Alexander Cherkashin amchercashin

## gist:f313c45ea3a28b5d727b32169b86eb11
=ПСТР(ЯЧЕЙКА("filename";A1);НАЙТИ("]";ЯЧЕЙКА("filename";A1))+1;255)

## app.js
const axios = require("axios");

async function getLastRecord(indexID) {
    const response = await axios.get("http://iss.moex.com/iss/history/engines/stock/markets/index/securities/" + indexID + ".json?limit=1");
    const lastRecord = response.data["history.cursor"]["data"][0][1];
    return lastRecord;
}

async function getDataAsync(indexID) {
    const lastRecord = await getLastRecord(indexID);

## elevator.py
from math import sqrt, ceil
def solve(room_number):
    s = room_number
    square_float = 1/2*((sqrt(3)*sqrt(3888*s**2-1)+108*s)**(1/3)/3**(2/3) + 1/(3**(1/3)*(sqrt(3)*sqrt(3888*s**2-1)+108*s)**(1/3)) - 1)
    level_float = (1/2)*(1+square_float)*square_float
    square = ceil(square_float)
    level = ceil(level_float)
    approx_position = level_float - level + 1
    position = round(approx_position / (1/square))


## sdsj-C.R
library(readr)
library(stringr)
library(dplyr)
library(ggplot2)
library(lubridate)
library(glmnet)
library(purrr)
library(tidyr)

train_gender <- read_csv("./data/customers_gender_train.csv", col_types = "ci")

## CUDnn.conf
/usr/local/cuda/lib64

## TensorFlow: connect to CUDnn
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda

## Restart to Windows from Ubuntu script
#!/bin/bash

sudo grub-reboot 2
reboot

## numberWithSpacesFormat.R
space <- function(x, ...) {format(x, ..., big.mark = " ", scientific = FALSE, trim = TRUE)}

## multivariateGaussian.R

multGaussian = function(X) {

        #Number of rows
        m <- nrow(X)

        #Column means
        mu <- colMeans(X)

        #Number of columns

## pca_var.R
pca_var <- function(X, percent) {
        #Normalize data before start
        X = scale(X)

        #Covariance matrix
        Sigma = (1 / nrow(X)) * t(X) %*% X

        #Singular value decomposition of covariance matrix
        Sigma_svd <- svd(Sigma)
	const axios = require("axios");

	async function getLastRecord(indexID) {
	const response = await axios.get("http://iss.moex.com/iss/history/engines/stock/markets/index/securities/" + indexID + ".json?limit=1");
	const lastRecord = response.data["history.cursor"]["data"][0][1];
	return lastRecord;
	}

	async function getDataAsync(indexID) {
	const lastRecord = await getLastRecord(indexID);
	from math import sqrt, ceil
	def solve(room_number):
	s = room_number
	square_float = 1/2((sqrt(3)sqrt(3888s2-1)+108s)(1/3)/3(2/3) + 1/(3*(1/3)(sqrt(3)sqrt(3888s*2-1)+108s)**(1/3)) - 1)
	level_float = (1/2)(1+square_float)square_float
	square = ceil(square_float)
	level = ceil(level_float)
	approx_position = level_float - level + 1
	position = round(approx_position / (1/square))
	library(readr)
	library(stringr)
	library(dplyr)
	library(ggplot2)
	library(lubridate)
	library(glmnet)
	library(purrr)
	library(tidyr)

	train_gender <- read_csv("./data/customers_gender_train.csv", col_types = "ci")
	export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
	export CUDA_HOME=/usr/local/cuda

	multGaussian = function(X) {

	#Number of rows
	m <- nrow(X)

	#Column means
	mu <- colMeans(X)

	#Number of columns
	pca_var <- function(X, percent) {
	#Normalize data before start
	X = scale(X)

	#Covariance matrix
	Sigma = (1 / nrow(X)) * t(X) %*% X

	#Singular value decomposition of covariance matrix
	Sigma_svd <- svd(Sigma)