/// <summary>
/// Initializes a new instance of the <see cref="ProximalSynapse"/> class and
/// sets its input source and initial permanance values.
/// </summary>
/// <param name="proximalSeg">
/// </param>
/// <param name="inputSource">
/// An <see cref="InputCell"/> providing source of the input to this synapse.
/// </param>
/// <param name="permanence">Initial permanence value.</param>
internal ProximalSynapse(ProximalSegment proximalSeg, InputCell inputSource, float permanence)
{
// Set fields
this.ProximalSegment = proximalSeg;
this.InputSource = inputSource;
this.Permanence = permanence;
SelectablelType = SelectableObjectType.ProximalSynapse;
}
/// <summary>
/// Initializes a new instance of the <see cref="ProximalSynapse"/> class and
/// sets its input source and initial permanance values.
/// </summary>
/// <param name="proximalSeg">
/// </param>
/// <param name="inputSource">
/// An <see cref="InputCell"/> providing source of the input to this synapse.
/// </param>
/// <param name="permanence">Initial permanence value.</param>
internal ProximalSynapse(ProximalSegment proximalSeg, InputCell inputSource, float permanence)
{
// Set fields
this.ProximalSegment = proximalSeg;
this.InputSource = inputSource;
this.Permanence = permanence;
SelectablelType = SelectableObjectType.ProximalSynapse;
}
/// <summary>
/// For each (position in inputSpaceRandomPositions):
/// 1. Create a new InputCell with input bit = position
/// 2. Attach a new ProximalSynapse
/// 3. Add the synapse to the synapse update list.
/// </summary>
/// <remarks>
/// Prior to receiving any inputs, the region is initialized by computing a list of
/// initial potential synapses for each column. This consists of a random set of inputs
/// selected from the input space. Each input is represented by a synapse and assigned
/// a random permanence value. The random permanence values are chosen with two
/// criteria. First, the values are chosen to be in a small range around connectedPerm
/// (the minimum permanence value at which a synapse is considered "connected"). This
/// enables potential synapses to become connected (or disconnected) after a small
/// number of training iterations. Second, each column has a natural center over the
/// input region, and the permanence values have a bias towards this center (they
/// have higher values near the center).
///
/// The concept of Locality Radius is an additional parameter to control how
/// far away synapse connections can be made instead of allowing connections anywhere.
/// The reason for this is that in the case of video images I wanted to experiment
/// with forcing each Column to only learn on a small section of the total input to
/// more effectively learn lines or corners in a small section.
/// </remarks>
internal void CreateProximalSegments()
{
// Calculates inputRadius for Columns from localityRadius
//var inputRadius = (int)Math.Round ( this.Region.LocalityRadius * this.Region.InputProportionX );
// JS
// JS - should there be 2 inputRadiae, for X and Y, to allow for non-square input fields?
//(1)
var inputRadius = (int) Math.Max( 1, Math.Round(this.Region.LocalityRadius * this.Region.InputProportionX));
//inputRadius = (int)Math.Max ( 1, Math.Round ( this.Region.PercentageInputPerColumn * this.Region.InputSize.Height * this.Region.InputSize.Width ) / 2 );
//var inputRadius = (int)Math.Max ( 1, Math.Round ( this.Region.LocalityRadius * Math.Max ( this.Region.InputProportionX, this.Region.InputProportionY ) ) );
// The coordinates of the input space for the Column
// Think of input space like a 'imaginary' square below the column center.
int minY, maxY, minX, maxX;
//(2)
if (this.Region.LocalityRadius > 0)
{
// Compute values of input square and cut radius on edges
minX = Math.Max ( 0, this.CentralPositionInInput.X - inputRadius);
maxX = Math.Min(this.Region.InputSize.Width - 1,
this.CentralPositionInInput.X + inputRadius);
minY = Math.Max ( 0, this.CentralPositionInInput.Y - inputRadius);
maxY = Math.Min(this.Region.InputSize.Height - 1,
this.CentralPositionInInput.Y + inputRadius);
}
else
{
minX = 0;
minY = 0;
maxX = this.Region.InputSize.Width - 1;
maxY = this.Region.InputSize.Height - 1;
}
// Compute input area
// (3)
int inputArea = (maxX - minX + 1) * (maxY - minY + 1);
// Proximal synapses per Column (input segment)
// TODO: give user some control over the number of synapses per segment
//var synapsesPerSegment =
//debug js
// (int) (inputArea * this.Region.PercentageInputPerColumn);
// (4)
var synapsesPerSegment = Math.Max(1, (int)(inputArea * this.Region.PercentageInputPerColumn));
// JS
//synapsesPerSegment = Math.Max ( 1, (int)(inputArea * this.Region.InputProportionX) );
// Updates minimum overlap value, i.e. the minimum number of inputs that must
// be active for a column to be considered during the inhibition step.
