
        assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix)))


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