PINE LIBRARY
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:
bisection_search(samples, value) Bissection Search
Parameters:
Returns: int.
label_points(points_x, points_y, centers_x, centers_y) labels each point index with cluster index and distance.
Parameters:
Returns: tuple with int array, float array.
kpp(points_x, points_y, n_clusters) K-Means++ Clustering adapted from Andy Allinger.
Parameters:
Returns: tuple with 2 arrays, float array, int array.
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.
Pine library
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