//debug js
//this.MinOverlap =
// (int) Math.Round(synapsesPerSegment * this.Region.PercentageMinOverlap);
// (5)
this.MinOverlap = Math.Max(1,
(int)Math.Round ( synapsesPerSegment * this.Region.PercentageMinOverlap ));
// Create all possible x,y positions for this column input space
// (6)
var inputPositions = new List<Point>();
for (int y = minY; y <= maxY; y++)
{
for (int x = minX; x <= maxX; x++)
{
var inputPosition = new Point(x, y);
inputPositions.Add(inputPosition);
}
}
// Random sample of unique input positions (no duplicates).
// Tie the random seed to this Column's position for reproducibility
int randomSeed = (this.PositionInRegion.Y * this.Region.Size.Width) + this.PositionInRegion.X;
// (7)
IEnumerable<Point> inputRandomPositions =
inputPositions.RandomSample(synapsesPerSegment, randomSeed, false);
// Initialize the gaussian normal distribution
// The values are chosen to be in a small range around connectedPerm
// (the minimum permanence value at which a synapse is considered "connected")
var gausianNormalDistribution =
new Normal ( Synapse.InitialPermanence, Synapse.PermanenceIncrement );
gausianNormalDistribution.RandomSource = new Random ( randomSeed );
// Create proximal synapses to ramdom positions in input
int longerSide = Math.Max(this.Region.InputSize.Width, this.Region.InputSize.Height);
foreach (var position in inputRandomPositions)
{
var newInputCell = new InputCell(this.Region, position.X, position.Y);
if (this.Region.FullDefaultSpatialPermanence)
{
// Create new synapse and add it to segment
this.ProximalSegment.CreateSynapse(newInputCell, 1.0f);
}
else
{
// Get new value for permanence from distribution
double permanence = gausianNormalDistribution.Sample();
// Distance from 'center' of this column to the current position in the input space
// JS - replaced with more precise code from Ultrafast
//var distanceInputFromColumn = new Point ();
//distanceInputFromColumn.X = this.CentralPositionInInput.X - position.X;
//distanceInputFromColumn.Y = this.CentralPositionInInput.Y - position.Y;
//double distanceToInput = Math.Sqrt (
// (distanceInputFromColumn.X * distanceInputFromColumn.X) +
// (distanceInputFromColumn.Y * distanceInputFromColumn.Y) );
double distanceX = this.CentralPositionInInput.X - position.X;
double distanceY = this.CentralPositionInInput.Y - position.Y;
double distanceToInput = Math.Sqrt ( distanceX * distanceX + distanceY * distanceY );
// Each column has a natural center over the input region, and the
// permanence values have a bias towards this center (they have higher values near
// the center)
int radiusBiasPeak = 2;
double radiusBiasStandardDeviation = 2.0; // 0.25;
double localityBias = radiusBiasPeak / 2.0 *
Math.Exp(Math.Pow(distanceToInput /
(longerSide * radiusBiasStandardDeviation), 2) / -2);
//StreamWriter file = new StreamWriter ( "localityBias.txt", false );
//for (float longSide = 1f; longSide < 10.0f; longSide += 1f)
//{
// file.WriteLine ( "RadiusBiasPeak,distToInput,longerSide,SD,localityBias" );
// for (float distToInput = 0.0f; distToInput < 2.0f; distToInput += 0.1f)
// {
// localityBias = radiusBiasPeak / 2.0f *
// Math.Exp ( Math.Pow ( distToInput /
// (longSide * radiusBiasStandardDeviation), 2 ) / -2 );
// file.WriteLine ( String.Format ( "{0:0.00},{1:0.00},{2:0.00},{3:0.00},{4:0.0000000}", radiusBiasPeak, distToInput, longSide, radiusBiasStandardDeviation, localityBias ) );
// }
//}
//file.Close ();
double permanenceBias = Math.Min(1.0f, permanence * localityBias);
// Create new synapse and add it to segment
this.ProximalSegment.CreateSynapse(newInputCell, permanenceBias);
}
}
}
/// <summary>
/// For each (position in inputSpaceRandomPositions):
/// 1. Create a new InputCell with input bit = position
/// 2. Attach a new ProximalSynapse
/// 3. Add the synapse to the synapse update list.
