public void TestStateDependentGammaInfoFunctions()
{
const int numReactions = 3;
const int gammaSize = 5;
const int bcThreshold = 20;
var gammaInfo = new StateDependentGammaInfo(numReactions, gammaSize, bcThreshold);
const double weight = 1.0;
var n = new List <int[]> {
new[] { 20, 20, 20, 20, 20 }, new[] { 1, 2, 3, 4, 5 }, new[] { 10, 20, 30, 15, 5 }
};
var lambda = new List <double[]> {
new[] { 10.0, 20.0, 40.0, 100.0, 1000.0 }, new[] { 2.0, 4.0, 6.0, 8.0, 10.0 }, new[] { 20.0, 10.0, 60.0, 30.0, 10.0 }
};
gammaInfo.UpdateGamma(weight, n, lambda);
gammaInfo.SetIntermediateGamma();
Assert.That(new[] { 2.0, 1.0, 0.5, 0.2, 0.02 }, Is.EquivalentTo(gammaInfo.IntermediateGamma[0]));
Assert.That(new[] { 0.5, 0.5, 0.5, 0.5, 0.5 }, Is.EquivalentTo(gammaInfo.IntermediateGamma[1]));
Assert.That(new[] { 0.5, 2.0, 0.5, 0.5, 0.5 }, Is.EquivalentTo(gammaInfo.IntermediateGamma[2]));
var startPC = new[] { 0.0, 0.2, 0.5 };
var endPC = new[] { 0.5, 0.8, 1.0 };
var ppc = new List <double[]>();
ppc.AddRange(gammaInfo.IntermediatePropensityCutoff);
gammaInfo.UpdatePropensityCutoff(startPC, endPC);
Assert.That(ppc, Is.EqualTo(gammaInfo.PreviousIntermediatePropensityCutoff));
Assert.That(new[] { 0.1, 0.2, 0.1 * 3, 0.4 }, Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[0]));
Assert.That(new[] { 0.2 + (0.8 - 0.2) / 5, 0.2 + (0.8 - 0.2) / 5 * 2, 0.2 + (0.8 - 0.2) / 5 * 3, 0.2 + (0.8 - 0.2) / 5 * 4 }, Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[1]));
Assert.That(new[] { 0.6, 0.7, 0.8, 0.9 }, Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[2]));
var ppc2 = new List <double[]>();
ppc2.AddRange(gammaInfo.IntermediatePropensityCutoff);
gammaInfo.MergeBins();
Assert.That(new[] { 0.1, 0.2, 0.1 * 3, 0.4 }, Is.EqualTo(gammaInfo.PreviousIntermediatePropensityCutoff[0]));
Assert.That(new[] { 0.2 + (0.8 - 0.2) / 5, 0.2 + (0.8 - 0.2) / 5 * 2, 0.2 + (0.8 - 0.2) / 5 * 3, 0.2 + (0.8 - 0.2) / 5 * 4 }, Is.EqualTo(gammaInfo.PreviousIntermediatePropensityCutoff[1]));
Assert.That(new[] { 0.6, 0.7, 0.8, 0.9 }, Is.EqualTo(gammaInfo.PreviousIntermediatePropensityCutoff[2]));
Assert.That(ppc2[0], Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[0]));
Assert.That(new[] { 1.0 }, Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[1]));
Assert.That(new[] { 0.7, 0.8 }, Is.EqualTo(gammaInfo.IntermediatePropensityCutoff[2]));
gammaInfo.UpdateStructure();
var gammaNum = GetHiddenField <List <double[]> >("_gammaNum", gammaInfo);
Assert.AreEqual(new double[gammaSize], gammaNum[0]);
Assert.AreEqual(new double[1], gammaNum[1]);
Assert.AreEqual(new double[3], gammaNum[2]);
}
public void TestStateDependentGammaInfo()
{
const int numReactions = 3;
const int gammaSize = 5;
const int bcThreshold = 20;
var gammaInfo = new StateDependentGammaInfo(numReactions, gammaSize, bcThreshold);
Assert.That(Enumerable.Repeat(1.0, gammaSize).ToArray(), Is.EquivalentTo(gammaInfo.IntermediateGamma[0]));
Assert.That(Enumerable.Repeat(1.0, gammaSize).ToArray(), Is.EquivalentTo(gammaInfo.IntermediateGamma[1]));
Assert.That(Enumerable.Repeat(1.0, gammaSize).ToArray(), Is.EquivalentTo(gammaInfo.IntermediateGamma[2]));
List <double[]> gamma, pc;
var rates = new[] { 1.0, 1.0, 1.0 };
CreateGammaPC(gammaSize, rates, out gamma, out pc);
Assert.That(pc, Is.EquivalentTo(gammaInfo.IntermediatePropensityCutoff));
}
public void TestUpdateBiasingParameters()
{
const string configString = @"{""solver"":""SDWSSA"", ""sdwSSA"":{""crossEntropyThreshold"":0.005, ""crossEntropyRuns"":1000000, ""biasingParametersFileName"":""resources\\rever_isom_CEinfo.json""}}";
Configuration.CurrentConfiguration = Configuration.ConfigurationFromString(configString);
var modelInfo = EmodlLoader.LoadEMODLFile("resources\\rever_isom.emodl");
const double duration = 10.0;
const int repeats = 1000000;
const int samples = 1;
var solver = new sdwSSA(modelInfo, duration, repeats, samples);
var gammaInfo = new StateDependentGammaInfo(2, 10, 20);
var method = GetHiddenMethod("UpdateBiasingParameters", solver);
var inputObjectArray = new object[2];
inputObjectArray[0] = gammaInfo;
inputObjectArray[1] = 35;
method.Invoke(solver, inputObjectArray);
var biasingParameters = GetHiddenField <BiasingParameters>("_biasingParameters", solver);
Assert.AreEqual(35, biasingParameters.RareEvent.Thresholds[2]);
}
