using System;
using System.Collections.Generic;
using System.Text;
using Tensorflow;
using NumSharp.Core;
using System.Linq;

namespace TensorFlowNET.Examples
{
    /// <summary>
    /// https://github.com/nicolov/naive_bayes_tensorflow
    /// </summary>
    public class NaiveBayesClassifier : Python, IExample
    { 
        public void Run()
        {
            np.array<float>(1.0f, 1.0f);
            // var X = np.array<float>(np.array<float>(1.0f, 1.0f), np.array<float>(2.0f, 2.0f), np.array<float>(1.0f, -1.0f), np.array<float>(2.0f, -2.0f), np.array<float>(-1.0f, -1.0f), np.array<float>(-1.0f, 1.0f),);
            // var X = np.array<float[]>(new float[][] { new float[] { 1.0f, 1.0f}, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
            var X = np.array<float>(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
            var y = np.array<int>(0,0,1,1,2,2);

            fit(X, y);
            // Create a regular grid and classify each point 
            
        }

        public void fit(NDArray X, NDArray y)
        {
            NDArray unique_y = y.unique<long>();
            
            Dictionary<long, List<NDArray>> dic = new Dictionary<long, List<NDArray>>();
            // Init uy in dic
            foreach (int uy in unique_y.Data<int>())
            {
                dic.Add(uy, new List<NDArray>());
            }
            // Separate training points by class 
            // Shape : nb_classes * nb_samples * nb_features
            int maxCount = 0;
            for (int i = 0; i < y.size; i++)
            {
                long curClass = (long)y[i];
                List<NDArray> l = dic[curClass];
                l.Add(X[i] as NDArray);
                if (l.Count > maxCount)
                {
                    maxCount = l.Count;
                }
                dic[curClass] = l;
            }
            NDArray points_by_class = np.zeros(new int[] { dic.Count, maxCount, X.shape[1] });
            foreach (KeyValuePair<long, List<NDArray>> kv in dic)
            {
                var cls = kv.Value.ToArray();
                for (int i = 0; i < dic.Count; i++)
                {
                    points_by_class[i] = dic[i];
                }
            }

            // estimate mean and variance for each class / feature
            // shape : nb_classes * nb_features
            var cons = tf.constant(points_by_class);
            Tuple<Tensor, Tensor> tup = tf.nn.moments(cons, new int[]{1});
            var mean = tup.Item1;
            var variance = tup.Item2;
            // Create a 3x2 univariate normal distribution with the 
            // Known mean and variance           
            // var dist = tf.distributions.Normal(loc=mean, scale=tf.sqrt(variance));

        }

        public void predict (NDArray X)
        {
            // assert self.dist is not None
            // nb_classes, nb_features = map(int, self.dist.scale.shape)


            throw new NotFiniteNumberException();
        }
    }
}