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# Unsupervised classification classe

See the file `test_kmeans.py` for an example.

## KMeans

class `classification.``KMeans`

  KMeans clustering algorithm adapted to hyperspectral imaging

  `display`(colorMap='Accent', suffix=None)

    Display the cluster map.

    Parameters:
      path: string
        The path where to put the plot.
      colorMap: string [default ‘Accent’]
        A color map element of [‘Accent’, ‘Dark2’, ‘Paired’, ‘Pastel1’, ‘Pastel2’, ‘Set1’, ‘Set2’, ‘Set3’], “Accent” is the default and it fall back on “Jet”.
      suffix: string [default None]
        Add a suffix to the title.
  `plot`(path, colorMap='Accent', suffix=None)

    Plot the cluster map.

    Parameters:
      path: string
        The path where to put the plot.
      colorMap: string [default ‘Accent’]
        A color map element of [‘Accent’, ‘Dark2’, ‘Paired’, ‘Pastel1’, ‘Pastel2’, ‘Set1’, ‘Set2’, ‘Set3’], “Accent” is the default and it fall back on “Jet”.
      suffix: string [default None]
        Add a suffix to the file name.
  `predict`(M, n_clusters=5, n_jobs=1, init='k-means++')

    KMeans clustering algorithm adapted to hyperspectral imaging. It is a simple wrapper to the scikit-learn version.

    Parameters:
      M: numpy array
        A HSI cube (m x n x p).
      n_clusters: int [default 5]
        The number of clusters to generate.
      n_jobs: int [default 1]
        Taken from scikit-learn doc: The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel. If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.
      init: string or array [default ‘k-means++’]
        Taken from scikit-learn doc: Method for initialization, defaults to k-means++: k-means++ : selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details. random: choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
    Returns: numpy array
      A cluster map (m x n x c), c is the clusters number .

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