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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 2 of the License, or
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* (at your option) any later version.
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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* You should have received a copy of the GNU General Public License
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* along with this program; if not, write to the Free Software
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* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
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* NeuralConnection.java
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* Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
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package weka.classifiers.functions.neural;
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import java.awt.Color;
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import java.awt.Graphics;
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import java.io.Serializable;
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* Abstract unit in a NeuralNetwork.
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* @author Malcolm Ware (mfw4@cs.waikato.ac.nz)
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* @version $Revision: 1.6 $
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public abstract class NeuralConnection
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implements Serializable {
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/** for serialization */
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private static final long serialVersionUID = -286208828571059163L;
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//bitwise flags for the types of unit.
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/** This unit is not connected to any others. */
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public static final int UNCONNECTED = 0;
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/** This unit is a pure input unit. */
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public static final int PURE_INPUT = 1;
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/** This unit is a pure output unit. */
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public static final int PURE_OUTPUT = 2;
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/** This unit is an input unit. */
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public static final int INPUT = 4;
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/** This unit is an output unit. */
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public static final int OUTPUT = 8;
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/** This flag is set once the unit has a connection. */
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public static final int CONNECTED = 16;
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/////The difference between pure and not is that pure is used to feed
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/////the neural network the attribute values and the errors on the outputs
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/////Beyond that they do no calculations, and have certain restrictions
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/////on the connections they can make.
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/** The list of inputs to this unit. */
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protected NeuralConnection[] m_inputList;
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/** The list of outputs from this unit. */
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protected NeuralConnection[] m_outputList;
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/** The numbering for the connections at the other end of the input lines. */
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protected int[] m_inputNums;
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/** The numbering for the connections at the other end of the out lines. */
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protected int[] m_outputNums;
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/** The number of inputs. */
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protected int m_numInputs;
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/** The number of outputs. */
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protected int m_numOutputs;
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/** The output value for this unit, NaN if not calculated. */
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protected double m_unitValue;
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/** The error value for this unit, NaN if not calculated. */
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protected double m_unitError;
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/** True if the weights have already been updated. */
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protected boolean m_weightsUpdated;
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/** The string that uniquely (provided naming is done properly) identifies
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protected String m_id;
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/** The type of unit this is. */
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protected int m_type;
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/** The x coord of this unit purely for displaying purposes. */
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protected double m_x;
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/** The y coord of this unit purely for displaying purposes. */
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protected double m_y;
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* Constructs The unit with the basic connection information prepared for
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* @param id the unique id of the unit
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public NeuralConnection(String id) {
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m_inputList = new NeuralConnection[0];
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m_outputList = new NeuralConnection[0];
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m_inputNums = new int[0];
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m_outputNums = new int[0];
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m_unitValue = Double.NaN;
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m_unitError = Double.NaN;
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m_weightsUpdated = false;
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m_type = UNCONNECTED;
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* @return The identity string of this unit.
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public String getId() {
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* @return The type of this unit.
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public int getType() {
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* @param t The new type of this unit.
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public void setType(int t) {
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* Call this to reset the unit for another run.
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* It is expected by that this unit will call the reset functions of all
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* input units to it. It is also expected that this will not be done
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* if the unit has already been reset (or atleast appears to be).
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public abstract void reset();
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* Call this to get the output value of this unit.
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* @param calculate True if the value should be calculated if it hasn't been
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* @return The output value, or NaN, if the value has not been calculated.
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public abstract double outputValue(boolean calculate);
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* Call this to get the error value of this unit.
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* @param calculate True if the value should be calculated if it hasn't been
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* @return The error value, or NaN, if the value has not been calculated.
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public abstract double errorValue(boolean calculate);
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* Call this to get the weight value on a particular connection.
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* @param n The connection number to get the weight for, -1 if The threshold
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* weight should be returned.
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* @return This function will default to return 1. If overridden, it should
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* return the value for the specified connection or if -1 then it should
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* return the threshold value. If no value exists for the specified
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* connection, NaN will be returned.
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public double weightValue(int n) {
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* Call this function to update the weight values at this unit.
