using System;
using System.Collections.Generic;

namespace GradientBoostingExample
{
    public class GradientBoosting
    {
        private List<double> target;
        private List<List<double>> features;
        private List<double> predictions;
        private List<double> residuals;
        private List<double> weights;
        private List<double> trees;
        private int numIterations;
        private double learningRate;

        public GradientBoosting(List<double> target, List<List<double>> features, int numIterations, double learningRate)
        {
            this.target = target;
            this.features = features;
            this.numIterations = numIterations;
            this.learningRate = learningRate;
            predictions = new List<double>();
            residuals = new List<double>();
            weights = new List<double>();
            trees = new List<double>();
        }

        public void Fit()
        {
            // Initialize the predictions with the mean of the target values
            double mean = CalculateMean(target);
            predictions = new List<double>(new double[target.Count]);
            predictions.Fill(mean);

            for (int i = 0; i < numIterations; i++)
            {
                // Calculate the residuals
                CalculateResiduals();

                // Fit a regression tree to the residuals
                RegressionTree tree = new RegressionTree(features, residuals);
                tree.Fit();

                // Update the predictions using the new tree
                UpdatePredictions(tree);

                // Update the weights based on the residuals
                UpdateWeights();

                // Add the tree to the list of trees
                trees.Add(learningRate);
            }
        }

        private void CalculateResiduals()
        {
            residuals.Clear();

            for (int i = 0; i < target.Count; i++)
            {
                double residual = target[i] - predictions[i];
                residuals.Add(residual);
            }
        }

        private void UpdatePredictions(RegressionTree tree)
        {
            for (int i = 0; i < predictions.Count; i++)
            {
                double prediction = predictions[i] + learningRate * tree.Predict(features[i]);
                predictions[i] = prediction;
            }
        }

        private void UpdateWeights()
        {
            for (int i = 0; i < residuals.Count; i++)
            {
                double weight = Math.Abs(residuals[i]);
                weights.Add(weight);
            }
        }

        private double CalculateMean(List<double> values)
        {
            double sum = 0;

            foreach (double value in values)
            {
                sum += value;
            }

            return sum / values.Count;
        }

        public double Predict(List<double> input)
        {
            double prediction = 0;

            for (int i = 0; i < trees.Count; i++)
            {
                prediction += learningRate * trees[i];
            }

            return prediction;
        }
    }

    public class RegressionTree
    {
        private List<List<double>> features;
        private List<double> target;
        private double prediction;

        public RegressionTree(List<List<double>> features, List<double> target)
        {
            this.features = features;
            this.target = target;
        }

        public void Fit()
        {
            // Implement your own algorithm to build the regression tree here
        }

        public double Predict(List<double> input)
        {
            return prediction;
        }
    }

    public static class ListExtensions
    {
        public static void Fill<T>(this IList<T> list, T value)
        {
            for (int i = 0; i < list.Count; i++)
            {
                list[i] = value;
            }
        }
    }

    public class Program
    {
        public static void Main(string[] args)
        {
            // Example usage
            List<double> target = new List<double> { 1, 2, 3, 4, 5 };
            List<List<double>> features = new List<List<double>>
            {
                new List<double> { 1, 1 },
                new List<double> { 2, 2 },
                new List<double> { 3, 3 },
                new List<double> { 4, 4 },
                new List<double> { 5, 5 }
            };

            GradientBoosting gb = new GradientBoosting(target, features, 100, 0.1);
            gb.Fit();

            List<double> input = new List<double> { 6, 6 };
            double prediction = gb.Predict(input);
            Console.WriteLine("Prediction: " + prediction);
        }
    }
}
