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Sasha Nik venik

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venik /
Last active Apr 23, 2019
Bash script for local building TensorFlow on Mac/Linux with all CPU optimizations (default pip package has only SSE)
#!/usr/bin/env bash
# Author: Sasha Nikiforov
# source of inspiration
# Detect platform
if [ "$(uname)" == "Darwin" ]; then
# MacOS
venik / diff.ts
Created Aug 31, 2017
Deep diff between two typescript objects with lodash, returns the difference
View diff.ts
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 / rsafrompub.ts
Created Sep 12, 2017
jsrsasign doesnt support creating RSA object from public in the ASN.1 form, one needs to extract modulus and exponent manually (typescript)
View rsafrompub.ts
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)
const modulus = pubKeyDecoded.substr(50, 128);
const exp = pubKeyDecoded.substr(182, pubKeyDecoded.length);
return KEYUTIL.getKey({n: modulus, e: exp});
#!/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
View latex
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] =
#!/bin/env python3
from import imread
import numpy as np
import matplotlib.pyplot as plt
import as cm
# Load the picture
img = imread('./lenin.jpg', flatten=True, mode=None)
# decompose the picture matrix
#!/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
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 = {}
View ch9_helper.r
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) {
View list_view_item_pokemon.xml
<layout xmlns:android=""
<import type="android.view.View"/>
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