public void TestMultistepSingleValue()
{
_classifier = new CLAClassifier(new[] { 1, 2 });
// Only should return one actual value bucket.
Classification <double> result = null;
int recordNum = 0;
for (int i = 0; i < 10; i++, recordNum++)
{
result = Compute <double>(_classifier, recordNum, new[] { 1, 5 }, 0, 10);
}
Assert.IsTrue(Arrays.AreEqual(new double[] { 10.0 }, result.GetActualValues()));
// Should have a probability of 100% for that bucket.
Assert.IsTrue(Arrays.AreEqual(new double[] { 1.0 }, result.GetStats(1)));
Assert.IsTrue(Arrays.AreEqual(new double[] { 1.0 }, result.GetStats(2)));
}
public void TestMultistepSingleValue()
{
var classifier = new SDRClassifier(new[] { 1, 2 });
Classification <double> retVal = null;
for (int i = 0; i < 10; i++)
{
retVal = _compute(classifier, i, new[] { 1, 5 }, 0, 10);
}
// Since overlap - should be previous with high likelihood
double[] actValues = retVal.GetActualValues();
Assert.AreEqual((double)actValues[0], 10);
double[] resultDoubles1 = (double[])retVal.GetStats(1);
double[] resultDoubles2 = (double[])retVal.GetStats(2);
Assert.AreEqual(resultDoubles1[0], 1);
Assert.AreEqual(resultDoubles2[0], 1);
}
private static Func <IInference, int, int> CreateDayOfWeekInferencePrintout(bool on)
{
int cycles = 1;
return((IInference inf, int cellsPerColumn) =>
{
Classification <Object> result = inf.GetClassification("dayOfWeek");
double day = MapToInputData((int[])inf.GetLayerInput());
if (day == 1.0)
{
if (on)
{
Console.WriteLine("\n=========================");
Console.WriteLine("CYCLE: " + cycles);
}
cycles++;
}
if (on)
{
Console.WriteLine("RECORD_NUM: " + inf.GetRecordNum());
Console.WriteLine("ScalarEncoder Input = " + day);
Console.WriteLine("ScalarEncoder Output = " + Arrays.ToString(inf.GetEncoding()));
Console.WriteLine("SpatialPooler Output = " + Arrays.ToString(inf.GetFeedForwardActiveColumns()));
if (inf.GetPreviousPredictiveCells() != null)
{
Console.WriteLine("TemporalMemory Previous Prediction = " +
Arrays.ToString(SDR.CellsAsColumnIndices(inf.GetPreviousPredictiveCells(), cellsPerColumn)));
}
Console.WriteLine("TemporalMemory Actives = " + Arrays.ToString(SDR.AsColumnIndices(inf.GetSdr(), cellsPerColumn)));
Console.Write("CLAClassifier prediction = " +
result.GetMostProbableValue(1) + " --> " + result.GetMostProbableValue(1));
Console.WriteLine(" | CLAClassifier 1 step prob = " + Arrays.ToString(result.GetStats(1)) + "\n");
}
return cycles;
});
}
