Python neighbors.kneighbors_graph方法代码示例

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def text_to_graph(text):
    import networkx as nx
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.neighbors import kneighbors_graph

    # use tfidf to transform texts into feature vectors
    vectorizer = TfidfVectorizer()
    vectors = vectorizer.fit_transform(text)

    # build the graph which is full-connected
    N = vectors.shape[0]
    mat = kneighbors_graph(vectors, N, metric='cosine', mode='distance', include_self=True)
    mat.data = 1 - mat.data  # to similarity

    g = nx.from_scipy_sparse_matrix(mat, create_using=nx.Graph())

    return g 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_isomap_simple_grid():
    # Isomap should preserve distances when all neighbors are used
    N_per_side = 5
    Npts = N_per_side ** 2
    n_neighbors = Npts - 1

    # grid of equidistant points in 2D, n_components = n_dim
    X = np.array(list(product(range(N_per_side), repeat=2)))

    # distances from each point to all others
    G = neighbors.kneighbors_graph(X, n_neighbors,
                                   mode='distance').toarray()

    for eigen_solver in eigen_solvers:
        for path_method in path_methods:
            clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2,
                                  eigen_solver=eigen_solver,
                                  path_method=path_method)
            clf.fit(X)

            G_iso = neighbors.kneighbors_graph(clf.embedding_,
                                               n_neighbors,
                                               mode='distance').toarray()
            assert_array_almost_equal(G, G_iso) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_kneighbors_graph_sparse(seed=36):
    # Test kneighbors_graph to build the k-Nearest Neighbor graph
    # for sparse input.
    rng = np.random.RandomState(seed)
    X = rng.randn(10, 10)
    Xcsr = csr_matrix(X)

    for n_neighbors in [1, 2, 3]:
        for mode in ["connectivity", "distance"]:
            assert_array_almost_equal(
                neighbors.kneighbors_graph(X,
                                           n_neighbors,
                                           mode=mode).toarray(),
                neighbors.kneighbors_graph(Xcsr,
                                           n_neighbors,
                                           mode=mode).toarray()) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def fit(self, X):
        """Fit the clustering model
        Parameters
        ----------
        X : array_like
            the data to be clustered: shape = [n_samples, n_features]
        """
        X = np.asarray(X, dtype=float)

        self.X_train_ = X

        # generate a sparse graph using the k nearest neighbors of each point
        G = kneighbors_graph(X, n_neighbors=self.n_neighbors, mode='distance')

        # Compute the minimum spanning tree of this graph
        self.full_tree_ = minimum_spanning_tree(G, overwrite=True)

        # Find the cluster labels
        self.n_components_, self.labels_, self.cluster_graph_ =\
            self.compute_clusters()

        return self 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def _get_adj_from_data(X, k, **kwargs):
    """
    Computes adjacency matrix of a K-NN graph from the given data.
    :param X: rank 1 np.array, the 2D coordinates of pixels on the grid.
    :param kwargs: kwargs for sklearn.neighbors.kneighbors_graph (see docs
    [here](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.kneighbors_graph.html)).
    :return: scipy sparse matrix.
    """
    A = kneighbors_graph(X, k, **kwargs).toarray()
    A = sp.csr_matrix(np.maximum(A, A.T))

    return A 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_isomap_reconstruction_error():
    # Same setup as in test_isomap_simple_grid, with an added dimension
    N_per_side = 5
    Npts = N_per_side ** 2
    n_neighbors = Npts - 1

    # grid of equidistant points in 2D, n_components = n_dim
    X = np.array(list(product(range(N_per_side), repeat=2)))

    # add noise in a third dimension
    rng = np.random.RandomState(0)
    noise = 0.1 * rng.randn(Npts, 1)
    X = np.concatenate((X, noise), 1)

    # compute input kernel
    G = neighbors.kneighbors_graph(X, n_neighbors,
                                   mode='distance').toarray()

    centerer = preprocessing.KernelCenterer()
    K = centerer.fit_transform(-0.5 * G ** 2)

    for eigen_solver in eigen_solvers:
        for path_method in path_methods:
            clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2,
                                  eigen_solver=eigen_solver,
                                  path_method=path_method)
            clf.fit(X)

            # compute output kernel
            G_iso = neighbors.kneighbors_graph(clf.embedding_,
                                               n_neighbors,
                                               mode='distance').toarray()

            K_iso = centerer.fit_transform(-0.5 * G_iso ** 2)

