i2pi

#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include <unistd.h>

#include "gl.h"

i2pi

https://www.youtube.com/watch?v=hIqEHc9_Bwo
https://www.youtube.com/watch?v=PwedoZZHadY
https://www.youtube.com/watch?v=tM_HRwqKzFk
https://www.youtube.com/watch?v=zufLcLT8fmI
https://www.youtube.com/watch?v=hdNHKUCbNmE
https://www.youtube.com/watch?v=4Ge2h8Apgd8
https://www.youtube.com/watch?v=wBkvzbw5cDM
https://www.youtube.com/watch?v=-8KmEO0hrz0
https://www.youtube.com/watch?v=gBLalqndgYQ
https://www.youtube.com/watch?v=QgmTsvIMD-k

i2pi

i2pi

/*

		   ** Disk Funtions **

  ⁄ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ∑
  ≥  JAOS  0.0.1 -  Josh and Andrew's Operating System   ∫
  √ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ∂
  ≥  THIS IS DESIGNED FOR VERSION 0.0.1 OF THE JAOS CODE ∫
  √ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ∂
  ≥							 ∫

i2pi

ld += ldd; 
if(ld>0.5f) ld -= 1.0f;
ldd += ledg * (lddd - ldd); 
lde *= 0.99983f; //decay
tmp = lf + lm * lde;
if(tmp<0.0f) tmp=0.0f;  else if(tmp>1.1f) tmp=1.1f; lf1 -= tmp * (lf0 + lq * lf1 + lde * ld);  
lf0 += tmp * lf1;
tmp = 0.45f * lf0;
if(tmp>0.10f) tmp = 0.12f; 
l += tmp;

i2pi

s=embed(rt(5,1),2);plot(t(replicate(1e4,s[4,]<<-(s[4,]+s[sample(3,1),])/2)),cex=.1)

i2pi

#
# This file gives a quick demonstration of a few ML techniques that
# are available in R. The file is designed as a walk through, so instead
# of simply running the entire file at once, copy and paste the logical
# blocks and check the output as we go along.
#
# Joshua Reich (josh@simple.com)     
# April 2, 2009
# Updated: February 21, 2012
#

i2pi

> difftime(end_day, start_day)
Time difference of 209.0417 days
> difftime(end_day, start_day, 'day')
Time difference of 208.8333 days
> start_day
[1] "2011-06-01 EDT"
> end_day
[1] "2011-12-27 EST"

i2pi

lm(formula = Convert ~ factor(form_version), data = round1)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.4963 -0.4478 -0.3778  0.5522  0.6222 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            0.496296   0.042682  11.628   <2e-16 ***
factor(form_version)1 -0.118519   0.060362  -1.963   0.0499 *  

i2pi

d <- read.csv('even_sample.csv')

# Fake 
d$Convert <- (runif(nrow(d)) < 0.27)

sent <- aggregate(rep(1, nrow(d)), list(Date=d$Date), sum)
conv <- aggregate(d$Convert, list(Date=d$Date), sum)

by_date <- merge(sent,conv, by='Date')
colnames(by_date)[2:3] <- c('sent', 'conv')