/// </summary>
/// <remarks>
/// Prior to receiving any inputs, the region is initialized by computing a list of
/// initial potential synapses for each column. This consists of a random set of inputs
/// selected from the input space. Each input is represented by a synapse and assigned
/// a random permanence value. The random permanence values are chosen with two
/// criteria. First, the values are chosen to be in a small range around connectedPerm
/// (the minimum permanence value at which a synapse is considered "connected"). This
/// enables potential synapses to become connected (or disconnected) after a small
/// number of training iterations. Second, each column has a natural center over the
/// input region, and the permanence values have a bias towards this center (they
/// have higher values near the center).
///
/// The concept of Locality Radius is an additional parameter to control how
/// far away synapse connections can be made instead of allowing connections anywhere.
/// The reason for this is that in the case of video images I wanted to experiment
/// with forcing each Column to only learn on a small section of the total input to
/// more effectively learn lines or corners in a small section.
/// </remarks>
internal void CreateProximalSegments()
{
// Calculates inputRadius for Columns from localityRadius
//var inputRadius = (int)Math.Round ( this.Region.LocalityRadius * this.Region.InputProportionX );
// JS
// JS - should there be 2 inputRadiae, for X and Y, to allow for non-square input fields?
//(1)
var inputRadius = (int)Math.Max(1, Math.Round(this.Region.LocalityRadius * this.Region.InputProportionX));
//inputRadius = (int)Math.Max ( 1, Math.Round ( this.Region.PercentageInputPerColumn * this.Region.InputSize.Height * this.Region.InputSize.Width ) / 2 );
//var inputRadius = (int)Math.Max ( 1, Math.Round ( this.Region.LocalityRadius * Math.Max ( this.Region.InputProportionX, this.Region.InputProportionY ) ) );
// The coordinates of the input space for the Column
// Think of input space like a 'imaginary' square below the column center.
int minY, maxY, minX, maxX;
//(2)
if (this.Region.LocalityRadius > 0)
{
// Compute values of input square and cut radius on edges
minX = Math.Max(0, this.CentralPositionInInput.X - inputRadius);
maxX = Math.Min(this.Region.InputSize.Width - 1,
this.CentralPositionInInput.X + inputRadius);
minY = Math.Max(0, this.CentralPositionInInput.Y - inputRadius);
maxY = Math.Min(this.Region.InputSize.Height - 1,
this.CentralPositionInInput.Y + inputRadius);
}
else
{
minX = 0;
minY = 0;
maxX = this.Region.InputSize.Width - 1;
maxY = this.Region.InputSize.Height - 1;
}
// Compute input area
// (3)
int inputArea = (maxX - minX + 1) * (maxY - minY + 1);
// Proximal synapses per Column (input segment)
// TODO: give user some control over the number of synapses per segment
//var synapsesPerSegment =
//debug js
// (int) (inputArea * this.Region.PercentageInputPerColumn);
// (4)
var synapsesPerSegment = Math.Max(1, (int)(inputArea * this.Region.PercentageInputPerColumn));
// JS
//synapsesPerSegment = Math.Max ( 1, (int)(inputArea * this.Region.InputProportionX) );
// Updates minimum overlap value, i.e. the minimum number of inputs that must
// be active for a column to be considered during the inhibition step.
//debug js
//this.MinOverlap =
// (int) Math.Round(synapsesPerSegment * this.Region.PercentageMinOverlap);
// (5)
this.MinOverlap = Math.Max(1,
(int)Math.Round(synapsesPerSegment * this.Region.PercentageMinOverlap));
// Create all possible x,y positions for this column input space
// (6)
var inputPositions = new List <Point>();
for (int y = minY; y <= maxY; y++)
{
for (int x = minX; x <= maxX; x++)
{
var inputPosition = new Point(x, y);
inputPositions.Add(inputPosition);
}
}
// Random sample of unique input positions (no duplicates).
