Python numpy.tril方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def create_matrix(dim, alpha=0.95, smallest_coef=0.1, largest_coef=.9):
    ''' Based o scikit-learn make_sparse_spd_matrix'''
    chol = -np.eye(dim)
    aux = np.random.rand(dim, dim)
    aux[aux < alpha] = 0
    aux[aux > alpha] = (smallest_coef
                        + (largest_coef - smallest_coef)
                        * np.random.rand(np.sum(aux > alpha)))
    aux = np.tril(aux, k=-1)

    # Permute the lines: we don't want to have asymmetries in the final
    # SPD matrix
    permutation = np.random.permutation(dim)
    aux = aux[permutation].T[permutation]
    chol += aux
    A = sp.csc_matrix(np.dot(chol.T, chol))

    x = np.random.rand(dim)
    b = A.dot(x)

    return A,b,x 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def select_merge_data(self, u_feas, label, label_to_images,  ratio_n,  dists):
        dists.add_(torch.tril(100000 * torch.ones(len(u_feas), len(u_feas))))#blocking the triangle

        cnt = torch.FloatTensor([len(label_to_images[label[idx]]) for idx in range(len(u_feas))])
        dists += ratio_n * (cnt.view(1, len(cnt)) + cnt.view(len(cnt), 1))  # dist += |A|+|B|
        
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if label[idx] == label[j]:
                    dists[idx, j] = 100000                  # set the distance within the same cluster

        dists = dists.numpy()
        ind = np.unravel_index(np.argsort(dists, axis=None), dists.shape)          # with axis=None all numbers are sorted and unravel_index transforms the sorted index into ind for each dimension
        idx1 = ind[0]          # the first dimension index
        idx2 = ind[1]           # the second dimension index
        return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def select_merge_data_v2(self, u_feas, labels, linkages):
        linkages+=(np.tril(100000 * np.ones((len(u_feas), len(u_feas)))))  # blocking the triangle

        print('Linkage adding')
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if labels[idx] == labels[j]:
                    linkages[idx, j] = 100000  # set the distance within the same cluster

        ind = np.unravel_index(np.argsort(linkages, axis=None),
                               linkages.shape)  # with axis=None all numbers are sorted and unravel_index transforms the sorted index into ind for each dimension
        idx1 = ind[0]  # the first cluster index
        idx2 = ind[1]  # the second cluster index
        print('Linkage add finished')
        return idx1, idx2


        #after 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def select_merge_data(self, u_feas, label, label_to_images,  ratio_n,  dists):
        dists.add_(torch.tril(100000 * torch.ones(len(u_feas), len(u_feas))))

        cnt = torch.FloatTensor([len(label_to_images[label[idx]]) for idx in range(len(u_feas))])
        dists += ratio_n * (cnt.view(1, len(cnt)) + cnt.view(len(cnt), 1))  
        
        for idx in range(len(u_feas)):
            for j in range(idx + 1, len(u_feas)):
                if label[idx] == label[j]:
                    dists[idx, j] = 100000                 

        dists = dists.numpy()
        ind = np.unravel_index(np.argsort(dists, axis=None), dists.shape)          
        idx1 = ind[0]          
        idx2 = ind[1]          
        return idx1, idx2 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_tril_triu_ndim3():
    for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
        a = np.array([
            [[1, 1], [1, 1]],
            [[1, 1], [1, 0]],
            [[1, 1], [0, 0]],
            ], dtype=dtype)
        a_tril_desired = np.array([
            [[1, 0], [1, 1]],
            [[1, 0], [1, 0]],
            [[1, 0], [0, 0]],
            ], dtype=dtype)
        a_triu_desired = np.array([
            [[1, 1], [0, 1]],
            [[1, 1], [0, 0]],
            [[1, 1], [0, 0]],
            ], dtype=dtype)
        a_triu_observed = np.triu(a)
        a_tril_observed = np.tril(a)
        assert_array_equal(a_triu_observed, a_triu_desired)
        assert_array_equal(a_tril_observed, a_tril_desired)
        assert_equal(a_triu_observed.dtype, a.dtype)
        assert_equal(a_tril_observed.dtype, a.dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_tril_triu_dtype():
    # Issue 4916
    # tril and triu should return the same dtype as input
    for c in np.typecodes['All']:
        if c == 'V':
            continue
        arr = np.zeros((3, 3), dtype=c)
        assert_equal(np.triu(arr).dtype, arr.dtype)
        assert_equal(np.tril(arr).dtype, arr.dtype)

