Python sparse.eye方法代码示例

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def _N(self,s,r):
        """
        Lagrange's interpolate function
        params:
            s,r:natural position of evalue point.2-array.
        returns:
            2x(2x4) shape function matrix.
        """
        la1=(1-s)/2
        la2=(1+s)/2
        lb1=(1-r)/2
        lb2=(1+r)/2
        N1=la1*lb1
        N2=la1*lb2
        N3=la2*lb1
        N4=la2*lb2

        N=np.hstack(N1*np.eye(2),N2*np.eye(2),N3*np.eye(2),N4*np.eye(2))
        return N 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def chebyshev_polynomials(adj, k):
    """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation)."""
    print("Calculating Chebyshev polynomials up to order {}...".format(k))

    adj_normalized = normalize_adj(adj)
    laplacian = sp.eye(adj.shape[0]) - adj_normalized
    largest_eigval, _ = eigsh(laplacian, 1, which='LM')
    scaled_laplacian = (
        2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])

    t_k = list()
    t_k.append(sp.eye(adj.shape[0]))
    t_k.append(scaled_laplacian)

    def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):
        s_lap = sp.csr_matrix(scaled_lap, copy=True)
        return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two

    for i in range(2, k+1):
        t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian))

    return sparse_to_tuple(t_k) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def learn_embedding(self):
        graph = self.g.G
        graph = graph.to_undirected()
        t1 = time()
        A = nx.to_scipy_sparse_matrix(graph)
        # print(np.sum(A.todense(), axis=0))
        normalize(A, norm='l1', axis=1, copy=False)
        I_n = sp.eye(graph.number_of_nodes())
        I_min_A = I_n - A
        print(I_min_A)
        u, s, vt = lg.svds(I_min_A, k=self._d + 1, which='SM')
        t2 = time()
        self._X = vt.T
        self._X = self._X[:, 1:]
        return self._X, (t2 - t1)
        # I_n = sp.eye(graph.number_of_nodes()) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def normalize_adjacency(graph, args):
    """
    Method to calculate a sparse degree normalized adjacency matrix.
    :param graph: Sparse graph adjacency matrix.
    :return A: Normalized adjacency matrix.
    """
    for node in graph.nodes():
        graph.add_edge(node, node)
    ind = range(len(graph.nodes()))
    degs = [1.0/graph.degree(node) for node in graph.nodes()]
    L = sparse.csr_matrix(nx.laplacian_matrix(graph),
                          dtype=np.float32)

    degs = sparse.csr_matrix(sparse.coo_matrix((degs, (ind, ind)),
                                               shape=L.shape,
                                               dtype=np.float32))

    propagator = sparse.eye(L.shape[0])-args.gamma*degs.dot(L)
    return propagator 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def dfs_trunk(sim, A,alpha = 0.99, QUERYKNN = 10, maxiter = 8, K = 100, tol = 1e-3):
    qsim = sim_kernel(sim).T
    sortidxs = np.argsort(-qsim, axis = 1)
    for i in range(len(qsim)):
        qsim[i,sortidxs[i,QUERYKNN:]] = 0
    qsims = sim_kernel(qsim)
    W = sim_kernel(A)
    W = csr_matrix(topK_W(W, K))
    out_ranks = []
    t =time()
    for i in range(qsims.shape[0]):
        qs =  qsims[i,:]
        tt = time() 
        w_idxs, W_trunk = find_trunc_graph(qs, W, 2);
        Wn = normalize_connection_graph(W_trunk)
        Wnn = eye(Wn.shape[0]) - alpha * Wn
        f,inf = s_linalg.minres(Wnn, qs[w_idxs], tol=tol, maxiter=maxiter)
        ranks = w_idxs[np.argsort(-f.reshape(-1))]
        missing = np.setdiff1d(np.arange(A.shape[1]), ranks)
        out_ranks.append(np.concatenate([ranks.reshape(-1,1), missing.reshape(-1,1)], axis = 0))
    #print time() -t, 'qtime'
    out_ranks = np.concatenate(out_ranks, axis = 1)
    return out_ranks 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def localpooling_filter(A, symmetric=True):
    r"""
    Computes the graph filter described in
    [Kipf & Welling (2017)](https://arxiv.org/abs/1609.02907).
    :param A: array or sparse matrix with rank 2 or 3;
    :param symmetric: boolean, whether to normalize the matrix as
    \(\D^{-\frac{1}{2}}\A\D^{-\frac{1}{2}}\) or as \(\D^{-1}\A\);
    :return: array or sparse matrix with rank 2 or 3, same as A;
    """
    fltr = A.copy()
    if sp.issparse(A):
        I = sp.eye(A.shape[-1], dtype=A.dtype)
    else:
        I = np.eye(A.shape[-1], dtype=A.dtype)
    if A.ndim == 3:
        for i in range(A.shape[0]):
            A_tilde = A[i] + I
            fltr[i] = normalized_adjacency(A_tilde, symmetric=symmetric)
    else:
        A_tilde = A + I
        fltr = normalized_adjacency(A_tilde, symmetric=symmetric)

    if sp.issparse(fltr):
        fltr.sort_indices()
    return fltr 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def whiten(self, X):
        """
        SUR whiten method.

        Parameters
        -----------
        X : list of arrays
            Data to be whitened.

        Returns
        -------
        If X is the exogenous RHS of the system.
        ``np.dot(np.kron(cholsigmainv,np.eye(M)),np.diag(X))``

        If X is the endogenous LHS of the system.

