To implement stochastic, linear regression, and standard deviation calculations in C++ for data analysis, you can use various mathematical formulas and algorithms.
Let's start with calculating the mean and standard deviation of a set of data using C++:
TEXT/X-C++SRC
1#include <iostream>
2#include <vector>
3#include <cmath>
4
5// Function to calculate the mean of a vector
6double calculateMean(const std::vector<double>& data) {
7 double sum = 0;
8
9 for (double value : data) {
10 sum += value;
11 }
12
13 return sum / data.size();
14}
15
16// Function to calculate the standard deviation of a vector
17// Using the population standard deviation formula
18double calculateStandardDeviation(const std::vector<double>& data) {
19 double mean = calculateMean(data);
20 double sum = 0;
21
22 for (double value : data) {
23 sum += pow(value - mean, 2);
24 }
25
26 return sqrt(sum / data.size());
27}
28
29int main() {
30 std::vector<double> data = {2.5, 1.3, 4.7, 3.2, 1.9};
31
32 double mean = calculateMean(data);
33 double standardDeviation = calculateStandardDeviation(data);
34
35 std::cout << "Mean: " << mean << std::endl;
36 std::cout << "Standard Deviation: " << standardDeviation << std::endl;
37
38 return 0;
39}
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38
}
// Function to calculate the mean of a vector
double calculateMean(const std::vector<double>& data) {
double sum = 0;
for (double value : data) {
sum += value;
}
return sum / data.size();
}
// Function to calculate the standard deviation of a vector
double calculateStandardDeviation(const std::vector<double>& data) {
double mean = calculateMean(data);
double sum = 0;
for (double value : data) {
sum += pow(value - mean, 2);
}
return sqrt(sum / data.size());
}
int main() {
std::vector<double> data = {2.5, 1.3, 4.7, 3.2, 1.9};
OUTPUT
:001 > Cmd/Ctrl-Enter to run, Cmd/Ctrl-/ to comment