/// <summary>
/// Invoked as the last step in learning of the SP.
/// </summary>
/// <param name="input">The input of the SP in the current cycle.</param>
/// <param name="output">The output SDR of the Spatial Pooler compute cycle.</param>
/// <returns></returns>
public bool Compute(int[] input, int[] output)
{
bool res = false;
double avgDerivation = -1;
// We take the hash value of the input.
var inpHash = GetHash(input);
//
// Here we track the number of active columns for every cycle for every input.
// We want that this number for every input is approximately the same.
if (m_NumOfActiveColsForInput.ContainsKey(inpHash))
{
ArrayUtils.PushToInterval(m_NumOfActiveColsForInput[inpHash], m_MaxPreviousElements, output.Count(c => c == 1));
}
//
// If the pattern appears for the first time, add it to dictionary of seen patterns.
if (!m_InOutMap.ContainsKey(inpHash))
{
m_InOutMap.Add(inpHash, output);
m_NumOfActiveColsForInput.Add(inpHash, new int[m_MaxPreviousElements]);
m_NumOfStableCyclesForInput.Add(inpHash, 0);
}
else
{
if (m_Cycle >= this.m_MinCycles)
{
this.m_HtmMemory.HtmConfig.MaxBoost = 0.0;
this.m_HtmMemory.HtmConfig.MinPctOverlapDutyCycles = 0.0;
this.m_HtmMemory.HtmConfig.MinPctActiveDutyCycles = 0.0;
}
// If the input has been already seen, we calculate the similarity between already seen input
// and the new input. The similarity is calculated as a correlation function.
var similarity = CalcArraySimilarity(ArrayUtils.IndexWhere(m_InOutMap[inpHash], k => k == 1), ArrayUtils.IndexWhere(output, k => k == 1));
// We replace the existing value with the new one.
m_InOutMap[inpHash] = output;
//
// We cannot expect the 100% for the entire learning cycle. Sometimes some
// SDR appear with few more or less bits than in the previous cycle.
// If this happen we take the new SDR (output) as the winner and put it in the map.
if (similarity >= m_RequiredSimilarityThreshold)
{
// We calculate here the average change of the SDR for the given input.
avgDerivation = ArrayUtils.AvgDelta(m_NumOfActiveColsForInput[inpHash]);
//
// If there is no change (SDR is stable) we count nuber of stable cycles.
// If the average value is not 0, then we reset the number of stable cycles.
if (avgDerivation == 0)
{
m_NumOfStableCyclesForInput[inpHash] = m_NumOfStableCyclesForInput[inpHash] + 1;
}
else
{
m_NumOfStableCyclesForInput[inpHash] = 0;
}
if (m_Cycle >= this.m_MinCycles)
{
if (m_NumOfStableCyclesForInput[inpHash] > m_RequiredNumOfStableCycles && IsInStableState(m_NumOfStableCyclesForInput, m_RequiredNumOfStableCycles))
{
// We fire event when changed from instable to stable.
if (!m_IsStable)
{
this.m_OnStabilityStatusChanged(true, m_InOutMap.Keys.Count, avgDerivation, m_Cycle);
}
m_IsStable = true;
res = true;
}
}
}
else
{
m_NumOfStableCyclesForInput[inpHash] = 0;
// If the new SDR output for the already seen input
if (m_IsStable)
{
// THIS SHOULD NEVER HAPPEN! MEANS FROM STABLE TO INSTABLE!
m_IsStable = false;
this.m_OnStabilityStatusChanged(false, m_InOutMap.Keys.Count, avgDerivation, m_Cycle);
}
}
}
this.m_Cycle++;
return(res);
}