// Tie the random seed to this Column's position for reproducibility
int randomSeed = (this.PositionInRegion.Y * this.Region.Size.Width) + this.PositionInRegion.X;
// (7)
IEnumerable <Point> inputRandomPositions =
inputPositions.RandomSample(synapsesPerSegment, randomSeed, false);
// Initialize the gaussian normal distribution
// The values are chosen to be in a small range around connectedPerm
// (the minimum permanence value at which a synapse is considered "connected")
var gausianNormalDistribution =
new Normal(Synapse.InitialPermanence, Synapse.PermanenceIncrement);
gausianNormalDistribution.RandomSource = new Random(randomSeed);
// Create proximal synapses to ramdom positions in input
int longerSide = Math.Max(this.Region.InputSize.Width, this.Region.InputSize.Height);
foreach (var position in inputRandomPositions)
{
var newInputCell = new InputCell(this.Region, position.X, position.Y);
if (this.Region.FullDefaultSpatialPermanence)
{
// Create new synapse and add it to segment
this.ProximalSegment.CreateSynapse(newInputCell, 1.0f);
}
else
{
// Get new value for permanence from distribution
double permanence = gausianNormalDistribution.Sample();
// Distance from 'center' of this column to the current position in the input space
// JS - replaced with more precise code from Ultrafast
//var distanceInputFromColumn = new Point ();
//distanceInputFromColumn.X = this.CentralPositionInInput.X - position.X;
//distanceInputFromColumn.Y = this.CentralPositionInInput.Y - position.Y;
//double distanceToInput = Math.Sqrt (
// (distanceInputFromColumn.X * distanceInputFromColumn.X) +
// (distanceInputFromColumn.Y * distanceInputFromColumn.Y) );
double distanceX = this.CentralPositionInInput.X - position.X;
double distanceY = this.CentralPositionInInput.Y - position.Y;
double distanceToInput = Math.Sqrt(distanceX * distanceX + distanceY * distanceY);
// Each column has a natural center over the input region, and the
// permanence values have a bias towards this center (they have higher values near
// the center)
int radiusBiasPeak = 2;
double radiusBiasStandardDeviation = 2.0; // 0.25;
double localityBias = radiusBiasPeak / 2.0 *
Math.Exp(Math.Pow(distanceToInput /
(longerSide * radiusBiasStandardDeviation), 2) / -2);
//StreamWriter file = new StreamWriter ( "localityBias.txt", false );
//for (float longSide = 1f; longSide < 10.0f; longSide += 1f)
//{
// file.WriteLine ( "RadiusBiasPeak,distToInput,longerSide,SD,localityBias" );
// for (float distToInput = 0.0f; distToInput < 2.0f; distToInput += 0.1f)
// {
// localityBias = radiusBiasPeak / 2.0f *
// Math.Exp ( Math.Pow ( distToInput /
// (longSide * radiusBiasStandardDeviation), 2 ) / -2 );
// file.WriteLine ( String.Format ( "{0:0.00},{1:0.00},{2:0.00},{3:0.00},{4:0.0000000}", radiusBiasPeak, distToInput, longSide, radiusBiasStandardDeviation, localityBias ) );
// }
//}
//file.Close ();
double permanenceBias = Math.Min(1.0f, permanence * localityBias);
// Create new synapse and add it to segment
this.ProximalSegment.CreateSynapse(newInputCell, permanenceBias);
}
}
}
/// <summary>
/// Create a new synapse for this segment attached to the specified input cell.
/// </summary>
/// <param name="inputSource">the input source of the synapse to create.</param>
/// <param name="initialPermanence">the initial permanence of the synapse.</param>
internal void CreateSynapse(InputCell inputSource, double initialPermanence)
{
var newSynapse = new ProximalSynapse(this, inputSource, (float)initialPermanence);
this.Synapses.Add(newSynapse);
}
/// <summary>
/// Create a new synapse for this segment attached to the specified input cell.
/// </summary>
/// <param name="inputSource">the input source of the synapse to create.</param>
/// <param name="initialPermanence">the initial permanence of the synapse.</param>
internal void CreateSynapse(InputCell inputSource, double initialPermanence)
{
var newSynapse = new ProximalSynapse(this, inputSource, (float)initialPermanence);
this.Synapses.Add(newSynapse);
}