    # check special cases
    arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'],
                    ['2004-01-01T12:00', '2003-01-03T13:45']],
                   dtype='datetime64')
    assert_equal(np.triu(arr).dtype, arr.dtype)
    assert_equal(np.tril(arr).dtype, arr.dtype)

    arr = np.zeros((3,3), dtype='f4,f4')
    assert_equal(np.triu(arr).dtype, arr.dtype)
    assert_equal(np.tril(arr).dtype, arr.dtype) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def testSolveSymPos(self):
        import scipy.linalg
        np.random.seed(1)

        data = np.random.randint(1, 10, (20, 20))
        data_l = np.tril(data)
        data1 = data_l.dot(data_l.T)
        data2 = np.random.randint(1, 10, (20, ))

        A = tensor(data1, chunk_size=5)
        b = tensor(data2, chunk_size=5)

        x = solve(A, b, sym_pos=True)

        res = self.executor.execute_tensor(x, concat=True)[0]
        np.testing.assert_allclose(res, scipy.linalg.solve(data1, data2))
        res = self.executor.execute_tensor(A.dot(x), concat=True)[0]
        np.testing.assert_allclose(res, data2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_tril_triu_ndim3():
    for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']:
        a = np.array([
            [[1, 1], [1, 1]],
            [[1, 1], [1, 0]],
            [[1, 1], [0, 0]],
            ], dtype=dtype)
        a_tril_desired = np.array([
            [[1, 0], [1, 1]],
            [[1, 0], [1, 0]],
            [[1, 0], [0, 0]],
            ], dtype=dtype)
        a_triu_desired = np.array([
            [[1, 1], [0, 1]],
            [[1, 1], [0, 0]],
            [[1, 1], [0, 0]],
            ], dtype=dtype)
        a_triu_observed = np.triu(a)
        a_tril_observed = np.tril(a)
        yield assert_array_equal, a_triu_observed, a_triu_desired
        yield assert_array_equal, a_tril_observed, a_tril_desired
        yield assert_equal, a_triu_observed.dtype, a.dtype
        yield assert_equal, a_tril_observed.dtype, a.dtype 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def parity_code(n_modes):
    """ The parity transform (arXiv:1208.5986) as binary code. This code is
    very similar to the Jordan-Wigner transform, but with long update strings
    instead of parity strings. It does not save qubits: n_qubits = n_modes.

    Args:
        n_modes (int): number of modes

    Returns (BinaryCode): The parity transform BinaryCode
    """
    dec_mtx = numpy.reshape(([1] + [0] * (n_modes - 1)) +
                            ([1, 1] + (n_modes - 1) * [0]) * (n_modes - 2) +
                            [1, 1], (n_modes, n_modes))
    enc_mtx = numpy.tril(numpy.ones((n_modes, n_modes), dtype=int))

    return BinaryCode(enc_mtx, linearize_decoder(dec_mtx)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_syrk(self):
        for f in _get_func('syrk'):
            c = f(a=self.a, alpha=1.)
            assert_array_almost_equal(np.triu(c), np.triu(self.t))

            c = f(a=self.a, alpha=1., lower=1)
            assert_array_almost_equal(np.tril(c), np.tril(self.t))

            c0 = np.ones(self.t.shape)
            c = f(a=self.a, alpha=1., beta=1., c=c0)
            assert_array_almost_equal(np.triu(c), np.triu(self.t+c0))

            c = f(a=self.a, alpha=1., trans=1)
            assert_array_almost_equal(np.triu(c), np.triu(self.tt))

