python类asmatrix()的实例源码

layers_test.py 文件源码 项目:DeepLearning_VirtualReality_BigData_Project 作者: rashmitripathi 项目源码 文件源码 阅读 32 收藏 0 点赞 0 评论 0
def testVertConvWithVaryingImage(self):
    image = np.asmatrix(('1.0 2.0 3.0;' '1.1 2.0 4.0;' '-4.3 0.0 8.9'))

    expected = np.asmatrix(('-0.1 0.0 -1.0;' ' 5.4 2.0 -4.9'))
    expected = np.reshape(np.asarray(expected), (1, 2, 3, 1))

    tf_image = constant_op.constant(
        image, shape=(1, 3, 3, 1), dtype=dtypes.float32)
    vert_gradients = layers_lib.conv2d_in_plane(
        tf_image,
        weights_initializer=init_ops.constant_initializer([1, -1]),
        kernel_size=[2, 1],
        padding='VALID',
        activation_fn=None)
    init_op = variables_lib.global_variables_initializer()

    with self.test_session() as sess:
      sess.run(init_op)
      result = sess.run(vert_gradients)

      self.assertAllClose(result, expected, rtol=1e-5, atol=1e-5)
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 improve_admm(x0, prob, *args, **kwargs):
    num_iters = kwargs.get('num_iters', 1000)
    viol_lim = kwargs.get('viol_lim', 1e4)
    tol = kwargs.get('tol', 1e-2)
    rho = kwargs.get('rho', None)
    phase1 = kwargs.get('phase1', True)

    if rho is not None:
        lmb0, P0Q = map(np.asmatrix, LA.eigh(prob.f0.P.todense()))
        lmb_min = np.min(lmb0)
        if lmb_min + prob.m*rho < 0:
            logging.error("rho parameter is too small, z-update not convex.")
            logging.error("Minimum possible value of rho: %.3f\n", -lmb_min/prob.m)
            logging.error("Given value of rho: %.3f\n", rho)
            raise Exception("rho parameter is too small, need at least %.3f." % rho)

    # TODO: find a reasonable auto parameter
    if rho is None:
        lmb0, P0Q = map(np.asmatrix, LA.eigh(prob.f0.P.todense()))
        lmb_min = np.min(lmb0)
        lmb_max = np.max(lmb0)
        if lmb_min < 0: rho = 2.*(1.-lmb_min)/prob.m
        else: rho = 1./prob.m
        rho *= 50.
        logging.warning("Automatically setting rho to %.3f", rho)

    if phase1:
        x1 = prob.better(x0, admm_phase1(x0, prob, tol, num_iters))
    else:
        x1 = x0
    x2 = prob.better(x1, admm_phase2(x1, prob, rho, tol, num_iters, viol_lim))
    return x2
linalg.py 文件源码 项目:hidi 作者: VEVO 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
def dot(X, Y):
    if sparse.isspmatrix(X) and sparse.isspmatrix(Y):
        return X * Y
    elif sparse.isspmatrix(X) or sparse.isspmatrix(Y):
        return sparse.csr_matrix(X) * sparse.csr_matrix(Y)

    return np.asmatrix(X) * np.asmatrix(Y)
transformations.py 文件源码 项目:deep-prior 作者: moberweger 项目源码 文件源码 阅读 47 收藏 0 点赞 0 评论 0
def transformPoint2D(pt, M):
    """
    Transform point in 2D coordinates
    :param pt: point coordinates
    :param M: transformation matrix
    :return: transformed point
    """
    pt2 = numpy.asmatrix(M.reshape((3, 3))) * numpy.matrix([pt[0], pt[1], 1]).T
    return numpy.array([pt2[0] / pt2[2], pt2[1] / pt2[2]])
transformations.py 文件源码 项目:deep-prior 作者: moberweger 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def transformPoint3D(pt, M):
    """
    Transform point in 3D coordinates
    :param pt: point coordinates
    :param M: transformation matrix
    :return: transformed point
    """
    pt3 = numpy.asmatrix(M.reshape((4, 4))) * numpy.matrix([pt[0], pt[1], pt[2], 1]).T
    return numpy.array([pt3[0] / pt3[3], pt3[1] / pt3[3], pt3[2] / pt3[3]])
test_indexing.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 test_matrix_fancy(self):
        # The matrix class messes with the shape. While this is always
        # weird (getitem is not used, it does not have setitem nor knows
        # about fancy indexing), this tests gh-3110
        m = np.matrix([[1, 2], [3, 4]])

        assert_(isinstance(m[[0,1,0], :], np.matrix))

        # gh-3110. Note the transpose currently because matrices do *not*
        # support dimension fixing for fancy indexing correctly.
        x = np.asmatrix(np.arange(50).reshape(5,10))
        assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
matlib.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 eye(n,M=None, k=0, dtype=float):
    """
    Return a matrix with ones on the diagonal and zeros elsewhere.