            # make sure error agrees
            reconstruction_error = np.linalg.norm(K - K_iso) / Npts
            assert_almost_equal(reconstruction_error,
                                clf.reconstruction_error()) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_not_fitted_error_gets_raised():
    X = [[1]]
    neighbors_ = neighbors.NearestNeighbors()
    assert_raises(NotFittedError, neighbors_.kneighbors_graph, X)
    assert_raises(NotFittedError, neighbors_.radius_neighbors_graph, X) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_kneighbors_graph():
    # Test kneighbors_graph to build the k-Nearest Neighbor graph.
    X = np.array([[0, 1], [1.01, 1.], [2, 0]])

    # n_neighbors = 1
    A = neighbors.kneighbors_graph(X, 1, mode='connectivity',
                                   include_self=True)
    assert_array_equal(A.toarray(), np.eye(A.shape[0]))

    A = neighbors.kneighbors_graph(X, 1, mode='distance')
    assert_array_almost_equal(
        A.toarray(),
        [[0.00, 1.01, 0.],
         [1.01, 0., 0.],
         [0.00, 1.40716026, 0.]])

    # n_neighbors = 2
    A = neighbors.kneighbors_graph(X, 2, mode='connectivity',
                                   include_self=True)
    assert_array_equal(
        A.toarray(),
        [[1., 1., 0.],
         [1., 1., 0.],
         [0., 1., 1.]])

    A = neighbors.kneighbors_graph(X, 2, mode='distance')
    assert_array_almost_equal(
        A.toarray(),
        [[0., 1.01, 2.23606798],
         [1.01, 0., 1.40716026],
         [2.23606798, 1.40716026, 0.]])

    # n_neighbors = 3
    A = neighbors.kneighbors_graph(X, 3, mode='connectivity',
                                   include_self=True)
    assert_array_almost_equal(
        A.toarray(),
        [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_non_euclidean_kneighbors():
    rng = np.random.RandomState(0)
    X = rng.rand(5, 5)

    # Find a reasonable radius.
    dist_array = pairwise_distances(X).flatten()
    np.sort(dist_array)
    radius = dist_array[15]

    # Test kneighbors_graph
    for metric in ['manhattan', 'chebyshev']:
        nbrs_graph = neighbors.kneighbors_graph(
            X, 3, metric=metric, mode='connectivity',
            include_self=True).toarray()
        nbrs1 = neighbors.NearestNeighbors(3, metric=metric).fit(X)
        assert_array_equal(nbrs_graph, nbrs1.kneighbors_graph(X).toarray())

    # Test radiusneighbors_graph
    for metric in ['manhattan', 'chebyshev']:
        nbrs_graph = neighbors.radius_neighbors_graph(
            X, radius, metric=metric, mode='connectivity',
            include_self=True).toarray()
        nbrs1 = neighbors.NearestNeighbors(metric=metric, radius=radius).fit(X)
        assert_array_equal(nbrs_graph, nbrs1.radius_neighbors_graph(X).A)

    # Raise error when wrong parameters are supplied,
    X_nbrs = neighbors.NearestNeighbors(3, metric='manhattan')
    X_nbrs.fit(X)
    assert_raises(ValueError, neighbors.kneighbors_graph, X_nbrs, 3,
                  metric='euclidean')
    X_nbrs = neighbors.NearestNeighbors(radius=radius, metric='manhattan')
    X_nbrs.fit(X)
    assert_raises(ValueError, neighbors.radius_neighbors_graph, X_nbrs,
                  radius, metric='euclidean') 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_k_and_radius_neighbors_train_is_not_query():
    # Test kneighbors et.al when query is not training data

    for algorithm in ALGORITHMS:

        nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm)

        X = [[0], [1]]
        nn.fit(X)
        test_data = [[2], [1]]

        # Test neighbors.
        dist, ind = nn.kneighbors(test_data)
        assert_array_equal(dist, [[1], [0]])
        assert_array_equal(ind, [[1], [1]])
        dist, ind = nn.radius_neighbors([[2], [1]], radius=1.5)
        check_object_arrays(dist, [[1], [1, 0]])
        check_object_arrays(ind, [[1], [0, 1]])

