#include "hdbscan.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
#include <set>
#include <map>
#include <cstdint>
using namespace std;

string Hdbscan::getFileName() {
    return this->fileName;
}
/// <summary>
///	Loads the csv file as specified by the constructor.CSV
/// </summary>
/// <param name="numberOfalues">A List of attributes to be choosen</param>
/// <param name="skipHeader">Bool value to skip header or not</param>
/// <returns>1 if successful, 0 otherwise</returns>

int Hdbscan::loadCsv(int numberOfValues, bool skipHeader) {
    string attribute;

    string line = "";

    int currentAttributes;
    vector<vector<double>> dataset;

    string fileName = this->getFileName();
    ifstream file(fileName, ios::in);
    if (!file)
        return 0;
    if (skipHeader) {
        getline(file, line);
    }
    while (getline(file, line)) { // Read through each line
        stringstream s(line);
        vector<double> row;
        currentAttributes = numberOfValues;
        while (getline(s, attribute, ',') && currentAttributes != 0) {
            row.push_back(stod(attribute));
            currentAttributes--;
        }
        dataset.push_back(row);
    }
    this->dataset = dataset;
    return 1;
}

void Hdbscan::execute(int minPoints, int minClusterSize, string distanceMetric) {
    // Call The Runner Class here
    hdbscanRunner runner;
    hdbscanParameters parameters;
    uint32_t noisyPoints = 0;
    set<int> numClustersSet;
    map<int, int> clustersMap;
    vector<int> normalizedLabels;

    parameters.dataset = this->dataset;
    parameters.minPoints = minPoints;
    parameters.minClusterSize = minClusterSize;
    parameters.distanceFunction = distanceMetric;
    this->result = runner.run(parameters);
    this->labels_ = result.labels;
    this->outlierScores_ = result.outliersScores;
    for (uint32_t i = 0; i < result.labels.size(); i++) {
        if (result.labels[i] == 0) {
            noisyPoints++;
        } else {
            numClustersSet.insert(result.labels[i]);
        }
    }
    this->numClusters_ = numClustersSet.size();
    this->noisyPoints_ = noisyPoints;
    int iNdex = 1;
    for (auto it = numClustersSet.begin(); it != numClustersSet.end(); it++) {
        clustersMap[*it] = iNdex++;
    }
    for (int i = 0; i < labels_.size(); i++) {
        if (labels_[i] != 0)
            normalizedLabels.push_back(clustersMap[labels_[i]]);
        else if (labels_[i] == 0) {
            normalizedLabels.push_back(-1);
        }
    }
    this->normalizedLabels_ = normalizedLabels;
    this->membershipProbabilities_ = result.membershipProbabilities;
}

void Hdbscan::displayResult() {
    hdbscanResult result = this->result;
    uint32_t numClusters = 0;

    cout << "HDBSCAN clustering for " << this->dataset.size() << " objects." << endl;

    for (uint32_t i = 0; i < result.labels.size(); i++) {
        cout << result.labels[i] << " ";
    }

    cout << endl << endl;

    cout << "The Clustering contains " << this->numClusters_ << " clusters with "
         << this->noisyPoints_ << " noise Points." << endl;
}