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class Array | |
def fair_combination | |
arr = [] | |
n = length | |
m = (n / 2).floor | |
# Divide by interval | |
(1..m - 1).each do |j| | |
subarr = (0..n - 1).to_a | |
sub1 = [] |
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#include <iostream> | |
#include <cmath> | |
#include "UTRandom1.h" | |
#include "UTNormals.h" | |
using namespace std; | |
double SimpleMonteCarlo1(double Expiry, | |
double Strike, | |
double Spot, |
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import audioop | |
import math | |
import numpy as np | |
import pyaudio | |
import pyfirmata | |
import sys | |
import time | |
import serial | |
import wave |
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import numpy as np | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
def broadbent_adstock(grp, r): | |
adstock = np.zeros(len(grp)) | |
adstock[0] = grp[0] | |
for i in range(1,len(grp)): | |
adstock[i] = grp[i] + r*adstock[i-1] | |
return adstock |
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optimizeAdStock <- function(grp,sales){ | |
rmax <- 0 | |
corrmax <- -1 | |
rates <- seq(0.00, 1.00, by = 0.01) | |
corrs <- vector() | |
for(r in rates){ | |
adstock <- stats::filter(grp, filter=r, method = "recursive") | |
corr <- cor(adstock,sales) |
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library(tidyverse) | |
library(ggplot2) | |
set.seed(123) | |
sample_num <- 1000 | |
sigma <- 1 | |
# Generate Two Unit Root Processes | |
x <- c(0,cumsum(rnorm(sample_num-1, mean=0, sd=sigma))) | |
y <- c(0,cumsum(rnorm(sample_num-1, mean=0, sd=sigma))) |
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Company | TV Ad Budget | Impressions | |
---|---|---|---|
MILLERLITE | 50.1 | 32.1 | |
PEPSI | 74.1 | 99.6 | |
STROH'S | 19.3 | 11.7 | |
FED'LEXPRESS | 22.9 | 21.9 | |
BURGERKING | 82.4 | 60.8 | |
COCO-COLA | 40.1 | 78.6 | |
MCDONALD'S | 185.9 | 92.4 | |
MCI | 26.9 | 50.7 | |
DIETCOLA | 20.4 | 21.4 |