mitallast

from elasticsearch import Elasticsearch
import sys

indexes = sys.argv[1:]

client = Elasticsearch('host:port')

recovery = client.indices.recovery(",".join(indexes), human=True)

for index, state in recovery.items():

mitallast

from elasticsearch import Elasticsearch
from elasticsearch.helpers import parallel_bulk, scan
import json
import time
import codecs
import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.info("start reindex")

mitallast

package test;

import java.util.ArrayList;
import java.util.List;

public final class Currency {

    public static final Currency EU = new Currency("EU");
    public static final Currency USD = new Currency("USD");

mitallast

package test;

public class Test1 {

    public static class Node {
        private Node parent;
        private Node right;
        private Node left;
        private int value;

mitallast

int[] a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
int[] b = [1, 2, 3, 4, 9, 8, 7, 6, 5]
int[] e = [9, 1, 8, 2, 7, 3, 6, 4, 5]
int[] f = [9, 8, 7, 6, 5, 4, 3, 2, 1]

int[] c = [1, 2, 3, 4, 5, 6, 7, 8, 10]
int[] d = [1, 2, 3, 4, 5, 6, 7, 8, 8]
int[] g = [1, 8, 3, 4, 5, 6, 7, 8, 8]

mitallast


mitallast

# init

# L - error function
def gbm_L(y, z):
	return (y-z)**2

# derivative of L function without coefficient `2`
def dbm_L_der(y, z):
	# ignore coefficient 2
	return y - z

mitallast

$ go test -bench=.
testing: warning: no tests to run
PASS
BenchmarkJsonReflection-8	  100000	     14689 ns/op
BenchmarkFFJson-8        	  200000	      6345 ns/op
ok  	github.com/mitallast/goprotoserver/protocol	2.974s

mitallast

import pandas_datareader.data as web
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import numpy as np
import datetime

start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2015, 3, 8)
usd = web.DataReader("USD", 'google', start, end)

mitallast

from scikits.audiolab import Sndfile
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal

f = Sndfile('impulse.wav', 'r')
data = np.array(f.read_frames(f.nframes), dtype=np.float64)
f.close()

fs = f.samplerate