eavidan

import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
import time
import base64
import requests
import pickle
import numpy as np
import random

kell18

from math import log
import unittest


def dcg_at_k(scores):
    assert scores
    return scores[0] + sum(sc / log(ind, 2) for sc, ind in zip(scores[1:], range(2, len(scores)+1)))

def ndcg_at_k(predicted_scores, user_scores):
    assert len(predicted_scores) == len(user_scores)

mathewdgardner

#!/bin/bash

# Save docker images
ds() {
  docker images | \
    cut -d ' ' -f 1 | \
    tail -n +2 | \
    xargs -t -n 1 -I {} -P 4 \
      sh -c 'docker save {} | bzip2 > $(echo "{}" | sed "s/^.*\///").tar.bz2'
}

nkigen

#!/bin/bash
#<nellyk89@gmail.com>
# http://dummyimage.com/600x400/000/fff&text=100000

BASE_URL="http://dummyimage.com/"
DEFAULT_SIZE="600X400"
DEFAULT_BG_COLOR="000"
DEFAULT_COLOR="fff"
START=1001
END=4000

elsonidoq

from math import log
log2= lambda x:log(x,2)
from scipy import histogram, digitize, stats, mean, std
from collections import defaultdict

def mutual_information(x,y):
    return entropy(y) - conditional_entropy(x,y)

def conditional_entropy(x, y):
    """

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.