SignalProcessingClusteringKMeans

Library "SignalProcessingClusteringKMeans"
K-Means Clustering Method.

nearest(point_x, point_y, centers_x, centers_y) finds the nearest center to a point and returns its distance and center index.
Parameters:

point_x: float, x coordinate of point.
point_y: float, y coordinate of point.
centers_x: float array, x coordinates of cluster centers.
centers_y: float array, y coordinates of cluster centers.
@ returns tuple of int, float.

bisection_search(samples, value) Bissection Search
Parameters:

samples: float array, weights to compare.
value: float array, weights to compare.

Returns: int.

label_points(points_x, points_y, centers_x, centers_y) labels each point index with cluster index and distance.
Parameters:

points_x: float array, x coordinates of points.
points_y: float array, y coordinates of points.
centers_x: float array, x coordinates of points.
centers_y: float array, y coordinates of points.

Returns: tuple with int array, float array.

kpp(points_x, points_y, n_clusters) K-Means++ Clustering adapted from Andy Allinger.
Parameters:

points_x: float array, x coordinates of the points.
points_y: float array, y coordinates of the points.
n_clusters: int, number of clusters.

Returns: tuple with 2 arrays, float array, int array.

RicardoSantos

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RicardoSantos