        """
        nobs = self.nobs
        if X is self.endog: # definitely not a robust check
            return np.dot(np.kron(self.cholsigmainv,np.eye(nobs)),
                X.reshape(-1,1))
        elif X is self.sp_exog:
            return (sparse.kron(self.cholsigmainv,
                sparse.eye(nobs,nobs))*X).toarray()#*=dot until cast to array 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def encode_onehot(labels):
    """Convert label to onehot format.

    Parameters
    ----------
    labels : numpy.array
        node labels

    Returns
    -------
    numpy.array
        onehot labels
    """
    eye = np.eye(labels.max() + 1)
    onehot_mx = eye[labels]
    return onehot_mx 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def tensor2onehot(labels):
    """Convert label tensor to label onehot tensor.

    Parameters
    ----------
    labels : torch.LongTensor
        node labels

    Returns
    -------
    torch.LongTensor
        onehot labels tensor

    """

    eye = torch.eye(labels.max() + 1)
    onehot_mx = eye[labels]
    return onehot_mx.to(labels.device) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def normalize_adj_tensor(adj, sparse=False):
    """Normalize adjacency tensor matrix.
    """
    device = torch.device("cuda" if adj.is_cuda else "cpu")
    if sparse:
        # TODO if this is too slow, uncomment the following code,
        # but you need to install torch_scatter
        # return normalize_sparse_tensor(adj)
        adj = to_scipy(adj)
        mx = normalize_adj(adj)
        return sparse_mx_to_torch_sparse_tensor(mx).to(device)
    else:
        mx = adj + torch.eye(adj.shape[0]).to(device)
        rowsum = mx.sum(1)
        r_inv = rowsum.pow(-1/2).flatten()
        r_inv[torch.isinf(r_inv)] = 0.
        r_mat_inv = torch.diag(r_inv)
        mx = r_mat_inv @ mx
        mx = mx @ r_mat_inv
    return mx 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def chebyshev_polynomials(adj, k):
    """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation)."""
    print("Calculating Chebyshev polynomials up to order {}...".format(k))

    adj_normalized = normalize_adj(adj)
    laplacian = sp.eye(adj.shape[0]) - adj_normalized
    largest_eigval, _ = eigsh(laplacian, 1, which='LM')
    scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])

    t_k = list()
    t_k.append(sp.eye(adj.shape[0]))
    t_k.append(scaled_laplacian)

    def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):
        s_lap = sp.csr_matrix(scaled_lap, copy=True)
        return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two

    for i in range(2, k+1):
        t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian))

    return sparse_to_tuple(t_k) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def test_dual_infeasible_lp(self):

        # Dual infeasible example
        self.P = sparse.csc_matrix((2, 2))
        self.q = np.array([2, -1])
        self.A = sparse.eye(2, format='csc')
        self.l = np.array([0., 0.])
        self.u = np.array([np.inf, np.inf])

        self.model = osqp.OSQP()
        self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
                         **self.opts)

        # Solve problem with OSQP
        res = self.model.solve()

        # Assert close
        self.assertEqual(res.info.status_val,
                         constant('OSQP_DUAL_INFEASIBLE')) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def setUp(self):
        # Simple QP problem
        self.P = sparse.diags([11., 0.1], format='csc')
        self.P_new = sparse.eye(2, format='csc')
        self.q = np.array([3, 4])
        self.A = sparse.csc_matrix([[-1, 0], [0, -1], [-1, -3],
                                    [2, 5], [3, 4]])
        self.A_new = sparse.csc_matrix([[-1, 0], [0, -1], [-2, -2],
                                        [2, 5], [3, 4]])
        self.u = np.array([0, 0, -15, 100, 80])
        self.l = -np.inf * np.ones(len(self.u))
        self.n = self.P.shape[0]
        self.m = self.A.shape[0]
        self.opts = {'verbose': False,
                     'eps_abs': 1e-08,
                     'eps_rel': 1e-08,
                     'alpha': 1.6,
                     'max_iter': 3000,
                     'warm_start': True}
        self.model = osqp.OSQP()
        self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
                         **self.opts) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def setUp(self):
        """
        Setup unconstrained quadratic problem
        """
        # Unconstrained QP problem
        sp.random.seed(4)

        self.n = 30
        self.m = 0
        P = sparse.diags(np.random.rand(self.n)) + 0.2*sparse.eye(self.n)
        self.P = P.tocsc()
        self.q = np.random.randn(self.n)
        self.A = sparse.csc_matrix((self.m, self.n))
        self.l = np.array([])
        self.u = np.array([])
        self.opts = {'verbose': False,
                     'eps_abs': 1e-08,
                     'eps_rel': 1e-08,
                     'polish': False}
        self.model = osqp.OSQP()
        self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
                         **self.opts) 

# 需要导入模块: from scipy import sparse [as 别名]
# 或者: from scipy.sparse import eye [as 别名]
def sakurai(n):
    """ Example taken from
        T. Sakurai, H. Tadano, Y. Inadomi and U. Nagashima
        A moment-based method for large-scale generalized eigenvalue problems
        Appl. Num. Anal. Comp. Math. Vol. 1 No. 2 (2004) """

    A = sparse.eye(n, n)
    d0 = array(r_[5,6*ones(n-2),5])
    d1 = -4*ones(n)
    d2 = ones(n)
    B = sparse.spdiags([d2,d1,d0,d1,d2],[-2,-1,0,1,2],n,n)

    k = arange(1,n+1)
    w_ex = sort(1./(16.*pow(cos(0.5*k*pi/(n+1)),4)))  # exact eigenvalues

    return A,B, w_ex