public void TestConstructor4()
{
var rng = new PredictableRandomNumberGenerator();
var gt = new Genotype('C', Dominance.Recessive, Dominance.Recessive, rng);
Assert.AreEqual("cc", gt.ToString());
}
public void TestCombineGenotype4()
{
var rng = new PredictableRandomNumberGenerator();
var gt = new Genotype('C', Dominance.Dominant, Dominance.Recessive, rng);
var otherGt = new Genotype('C', Dominance.Recessive, Dominance.Recessive, rng);
TestGenotypeCombinationRatio(gt, otherGt, "Cc", 0.50m);
TestGenotypeCombinationRatio(gt, otherGt, "cc", 0.50m);
}
public void TestCombineGenotype3()
{
var rng = new PredictableRandomNumberGenerator();
var gt = new Genotype('C', Dominance.Recessive, Dominance.Recessive, rng);
var otherGt = new Genotype('C', Dominance.Dominant, Dominance.Dominant, rng);
var resultingGenotype = gt.CombineGenotypes(otherGt);
TestGenotypeCombinationRatio(gt, otherGt, "Cc", 1.0m);
}
[TestMethod]//NEED TO: Add more tests
public void TestCalculatePhenotypicRatio1()
{
var rng = new PredictableRandomNumberGenerator();
var gt = new Genotype('C', Dominance.Dominant, Dominance.Recessive, rng); //Cc
var otherGt = new Genotype('C', Dominance.Recessive, Dominance.Recessive, rng); //cc
Phenotype p = new Phenotype(null, null, null); // TO DO: fill in
string phenotypicRatio = p.CalculatePhenotypicRatio(otherGt, gt, 2); //Aa Bb
Assert.AreEqual("1:3", phenotypicRatio);
}
public void TestLoadFile()
{
var rng = new PredictableRandomNumberGenerator();
var gp = new GenotypeRepository();
var g = new Genotype('A', Dominance.Dominant, Dominance.Recessive, rng);
var myPersonRepository = new PersonRepository();
string[] records = new string[1000];
myPersonRepository.LoadFile("PersonsSeedData.txt");
var ListofPersons = new List <Person>();
ListofPersons = myPersonRepository.ListPersons();
var TestPerson = new Person("Abigail Adams", Sex.Female, true, gp);
Assert.AreEqual("Abigail Adams Female A a", ListofPersons[5].ToString());
}
public void CanAssertNeuralNetworkSuccess()
{
var neuralNetwork = ExplicitNeuralNetworkBuilder
.CreateForTest(NeuralNetworkContext.MaximumPrecision, PredictableRandomNumberGenerator.Create())
.InputLayer(l => l.Neurons(
n => n.Id(1).ErrorGradient(1).Output(1).Activation(ActivationType.Sigmoid),
n => n.Id(2).ErrorGradient(1).Output(1).Activation(ActivationType.Sigmoid)))
.HiddenLayer(l => l.Neurons(
n => n.Id(3).ErrorGradient(2).Output(2).Activation(ActivationType.Sigmoid),
n => n.Id(4).ErrorGradient(2).Output(2).Activation(ActivationType.Sigmoid)))
.OutputLayer(l => l.Neurons(
n => n.Id(5).ErrorGradient(3).Output(3).Activation(ActivationType.Sigmoid),
n => n.Id(6).ErrorGradient(3).Output(3).Activation(ActivationType.Sigmoid)))
.Synapses(
s => s.SynapseBetween(inputNeuronId: 1, outputNeuronId: 3, weight: 1),
s => s.SynapseBetween(inputNeuronId: 1, outputNeuronId: 4, weight: 1),
s => s.SynapseBetween(inputNeuronId: 2, outputNeuronId: 3, weight: 1),
s => s.SynapseBetween(inputNeuronId: 2, outputNeuronId: 4, weight: 1),
s => s.SynapseBetween(inputNeuronId: 3, outputNeuronId: 5, weight: 2),
s => s.SynapseBetween(inputNeuronId: 3, outputNeuronId: 6, weight: 2),
s => s.SynapseBetween(inputNeuronId: 4, outputNeuronId: 5, weight: 2),
s => s.SynapseBetween(inputNeuronId: 4, outputNeuronId: 6, weight: 2))
.Build();
var neuralNetworkAssertor = new NeuralNetworkAssertor.Builder()
.InputLayer(l => l.Neurons(
n => n.Id(1).Output(1).ErrorGradient(1).OutputSynapses(
s => s.OutputNeuronId(3).Weight(1),
s => s.OutputNeuronId(4).Weight(1)),
n => n.Id(2).Output(1).ErrorGradient(1).OutputSynapses(
s => s.OutputNeuronId(3).Weight(1),
s => s.OutputNeuronId(4).Weight(1))))
.HiddenLayers(l => l.Neurons(
n => n
.Id(3).Output(2).ErrorGradient(2)
.InputSynapses(
s => s.InputNeuronId(1).Weight(1),
s => s.InputNeuronId(2).Weight(1))
.OutputSynapses(
s => s.OutputNeuronId(5).Weight(2),
s => s.OutputNeuronId(6).Weight(2)),
n => n
.Id(4).Output(2).ErrorGradient(2)
.InputSynapses(
s => s.InputNeuronId(1).Weight(1),
s => s.InputNeuronId(2).Weight(1))
.OutputSynapses(
s => s.OutputNeuronId(5).Weight(2),
s => s.OutputNeuronId(6).Weight(2))))
.OutputLayer(l => l.Neurons(
n => n.Id(5).Output(3).ErrorGradient(3).InputSynapses(
s => s.InputNeuronId(3).Weight(2),
s => s.InputNeuronId(4).Weight(2)),
n => n.Id(6).Output(3).ErrorGradient(3).InputSynapses(
s => s.InputNeuronId(3).Weight(2),
s => s.InputNeuronId(4).Weight(2))))
.Build();
neuralNetworkAssertor.Assert(neuralNetwork);
}
