RicardoSantos

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.

Pine library

In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can use it freely. Cheers to the author!

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