mitallast

class ApacheAnt < Formula
  homepage "http://archive.apache.org/dist/ant/binaries/apache-ant-1.7.1-bin.tar.gz"
  url "http://archive.apache.org/dist/ant/binaries/apache-ant-1.7.1-bin.tar.gz"
  sha1 "6344bb150f22dc335462bafa87de45085d51a5be"

  def install
    rm Dir["bin/*.{bat,cmd,dll,exe}"]
    libexec.install Dir["*"]
    bin.install_symlink Dir["#{libexec}/bin/*"]
    if build.with? "ivy"

mitallast

package granat.dp.components.image;

import java.nio.file.DirectoryStream;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Iterator;

public class TestIterate {
    public static void main(String[] arg) throws Exception {

mitallast

<h2>Загрузка картинки</h2>
<form method="post" action="/admin/images/upload.json" enctype="multipart/form-data" id="image-upload-form"
        data-success="/admin/images/">
    <div class="row">
        <div class="form-group col-md-6">
            <label for="image" class="control-label">картинка</label>
            <input type="file" class="form-control" id="image" name="image" required/>
        </div>
    </div>
    <button type="submit" class="btn btn-default">Сохранить</button>

mitallast

Mac-Pro:flac mitallast$ ls
metallica.flac
Mac-Pro:flac mitallast$ ffmpeg -i metallica.flac metallica.wav
ffmpeg version 2.8 Copyright (c) 2000-2015 the FFmpeg developers
  built with Apple LLVM version 6.1.0 (clang-602.0.53) (based on LLVM 3.6.0svn)
  configuration: --prefix=/usr/local/Cellar/ffmpeg/2.8 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-opencl --enable-libx264 --enable-libmp3lame --enable-libvo-aacenc --enable-libxvid --enable-vda
  libavutil      54. 31.100 / 54. 31.100
  libavcodec     56. 60.100 / 56. 60.100
  libavformat    56. 40.101 / 56. 40.101
  libavdevice    56.  4.100 / 56.  4.100

mitallast

# retrieve_metric_avg.lua $operation_type
local operation_type = ARGV[1];
local count = redis.call("HGET", "operations_count", operation_type)
local total = redis.call("HGET", "operations_total", operation_type)
if not count or not total then
    return 0
else
    local avg = tonumber(total)/tonumber(count)
    return tostring(avg)
end

mitallast

; -*- mode: clojure; -*-
; vim: filetype=clojure

(logging/init {:file "/var/log/riemann/riemann.log"})

; Listen on the local interface over TCP (5555), UDP (5555), and websockets
; (5556)
(let [host "127.0.0.1"]
  (tcp-server {:host host})
  (udp-server {:host host})

mitallast

# -*- coding: utf-8 -*-
from grab import Grab
import logging
import pickledb
from urlparse import urlparse, parse_qs

# logging.basicConfig(level=logging.DEBUG)
db = pickledb.load('guitarplayer.db', False)

keywords = ['prs', 'paul', 'reed', 'smith', u'прс']

mitallast

CREATE LANGUAGE PLPYTHONU;

CREATE OR REPLACE
FUNCTION merge_json(left JSON, right JSON)
  RETURNS JSON AS $$
  import simplejson as json
  def merge(source, destination):
    for key, value in source.items():
      if isinstance(value, dict):
        # get node or create one

mitallast

from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.classification import NaiveBayes
from pyspark.mllib.feature import HashingTF

textFile = sc.textFile("sells.csv")
htf = HashingTF(100000)
data = textFile.map(lambda line: line.split(',', 1)).map(lambda parts: LabeledPoint(parts[0], htf.transform(parts[1].split(" "))))
d_train, d_test = data.randomSplit([0.6, 0.4])
model = NaiveBayes.train(d_train)
prediction_and_labels = d_test.map(lambda point: (model.predict(point.features), point.label))

mitallast

package org.questions;

import com.google.common.collect.Iterators;
import com.google.common.primitives.SignedBytes;
import com.google.common.primitives.UnsignedBytes;

import java.util.*;

public class Test {