urigoren

from itertools import combinations
from functools import reduce
from pprint import pprint as pr
  
def all_arrangements(k):
	m=k*(k+1)/2
	m_bits_on=set([tuple(reduce(lambda x,y:x[:y]+[1]+x[y+1:],c,[0]*(2*m+1))) for c in combinations(range(2*m+1),m)])
	return set([tuple(sorted(filter(lambda i:i>0,reduce(lambda x,y: x+[y] if y==0 else x[:-1]+[x[-1]+1,],p,[0])))) for p in m_bits_on])

def move(t,n=1):

urigoren

<?php
define('SLACK_WEBHOOK', 'https://hooks.slack.com/services/xxx/yyy/zzz');
define('TELEGRAM_BOT_TOKEN', '...');
define('TELEGRAM_CHAT_ID', '12345');

function slack($txt) {
  $msg = array('text' => $txt);
  $c = curl_init(SLACK_WEBHOOK);
  curl_setopt($c, CURLOPT_RETURNTRANSFER, true);
  curl_setopt($c, CURLOPT_SSL_VERIFYPEER, false);

urigoren

import numpy as np
from scipy import sparse
import collections

"""
#SPARK Co-Occurence Matrix
#Format: (vertex, vertex) : count
import json, operator, itertools
def cooccur_matrix(srcHdfs, product2vertex):
    ret = sc.textFile(srcHdfs)\

urigoren

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

urigoren

from itertools import chain
from functools import reduce
import operator

"""
Usage of this module:

<iterable> >> function3 * function2 * function1 >> aggregation

for example:

urigoren

hadoop fs -text $1>hvim.txt
vim hvim.txt
hadoop fs -rm -skipTrash $1
hadoop fs -copyFromLocal hvim.txt $1
rm hvim.txt
hadoop fs -chmod 777 $1

urigoren

import numpy as np
from scipy.sparse import linalg, eye, csr_matrix
from sklearn.preprocessing import normalize
from sklearn.metrics.pairwise import pairwise_distances
from collections import defaultdict



class MarkovClustering:
    def __init__(self, matrix, metric="cosine", bias=1):

urigoren

import ast, inspect, textwrap

whitelist = {'math', 'itertools', 'collections', 'functools', 'operator',
                 'json', 'pickle', 'string', 'types', 'statistics', 'fractions', 'decimal'}


def pure(f):
    """pure decorator"""
    f.pure = True
    return f

urigoren

import numpy as np
from matplotlib import pyplot as plt

def polyfit_plot(x, y, p=1):
    plt.scatter(x, y)
    axes = plt.gca()
    coeff = np.polyfit(x, y, p)
    X_plot = np.linspace(axes.get_xlim()[0],axes.get_xlim()[1],100)
    print (np.corrcoef(x,y)[0,1])
    Y_plot = 0

urigoren

Moved to: https://github.com/urigoren/nre

use

pip install nre