urigoren

import sys, random, json
from gensim.models import Word2Vec
from argparse import ArgumentParser
from collections import Counter
from pathlib import Path

__dir__= Path(__file__).absolute().parent


class DirSentences(object):

urigoren

import json, boto3
s3 = boto3.resource("s3").Bucket("bucket")
json.load_s3 = lambda f: json.load(s3.Object(key=f).get()["Body"])
json.dump_s3 = lambda obj, f: s3.Object(key=f).put(Body=json.dumps(obj))

urigoren

set splitbelow
nnoremap <F4> :set hlsearch! nohlsearch?<CR>
imap <F4> <C-O><F4>

nnoremap <F2> :let @/="qoXouQoz"<CR>:set invpaste paste?<CR>
set pastetoggle=<F2>
nnoremap <silent> <F3> :let @/ .= '\\|\<'.expand('<cword>').'\>'<cr>n
nnoremap <buffer> <F5> :w<cr>:exec '!/usr/bin/python3' shellescape(@%, 1)<cr>
syntax on

urigoren

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

use

pip install nre

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

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 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

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

from itertools import chain
from functools import reduce
import operator

"""
Usage of this module:

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

for example:

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...')