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* After the weights have been updated at this unit, All the
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* input connections will then be called from this to have their
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* @param l The learning Rate to use.
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* @param m The momentum to use.
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public void updateWeights(double l, double m) {
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//the action the subclasses should perform is upto them
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//but if they coverride they should make a call to this to
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//call the method for all their inputs.
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if (!m_weightsUpdated) {
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for (int noa = 0; noa < m_numInputs; noa++) {
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m_inputList[noa].updateWeights(l, m);
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m_weightsUpdated = true;
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* Use this to get easy access to the inputs.
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* It is not advised to change the entries in this list
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* (use the connecting and disconnecting functions to do that)
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* @return The inputs list.
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public NeuralConnection[] getInputs() {
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* Use this to get easy access to the outputs.
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* It is not advised to change the entries in this list
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* (use the connecting and disconnecting functions to do that)
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* @return The outputs list.
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public NeuralConnection[] getOutputs() {
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* Use this to get easy access to the input numbers.
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* It is not advised to change the entries in this list
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* (use the connecting and disconnecting functions to do that)
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* @return The input nums list.
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public int[] getInputNums() {
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* Use this to get easy access to the output numbers.
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* It is not advised to change the entries in this list
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* (use the connecting and disconnecting functions to do that)
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* @return The outputs list.
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public int[] getOutputNums() {
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* @return the x coord.
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public double getX() {
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* @return the y coord.
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public double getY() {
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* @param x The new value for it's x pos.
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public void setX(double x) {
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* @param y The new value for it's y pos.
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public void setY(double y) {
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* Call this function to determine if the point at x,y is on the unit.
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* @param g The graphics context for font size info.
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* @param x The x coord.
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* @param y The y coord.
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* @param w The width of the display.
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* @param h The height of the display.
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* @return True if the point is on the unit, false otherwise.
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public boolean onUnit(Graphics g, int x, int y, int w, int h) {
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int m = (int)(m_x * w);
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int c = (int)(m_y * h);
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if (x > m + 10 || x < m - 10 || y > c + 10 || y < c - 10) {
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* Call this function to draw the node.
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* @param g The graphics context.
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* @param w The width of the drawing area.
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* @param h The height of the drawing area.
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public void drawNode(Graphics g, int w, int h) {
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if ((m_type & OUTPUT) == OUTPUT) {
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g.setColor(Color.orange);
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g.setColor(Color.red);
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g.fillOval((int)(m_x * w) - 9, (int)(m_y * h) - 9, 19, 19);
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g.setColor(Color.gray);
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g.fillOval((int)(m_x * w) - 5, (int)(m_y * h) - 5, 11, 11);
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* Call this function to draw the node highlighted.
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* @param g The graphics context.
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* @param w The width of the drawing area.
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* @param h The height of the drawing area.
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public void drawHighlight(Graphics g, int w, int h) {
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g.setColor(Color.yellow);
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g.fillOval((int)(m_x * w) - 5, (int)(m_y * h) - 5, 11, 11);
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* Call this function to draw the nodes input connections.
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* @param g The graphics context.
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* @param w The width of the drawing area.
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* @param h The height of the drawing area.
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public void drawInputLines(Graphics g, int w, int h) {
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g.setColor(Color.black);
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int px = (int)(m_x * w);
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int py = (int)(m_y * h);
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for (int noa = 0; noa < m_numInputs; noa++) {
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g.drawLine((int)(m_inputList[noa].getX() * w)
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, (int)(m_inputList[noa].getY() * h)
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* Call this function to draw the nodes output connections.
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* @param g The graphics context.
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* @param w The width of the drawing area.
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* @param h The height of the drawing area.
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public void drawOutputLines(Graphics g, int w, int h) {
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g.setColor(Color.black);
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int px = (int)(m_x * w);
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int py = (int)(m_y * h);
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for (int noa = 0; noa < m_numOutputs; noa++) {
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, (int)(m_outputList[noa].getX() * w)
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, (int)(m_outputList[noa].getY() * h));
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* This will connect the specified unit to be an input to this unit.
