Predictor.cpp

#include <iostream>
#include <fstream>
#include <curl/curl.h>
#include <mlpack/core.hpp>
#include <mlpack/methods/linear_regression/linear_regression.hpp>
#include <mlpack/methods/ridge_regression/ridge_regression.hpp>

using namespace mlpack;
using namespace mlpack::regression;

size_t WriteCallback(void* contents, size_t size, size_t nmemb, void* userp) {
    ((std::string*)userp)->append((char*)contents, size * nmemb);
    return size * nmemb;
}

void fetchData(const std::string& url, const std::string& filename) {
    CURL* curl;
    CURLcode res;
    std::string readBuffer;

    curl = curl_easy_init();
    if (curl) {
        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, WriteCallback);
        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);
        res = curl_easy_perform(curl);
        curl_easy_cleanup(curl);

        if (res == CURLE_OK) {
            std::ofstream outFile(filename);
            outFile << readBuffer;
            outFile.close();
        } else {
            std::cerr << "curl_easy_perform() failed: " << curl_easy_strerror(res) << std::endl;
        }
    }
}

void preprocessData(const std::string& inputFile, const std::string& outputFile) {
    std::ifstream inFile(inputFile);
    std::ofstream outFile(outputFile);

    std::string line;
    std::getline(inFile, line); // Skip the header

    while (std::getline(inFile, line)) {
        for (char& c : line) {
            if (c == ',') {
                c = ' ';
            }
        }
        outFile << line << std::endl;
    }

    inFile.close();
    outFile.close();
}

void trainModel(const std::string& filename) {
    arma::mat dataset;
    data::Load(filename, dataset, true);

    arma::rowvec responses = dataset.row(dataset.n_rows - 1);
    dataset.shed_row(dataset.n_rows - 1);

    RidgeRegression lr(dataset, responses, 0.1); // L2 regularization with lambda = 0.1

    data::Save("ridge_regression_model.xml", "model", lr);
}

void predict(const std::string& modelFile, const arma::mat& testSet) {
    RidgeRegression lr;
    data::Load(modelFile, "model", lr);

    arma::rowvec predictions;
    lr.Predict(testSet, predictions);

    std::cout << "Predictions: " << predictions << std::endl;
}

int main() {
    std::string apiKey = "your_api_key";
    std::string symbol = "RELIANCE.BSE";
    std::string url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=" + symbol + "&apikey=" + apiKey + "&datatype=csv";
    std::string rawFilename = "raw_data.csv";
    std::string processedFilename = "data.csv";

    fetchData(url, rawFilename);

    preprocessData(rawFilename, processedFilename);

    trainModel(processedFilename);

    arma::mat testSet;
    data::Load(processedFilename, testSet, true);
    predict("ridge_regression_model.xml", testSet);

    return 0;
}