Module weighted_percentile

Compute weighted percentiles. If the weights are equal, this is the same as normal percentiles. Elements of the data and wt arrays correspond to each other and must have equal length (unless wt is None).

Parameters:

• data (A numpy.ndarray array or a list of numbers.) - The data.
• wt (None or a numpy.ndarray array or a list of numbers. All the weights must be non-negative and the sum must be greater than zero.) - How important is a given piece of data.
• percentiles (a list of numbers between 0 and 1.) - what percentiles to use. (Not really percentiles, as the range is 0-1 rather than 0-100.)

Returns: [ float, ... ]

the weighted percentiles of the data.

The weighted median is the point where half the weight is above and half the weight is below. If the weights are equal, this is the same as the median. Elements of the data and wt arrays correspond to each other and must have equal length (unless wt is None).

Parameters:

• data (A numpy.ndarray array or a list of numbers.) - The data.
• wt (None or a numpy.ndarray array or a list of numbers. All the weights must be non-negative and the sum must be greater than zero.) - How important is a given piece of data.

Returns: float

the weighted median of the data.

Takes a weighted component-by-component median of a sequence of vectors.

Parameters:

• list_of_vectors (any sequence of lists or numpy.ndarray. All the inside lists must be of the same length.) - the data to be combined
• wt (a vector of weights (one weight for each input vector) or None.) - sequence of numbers or None

Returns: numpy.ndarray

the median vector.