honake

honake

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)))

honake

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)

honake

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

honake

import audioop
import math
import numpy as np
import pyaudio
import pyfirmata
import sys
import time
import serial
import wave

honake

#include <iostream>
#include <cmath>
#include "UTRandom1.h"
#include "UTNormals.h"

using namespace std;

double SimpleMonteCarlo1(double Expiry,
	double Strike,
	double Spot,

honake

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 = []