public void TestMethod2()
{
Topology t = new Topology(new int[] { 2048, 40 });
int[] coords = new int[] { 200, 10 };
var indx = HtmCompute.GetFlatIndexFromCoordinates(coords, t.HtmTopology);
}
/**
*
*
*
* @param shape
* @param useColumnMajorOrdering
*
*
*
*/
/// <summary>
/// Constructs a new <see cref="Coordinator"/> object to be configured with specified dimensions and major ordering.
/// </summary>
/// <param name="shape">the dimensions of this sparse array</param>
/// <param name="useColumnMajorOrdering">flag indicating whether to use column ordering or row major ordering. if false
/// (the default), then row major ordering will be used. If true, then column major
/// ordering will be used.</param>
public Topology(int[] shape, bool useColumnMajorOrdering)
{
this.dimensions = shape;
this.numDimensions = shape.Length;
this.dimensionMultiples = InitDimensionMultiples(
useColumnMajorOrdering ? HtmCompute.Reverse(shape) : shape);
isColumnMajor = useColumnMajorOrdering;
}
/// <summary>
/// Initialize all columns inside of partition and connect them to sensory input.
/// It returns the average connected span of the partition.
/// </summary>
/// <param name="msg"></param>
private void createAndConnectColumns(ConnectAndConfigureColumnsMsg msg)
{
DateTime startTime = DateTime.Now;
Log(msg, Self, "Entered.");
List <double> avgConnections = new List <double>();
Random rnd;
if (this.config.RandomGenSeed > 0)
{
rnd = new Random(this.config.RandomGenSeed);
}
else
{
rnd = new Random();
}
if (this.dict.Count == 0)
{
}
foreach (var element in this.dict)
{
if (this.config == null)
{
throw new ArgumentException($"HtmConfig must be set in the message.");
}
int colIndx = (int)element.Key;
// Gets RF
var potential = HtmCompute.MapPotential(this.config, colIndx, rnd);
var column = (Column)this.dict[colIndx];
// This line initializes all synases in the potential pool of synapses.
// It creates the pool on proximal dendrite segment of the column.
// After initialization permancences are set to zero.
//connectColumnToInputRF(c.HtmConfig, data.Potential, data.Column);
column.CreatePotentialPool(this.config, potential, -1);
var perms = HtmCompute.InitSynapsePermanences(this.config, potential, rnd);
avgConnections.Add(HtmCompute.CalcAvgSpanOfConnectedSynapses(column, this.config));
//Log(msg, Self, $".:). {dict.Count}/{dict.Keys.Min()}/{dict.Keys.Min()} - duration: {(DateTime.Now - startTime).TotalSeconds}");
HtmCompute.UpdatePermanencesForColumn(this.config, perms, column, potential, true);
}
double avgConnectedSpan = ArrayUtils.average(avgConnections.ToArray());
Log(msg, Self, "Completed.");
Sender.Tell(avgConnectedSpan, Self);
Log(msg, Self, $"Response sent. {(DateTime.Now - startTime).TotalSeconds}");
}
public void TestMethod3()
{
Topology t = new Topology(new int[] { 2048, 40 });
int[] coords = new int[] { 200, 10 };
var indx = HtmCompute.GetFlatIndexFromCoordinates(coords, t.HtmTopology);
var coords2 = HtmCompute.GetCoordinatesFromIndex(indx, t.HtmTopology);
Assert.AreEqual(coords[0], coords2[0]);
Assert.AreEqual(coords[1], coords2[1]);
}
/// <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);
}
public void NeighborhoodTest()
{
var parameters = GetDefaultParams();
int cellsDim1 = 64;
int cellsDim2 = 64;
parameters.setInputDimensions(new int[] { 32 });
parameters.setColumnDimensions(new int[] { cellsDim1 });
var sp = new SpatialPooler();
var mem = new Connections();
parameters.apply(mem);
sp.Init(mem);
for (int rad = 1; rad < 10; rad++)
{
using (StreamWriter sw = new StreamWriter($"neighborhood-test-rad{rad}-center-from-{cellsDim1}-to-{0}.csv"))
{
sw.WriteLine($"{cellsDim1}|{cellsDim2}|{rad}|First column defines center of neiborhood. All other columns define indicies of neiborhood columns");
for (int center = 0; center < 64; center++)
{
var nbs = HtmCompute.GetNeighborhood(center, rad, mem.HtmConfig.ColumnTopology.HtmTopology);
StringBuilder sb = new StringBuilder();
sb.Append(center);
sb.Append('|');
foreach (var neighobordCellIndex in nbs)
{
sb.Append(neighobordCellIndex);
sb.Append('|');
}
string str = sb.ToString();
sw.WriteLine(str.TrimEnd('|'));
}
}
}
}
void adaptSynapses(AdaptSynapsesMsg msg)
{
ParallelOptions opts = new ParallelOptions();
opts.MaxDegreeOfParallelism = msg.ColumnKeys.Count;
Parallel.ForEach(msg.ColumnKeys, opts, (colPair) =>
{
Column activeColumn = (Column)this.dict[colPair.Key];
//Pool pool = c.getPotentialPools().get(activeColumns[i]);
Pool pool = activeColumn.ProximalDendrite.RFPool;
double[] perm = pool.getDensePermanences(this.config.NumInputs);
int[] indexes = pool.getSparsePotential();
ArrayUtils.raiseValuesBy(msg.PermanenceChanges, perm);
HtmCompute.UpdatePermanencesForColumn(this.config, perm, activeColumn, indexes, true);
});
// We send this to ensure reliable messaging. No other result is required here.
Sender.Tell(0, Self);
}
/// <summary>
/// Initialize all columns inside of partition and connect them to sensory input.
/// It returns the average connected span of the partition.
/// </summary>
/// <param name="msg"></param>
//TODO remove unnecessary argument
private double CreateAndConnectColumns(ConnectAndConfigureColumnsMsg msg)
{
Debug.Write(".");
List <double> avgConnections = new List <double>();
Random rnd;
if (this.HtmConfig.RandomGenSeed > 0)
{
rnd = new Random(this.HtmConfig.RandomGenSeed);
}
else
{
rnd = new Random();
}
foreach (var element in this.Dict)
{
if (this.HtmConfig == null)
{
throw new ArgumentException($"HtmConfig must be set in the message.");
}
int colIndx = -1;
Column column;
if (element.Key is string)
{
if (!int.TryParse(element.Key as string, out colIndx))
{
throw new ArgumentException($"The key must be of type 'int' or string convertable to 'int");
}
column = (Column)this.Dict[element.Key];
}
else
{
colIndx = (int)element.Key;
column = (Column)this.Dict[colIndx];
}
// Gets RF
var potential = HtmCompute.MapPotential(this.HtmConfig, colIndx, rnd);
// This line initializes all synases in the potential pool of synapses.
// It creates the pool on proximal dendrite segment of the column.
// After initialization permancences are set to zero.
//connectColumnToInputRF(c.HtmConfig, data.Potential, data.Column);
column.CreatePotentialPool(this.HtmConfig, potential, -1);
var perms = HtmCompute.InitSynapsePermanences(this.HtmConfig, potential, rnd);
avgConnections.Add(HtmCompute.CalcAvgSpanOfConnectedSynapses(column, this.HtmConfig));
HtmCompute.UpdatePermanencesForColumn(this.HtmConfig, perms, column, potential, true);
}
Debug.Write(".");
double avgConnectedSpan = ArrayUtils.Average(avgConnections.ToArray());
Debug.Write(".");
return(avgConnectedSpan);
}
