latools/filtering/clustering.py
def cluster_kmeans(data, n_clusters, **kwargs):
    """
    Identify clusters using K - Means algorithm.

    Parameters
    ----------
    data : array_like
        array of size [n_samples, n_features].
    n_clusters : int
        The number of clusters expected in the data.

    Returns
    -------
    dict
        boolean array for each identified cluster.
    """
    km = cl.KMeans(n_clusters, **kwargs)
    kmf = km.fit(data)

    labels = kmf.labels_

    return labels, [np.nan]
def cluster_kmeans(data, n_clusters, **kwargs):
    km = cl.KMeans(n_clusters, **kwargs)
    kmf = km.fit(data)

    labels = kmf.labels_

    return labels, [np.nan]
def cluster_kmeans ( data , n_clusters , * * kwargs ) : km = cl . KMeans ( n_clusters , * * kwargs ) kmf = km . fit ( data ) labels = kmf . labels_ return labels , [ np . nan ]
Identify clusters using K - Means algorithm.

    Parameters
    ----------
    data : array_like
        array of size [n_samples, n_features].
    n_clusters : int
        The number of clusters expected in the data.

    Returns
    -------
    dict
        boolean array for each identified cluster.
Identify clusters using K - Means algorithm .