    #prints '0-th dimension must be fixed to 3 but got 5', FIXME: suppress?
    # FIXME: how to catch the _fblas.error? 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_syr2k(self):
        for f in _get_func('syr2k'):
            c = f(a=self.a, b=self.b, alpha=1.)
            assert_array_almost_equal(np.triu(c), np.triu(self.t))

            c = f(a=self.a, b=self.b, alpha=1., lower=1)
            assert_array_almost_equal(np.tril(c), np.tril(self.t))

            c0 = np.ones(self.t.shape)
            c = f(a=self.a, b=self.b, alpha=1., beta=1., c=c0)
            assert_array_almost_equal(np.triu(c), np.triu(self.t+c0))

            c = f(a=self.a, b=self.b, alpha=1., trans=1)
            assert_array_almost_equal(np.triu(c), np.triu(self.tt))

    #prints '0-th dimension must be fixed to 3 but got 5', FIXME: suppress? 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_al_mohy_higham_2012_experiment_1(self):
        # Fractional powers of a tricky upper triangular matrix.
        A = _get_al_mohy_higham_2012_experiment_1()

        # Test remainder matrix power.
        A_funm_sqrt, info = funm(A, np.sqrt, disp=False)
        A_sqrtm, info = sqrtm(A, disp=False)
        A_rem_power = _matfuncs_inv_ssq._remainder_matrix_power(A, 0.5)
        A_power = fractional_matrix_power(A, 0.5)
        assert_array_equal(A_rem_power, A_power)
        assert_allclose(A_sqrtm, A_power)
        assert_allclose(A_sqrtm, A_funm_sqrt)

        # Test more fractional powers.
        for p in (1/2, 5/3):
            A_power = fractional_matrix_power(A, p)
            A_round_trip = fractional_matrix_power(A_power, 1/p)
            assert_allclose(A_round_trip, A, rtol=1e-2)
            assert_allclose(np.tril(A_round_trip, 1), np.tril(A, 1)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def test_readinto_partial_sliding17(self, name, BQM, version):

        bqm = BQM(np.tril(np.arange(25).reshape((5, 5))), 'BINARY')
        bqm.offset = -6

        fv = FileView(bqm, version=version)

        buff = fv.readall()

        for pos in range(fv.quadratic_end):
            self.assertEqual(pos, fv.seek(pos))
            subbuff = bytearray(17)
            num_read = fv.readinto(subbuff)
            self.assertGreater(num_read, 0)
            self.assertEqual(subbuff[:num_read], buff[pos:pos+num_read])

    # Ocean only supports 64bit python 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def tril(m, k=0):  # pylint: disable=missing-docstring
  m = asarray(m).data
  m_shape = m.shape.as_list()

  if len(m_shape) < 2:
    raise ValueError('Argument to tril must have rank at least 2')

  if m_shape[-1] is None or m_shape[-2] is None:
    raise ValueError('Currently, the last two dimensions of the input array '
                     'need to be known.')

  z = tf.constant(0, m.dtype)

  mask = tri(*m_shape[-2:], k=k, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.where(tf.broadcast_to(mask, tf.shape(m)), m, z)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import tril [as 別名]
def forward(self, inputs):
    q, k, v = inputs

    if self._mode == 'predict':
      self.state = _fast_inference_update_state(inputs, self.state)
      (k, v, mask, _) = self.state
    else:
      mask_size = q.shape[-2]
      # Not all backends define jnp.tril. However, using np.tril is inefficient
      # in that it creates a large global constant. TODO(kitaev): try to find an
      # alternative that works across all backends.
      if fastmath.backend_name() == 'jax':
        mask = jnp.tril(
            jnp.ones((1, mask_size, mask_size), dtype=np.bool_), k=0)
      else:
        mask = np.tril(
            np.ones((1, mask_size, mask_size), dtype=np.bool_), k=0)

    res = DotProductAttention(
        q, k, v, mask, dropout=self._dropout, mode=self._mode, rng=self.rng)
    return res