block/test.py at master · bamos/block · GitHub

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#!/usr/bin/env python3

import numpy as np
import numpy.random as npr
import scipy.sparse.linalg as sla

from block import block, block_diag


def test_np():
    npr.seed(0)

    nx, nineq, neq = 4, 6, 7
    Q = npr.randn(nx, nx)
    G = npr.randn(nineq, nx)
    A = npr.randn(neq, nx)
    D = np.diag(npr.rand(nineq))

    K_ = np.bmat((
        (Q, np.zeros((nx, nineq)), G.T, A.T),
        (np.zeros((nineq, nx)), D, np.eye(nineq), np.zeros((nineq, neq))),
        (G, np.eye(nineq), np.zeros((nineq, nineq + neq))),
        (A, np.zeros((neq, nineq + nineq + neq)))
    ))

    K = block((
        (Q,   0, G.T, A.T),
        (0,   D, 'I',   0),
        (G, 'I',   0,   0),
        (A,   0,   0,   0)
    ))

    assert np.allclose(K_, K)


def test_diag():
    n0, n1, n2, n3 = 4, 5, 6, 7
    A = npr.randn(n0, n1)
    B = npr.randn(n2, n3)

    K_ = np.bmat((
        (A, np.zeros((n0, n3))),
        (np.zeros((n2, n1)), B)
    ))

    K = block_diag((A, B))

    assert np.allclose(K_, K)


def test_torch():
    import torch
    from torch.autograd import Variable

    torch.manual_seed(0)

    nx, nineq, neq = 4, 6, 7
    Q = torch.randn(nx, nx)
    G = torch.randn(nineq, nx)
    A = torch.randn(neq, nx)
    D = torch.diag(torch.rand(nineq))

    K_ = torch.cat((
        torch.cat((Q, torch.zeros(nx, nineq).type_as(Q), G.t(), A.t()), 1),
        torch.cat((torch.zeros(nineq, nx).type_as(Q), D,
                   torch.eye(nineq).type_as(Q),
                   torch.zeros(nineq, neq).type_as(Q)), 1),
        torch.cat((G, torch.eye(nineq).type_as(Q), torch.zeros(
            nineq, nineq + neq).type_as(Q)), 1),
        torch.cat((A, torch.zeros((neq, nineq + nineq + neq))), 1)
    ))

    K = block((
        (Q,   0, G.t(), A.t()),
        (0,   D,   'I',     0),
        (G, 'I',     0,     0),
        (A,   0,     0,     0)
    ))

    assert (K - K_).norm() == 0.0
    K = block((
        (Variable(Q),   0, G.t(), Variable(A.t())),
        (0,   Variable(D),   'I',     0),
        (Variable(G), 'I',     0,     0),
        (A,   0,     0,     0)
    ))

    assert (K.data - K_).norm() == 0.0


def test_linear_operator():
    npr.seed(0)

    nx, nineq, neq = 4, 6, 7
    Q = npr.randn(nx, nx)
    G = npr.randn(nineq, nx)
    A = npr.randn(neq, nx)
    D = np.diag(npr.rand(nineq))

    K_ = np.bmat((
        (Q, np.zeros((nx, nineq)), G.T, A.T),
        (np.zeros((nineq, nx)), D, np.eye(nineq), np.zeros((nineq, neq))),
        (G, np.eye(nineq), np.zeros((nineq, nineq + neq))),
        (A, np.zeros((neq, nineq + nineq + neq)))
    ))

    Q_lo = sla.aslinearoperator(Q)
    G_lo = sla.aslinearoperator(G)
    A_lo = sla.aslinearoperator(A)
    D_lo = sla.aslinearoperator(D)

    K = block((
        (Q_lo,    0,    G.T,    A.T),
        (0,    D_lo,    'I',      0),
        (G_lo,  'I',      0,      0),
        (A_lo,    0,      0,      0)
    ), arrtype=sla.LinearOperator)

    w1 = np.random.randn(K_.shape[1])
    assert np.allclose(K_.dot(w1), K.dot(w1))
    w2 = np.random.randn(K_.shape[0])
    assert np.allclose(K_.T.dot(w2), K.H.dot(w2))
    W = np.random.randn(*K_.shape)
    assert np.allclose(K_.dot(W), K.dot(W))


def test_empty():
    A = npr.randn(3, 0)
    B = npr.randn(3, 3)
    out = block([[A, B]])
    assert np.linalg.norm(out - B) == 0.0

    A = npr.randn(0, 3)
    B = npr.randn(3, 3)
    out = block([[A], [B]])
    assert np.linalg.norm(out - B) == 0.0


if __name__ == '__main__':
    test_np()
    test_torch()
    test_empty()
    test_linear_operator()
    test_diag()