module calkmeans

---

function calkmeans

calkmeans(
    image,
    k=None,
    algo=('auto', 'elkan'),
    max_iter=300,
    n_iter=10,
    nodata=-99999,
    **kwargs
)

Calibrating kmeans

This function allows to calibrate the kmeans algorithm. It is possible to obtain the best 'k' value and the best embedded algorithm in KMmeans.

Parameters:

• image: Optical images. It must be rasterio.io.DatasetReader with 3d.

• k: k This argument is None when the objective is to obtain the best 'k' value. f the objective is to select the best algorithm embedded in kmeans, please specify a 'k' value.

• max_iter: The maximum number of iterations allowed. Strictly related to KMeans. Please see

• https: //scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

• algo: It can be "auto" and 'elkan'. "auto" and "full" are deprecated and they will be removed in Scikit-Learn 1.3. They are both aliases for "lloyd".

• Changed in version 1.1: Renamed “full” to “lloyd”, and deprecated “auto” and “full”. Changed “auto” to use “lloyd” instead of “elkan”.

• n_iter: Iterations number to obtain the best 'k' value. 'n_iter' must be greater than the number of classes expected to be obtained in the classification. Default is 10.

• nodata: The NoData value to replace with -99999.

• **kwargs: These will be passed to scikit-learn KMeans, please see full lists at:

• https: //scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html.

Return: Labels of classification as numpy object with 2d.

Note:

If the idea is to find the optimal value of 'k' (clusters or classes), k = None as an argument of the function must be put, because the function find 'k' for which the intra-class inertia is stabilized. If the 'k' value is known and the idea is to find the best algorithm embedded in kmeans (that maximizes inter-class distances), k = n, which 'n' is a specific class number, must be put. It can be greater than or equal to 0.

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