/// <summary>
/// This method updates the permanence matrix with a column's new permanence values. The column is identified by its index, which reflects the row in
/// the matrix, and the permanence is given in 'sparse' form, (i.e. an array whose members are associated with specific indexes). It is in charge of
/// implementing 'clipping' - ensuring that the permanence values are always between 0 and 1 - and 'trimming' - enforcing sparseness by zeroing out all
/// permanence values below 'synPermTrimThreshold'. Every method wishing to modify the permanence matrix should do so through this method.
/// </summary>
/// <param name="htmConfig"></param>
/// <param name="perm">An array of permanence values for a column. The array is "sparse", i.e. it contains an entry for each input bit, even if the permanence value is 0.</param>
/// <param name="maskPotential">Indexes of potential connections to input neurons.</param>
/// <param name="raisePerm">a boolean value indicating whether the permanence values</param>
public void UpdatePermanencesForColumnSparse(HtmConfig htmConfig, double[] perm, int[] maskPotential, bool raisePerm)
{
if (raisePerm)
{
HtmCompute.RaisePermanenceToThresholdSparse(htmConfig, perm);
}
ArrayUtils.LessOrEqualXThanSetToY(perm, htmConfig.SynPermTrimThreshold, 0);
ArrayUtils.EnsureBetweenMinAndMax(perm, htmConfig.SynPermMin, htmConfig.SynPermMax);
SetProximalPermanencesSparse(htmConfig, perm, maskPotential);
}
/// <summary>
/// Sets the permanences for each <see cref="Synapse"/>. The number of synapses is set by the potentialPct variable which determines the number of input
/// bits a given column will be "attached" to which is the same number as the number of <see cref="Synapse"/>s
/// </summary>
/// <param name="htmConfig">the <see cref="Connections"/> memory</param>
/// <param name="perms">the floating point degree of connectedness</param>
public void SetPermanences(HtmConfig htmConfig, double[] perms)
{
this.ProximalDendrite.RFPool.ResetConnections();
// Every column contians a single row at index 0.
this.ConnectedInputCounterMatrix.ClearStatistics(0);
foreach (Synapse synapse in this.ProximalDendrite.Synapses)
{
this.SetPermanence(synapse, htmConfig.SynPermConnected, perms[synapse.InputIndex]);
if (perms[synapse.InputIndex] >= htmConfig.SynPermConnected)
{
this.ConnectedInputCounterMatrix.set(1, 0 /*this.Index*/, synapse.InputIndex);
}
}
}
/// <summary>
/// Sets the permanences for each {@link Synapse} specified by the indexes passed in which identify the input vector indexes associated with the
/// <see cref="Synapse"/>. The permanences passed in are understood to be in "sparse" format and therefore require the int array identify their
/// corresponding indexes.
/// </summary>
/// <param name="connectedCounts"></param>
/// <param name="htmConfig"></param>
/// <param name="perms">the floating point degree of connectedness</param>
/// <param name="inputIndexes"></param>
/// <remarks>
/// Note: This is the "sparse" version of this method.
/// </remarks>
public void SetPermanences(AbstractSparseBinaryMatrix connectedCounts, HtmConfig htmConfig, double[] perms, int[] inputIndexes)
{
var permConnThreshold = htmConfig.SynPermConnected;
RFPool.ResetConnections();
// c.getConnectedCounts().clearStatistics(ParentColumnIndex);
connectedCounts.ClearStatistics(0 /*this.ParentColumnIndex*/);
for (int i = 0; i < inputIndexes.Length; i++)
{
var synapse = RFPool.GetSynapseForInput(inputIndexes[i]);
synapse.Permanence = perms[i];
if (perms[i] >= permConnThreshold)
{
connectedCounts.set(1, 0 /*ParentColumnIndex*/, i);
}
}
}
/// <summary>
/// Creates connections between mini-columns and input neurons.
/// </summary>
/// <param name="htmConfig"></param>
/// <param name="inputVectorIndexes">Sensory cells providing spatial input that will be learned by SP.</param>
/// <param name="startSynapseIndex">Starting index.</param>
/// <returns></returns>
public Pool CreatePotentialPool(HtmConfig htmConfig, int[] inputVectorIndexes, int startSynapseIndex)
{
this.ProximalDendrite.Synapses.Clear();
var pool = new Pool(inputVectorIndexes.Length, htmConfig.NumInputs);
this.ProximalDendrite.RFPool = pool;
for (int i = 0; i < inputVectorIndexes.Length; i++)
{
var synapse = this.ProximalDendrite.CreateSynapse(startSynapseIndex + i, inputVectorIndexes[i]);
// All permanences are at the begining set to 0.
this.SetPermanence(synapse, htmConfig.SynPermConnected, 0);
}
return(pool);
}
/// <summary>
/// Gets the min,max and avg permanence of all connected synapses.
/// </summary>
/// <returns></returns>
public HtmStatistics GetStatistics(HtmConfig config)
{
double permSum = 0.0;
double max = 0.0;
double min = 2.0;
int connectedSynapses = 0;
foreach (var syn in this.ProximalDendrite.Synapses.Where(s => s.Permanence > config.SynPermConnected))
{
permSum += syn.Permanence;
if (syn.Permanence < min)
{
min = syn.Permanence;
}
if (syn.Permanence > max)
{
max = syn.Permanence;
}
connectedSynapses++;
}
return(new HtmStatistics
{
SynapticActivity = (double)connectedSynapses / (double)this.ProximalDendrite.Synapses.Count,
AvgPermanence = permSum / connectedSynapses,
MinPermanence = min,
MaxPermanence = max,
ConnectedSynapses = connectedSynapses,
Synapses = this.ProximalDendrite.Synapses.Count
});
}
/// <summary>
/// Sets the permanences on the <see cref="ProximalDendrite"/> <see cref="Synapse"/>s
/// </summary>
/// <param name="htmConfig"></param>
/// <param name="permanences">floating point degree of connectedness</param>
/// <param name="inputVectorIndexes"></param>
public void SetProximalPermanencesSparse(HtmConfig htmConfig, double[] permanences, int[] inputVectorIndexes)
{
this.ProximalDendrite.SetPermanences(this.ConnectedInputCounterMatrix, htmConfig, permanences, inputVectorIndexes);
}