py: boostrap generator

bootstrap.py

import numpy as np

class Bootstrap():
    def __init__(self, arr, n, statistic=np.mean):
        self.ii = 0
        self.n = n
        self.arr = np.array(arr).squeeze()
        self.statistic = statistic

    def __iter__(self):
        return self

    def __next__(self):
        if self.ii < self.n:
            self.ii += 1
            if self.statistic is not None:
                return self.statistic(np.random.choice(self.arr, self.arr.size))
            else:
                return np.random.choice(self.arr, self.arr.size)
        else:
            raise StopIteration()

    def all(self):
        return np.fromiter(self, 'f')

    def ci(self, intervals=[95.0]):
        intervals = list(intervals)
        n_percentiles = len(intervals)*2
        percentiles = [(v/2.0, 100.0-(v/2.0)) for v in intervals]
        reduced = self.all()
            
        return [np.percentile(reduced, pc, overwrite_input=True) for pc in percentiles]