How can i insert an array List as input of my neural network?

[kunaca]

I have multiples inputs , but most of them are List array and i dont know how can i configure my neural network to accept list array as an input.
Here i have a example XOR(but the inputs are just floats and i cant change to List array):

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Encog.Engine.Network.Activation;
using Encog.Neural.Networks;
using Encog.Neural.Networks.Layers;
using Encog.Neural.Networks.Structure;
using Encog.Neural.Networks.Training.Propagation.Back;
using Encog.ML.Data.Basic;
using Encog.ML.Data.Versatile;
using Encog.ML.Data;

namespace Encog
{
class Program
{
static void Main(string[] args)
{
#region Create DataSet
//previsao soma 0.5+0.5 para gerar saida 1.0
double[][] x =
{
new double []{0.1, 0.2},
new double []{0.4, 0.3},
new double []{0.0, 0.0},
new double []{0.15, 0.25}
};

        double[][] y =
        {
            new double []{0.3}, // soma do 1 array
            new double []{0.7},
            new double []{0.0},
            new double []{0.4}
        };
        IMLDataSet dataset = new BasicMLDataSet(x, y); // criar interface dataset

        #endregion

        #region Create neuralNetwork
        BasicNetwork network = new BasicNetwork();
        //Adiconar camadas
        network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 2)); // contador de neuronios - ultimo parametro. temos 2 numeros na 1ª camada de entrada
        //camada oculta, com mais neuronios que a camada principal
        network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
        //camada de saida so com 1 neuronio
        network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
        network.Structure.FinalizeStructure();
        network.Reset();//randomizar os pesos

        #endregion

        #region Create BackPropagation instance(tem outros algoritmos de treinamento sem ser backpropagation)

        // classe para treinamento
        Backpropagation train = new Backpropagation(network, dataset, 0.5, 0.1);
        #endregion

        #region Train the network
        // precisamos de 2 variaveis para treinar ou 

        int epoch = 0;
        do
        {
            train.Iteration();
            //mostrar erro a cada 100 epoch
            if (epoch % 100 == 0)
                Console.WriteLine("Error: {0}   Epoch: {1}", train.Error, epoch);

            epoch++; // sentinela  , contador
        }
        while (epoch < 6000);
        #endregion

        #region Test Network

        //vamos gerar 2 numeros
        for (int i = 0; i <= 10; i++) // 0.1 0.1 ->resultado: 0.2
                                     // 0.2 0.2 ->resultado: 0.4 etc...
        {
            //converter o n para double
            double num = 1.0 / (double)i;

            //entrada para rede neuronal
            double[] d = new double[] { num, num };
            IMLData input = new BasicMLData(d); // so aceita double

            IMLData output = network.Compute(input);
            // resultado
            double[] result = new double[output.Count];
            output.CopyTo(result, 0, output.Count);

            Console.WriteLine("{0} + {1} = {2}", num, num, result[0]);

        }
        Console.ReadKey();    
        #endregion
    }
}

}