TestHelper.cs

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namespace SpikingNeuronNetwork.Test
{
    using Lib;
    using Lib.Interfaces;
    using Microsoft.VisualStudio.TestTools.UnitTesting;
    using System;
    using System.Collections.Generic;
    using System.Linq;

    public class TestHelper
    {
        public static double Epsilon = 1e-4;

        public static void CompareSpikeTrains(IReadOnlyCollection<Spike> spikeTrain1, IReadOnlyList<Spike> spikeTrain2, double threshold)
        {
            //Find Mean-Squared in Spike Times Between the Different Methods
            Assert.AreEqual(spikeTrain1.Count, spikeTrain2.Count,
                            "The Two Spike Trains Contain Different Numbers of Spikes! Unable to Compare.\n\tTrain 1: " + String.Join(", ", spikeTrain1) + "\n\tTrain 2: " + String.Join(", ", spikeTrain2));

            if (spikeTrain1.Count == 0)
            {
                Console.WriteLine("The Two Spike Trains Produced No Output Spikes!");
                Console.WriteLine();
            }
            else
            {
                double meanSquaredError = 0;
                meanSquaredError +=
                    spikeTrain1.Select((t, j) => Math.Pow(t.Time - spikeTrain2[j].Time, 2)).Sum();
                meanSquaredError /= spikeTrain1.Count;
                Assert.IsTrue(meanSquaredError < threshold,
                              "Output Spike Mean-Squared Error Between The Two Spike Trains is greater than " + threshold);
                Console.WriteLine("Output Spike Mean-Squared Error Between The Two Spike Trains: ");
                Console.WriteLine("\t" + meanSquaredError);
                Console.WriteLine();
            }
        }

        public static void CompareNumericalAndEventDrivenSpikeTrains(ISpikingNeuron neuron, List<Spike> inputSpikeTimes, int maxNumOutputSpikes = 3)
        {
            inputSpikeTimes.RemoveAll(x => x.NeuronIndex >= neuron.NumInputs);

            // Display Neuron Properties
            Console.WriteLine(neuron);

            // Simulate Theta Neuron Numerically
            neuron.Method = SimulationMethod.Numerical;
            var outputSpikeTimesNumerical = neuron.RunThetaNeuron(inputSpikeTimes, maxNumOutputSpikes).ToList();

            //Simulate Theta Neuron with Event-Driven Methodology
            neuron.ResetState();
            neuron.Method = SimulationMethod.EventDriven;
            var outputSpikeTimesEventDriven = neuron.RunThetaNeuron(inputSpikeTimes, maxNumOutputSpikes).ToList();
            CompareSpikeTrains(outputSpikeTimesNumerical, outputSpikeTimesEventDriven, 0.01);
        }
    }
}