Module « scipy.cluster.vq »

Fonction kmeans2 - module scipy.cluster.vq

Signature de la fonction kmeans2

def kmeans2(data, k, iter=10, thresh=1e-05, minit='random', missing='warn', check_finite=True, *, seed=None) 

Description

kmeans2.__doc__

    Classify a set of observations into k clusters using the k-means algorithm.

    The algorithm attempts to minimize the Euclidean distance between
    observations and centroids. Several initialization methods are
    included.

    Parameters
    ----------
    data : ndarray
        A 'M' by 'N' array of 'M' observations in 'N' dimensions or a length
        'M' array of 'M' 1-D observations.
    k : int or ndarray
        The number of clusters to form as well as the number of
        centroids to generate. If `minit` initialization string is
        'matrix', or if a ndarray is given instead, it is
        interpreted as initial cluster to use instead.
    iter : int, optional
        Number of iterations of the k-means algorithm to run. Note
        that this differs in meaning from the iters parameter to
        the kmeans function.
    thresh : float, optional
        (not used yet)
    minit : str, optional
        Method for initialization. Available methods are 'random',
        'points', '++' and 'matrix':

        'random': generate k centroids from a Gaussian with mean and
        variance estimated from the data.

        'points': choose k observations (rows) at random from data for
        the initial centroids.

        '++': choose k observations accordingly to the kmeans++ method
        (careful seeding)

        'matrix': interpret the k parameter as a k by M (or length k
        array for 1-D data) array of initial centroids.
    missing : str, optional
        Method to deal with empty clusters. Available methods are
        'warn' and 'raise':

        'warn': give a warning and continue.

        'raise': raise an ClusterError and terminate the algorithm.
    check_finite : bool, optional
        Whether to check that the input matrices contain only finite numbers.
        Disabling may give a performance gain, but may result in problems
        (crashes, non-termination) if the inputs do contain infinities or NaNs.
        Default: True
    seed : {None, int, `numpy.random.Generator`,
            `numpy.random.RandomState`}, optional

        Seed for initializing the pseudo-random number generator.
        If `seed` is None (or `numpy.random`), the `numpy.random.RandomState`
        singleton is used.
        If `seed` is an int, a new ``RandomState`` instance is used,
        seeded with `seed`.
        If `seed` is already a ``Generator`` or ``RandomState`` instance then
        that instance is used.
        The default is None.

    Returns
    -------
    centroid : ndarray
        A 'k' by 'N' array of centroids found at the last iteration of
        k-means.
    label : ndarray
        label[i] is the code or index of the centroid the
        ith observation is closest to.

    See Also
    --------
    kmeans

    References
    ----------
    .. [1] D. Arthur and S. Vassilvitskii, "k-means++: the advantages of
       careful seeding", Proceedings of the Eighteenth Annual ACM-SIAM Symposium
       on Discrete Algorithms, 2007.

    Examples
    --------
    >>> from scipy.cluster.vq import kmeans2
    >>> import matplotlib.pyplot as plt

    Create z, an array with shape (100, 2) containing a mixture of samples
    from three multivariate normal distributions.

    >>> rng = np.random.default_rng()
    >>> a = rng.multivariate_normal([0, 6], [[2, 1], [1, 1.5]], size=45)
    >>> b = rng.multivariate_normal([2, 0], [[1, -1], [-1, 3]], size=30)
    >>> c = rng.multivariate_normal([6, 4], [[5, 0], [0, 1.2]], size=25)
    >>> z = np.concatenate((a, b, c))
    >>> rng.shuffle(z)

    Compute three clusters.

    >>> centroid, label = kmeans2(z, 3, minit='points')
    >>> centroid
    array([[ 2.22274463, -0.61666946],  # may vary
           [ 0.54069047,  5.86541444],
           [ 6.73846769,  4.01991898]])

    How many points are in each cluster?

    >>> counts = np.bincount(label)
    >>> counts
    array([29, 51, 20])  # may vary

    Plot the clusters.

    >>> w0 = z[label == 0]
    >>> w1 = z[label == 1]
    >>> w2 = z[label == 2]
    >>> plt.plot(w0[:, 0], w0[:, 1], 'o', alpha=0.5, label='cluster 0')
    >>> plt.plot(w1[:, 0], w1[:, 1], 'd', alpha=0.5, label='cluster 1')
    >>> plt.plot(w2[:, 0], w2[:, 1], 's', alpha=0.5, label='cluster 2')
    >>> plt.plot(centroid[:, 0], centroid[:, 1], 'k*', label='centroids')
    >>> plt.axis('equal')
    >>> plt.legend(shadow=True)
    >>> plt.show()