    Parameters
    ----------
    n : int
        Number of rows in the output.
    M : int, optional
        Number of columns in the output, defaults to `n`.
    k : int, optional
        Index of the diagonal: 0 refers to the main diagonal,
        a positive value refers to an upper diagonal,
        and a negative value to a lower diagonal.
    dtype : dtype, optional
        Data-type of the returned matrix.

    Returns
    -------
    I : matrix
        A `n` x `M` matrix where all elements are equal to zero,
        except for the `k`-th diagonal, whose values are equal to one.

    See Also
    --------
    numpy.eye : Equivalent array function.
    identity : Square identity matrix.

    Examples
    --------
    >>> import numpy.matlib
    >>> np.matlib.eye(3, k=1, dtype=float)
    matrix([[ 0.,  1.,  0.],
            [ 0.,  0.,  1.],
            [ 0.,  0.,  0.]])

    """
    return asmatrix(np.eye(n, M, k, dtype))
test_regression.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 test_matrix_std_argmax(self,level=rlevel):
        # Ticket #83
        x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
        self.assertEqual(x.std().shape, ())
        self.assertEqual(x.argmax().shape, ())
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def test_asmatrix(self):
        A = np.arange(100).reshape(10, 10)
        mA = asmatrix(A)
        A[0, 0] = -10
        assert_(A[0, 0] == mA[0, 0])
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_basic(self):
        x = asmatrix(np.zeros((3, 2), float))
        y = np.zeros((3, 1), float)
        y[:, 0] = [0.8, 0.2, 0.3]
        x[:, 1] = y > 0.5
        assert_equal(x, [[0, 1], [0, 0], [0, 0]])
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 21 收藏 0 点赞 0 评论 0
def test_scalar_indexing(self):
        x = asmatrix(np.zeros((3, 2), float))
        assert_equal(x[0, 0], x[0][0])
test_defmatrix.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 test_row_column_indexing(self):
        x = asmatrix(np.eye(2))
        assert_array_equal(x[0,:], [[1, 0]])
        assert_array_equal(x[1,:], [[0, 1]])
        assert_array_equal(x[:, 0], [[1], [0]])
        assert_array_equal(x[:, 1], [[0], [1]])
test_defmatrix.py 文件源码 项目:radar 作者: amoose136 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
def test_list_indexing(self):
        A = np.arange(6)
        A.shape = (3, 2)
        x = asmatrix(A)
        assert_array_equal(x[:, [1, 0]], x[:, ::-1])
        assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
lms.py 文件源码 项目:Spherical-robot 作者: Evan-Zhao 项目源码 文件源码 阅读 18 收藏 0 点赞 0 评论 0
def lms(x1: numpy.array, x2: numpy.array, N: int):
    # Verify argument shape.
    s1, s2 = x1.shape, x2.shape
    if len(s1) != 1 or len(s2) != 1 or s1[0] != s2[0]:
        raise Exception("Argument shape invalid, in 'lms' function")
    l = s1[0]

    # Coefficient matrix
    W = numpy.mat(numpy.zeros([1, 2 * N + 1]))
    # Coefficient (time) matrix
    Wt = numpy.mat(numpy.zeros([l, 2 * N + 1]))
    # Feedback (time) matrix
    y = numpy.mat(numpy.zeros([l, 1]))
    # Error (time) matrix
    e = numpy.mat(numpy.zeros([l, 1]))

    # Traverse channel data
    for i in range(N, l-N):
        x1_vec = numpy.asmatrix(x1[i-N:i+N+1])
        y[i] = x1_vec * numpy.transpose(W)
        e[i] = x2[i] - y[i]
        W += mu * e[i] * x1_vec
        Wt[i] = W

    # Find the coefficient matrix which has max maximum.
    Wt_maxs = numpy.max(Wt, axis=1)
    row_idx = numpy.argmax(Wt_maxs)
    max_W = Wt[row_idx]
    delay_count = numpy.argmax(max_W) - N

    plot(l, x1, x2, y, e)

    return delay_count
optimiz.py 文件源码 项目:Chalutier 作者: LaBaleineFr 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def evaluate_portefolio(wei, returns_vec):
    """ Given a repartition, compute expected return and risk from a portefolio

