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use crate::*;
mod normal_distribution {
const S2PI: f64 = 2.50662827463100050242E0;
// https://github.com/scipy/scipy/blob/v1.5.4/scipy/special/cephes/ndtri.c
const P0: [f64; 5] = [
-5.99633501014107895267E1,
9.80010754185999661536E1,
-5.66762857469070293439E1,
import numpy as np
from sklearn.metrics import average_precision_score

def chance_level_ap(n_all, n_positive, trials=1000):
    return np.mean([
        average_precision_score([1] * n_positive + [0] * (n_all - n_positive), np.random.permutation(n_all))
 for _ in range(trials)])
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#!/usr/bin/env python
import itertools
import sys
import os
import subprocess
import jinja2
def enumerate_combinations(**params):
#!/bin/bash
set -e
TARGET_DIR="$(dirname $(readlink -m $0))/.local"
WORKING_DIR="/tmp/"
LIBJPEG_VERSION="1.5.3"
OPENCV_VERSION="3.4.1"
eval "$(pyenv init -)"
PYENV_VERSION=$(pyenv version | cut -f 1 -d ' ')
class RangeWithTimeout(object):
def __init__(self, n=None, timeout_seconds=None):
self.n = n
self.timeout_seconds = timeout_seconds
self.i = 0
def __iter__(self):
self.start_datetime = datetime.datetime.now()