# mad

unknown

python

a year ago

927 B

2

Indexable

Never

^{}

import numpy as np def mad_based_outlier(points, thresh=3.5): """ Returns a boolean array with True if points are outliers and False otherwise. Parameters: points : An numobservations by numdimensions array of observations thresh : The modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers. """ if len(points.shape) == 1: points = points[:,None] median = np.median(points, axis=0) diff = np.sum((points - median)**2, axis=-1) diff = np.sqrt(diff) med_abs_deviation = np.median(diff) modified_z_score = 0.6745 * diff / med_abs_deviation return modified_z_score > thresh data = np.array([1, 1, 2, 2, 4, 6, 9]) outliers = mad_based_outlier(data) print(outliers) # [False False False False False False True]