Tomer Ben David tomer-ben-david

tomer-ben-david / longestSubsequenceNonRepeating.java

Created May 7, 2022 06:17

LeetCode 2 Find longest subsequence in string non repeating

	class Solution {
	public int lengthOfLongestSubstring(String s) {
	int l = 0;
	int result = 0;
	Set<Character> set = new HashSet<>();
	// We start with right pointer from 0, both left and right are at 0!.
	for (int r = 0; r < s.length(); r++) {
	// As long as we have duplicates move left first. Trick! at first 0,0 no duplicates!
	while (set.contains(s.charAt(r))) {
	set.remove(s.charAt(l)); // Note we remove what left points to! until we remove all duplicates with left.

tomer-ben-david / gnuplot

Created February 25, 2020 08:38

tomer-ben-david / gist:72f3cc7752d6debee8327071f828f29a

Created January 28, 2020 08:12

snippet tampermonkey repl

	// ==UserScript==
	// @name Interview Run Code
	// @namespace http://tampermonkey.net/
	// @version 0.1
	// @description try to take over the world!
	// @author You
	// @include https://repl.it/*
	// @include http://tomer-test-public.s3-website-us-west-2.amazonaws.com/*
	// @grant none
	// @require http://code.jquery.com/jquery-3.3.1.min.js

tomer-ben-david / redshift.rstudio.R

Last active September 10, 2018 12:01

redshift rstudio

	brew install libpq
	```R
	install.packages('RPostgres')
	library(RPostgres)
	pconn_r <- dbConnect(RPostgres::Postgres(),
	host = host,
	port = port,
	user = user,
	password = password,
	dbname = dbname,

tomer-ben-david / scala.md

Last active April 10, 2018 12:14

#scala

scala

mockito

import org.scalatest.mock.MockitoSugar
import org.mockito.Mockito._

with MockitoSugar

tomer-ben-david / scala.primer.scala

Last active April 8, 2018 07:42

	BigDecimal(1.002).setScale(2, BigDecimal.RoundingMode.HALF_UP).doubleValue() // Round double to #.##

	\| Spark Term \| Description \|
	\| ------------------------------------------------------------ \| ------------------------------------------------------------ \|
	\| Spreadsheet \| Think of data as spreadsheet \|
	\| Statistical learning \| Output = f(input) # => f(inputVariable) or f(inputVector), or f(independent variables) or Y = F(X) // X1,X2,.. \|
	\| Programming learning \| OutputAttributes = Program(InputAttributes) or Program(InputFeatures) or Model = Algorithm(Data) \|
	\| Error \| Y = f(X) + e # => You learn a function! \|
	\| Parametric learning \| No

tomer-ben-david / apply.function.to.matrix.R

Created January 20, 2018 10:11

apply function to matrix

	> df <- data.frame(x=c("spam", "spam", "ham"), y=c("some mail", "some other mail", "some third mail"))
	> add.string.func <- function(somestr) { paste("prefix-", somestr, "-postfix", sep = "") }
	> df[1,]
	x y
	1 spam some mail
	> df[,1]
	[1] spam spam ham
	Levels: ham spam
	> apply(X = df[1,], add.string.func, MARGIN = 1)
	1

tomer-ben-david / build.matrix.table.plot.R

Last active January 27, 2018 15:06

create matrix table r and plot load dataframe #R

	df <- data.frame(x=c("spam", "spam", "ham"), y=c("some mail", "some other mail", "some third mail"))
	names(df) <- c("Label", "Text")
	df$Label <- as.factor(df$Label) // Fill by label would not work if not factor.
	df$TextLength <- nchar(as.character(df$Text))
	View(df)
	ggplot(df, aes(x = TextLength, fill = Label)) + theme_bw() +
	geom_histogram(binwidth = 5) +
	labs(y = "Text Count", x = "Length of Text", title = "Distribution of text on labels")

tomer-ben-david / read.csv.from.url.R

Created January 19, 2018 06:53

R read csv from URL #R

	library(RCurl)
	library(bitops)
	URL = "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
	x = getURL(URL)
	out = read.csv(textConnection(x))
	head(out[1:6])

tomer-ben-david / r.read.text.file.R

Created January 13, 2018 09:19

R read text file

mylog <- readLines("./reputation") # => R read load text file

	BigDecimal(1.002).setScale(2, BigDecimal.RoundingMode.HALF_UP).doubleValue() // Round double to #.##

	\| Spark Term \| Description \|
	\| ------------------------------------------------------------ \| ------------------------------------------------------------ \|
	\| Spreadsheet \| Think of data as spreadsheet \|
	\| Statistical learning \| Output = f(input) # => f(inputVariable) or f(inputVector), or f(independent variables) or Y = F(X) // X1,X2,.. \|
	\| Programming learning \| OutputAttributes = Program(InputAttributes) or Program(InputFeatures) or Model = Algorithm(Data) \|
	\| Error \| Y = f(X) + e # => You learn a function! \|
	\| Parametric learning \| No