Python scipy.sparse.isspmatrix_lil() Examples

The following are 6 code examples of scipy.sparse.isspmatrix_lil(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy.sparse , or try the search function .
Example #1
Source File: matrix_utils.py    From nonnegfac-python with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def sparse_remove_row(X, to_remove):
    """ Delete rows from a sparse matrix

    Parameters
    ----------
    X : scipy.sparse matrix
    to_remove : a list of row indices to be removed.

    Returns
    -------
    Y : scipy.sparse matrix
    """
    if not sps.isspmatrix_lil(X):
        X = X.tolil()

    to_keep = [i for i in iter(range(0, X.shape[0])) if i not in to_remove]
    Y = sps.vstack([X.getrowview(i) for i in to_keep])
    return Y
Example #2
Source File: lil.py    From alphacsc with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def is_list_of_lil(z):
    return isinstance(z, list) and sparse.isspmatrix_lil(z[0])
Example #3
Source File: lil.py    From alphacsc with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def is_lil(z):
    return sparse.isspmatrix_lil(z)
Example #4
Source File: hierarchical.py    From Mastering-Elasticsearch-7.0 with MIT License 4 votes vote down vote up 
def _fix_connectivity(X, connectivity, affinity):
    """
    Fixes the connectivity matrix

        - copies it
        - makes it symmetric
        - converts it to LIL if necessary
        - completes it if necessary
    """
    n_samples = X.shape[0]
    if (connectivity.shape[0] != n_samples or
            connectivity.shape[1] != n_samples):
        raise ValueError('Wrong shape for connectivity matrix: %s '
                         'when X is %s' % (connectivity.shape, X.shape))

    # Make the connectivity matrix symmetric:
    connectivity = connectivity + connectivity.T

    # Convert connectivity matrix to LIL
    if not sparse.isspmatrix_lil(connectivity):
        if not sparse.isspmatrix(connectivity):
            connectivity = sparse.lil_matrix(connectivity)
        else:
            connectivity = connectivity.tolil()

    # Compute the number of nodes
    n_connected_components, labels = connected_components(connectivity)

    if n_connected_components > 1:
        warnings.warn("the number of connected components of the "
                      "connectivity matrix is %d > 1. Completing it to avoid "
                      "stopping the tree early." % n_connected_components,
                      stacklevel=2)
        # XXX: Can we do without completing the matrix?
        for i in range(n_connected_components):
            idx_i = np.where(labels == i)[0]
            Xi = X[idx_i]
            for j in range(i):
                idx_j = np.where(labels == j)[0]
                Xj = X[idx_j]
                D = pairwise_distances(Xi, Xj, metric=affinity)
                ii, jj = np.where(D == np.min(D))
                ii = ii[0]
                jj = jj[0]
                connectivity[idx_i[ii], idx_j[jj]] = True
                connectivity[idx_j[jj], idx_i[ii]] = True

    return connectivity, n_connected_components
Example #5
Source File: hierarchical.py    From Splunking-Crime with GNU Affero General Public License v3.0 4 votes vote down vote up 
def _fix_connectivity(X, connectivity, affinity):
    """
    Fixes the connectivity matrix

        - copies it
        - makes it symmetric
        - converts it to LIL if necessary
        - completes it if necessary
    """
    n_samples = X.shape[0]
    if (connectivity.shape[0] != n_samples or
            connectivity.shape[1] != n_samples):
        raise ValueError('Wrong shape for connectivity matrix: %s '
                         'when X is %s' % (connectivity.shape, X.shape))

    # Make the connectivity matrix symmetric:
    connectivity = connectivity + connectivity.T

    # Convert connectivity matrix to LIL
    if not sparse.isspmatrix_lil(connectivity):
        if not sparse.isspmatrix(connectivity):
            connectivity = sparse.lil_matrix(connectivity)
        else:
            connectivity = connectivity.tolil()

    # Compute the number of nodes
    n_components, labels = connected_components(connectivity)

    if n_components > 1:
        warnings.warn("the number of connected components of the "
                      "connectivity matrix is %d > 1. Completing it to avoid "
                      "stopping the tree early." % n_components,
                      stacklevel=2)
        # XXX: Can we do without completing the matrix?
        for i in xrange(n_components):
            idx_i = np.where(labels == i)[0]
            Xi = X[idx_i]
            for j in xrange(i):
                idx_j = np.where(labels == j)[0]
                Xj = X[idx_j]
                D = pairwise_distances(Xi, Xj, metric=affinity)
                ii, jj = np.where(D == np.min(D))
                ii = ii[0]
                jj = jj[0]
                connectivity[idx_i[ii], idx_j[jj]] = True
                connectivity[idx_j[jj], idx_i[ii]] = True

    return connectivity, n_components


###############################################################################
# Hierarchical tree building functions
Example #6
Source File: hierarchical.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def _fix_connectivity(X, connectivity, affinity):
    """
    Fixes the connectivity matrix

        - copies it
        - makes it symmetric
        - converts it to LIL if necessary
        - completes it if necessary
    """
    n_samples = X.shape[0]
    if (connectivity.shape[0] != n_samples or
            connectivity.shape[1] != n_samples):
        raise ValueError('Wrong shape for connectivity matrix: %s '
                         'when X is %s' % (connectivity.shape, X.shape))

    # Make the connectivity matrix symmetric:
    connectivity = connectivity + connectivity.T

    # Convert connectivity matrix to LIL
    if not sparse.isspmatrix_lil(connectivity):
        if not sparse.isspmatrix(connectivity):
            connectivity = sparse.lil_matrix(connectivity)
        else:
            connectivity = connectivity.tolil()

    # Compute the number of nodes
    n_components, labels = connected_components(connectivity)

    if n_components > 1:
        warnings.warn("the number of connected components of the "
                      "connectivity matrix is %d > 1. Completing it to avoid "
                      "stopping the tree early." % n_components,
                      stacklevel=2)
        # XXX: Can we do without completing the matrix?
        for i in xrange(n_components):
            idx_i = np.where(labels == i)[0]
            Xi = X[idx_i]
            for j in xrange(i):
                idx_j = np.where(labels == j)[0]
                Xj = X[idx_j]
                D = pairwise_distances(Xi, Xj, metric=affinity)
                ii, jj = np.where(D == np.min(D))
                ii = ii[0]
                jj = jj[0]
                connectivity[idx_i[ii], idx_j[jj]] = True
                connectivity[idx_j[jj], idx_i[ii]] = True

    return connectivity, n_components


###############################################################################
# Hierarchical tree building functions