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* @param n It's connection number for this connection.
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* @return True if the connection was made, false otherwise.
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protected boolean connectInput(NeuralConnection i, int n) {
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for (int noa = 0; noa < m_numInputs; noa++) {
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if (i == m_inputList[noa]) {
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if (m_numInputs >= m_inputList.length) {
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//then allocate more space to it.
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m_inputList[m_numInputs] = i;
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m_inputNums[m_numInputs] = n;
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* This will allocate more space for input connection information
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* if the arrays for this have been filled up.
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protected void allocateInputs() {
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NeuralConnection[] temp1 = new NeuralConnection[m_inputList.length + 15];
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int[] temp2 = new int[m_inputNums.length + 15];
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for (int noa = 0; noa < m_numInputs; noa++) {
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temp1[noa] = m_inputList[noa];
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temp2[noa] = m_inputNums[noa];
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* This will connect the specified unit to be an output to this unit.
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* @param n It's connection number for this connection.
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* @return True if the connection was made, false otherwise.
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protected boolean connectOutput(NeuralConnection o, int n) {
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for (int noa = 0; noa < m_numOutputs; noa++) {
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if (o == m_outputList[noa]) {
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if (m_numOutputs >= m_outputList.length) {
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//then allocate more space to it.
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m_outputList[m_numOutputs] = o;
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m_outputNums[m_numOutputs] = n;
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* Allocates more space for output connection information
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* if the arrays have been filled up.
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protected void allocateOutputs() {
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NeuralConnection[] temp1
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= new NeuralConnection[m_outputList.length + 15];
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int[] temp2 = new int[m_outputNums.length + 15];
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for (int noa = 0; noa < m_numOutputs; noa++) {
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temp1[noa] = m_outputList[noa];
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temp2[noa] = m_outputNums[noa];
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m_outputList = temp1;
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m_outputNums = temp2;
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* This will disconnect the input with the specific connection number
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* From this node (only on this end however).
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* @param i The unit to disconnect.
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* @param n The connection number at the other end, -1 if all the connections
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* to this unit should be severed.
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* @return True if the connection was removed, false if the connection was
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protected boolean disconnectInput(NeuralConnection i, int n) {
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boolean removed = false;
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for (int noa = 0; noa < m_numInputs; noa++) {
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if (i == m_inputList[noa] && (n == -1 || n == m_inputNums[noa])) {
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for (int noa = loc+1; noa < m_numInputs; noa++) {
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m_inputList[noa-1] = m_inputList[noa];
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m_inputNums[noa-1] = m_inputNums[noa];
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//set the other end to have the right connection number.
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m_inputList[noa-1].changeOutputNum(m_inputNums[noa-1], noa-1);
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} while (n == -1 && loc != -1);
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* This function will remove all the inputs to this unit.
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* In doing so it will also terminate the connections at the other end.
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public void removeAllInputs() {
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for (int noa = 0; noa < m_numInputs; noa++) {
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//this command will simply remove any connections this node has
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//with the other in 1 go, rather than seperately.
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m_inputList[noa].disconnectOutput(this, -1);
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//now reset the inputs.
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m_inputList = new NeuralConnection[0];
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setType(getType() & (~INPUT));
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if (getNumOutputs() == 0) {
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setType(getType() & (~CONNECTED));
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m_inputNums = new int[0];
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* Changes the connection value information for one of the connections.
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* @param n The connection number to change.
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* @param v The value to change it to.
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protected void changeInputNum(int n, int v) {
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if (n >= m_numInputs || n < 0) {
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* This will disconnect the output with the specific connection number
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* From this node (only on this end however).
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* @param o The unit to disconnect.
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* @param n The connection number at the other end, -1 if all the connections
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* to this unit should be severed.
