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iwiwi

iwiwi

iwiwi

iwiwi

#!/usr/bin/env ruby

#
# crawler.rb --- Lightweight parallel web graph crawler
#
# Usage:
#   ./crawler.rb START_URL TARGET_REGEXP
#
# Output:
#   stdout --- edge list (tab separated URLs)

iwiwi

import it.unimi.dsi.fastutil.ints.IntArrayFIFOQueue;
import it.unimi.dsi.fastutil.ints.IntArrays;
import it.unimi.dsi.logging.ProgressLogger;
import it.unimi.dsi.webgraph.GraphClassParser;
import it.unimi.dsi.webgraph.ImmutableGraph;
import it.unimi.dsi.webgraph.LazyIntIterator;

import java.io.*;
import java.util.*;

iwiwi

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,

iwiwi

#!/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 ' ')

iwiwi

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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