public void CanMakeAndReadZeroNetworkVector()
{
NetworkVector vector = new NetworkVector(2);
Assert.AreEqual(0.0, vector.ToArray()[0]);
Assert.AreEqual(0.0, vector.ToArray()[1]);
}
public void CanZeroNetworkVector()
{
NetworkVector vector = new NetworkVector(new double[] { 1, 2 });
vector.Zero();
Assert.AreEqual(0.0, vector.ToArray()[0]);
Assert.AreEqual(0.0, vector.ToArray()[1]);
}
public void CanMakeAndRead_1_2_NetworkVector()
{
NetworkVector vector = new NetworkVector(new double[] { 1, 2 });
double[] vectorValues = vector.ToArray();
Assert.AreEqual(1.0, vectorValues[0]);
Assert.AreEqual(2.0, vectorValues[1]);
}
public void CanApplyFunctionComponentwise()
{
NetworkVector.SingleVariableFunction f = x => x - 1;
NetworkVector vector = new NetworkVector(new double[] { 1, 2 });
NetworkVector newvector = NetworkVector.ApplyFunctionComponentWise(vector, f);
double[] vectorValues = newvector.ToArray();
Assert.AreEqual(0.0, vectorValues[0]);
Assert.AreEqual(1.0, vectorValues[1]);
}
public void CanCopy()
{
NetworkVector vector1 = new NetworkVector(new double[] { 1, 0 });
NetworkVector vector2 = vector1.Copy();
double[] vector1Values = vector1.ToArray();
double[] vector2Values = vector2.ToArray();
Assert.AreEqual(vector1Values[0], vector2Values[0]);
Assert.AreEqual(vector1Values[1], vector2Values[1]);
}
public void CanSubtract()
{
NetworkVector vector = new NetworkVector(new double[] { 1, 2, 3, 4, 5 });
vector.Subtract(vector);
double[] vectorValues = vector.ToArray();
for (int i = 0; i < vectorValues.Length; i++)
{
Assert.AreEqual(0.0, vectorValues[i]);
}
}
public void CanBackSigmoid()
{
NetworkVector result = nf_sigmoid.Run(vector_2);
double[] outarray = result.ToArray();
double[] inarray = vector_2.ToArray();
NetworkVector gradientCheck = new NetworkVector(
new double[] { sigmoidDerivative(inarray[0], outarray[0]),
sigmoidDerivative(inarray[1], outarray[1]) });
Assert.AreEqual(gradientCheck, nf_sigmoid.InputGradient(vector_2, vector_2, result));
}
public void CanLeftMultiply()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 0, 1 }, { 1, 0 }
});
NetworkVector vector = new NetworkVector(new double[] { 1, 1 });
NetworkVector result = matrix.LeftMultiply(vector);
double[] resultValues = result.ToArray();
Assert.AreEqual(1, resultValues[0]);
Assert.AreEqual(1, resultValues[1]);
}
public void CanMakeAndReadBigNetworkVector()
{
int size = 10000;
double[] array = new double[size];
for (int i = 0; i < size; i++)
{
array[i] = i;
}
NetworkVector vector = new NetworkVector(array);
double[] vectorValues = vector.ToArray();
for (int i = 0; i < size; i++)
{
Assert.AreEqual(array[i], vectorValues[i]);
}
}
public void CanSum()
{
int listSize = 100;
NetworkVector vector = new NetworkVector(new double[] { 0, 1, 2, 3, 4, 5 });
List <NetworkVector> list = new List <NetworkVector>();
for (int i = 0; i < listSize; i++)
{
list.Add(vector);
}
vector = NetworkVector.Sum(list);
double[] result = vector.ToArray();
for (int i = 0; i < vector.Dimension; i++)
{
Assert.AreEqual(i * listSize, result[i]);
}
}
public void RunProducesCorrectOutput()
{
SoftMaxUnit smu = new SoftMaxUnit(7);
NetworkVector input = new NetworkVector(new double[] { 1, 0, 0, 0, 0, 0, 1 });
NetworkVector output = smu.Run(input);
double sum = 5 + (2 * Math.E);
double one_value = Math.E / sum;
double zero_value = 1 / sum;
double delta = 0.000000001;
double[] outputvalues = output.ToArray();
double outputvaluessum = output.SumValues();
Assert.AreEqual(1.0, outputvaluessum, delta);
Assert.AreEqual(one_value, outputvalues[0], delta);
Assert.AreEqual(one_value, outputvalues[6], delta);
for (int i = 1; i < smu.NumberOfOutputs - 1; i++)
{
Assert.AreEqual(zero_value, outputvalues[i], delta);
}
}