imagepypelines.
confidence
(data, confidence=0.95)[source]¶Parameters: 


Returns:  the mean for this distributions float: +/ deviation for this confidence interval 
Return type:  float 
Example
>>> import numpy as np
>>> import imagepypelines as ip
>>> # create sample test 'accuracies' from a normal distribution
>>> # mean accuracy is 75%, std is 10% for this example
>>> accuracies = np.random.normal(.75, .1, 1000)
>>> # get 95% confidence interval
>>> mean, error = ip.confidence(accuracies,.95)