venik

#!/usr/bin/env bash

# Author: Sasha Nikiforov

# source of inspiration
# https://stackoverflow.com/questions/41293077/how-to-compile-tensorflow-with-sse4-2-and-avx-instructions

# Detect platform
if [ "$(uname)" == "Darwin" ]; then
	# MacOS

venik

difference(newObject: any, baseObject: any) {
    function changes(newObject: any, baseObject: any) {
        return _.transform(newObject, function(result, value, key) {
            if (!_.isEqual(value, baseObject[key])) {
                result[key] = (_.isObject(value) && _.isObject(baseObject[key])) ? changes(value, baseObject[key]) : value;
            }
        });
    }

    const rval = changes(newObject, baseObject);

venik

function getRsaFromPubKey(pubKeyB64: string): RSAKey {
    const pubKeyDecoded = b64tohex(pubKeyB64);

    // jsrsasign cannot build key out of PEM or ASN.1 string, so we have to extract modulus and exponent
    // you can get some idea what happens from the link below (keep in mind that in JS every char is 2 bytes)
    // https://crypto.stackexchange.com/questions/18031/how-to-find-modulus-from-a-rsa-public-key/18034#18034
    const modulus = pubKeyDecoded.substr(50, 128);
    const exp = pubKeyDecoded.substr(182, pubKeyDecoded.length);

    return KEYUTIL.getKey({n: modulus, e: exp});

venik

#!/usr/bin/env python
# Sasha Nikiforov

import numpy as np
import numpy.linalg as lg

from functools import reduce

# Not super optimal way to calculate N-th Fibonacci - taken
# from stackoverflow

venik

S_{N} = S_{N-2} + S_{N-1}

A = \left[\begin{array}{cc} 0 & 1\\ 1 & 1 \end{array}\right]

A \left[\begin{array}{cc} S_{N-2} \\S_{N-1} \end{array}\right] = 
\left[\begin{array}{cc} 0 & 1\\ 1 & 1 \end{array}\right] \left[\begin{array}{cc} S_{N-2} \\S_{N-1} \end{array}\right] = 
\left[\begin{array}{cc} S_{N-1} \\ S_{N} \end{array}\right]


\left[\begin{array}{cc} 0 & 1\\ 1 & 1 \end{array}\right] \left[\begin{array}{cc} 0 & 1\\ 1 & 1 \end{array}\right] \left[\begin{array}{cc} S_{0} \\S_{1} \end{array}\right] = 

venik

#!/bin/env python3
from scipy.ndimage.io import imread
import numpy as np

import matplotlib.pyplot as plt
import matplotlib.cm as cm

# Load the picture
img = imread('./lenin.jpg', flatten=True, mode=None)
# decompose the picture matrix

venik

#!/usr/bin/env python2
# -*- coding: utf-8 -*-

import sys
import time
from datetime import datetime

keyString = u'купить'
goodString = u'купюра'
timeThreshold = 90 # seconds timescale

venik

import numpy as np
import numpy.linalg as linalg
import matplotlib.pyplot as plt

corpus = []
corpus.append('I like deep learning')
corpus.append('I like NLP')
corpus.append('I enjoy flying')

word_index = {}

venik

x = rbind(mvrnorm(50, rep(0,10), diag(10)), mvrnorm(50, rep(c(1, 0), c(5, 5)), diag(10)))
y = rep(c(0, 1), c(50, 50))
dat = data.frame(x, y=as.factor(y))

svmfit = svm(y~., data=dat)

ex1 = function (times, svmfit) {
    errate = rep(0, times)
    test_size = 500
    for (i in 1:times) {

venik

<layout xmlns:android="http://schemas.android.com/apk/res/android"
        xmlns:app="http://schemas.android.com/apk/res-auto"
        xmlns:tools="http://schemas.android.com/tools">
    <data>
        <import type="android.view.View"/>

        <variable
                name="viewmodel"
                type="com.example.gql1.viewmodels.PokemonVM"/>
    </data>