sort_graph_by_row_values#

sklearn.neighbors.sort_graph_by_row_values(graph, copy=False, warn_when_not_sorted=True)[source]#

Sort a sparse graph such that each row is stored with increasing values.

Added in version 1.2.

Parameters:

graphsparse matrix of shape (n_samples, n_samples): Distance matrix to other samples, where only non-zero elements are considered neighbors. Matrix is converted to CSR format if not already.
copybool, default=False: If True, the graph is copied before sorting. If False, the sorting is performed inplace. If the graph is not of CSR format, copy must be True to allow the conversion to CSR format, otherwise an error is raised.
warn_when_not_sortedbool, default=True: If True, a EfficiencyWarning is raised when the input graph is not sorted by row values.

Returns:

graphsparse matrix of shape (n_samples, n_samples): Distance matrix to other samples, where only non-zero elements are considered neighbors. Matrix is in CSR format.

Examples

>>> from scipy.sparse import csr_matrix
>>> from sklearn.neighbors import sort_graph_by_row_values
>>> X = csr_matrix(
...     [[0., 3., 1.],
...      [3., 0., 2.],
...      [1., 2., 0.]])
>>> X.data
array([3., 1., 3., 2., 1., 2.])
>>> X_ = sort_graph_by_row_values(X)
>>> X_.data
array([1., 3., 2., 3., 1., 2.])