    :param wei: Weights for each currency
    :type wei: ndarray of float
    :return: expected return and risk
    :rtype: (float, float)
    """
    p = np.asmatrix(np.mean(returns_vec, axis=1))
    w = np.asmatrix(wei)
    c = np.asmatrix(np.cov(returns_vec))
    mu = w * p.T
    sigma = np.sqrt(w * c * w.T)
    return mu, sigma
LSFIR.py 文件源码 项目:Least-Squared-Error-Based-FIR-Filters 作者: fourier-being 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def lpfls2notch(N,wp,ws,wn1,wn2,W):
    M = (N-1)/2
    nq = np.arange(0,2*M+1)
    nb = np.arange(0,M+1)
    q = (wp/np.pi)*np.sinc((wp/np.pi)*nq) - W*(ws/np.pi)*np.sinc((ws/np.pi)*nq)
    b = (wp/np.pi)*np.sinc((wp/np.pi)*nb)
    q[0] = wp/np.pi + W*(1-ws/np.pi) # since sin(pi*n)/pi*n = 1, not 0
    b = np.asmatrix(b)
    b = b.transpose()

    Q1 = ln.toeplitz(q[0:M+1])
    Q2 = ln.hankel(q[0:M+1],q[M:])
    Q = Q1+Q2

    G1 = np.cos(wn1*nb)
    G2 = np.cos(wn2*nb)
    G = np.matrix([G1,G2])

    d = np.array([0,0])
    d = np.asmatrix(d)
    d = d.transpose()

    c = np.asmatrix(ln.solve(Q,b))

    mu = ln.solve(G*ln.inv(Q)*G.transpose(),G*c - d)

    a = c - ln.solve(Q,G.transpose()*mu)
    h = np.zeros(N)
    for i in nb:
        h[i] = 0.5*a[M-i]
        h[N-1-i] = h[i]
    h[M] = 2*h[M]
    hmax = max(np.absolute(h))
    for i in nq:
        h[i] = (8191/hmax)*h[i]
    return h
LSFIR.py 文件源码 项目:Least-Squared-Error-Based-FIR-Filters 作者: fourier-being 项目源码 文件源码 阅读 23 收藏 0 点赞 0 评论 0
def lpfls1notch(N,wp,ws,wn1,W):
    M = (N-1)/2
    nq = np.arange(0,2*M+1)
    nb = np.arange(0,M+1)
    q = (wp/np.pi)*np.sinc((wp/np.pi)*nq) - W*(ws/np.pi)*np.sinc((ws/np.pi)*nq)
    b = (wp/np.pi)*np.sinc((wp/np.pi)*nb)
    q[0] = wp/np.pi + W*(1-ws/np.pi) # since sin(pi*n)/pi*n = 1, not 0
    b = np.asmatrix(b)
    b = b.transpose()

    Q1 = ln.toeplitz(q[0:M+1])
    Q2 = ln.hankel(q[0:M+1],q[M:])
    Q = Q1+Q2

    G1 = np.cos(wn1*nb)
    G = np.matrix([G1])

    d = np.array([0])
    d = np.asmatrix(d)

    c = np.asmatrix(ln.solve(Q,b))

    mu = ln.solve(G*ln.inv(Q)*G.transpose(),G*c - d)

    a = c - ln.solve(Q,G.transpose()*mu)
    h = np.zeros(N)
    for i in nb:
        h[i] = 0.5*a[M-i]
        h[N-1-i] = h[i]
    h[M] = 2*h[M]
    hmax = max(np.absolute(h))
    for i in nq:
        h[i] = (8191/hmax)*h[i]
    return h
competitors.py 文件源码 项目:AND4NMF 作者: PrincetonML 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def decoding(self):
        D = self.A_true.shape[1]
        num_doc = self.Y.shape[1]
        Z = np.asmatrix(np.zeros((D, num_doc)))
        A = np.asarray(self.A.copy())
        Y = np.asarray(self.Y.copy())
        for i in range(num_doc):
            Yi = np.array(Y[:, i]).flatten()
            t, bla = nnls(A, Yi)
            Z[:, i] = np.asmatrix(t).transpose()
        Z = np.asmatrix(Z)
        return Z
competitors.py 文件源码 项目:AND4NMF 作者: PrincetonML 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
def decoding(self):
        D = self.A_true.shape[1]
        num_doc = self.Y.shape[1]
        Z = np.asmatrix(np.zeros((D, num_doc)))
        for i in range(num_doc):
            Yi = np.array(self.Y[:, i].copy()).flatten()
            A = np.asarray(self.A.copy())
            t, bla = nnls(A, Yi)
            Z[:, i] = np.asmatrix(t).transpose()
        Z = np.asmatrix(Z)
        return Z
competitors.py 文件源码 项目:AND4NMF 作者: PrincetonML 项目源码 文件源码 阅读 24 收藏 0 点赞 0 评论 0
def train(self):
        D = self.A_true.shape[1]
        for i in range(20):
            self.show_error()

            start = time.time()
            prior = self.sparsity / np.float(self.A_true.shape[1])
            lda = LDA(n_topics=D, random_state=0, doc_topic_prior = prior, max_iter=i)
            lda.fit(self.Y.transpose())
            end = time.time()
            self.time = end - start
            self.A = np.asmatrix(lda.components_.transpose())


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