        # Test the graph variants.
        assert_array_equal(
            nn.kneighbors_graph(test_data).A, [[0., 1.], [0., 1.]])
        assert_array_equal(
            nn.kneighbors_graph([[2], [1]], mode='distance').A,
            np.array([[0., 1.], [0., 0.]]))
        rng = nn.radius_neighbors_graph([[2], [1]], radius=1.5)
        assert_array_equal(rng.A, [[0, 1], [1, 1]]) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_k_and_radius_neighbors_X_None():
    # Test kneighbors et.al when query is None
    for algorithm in ALGORITHMS:

        nn = neighbors.NearestNeighbors(n_neighbors=1, algorithm=algorithm)

        X = [[0], [1]]
        nn.fit(X)

        dist, ind = nn.kneighbors()
        assert_array_equal(dist, [[1], [1]])
        assert_array_equal(ind, [[1], [0]])
        dist, ind = nn.radius_neighbors(None, radius=1.5)
        check_object_arrays(dist, [[1], [1]])
        check_object_arrays(ind, [[1], [0]])

        # Test the graph variants.
        rng = nn.radius_neighbors_graph(None, radius=1.5)
        kng = nn.kneighbors_graph(None)
        for graph in [rng, kng]:
            assert_array_equal(rng.A, [[0, 1], [1, 0]])
            assert_array_equal(rng.data, [1, 1])
            assert_array_equal(rng.indices, [1, 0])

        X = [[0, 1], [0, 1], [1, 1]]
        nn = neighbors.NearestNeighbors(n_neighbors=2, algorithm=algorithm)
        nn.fit(X)
        assert_array_equal(
            nn.kneighbors_graph().A,
            np.array([[0., 1., 1.], [1., 0., 1.], [1., 1., 0]])) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_include_self_neighbors_graph():
    # Test include_self parameter in neighbors_graph
    X = [[2, 3], [4, 5]]
    kng = neighbors.kneighbors_graph(X, 1, include_self=True).A
    kng_not_self = neighbors.kneighbors_graph(X, 1, include_self=False).A
    assert_array_equal(kng, [[1., 0.], [0., 1.]])
    assert_array_equal(kng_not_self, [[0., 1.], [1., 0.]])

    rng = neighbors.radius_neighbors_graph(X, 5.0, include_self=True).A
    rng_not_self = neighbors.radius_neighbors_graph(
        X, 5.0, include_self=False).A
    assert_array_equal(rng, [[1., 1.], [1., 1.]])
    assert_array_equal(rng_not_self, [[0., 1.], [1., 0.]]) 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def test_knn_forcing_backend(backend, algorithm):
    # Non-regression test which ensure the knn methods are properly working
    # even when forcing the global joblib backend.
    with parallel_backend(backend):
        X, y = datasets.make_classification(n_samples=30, n_features=5,
                                            n_redundant=0, random_state=0)
        X_train, X_test, y_train, y_test = train_test_split(X, y)

        clf = neighbors.KNeighborsClassifier(n_neighbors=3,
                                             algorithm=algorithm,
                                             n_jobs=3)
        clf.fit(X_train, y_train)
        clf.predict(X_test)
        clf.kneighbors(X_test)
        clf.kneighbors_graph(X_test, mode='distance').toarray() 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def hierarchy(feat, n_clusters=2, knn=30):
    from sklearn.neighbors import kneighbors_graph
    knn_graph = kneighbors_graph(feat, knn, include_self=False)
    hierarchy = cluster.AgglomerativeClustering(n_clusters=n_clusters,
                                                connectivity=knn_graph,
                                                linkage='ward').fit(feat)
    return hierarchy.labels_ 

# 需要导入模块: from sklearn import neighbors [as 别名]
# 或者: from sklearn.neighbors import kneighbors_graph [as 别名]
def _calc_scores(self, x):
        graph = kneighbors_graph(
            x,
            n_neighbors=self.p,
        )
        # Construct the heat matrix
        w = np.zeros([x.shape[0], x.shape[0]])
        rows, cols = graph.nonzero()
        for i, j in zip(rows, cols):
            w[i, j] = math.exp(-np.linalg.norm(x[i] - x[j])**2/self.sigma)

        # Compute degree and Laplacian matrices
        degree_vector = np.sum(w, 1)
        degree = np.diag(degree_vector)
        laplacian = degree - w

        # Solve the eigen-problem
        values, vectors = eigh(laplacian, degree)
        smallest = vectors[:, 0:self.clusters].T

        # Find coefficients for each cluster
        coefs = []
        for i in range(self.clusters):
            this_coefs = self._create_regressor().fit(x, smallest[i]).coef_
            coefs.append(this_coefs)
        coefs = np.array(coefs)

        # Compute MCFS-scores
        scores = np.max(coefs, 0)
        return scores