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A Java based Neuron Modeling framework
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package model.MARK_II.parameters;
import model.MARK_II.generalAlgorithm.ColumnPosition;
import java.util.Set;
/**
* Uses the equation found here https://github.com/quinnliu/WalnutiQ/issues/21
* for computating a score for the SDR created by spatial pooling.
*
* Input from constructor: set of active column positions produced by spatial
* pooling.
*
* Output from calling public method: a score where the larger the score
* represents a better sparse distributed representation.
*
* @author Quinn Liu ([email protected])
* @version Apr 15, 2014
*/
public class SDRScoreCalculator {
private Set columnActivityAfterSeeingImage;
private double desiredPercentageOfActiveColumns;
private int totalNumberOfColumnsInRegion;
/**
* Computes the SDR score for the given set of column positions.
*
* @param columnActivityAfterSeeingImage
* @param desiredPercentageOfActiveColumns
* @param totalNumberOfColumnsInRegion
*/
public SDRScoreCalculator(
Set columnActivityAfterSeeingImage,
double desiredPercentageOfActiveColumns,
int totalNumberOfColumnsInRegion) {
this.columnActivityAfterSeeingImage = columnActivityAfterSeeingImage;
this.desiredPercentageOfActiveColumns = desiredPercentageOfActiveColumns;
this.totalNumberOfColumnsInRegion = totalNumberOfColumnsInRegion;
}
public void updateParameters(
Set columnActivityAfterSeeingImage,
double desiredPercentageOfActiveColumns,
int totalNumberOfColumnsInRegion) {
this.columnActivityAfterSeeingImage = columnActivityAfterSeeingImage;
this.desiredPercentageOfActiveColumns = desiredPercentageOfActiveColumns;
this.totalNumberOfColumnsInRegion = totalNumberOfColumnsInRegion;
}
/**
* Please look here for a visual of the followin sparsityScore formula:
* https://github.com/quinnliu/WalnutiQ/issues/21
*
* @return the sparsity score. The smaller the score the more sparsly
* distributed the active columns are.
*/
public double computeSparsityScore() {
double totalDistanceToNearestActiveColumnForAllActiveColumns = 0.0;
if (this.columnActivityAfterSeeingImage.size() <= 1) {
return 0;
}
// iterate through all column positions
for (ColumnPosition currentColumnPosition : this.columnActivityAfterSeeingImage) {
int row1 = currentColumnPosition.getRow();
int column1 = currentColumnPosition.getColumn();
Set columnActivity = this.columnActivityAfterSeeingImage;
// find nearest active column distance(1 million is a random large
// number)
double distanceToNearestActiveColumn = 1000000;
for (ColumnPosition otherColumnPosition : columnActivity) {
int row2 = otherColumnPosition.getRow();
int column2 = otherColumnPosition.getColumn();
// if you find the same columnPosition as (row1, column1) skip
if (row1 == row2 && column1 == column2) {
continue;
}
double distanceBetween_row1column1_row2column2 = Math.sqrt(Math.pow(row1 - row2,
2) + Math.pow(column1 - column2, 2));
if (distanceBetween_row1column1_row2column2 < distanceToNearestActiveColumn) {
distanceToNearestActiveColumn = distanceBetween_row1column1_row2column2;
}
}
totalDistanceToNearestActiveColumnForAllActiveColumns += distanceToNearestActiveColumn;
}
double averageDistanceToNearestActiveColumn = totalDistanceToNearestActiveColumnForAllActiveColumns
/ columnActivityAfterSeeingImage.size();
// the best averageDistanceToNearestActiveColumn
return -averageDistanceToNearestActiveColumn;
}
/**
* @return the number of active columns score. The smaller the score the
* closer the numberOfActiveColumns was to
* desiredNumberOfActiveColumns.
*/
public double computeNumberOfActiveColumnsScore() {
int desiredNumberOfActiveColumns = (int) (this.totalNumberOfColumnsInRegion
* this.desiredPercentageOfActiveColumns / 100);
int actualNumberOfActiveColumns = this.columnActivityAfterSeeingImage
.size();
double difference = Math.abs(desiredNumberOfActiveColumns
- actualNumberOfActiveColumns);
if (difference < 0.1) {
difference = 0.1; // 1 / 0.1 = 10 which is a great score
// as it should be since desired was = to actual
}
return -(1 / difference);
}
/**
* @return SDR score computed from given set of active column positions.
*/
public double computeSDRScore() {
return this.computeSparsityScore()
+ this.computeNumberOfActiveColumnsScore();
}
}