Simulation of Big Data Processing Gaming Project in C++

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#include <iostream>
#include <vector>
#include <cmath>
#include <cstdlib>
#include <ctime>

using namespace std;

// Function to generate a large dataset of random integers
vector<int> generateDataset(size_t size, int minValue, int maxValue) {
    vector<int> dataset(size);
    for (size_t i = 0; i < size; ++i) {
        dataset[i] = minValue + rand() % (maxValue - minValue + 1);
    }
    return dataset;
}

// Function to calculate the mean of a dataset
double calculateMean(const vector<int>& dataset) {
    double sum = 0;
    for (int value : dataset) {
        sum += value;
    }
    return sum / dataset.size();
}

// Function to calculate the standard deviation of a dataset
double calculateStandardDeviation(const vector<int>& dataset, double mean) {
    double sum = 0;
    for (int value : dataset) {
        sum += (value - mean) * (value - mean);
    }
    return sqrt(sum / dataset.size());
}

int main() {
    srand(static_cast<unsigned>(time(0))); // Seed for random number generation

    size_t datasetSize = 1000000; // Size of the dataset
    int minValue = 0; // Minimum value in the dataset
    int maxValue = 100; // Maximum value in the dataset

    // Generate the dataset
    vector<int> dataset = generateDataset(datasetSize, minValue, maxValue);

    // Calculate mean and standard deviation
    double mean = calculateMean(dataset);
    double stdDev = calculateStandardDeviation(dataset, mean);

    // Print results
    cout << "Dataset Size: " << datasetSize << endl;
    cout << "Mean: " << mean << endl;
    cout << "Standard Deviation: " << stdDev << endl;

    return 0;
}

#include <iostream>

#include <vector>

#include <cmath>

#include <cstdlib>

#include <ctime>

using namespace std;

// Function to generate a large dataset of random integers

vector<int> generateDataset(size_t size, int minValue, int maxValue) {

vector<int> dataset(size);

for (size_t i = 0; i < size; ++i) {

dataset[i] = minValue + rand() % (maxValue - minValue + 1);

}

return dataset;

}

// Function to calculate the mean of a dataset

double calculateMean(const vector<int>& dataset) {

double sum = 0;

for (int value : dataset) {

sum += value;

}

return sum / dataset.size();

}

// Function to calculate the standard deviation of a dataset

double calculateStandardDeviation(const vector<int>& dataset, double mean) {

double sum = 0;

for (int value : dataset) {

sum += (value - mean) * (value - mean);

}

return sqrt(sum / dataset.size());

}

int main() {

srand(static_cast<unsigned>(time(0))); // Seed for random number generation

size_t datasetSize = 1000000; // Size of the dataset

int minValue = 0; // Minimum value in the dataset

int maxValue = 100; // Maximum value in the dataset

// Generate the dataset

vector<int> dataset = generateDataset(datasetSize, minValue, maxValue);

// Calculate mean and standard deviation

double mean = calculateMean(dataset);

double stdDev = calculateStandardDeviation(dataset, mean);

// Print results

cout << "Dataset Size: " << datasetSize << endl;

cout << "Mean: " << mean << endl;

cout << "Standard Deviation: " << stdDev << endl;

return 0;

}

Explanation

Headers:
- <iostream>: For input and output operations.
- <vector>: For using the std::vector container.
- <cmath>: For mathematical functions like sqrt()
  
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- <cstdlib>: For rand() and srand() functions.
- <ctime>: For time() function to seed the random number generator.
Function generateDataset:
- Parameters:
  - size: Number of elements in the dataset.
  - minValue and maxValue: Range of random integers.
- Functionality:
  - Generates a dataset of random integers within the specified range and size.
Function calculateMean:
- Parameters:
  - const vector<int>& dataset: The dataset for which to calculate the mean.
- Functionality:
  - Computes the mean (average) of the dataset by summing all values and dividing by the size.
Function calculateStandardDeviation:
- Parameters:
  - const vector<int>& dataset: The dataset for which to calculate the standard deviation.
  - double mean: The mean of the dataset.
- Functionality:
  - Computes the standard deviation by calculating the square root of the average of squared deviations from the mean.
Main Function:
- Initialization:
  - Seeds the random number generator.
  - Defines dataset size and value range.
  - Generates the dataset.
- Processing:
  - Calculates the mean and standard deviation of the dataset.
- Output:
  - Prints the size of the dataset, mean, and standard deviation.

Notes:

Big Data Simulation: This example simulates the processing of a large dataset by performing statistical calculations.

Efficiency: For actual big data processing, more efficient algorithms and libraries are needed to handle extremely large datasets.
Extensions: The program can be extended with additional statistical operations, data transformations, or integration with actual big data processing frameworks.

Explanation

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