179 lines
6.5 KiB
Python
179 lines
6.5 KiB
Python
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"""Nearest Neighbors graph functions"""
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# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
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#
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# License: BSD 3 clause (C) INRIA, University of Amsterdam
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from .base import KNeighborsMixin, RadiusNeighborsMixin
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from .unsupervised import NearestNeighbors
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def _check_params(X, metric, p, metric_params):
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"""Check the validity of the input parameters"""
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params = zip(['metric', 'p', 'metric_params'],
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[metric, p, metric_params])
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est_params = X.get_params()
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for param_name, func_param in params:
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if func_param != est_params[param_name]:
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raise ValueError(
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"Got %s for %s, while the estimator has %s for "
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"the same parameter." % (
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func_param, param_name, est_params[param_name]))
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def _query_include_self(X, include_self):
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"""Return the query based on include_self param"""
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if include_self:
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query = X._fit_X
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else:
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query = None
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return query
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def kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski',
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p=2, metric_params=None, include_self=False, n_jobs=1):
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"""Computes the (weighted) graph of k-Neighbors for points in X
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Read more in the :ref:`User Guide <unsupervised_neighbors>`.
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Parameters
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----------
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X : array-like or BallTree, shape = [n_samples, n_features]
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Sample data, in the form of a numpy array or a precomputed
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:class:`BallTree`.
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n_neighbors : int
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Number of neighbors for each sample.
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mode : {'connectivity', 'distance'}, optional
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Type of returned matrix: 'connectivity' will return the connectivity
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matrix with ones and zeros, and 'distance' will return the distances
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between neighbors according to the given metric.
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metric : string, default 'minkowski'
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The distance metric used to calculate the k-Neighbors for each sample
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point. The DistanceMetric class gives a list of available metrics.
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The default distance is 'euclidean' ('minkowski' metric with the p
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param equal to 2.)
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p : int, default 2
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Power parameter for the Minkowski metric. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric_params : dict, optional
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additional keyword arguments for the metric function.
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include_self : bool, default=False.
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Whether or not to mark each sample as the first nearest neighbor to
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itself. If `None`, then True is used for mode='connectivity' and False
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for mode='distance' as this will preserve backwards compatibilty.
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n_jobs : int, optional (default = 1)
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The number of parallel jobs to run for neighbors search.
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If ``-1``, then the number of jobs is set to the number of CPU cores.
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Returns
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-------
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A : sparse matrix in CSR format, shape = [n_samples, n_samples]
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A[i, j] is assigned the weight of edge that connects i to j.
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Examples
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--------
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>>> X = [[0], [3], [1]]
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>>> from sklearn.neighbors import kneighbors_graph
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>>> A = kneighbors_graph(X, 2, mode='connectivity', include_self=True)
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>>> A.toarray()
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array([[ 1., 0., 1.],
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[ 0., 1., 1.],
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[ 1., 0., 1.]])
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See also
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--------
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radius_neighbors_graph
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"""
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if not isinstance(X, KNeighborsMixin):
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X = NearestNeighbors(n_neighbors, metric=metric, p=p,
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metric_params=metric_params, n_jobs=n_jobs).fit(X)
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else:
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_check_params(X, metric, p, metric_params)
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query = _query_include_self(X, include_self)
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return X.kneighbors_graph(X=query, n_neighbors=n_neighbors, mode=mode)
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def radius_neighbors_graph(X, radius, mode='connectivity', metric='minkowski',
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p=2, metric_params=None, include_self=False, n_jobs=1):
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"""Computes the (weighted) graph of Neighbors for points in X
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Neighborhoods are restricted the points at a distance lower than
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radius.
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Read more in the :ref:`User Guide <unsupervised_neighbors>`.
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Parameters
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----------
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X : array-like or BallTree, shape = [n_samples, n_features]
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Sample data, in the form of a numpy array or a precomputed
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:class:`BallTree`.
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radius : float
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Radius of neighborhoods.
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mode : {'connectivity', 'distance'}, optional
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Type of returned matrix: 'connectivity' will return the connectivity
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matrix with ones and zeros, and 'distance' will return the distances
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between neighbors according to the given metric.
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metric : string, default 'minkowski'
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The distance metric used to calculate the neighbors within a
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given radius for each sample point. The DistanceMetric class
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gives a list of available metrics. The default distance is
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'euclidean' ('minkowski' metric with the param equal to 2.)
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p : int, default 2
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Power parameter for the Minkowski metric. When p = 1, this is
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equivalent to using manhattan_distance (l1), and euclidean_distance
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(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.
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metric_params : dict, optional
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additional keyword arguments for the metric function.
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include_self : bool, default=False
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Whether or not to mark each sample as the first nearest neighbor to
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itself. If `None`, then True is used for mode='connectivity' and False
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for mode='distance' as this will preserve backwards compatibilty.
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n_jobs : int, optional (default = 1)
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The number of parallel jobs to run for neighbors search.
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If ``-1``, then the number of jobs is set to the number of CPU cores.
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Returns
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-------
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A : sparse matrix in CSR format, shape = [n_samples, n_samples]
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A[i, j] is assigned the weight of edge that connects i to j.
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Examples
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--------
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>>> X = [[0], [3], [1]]
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>>> from sklearn.neighbors import radius_neighbors_graph
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>>> A = radius_neighbors_graph(X, 1.5, mode='connectivity', include_self=True)
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>>> A.toarray()
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array([[ 1., 0., 1.],
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[ 0., 1., 0.],
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[ 1., 0., 1.]])
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See also
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--------
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kneighbors_graph
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"""
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if not isinstance(X, RadiusNeighborsMixin):
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X = NearestNeighbors(radius=radius, metric=metric, p=p,
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metric_params=metric_params, n_jobs=n_jobs).fit(X)
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else:
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_check_params(X, metric, p, metric_params)
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query = _query_include_self(X, include_self)
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return X.radius_neighbors_graph(query, radius, mode)
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