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* @return True if the connection was removed, false if the connection was
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protected boolean disconnectOutput(NeuralConnection o, int n) {
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boolean removed = false;
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for (int noa = 0; noa < m_numOutputs; noa++) {
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if (o == m_outputList[noa] && (n == -1 || n == m_outputNums[noa])) {
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for (int noa = loc+1; noa < m_numOutputs; noa++) {
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m_outputList[noa-1] = m_outputList[noa];
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m_outputNums[noa-1] = m_outputNums[noa];
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//set the other end to have the right connection number
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m_outputList[noa-1].changeInputNum(m_outputNums[noa-1], noa-1);
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} while (n == -1 && loc != -1);
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* This function will remove all outputs to this unit.
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* In doing so it will also terminate the connections at the other end.
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public void removeAllOutputs() {
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for (int noa = 0; noa < m_numOutputs; noa++) {
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//this command will simply remove any connections this node has
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//with the other in 1 go, rather than seperately.
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m_outputList[noa].disconnectInput(this, -1);
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//now reset the inputs.
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m_outputList = new NeuralConnection[0];
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m_outputNums = new int[0];
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setType(getType() & (~OUTPUT));
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if (getNumInputs() == 0) {
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setType(getType() & (~CONNECTED));
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* Changes the connection value information for one of the connections.
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* @param n The connection number to change.
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* @param v The value to change it to.
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protected void changeOutputNum(int n, int v) {
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if (n >= m_numOutputs || n < 0) {
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* @return The number of input connections.
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public int getNumInputs() {
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* @return The number of output connections.
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public int getNumOutputs() {
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* Connects two units together.
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* @param s The source unit.
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* @param t The target unit.
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* @return True if the units were connected, false otherwise.
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public static boolean connect(NeuralConnection s, NeuralConnection t) {
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if (s == null || t == null) {
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//this ensures that there is no existing connection between these
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//two units already. This will also cause the current weight there to be
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if ((t.getType() & PURE_INPUT) == PURE_INPUT) {
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return false; //target is an input node.
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if ((s.getType() & PURE_OUTPUT) == PURE_OUTPUT) {
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return false; //source is an output node
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if ((s.getType() & PURE_INPUT) == PURE_INPUT
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&& (t.getType() & PURE_OUTPUT) == PURE_OUTPUT) {
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return false; //there is no actual working node in use
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if ((t.getType() & PURE_OUTPUT) == PURE_OUTPUT && t.getNumInputs() > 0) {
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return false; //more than 1 node is trying to feed a particular output
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if ((t.getType() & PURE_OUTPUT) == PURE_OUTPUT &&
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(s.getType() & OUTPUT) == OUTPUT) {
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return false; //an output node already feeding out a final answer
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if (!s.connectOutput(t, t.getNumInputs())) {
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if (!t.connectInput(s, s.getNumOutputs() - 1)) {
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s.disconnectOutput(t, t.getNumInputs());
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//now ammend the type.
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if ((s.getType() & PURE_INPUT) == PURE_INPUT) {
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t.setType(t.getType() | INPUT);
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else if ((t.getType() & PURE_OUTPUT) == PURE_OUTPUT) {
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s.setType(s.getType() | OUTPUT);
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t.setType(t.getType() | CONNECTED);
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s.setType(s.getType() | CONNECTED);
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* Disconnects two units.
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* @param s The source unit.
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* @param t The target unit.
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* @return True if the units were disconnected, false if they weren't
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* (probably due to there being no connection).
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public static boolean disconnect(NeuralConnection s, NeuralConnection t) {
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if (s == null || t == null) {
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boolean stat1 = s.disconnectOutput(t, -1);
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boolean stat2 = t.disconnectInput(s, -1);
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if (stat1 && stat2) {
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if ((s.getType() & PURE_INPUT) == PURE_INPUT) {
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t.setType(t.getType() & (~INPUT));
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else if ((t.getType() & (PURE_OUTPUT)) == PURE_OUTPUT) {
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s.setType(s.getType() & (~OUTPUT));
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if (s.getNumInputs() == 0 && s.getNumOutputs() == 0) {
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s.setType(s.getType() & (~CONNECTED));
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if (t.getNumInputs() == 0 && t.getNumOutputs() == 0) {
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t.setType(t.getType() & (~CONNECTED));
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return stat1 && stat2;
added
removed
weka/classifiers/functions/neural/NeuralConnection.java
