public void SimpleSlidingParameters()
{
var list = new List <double>(20);
for (int i = 0; i < 20; i++)
{
list.Add(i);
}
var input = new SlidingWindowMLDataProvider(list, 10, -5, 3);
Assert.AreEqual(6, input.Count);
Assert.AreEqual(0.0, input[0, 0]);
Assert.AreEqual(0.0, input[0, 5]);
Assert.AreEqual(4.0, input[0, 9]);
Assert.AreEqual(17.0, input[6, 4]);
Assert.AreEqual(0.0, input[6, 9]);
input.PadWithNearest = true;
Assert.AreEqual(19.0, input[6, 9]);
Assert.AreEqual(19.0, input[6, 90]); // no range error presently
input.DefaultPadValue = -1.0;
input.PadWithNearest = false;
Assert.AreEqual(-1.0, input[0, 0]);
Assert.AreEqual(-1.0, input[6, 9]);
}
public void BasicSlidingSineSignal()
{
var listSize = 30 * 200;
var inputList = new List <double>(listSize);
var idealList = new List <double>(listSize);
var rand = new Random(23);
for (int i = 0; i < listSize; i++)
{
idealList.Add(Math.Sin(Math.PI * 2.0 * i / 30));
inputList.Add(idealList[idealList.Count - 1] + (rand.NextDouble() - 0.5) * 0.1);
}
var input = new SlidingWindowMLDataProvider(inputList, 10, 0, 1);
var ideal = new SlidingWindowMLDataProvider(idealList, 2, 11, 1); // predecit the eleventh, twelth item from the ten previous to it
var ds = new DynamicMLDataSet(input, ideal);
Assert.AreEqual(10, input.WindowSize);
Assert.AreEqual(10, ds.InputSize);
Assert.AreEqual(2, ds.IdealSize);
Assert.AreEqual(listSize, ds.Count);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(ds.InputSize));
network.AddLayer(new BasicLayer(ds.InputSize + 3));
network.AddLayer(new BasicLayer(ds.IdealSize));
network.Structure.FinalizeStructure();
network.Reset(42);
var trainer = new Encog.Neural.Networks.Training.Propagation.Resilient.ResilientPropagation(network, ds);
int maxIteration = 300;
int iteration = 0;
do
{
trainer.Iteration();
Debug.WriteLine(++iteration + ": Error = " + trainer.Error);
} while(trainer.Error > 0.001 && maxIteration > iteration);
Assert.IsTrue(iteration < maxIteration);
}
public void SimpleSlidingGap()
{
var list = new List <double>(20);
for (int i = 0; i < 20; i++)
{
list.Add(i);
}
var input = new SlidingWindowMLDataProvider(list, 12, 0, 1, 4);
Assert.AreEqual(20, input.Count);
Assert.AreEqual(3, input.Size);
Assert.AreEqual(0.0, input[0, 0]);
Assert.AreEqual(4.0, input[0, 1]);
Assert.AreEqual(8.0, input[0, 2]);
Assert.AreEqual(1.0, input[1, 0]);
Assert.AreEqual(19.0, input[19, 0]);
Assert.AreEqual(0.0, input[19, 1]);
}
public void BasicSlidingSineSignal()
{
var listSize = 30 * 200;
var inputList = new List<double>(listSize);
var idealList = new List<double>(listSize);
var rand = new Random(23);
for(int i = 0; i < listSize; i++)
{
idealList.Add(Math.Sin(Math.PI * 2.0 * i / 30));
inputList.Add(idealList[idealList.Count - 1] + (rand.NextDouble() - 0.5) * 0.1);
}
var input = new SlidingWindowMLDataProvider(inputList, 10, 0, 1);
var ideal = new SlidingWindowMLDataProvider(idealList, 2, 11, 1); // predecit the eleventh, twelth item from the ten previous to it
var ds = new DynamicMLDataSet(input, ideal);
Assert.AreEqual(10, input.WindowSize);
Assert.AreEqual(10, ds.InputSize);
Assert.AreEqual(2, ds.IdealSize);
Assert.AreEqual(listSize, ds.Count);
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(ds.InputSize));
network.AddLayer(new BasicLayer(ds.InputSize + 3));
network.AddLayer(new BasicLayer(ds.IdealSize));
network.Structure.FinalizeStructure();
network.Reset(42);
var trainer = new Encog.Neural.Networks.Training.Propagation.Resilient.ResilientPropagation(network, ds);
int maxIteration = 300;
int iteration = 0;
do
{
trainer.Iteration();
Debug.WriteLine(++iteration + ": Error = " + trainer.Error);
} while(trainer.Error > 0.001 && maxIteration > iteration);
Assert.IsTrue(iteration < maxIteration);
}
public void SimpleSlidingGap()
{
var list = new List<double>(20);
for(int i = 0; i < 20; i++) list.Add(i);
var input = new SlidingWindowMLDataProvider(list, 12, 0, 1, 4);
Assert.AreEqual(20, input.Count);
Assert.AreEqual(3, input.Size);
Assert.AreEqual(0.0, input[0, 0]);
Assert.AreEqual(4.0, input[0, 1]);
Assert.AreEqual(8.0, input[0, 2]);
Assert.AreEqual(1.0, input[1, 0]);
Assert.AreEqual(19.0, input[19, 0]);
Assert.AreEqual(0.0, input[19, 1]);
}
public void SimpleSlidingParameters()
{
var list = new List<double>(20);
for(int i = 0; i < 20; i++) list.Add(i);
var input = new SlidingWindowMLDataProvider(list, 10, -5, 3);
Assert.AreEqual(6, input.Count);
Assert.AreEqual(0.0, input[0, 0]);
Assert.AreEqual(0.0, input[0, 5]);
Assert.AreEqual(4.0, input[0, 9]);
Assert.AreEqual(17.0, input[6, 4]);
Assert.AreEqual(0.0, input[6, 9]);
input.PadWithNearest = true;
Assert.AreEqual(19.0, input[6, 9]);
Assert.AreEqual(19.0, input[6, 90]); // no range error presently
input.DefaultPadValue = -1.0;
input.PadWithNearest = false;
Assert.AreEqual(-1.0, input[0, 0]);
Assert.AreEqual(-1.0, input[6, 9]);
}
