def component_layout(
    data, n_components, component_labels, dim, metric="euclidean", metric_kwds={}
):
    """Provide a layout relating the separate connected components. This is done
    by taking the centroid of each component and then performing a spectral embedding
    of the centroids.

    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The source data -- required so we can generate centroids for each
        connected component of the graph.

    n_components: int
        The number of distinct components to be layed out.

    component_labels: array of shape (n_samples)
        For each vertex in the graph the label of the component to
        which the vertex belongs.

    dim: int
        The chosen embedding dimension.

    metric: string or callable (optional, default 'euclidean')
        The metric used to measure distances among the source data points.

    metric_kwds: dict (optional, default {})
        Keyword arguments to be passed to the metric function.

    Returns
    -------
    component_embedding: array of shape (n_components, dim)
        The ``dim``-dimensional embedding of the ``n_components``-many
        connected components.
    """

    component_centroids = np.empty((n_components, data.shape[1]), dtype=np.float64)

    for label in range(n_components):
        component_centroids[label] = data[component_labels == label].mean(axis=0)

    distance_matrix = pairwise_distances(
        component_centroids, metric=metric, **metric_kwds
    )
    affinity_matrix = np.exp(-distance_matrix ** 2)

    component_embedding = SpectralEmbedding(
        n_components=dim, affinity="precomputed"
    ).fit_transform(affinity_matrix)
    component_embedding /= component_embedding.max()

    return component_embedding
def component_layout ( data , n_components , component_labels , dim , metric = "euclidean" , metric_kwds = { } ) : component_centroids = np . empty ( ( n_components , data . shape [ 1 ] ) , dtype = np . float64 ) for label in range ( n_components ) : component_centroids [ label ] = data [ component_labels == label ] . mean ( axis = 0 ) distance_matrix = pairwise_distances ( component_centroids , metric = metric , ** metric_kwds ) affinity_matrix = np . exp ( - distance_matrix ** 2 ) component_embedding = SpectralEmbedding ( n_components = dim , affinity = "precomputed" ) . fit_transform ( affinity_matrix ) component_embedding /= component_embedding . max ( ) return component_embedding
Provide a layout relating the separate connected components. This is done
    by taking the centroid of each component and then performing a spectral embedding
    of the centroids.

    Parameters
    ----------
    data: array of shape (n_samples, n_features)
        The source data -- required so we can generate centroids for each
        connected component of the graph.

    n_components: int
        The number of distinct components to be layed out.

    component_labels: array of shape (n_samples)
        For each vertex in the graph the label of the component to
        which the vertex belongs.

    dim: int
        The chosen embedding dimension.

    metric: string or callable (optional, default 'euclidean')
        The metric used to measure distances among the source data points.

    metric_kwds: dict (optional, default {})
        Keyword arguments to be passed to the metric function.

    Returns
    -------
    component_embedding: array of shape (n_components, dim)
        The ``dim``-dimensional embedding of the ``n_components``-many
        connected components.