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Find outliers in a set of points. This is based on: http://stackoverflow.com/a/22357811/8879 (with slight modifications)
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import numpy as np | |
def is_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. | |
Returns: | |
-------- | |
mask : A numobservations-length boolean array. | |
References: | |
---------- | |
Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and | |
Handle Outliers", The ASQC Basic References in Quality Control: | |
Statistical Techniques, Edward F. Mykytka, Ph.D., Editor. | |
""" | |
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 = max(np.median(diff), 1e-10) | |
modified_z_score = 0.6745 * diff / med_abs_deviation | |
return modified_z_score > thresh |
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