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A Java based Neuron Modeling framework
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package model.MARK_II.util;
import model.MARK_II.region.*;
import java.awt.*;
import java.util.Set;
/**
* @author Quinn Liu ([email protected])
* @version June 18, 2013
*/
public class RegionConsoleViewer {
/**
* Returns a 2-D array of chars representing each Column's activeState
* within a Region inside of a SpatialPooler object. 'a' represents an
* active Column while 'i' represents an inactive Column for the current
* time step.
*
* @param region
* @return A 2-D char array of Columns' active states.
*/
public static char[][] getColumnActiveStatesCharArray(Region region) {
char[][] columnActiveStates = new char[region.getNumberOfRowsAlongRegionYAxis()][region
.getNumberOfColumnsAlongRegionXAxis()];
Column[][] columns = region.getColumns();
for (int row = 0; row < columnActiveStates.length; row++) {
for (int column = 0; column < columnActiveStates[row].length; column++) {
if (columns[row][column].getActiveState()) {
// 'a' represents an active Column at a specific time step
columnActiveStates[row][column] = 'a';
} else {
// 'i' represents an inactive Column at a specific time step
columnActiveStates[row][column] = 'i';
}
}
}
return columnActiveStates;
}
/**
* Returns a 2-D array of chars representing each Column's activeState
* within a Region inside of a SpatialPooler object. 'a' represents an
* active Column while 'i' represents an inactive Column for the current
* time step.
*
* @param region
* @return A 2-D char array of Columns' predictive states.
*/
public static char[][] getColumnPredictiveStatesCharArray(Region region) {
char[][] columnPredictiveStates = new char[region.getNumberOfRowsAlongRegionYAxis()][region
.getNumberOfColumnsAlongRegionXAxis()];
Column[][] columns = region.getColumns();
for (int row = 0; row < columnPredictiveStates.length; row++) {
for (int column = 0; column < columnPredictiveStates[row].length; column++) {
Neuron[] neurons = columns[row][column].getNeurons();
for (Neuron neuron : neurons) {
if (neuron.getPredictingState()) {
// 'p' represents an Column with a Neuron that has predictive state on
columnPredictiveStates[row][column] = 'p';
break;
} else {
// 'n' represents an Column with a Neuron that does NOTE havehas predictive state on
columnPredictiveStates[row][column] = 'n';
}
}
}
}
return columnPredictiveStates;
}
/**
* Returns a 2-D array of integers representing each Column's overlapScore
* within a Region inside of a SpatialPooler object.
*
* @param region
* @return A 2-D integer array of Columns' overlapScores.
*/
public static int[][] getColumnOverlapScoresIntArray(Region region) {
int[][] columnOverlapScores = new int[region.getNumberOfRowsAlongRegionYAxis()][region
.getNumberOfColumnsAlongRegionXAxis()];
Column[][] columns = region.getColumns();
for (int row = 0; row < columnOverlapScores.length; row++) {
for (int column = 0; column < columnOverlapScores[row].length; column++) {
columnOverlapScores[row][column] = columns[row][column].getOverlapScore();
}
}
return columnOverlapScores;
}
/**
* 0 means the SensorCell is disconnected from the Synapse at that location.
* Any number 2-9 represents the permanenceValue of the Synapse at that
* location rounded down in the tenth decimal place.
*
* For example, a Synapse with a permanenceValue of 0.36 will be represented
* as a 3 in the int[][] array being returned.
*/
public static int[][] getSynapsePermanencesIntArray(Region region) {
Dimension bottomLayerDimensions = region.getBottomLayerXYAxisLength();
int[][] synapsePermanences = new int[bottomLayerDimensions.width][bottomLayerDimensions.height];
Column[][] columns = region.getColumns();
for (int row = 0; row < columns.length; row++) {
for (int column = 0; column < columns[row].length; column++) {
Set> synapses = columns[row][column]
.getProximalSegment().getSynapses();
for (Synapse| synapse : synapses) {
if (synapse.getPermanenceValue() < 0.2) {
synapsePermanences[synapse.getCellColumn()][synapse
.getCellRow()] = 0;
} else {
// permanenceTimesTen = round((0.9999 - 0.05555) * 10)
int permanenceTimesTen = (int) Math.round(((synapse
.getPermanenceValue() - 0.055555) * 10));
synapsePermanences[synapse.getCellColumn()][synapse
.getCellRow()] = permanenceTimesTen;
}
}
}
}
return synapsePermanences;
}
/**
* Prints a byte 2-D array to the console.
*
* @param doubleByteArray The 2-D byte array to be printed.
*/
public static void printDoubleByteArray(byte[][] doubleByteArray) {
for (int row = 0; row < doubleByteArray.length; row++) {
System.out.println();
for (int column = 0; column < doubleByteArray[row].length; column++) {
System.out.print(doubleByteArray[row][column]);
}
}
}
/**
* Prints a int 2-D array to the console.
*
* @param doubleIntArray The 2-D int array to be printed.
*/
public static void printDoubleIntArray(int[][] doubleIntArray) {
for (int row = 0; row < doubleIntArray.length; row++) {
System.out.println();
for (int column = 0; column < doubleIntArray[row].length; column++) {
System.out.printf("%1d", doubleIntArray[row][column]);
}
}
}
/**
* Returns a char 2-D array to the console.
*
* @param doubleCharArray The 2-D char array to be printed.
* @return The 2-D char array as a String.
*/
public static String doubleCharArrayAsString(char[][] doubleCharArray) {
String doubleCharArrayAsString = "";
for (int row = 0; row < doubleCharArray.length; row++) {
boolean isAtBeginning = (row == 0);
boolean isAtEnd = (row == doubleCharArray.length);
if (isAtBeginning || isAtEnd) {
// don't add anything
} else {
doubleCharArrayAsString += "\n";
}
for (int column = 0; column < doubleCharArray[row].length; column++) {
doubleCharArrayAsString += doubleCharArray[row][column];
}
}
return doubleCharArrayAsString;
}
public static void printDoubleCharArray(char[][] doubleCharArray) {
System.out.print(doubleCharArrayAsString(doubleCharArray));
}
}
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