CustomNode.java.wp: Difference between revisions
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[[WikiPeerCode]] | [[WikiPeerCode]] | ||
<pre>/* | <pre> | ||
/* | |||
* CustomNode.java | * CustomNode.java | ||
* | * | ||
* Created on January 22, 2005, 2:41 PM | * Created on January 22, 2005, 2:41 PM | ||
* Modified on June | * Modified on June 18, 2006, 13:55 by Jack | ||
* | * | ||
* This class contains the parameters and methods | * This class contains the parameters and methods | ||
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// the degree by which the historical opinion is weighted | // the degree by which the historical opinion is weighted | ||
public static double histWeight; | public static double histWeight; | ||
// the (negative of) slope of skill vs variance of noise | |||
public static double noiseSlope; | |||
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// Outputs: double, the noise variance | // Outputs: double, the noise variance | ||
public static double getNoiseFromSkill(double skill){ | public static double getNoiseFromSkill(double skill){ | ||
return skill | return (1-skill)*noiseSlope; | ||
} | } | ||
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public static double getHistWeight() {return histWeight;} | public static double getHistWeight() {return histWeight;} | ||
public static void setHistWeight(double i) {histWeight = i;} | public static void setHistWeight(double i) {histWeight = i;} | ||
public static double getNoiseSlope() {return noiseSlope;} | |||
public static void setNoiseSlope(double i) {noiseSlope = i;} | |||
} | } | ||
</pre> | </pre> |
Revision as of 23:09, 18 June 2006
/* * CustomNode.java * * Created on January 22, 2005, 2:41 PM * Modified on June 18, 2006, 13:55 by Jack * * This class contains the parameters and methods * for an agent acting on the network */ package RepMod; import uchicago.src.sim.gui.NetworkDrawable; import uchicago.src.sim.network.DefaultDrawableNode; import uchicago.src.sim.gui.DrawableNonGridNode; import uchicago.src.sim.util.Random; import uchicago.src.sim.gui.OvalNetworkItem; import java.awt.Color; import uchicago.src.sim.gui.ColorMap; import uchicago.src.sim.engine.CustomProbeable; import java.util.ArrayList; /** * * @author Jack Waddell * */ // DefaultDrawableNode lets us draw it in the GUI // CustomProbeable lets us define which attributes appear when // probed in the GUI public class CustomNode extends DefaultDrawableNode implements CustomProbeable { //*********************************** // Static Parameters // tracks next unique id public static int nextID = 0; public static GUIModel guiModel = null; public static RepMod model; // voteType, whether to use democratic or meritocratic votes // 0 = democratic // 1 = meritocratic public static int voteType; // the degree by which the historical opinion is weighted public static double histWeight; // the (negative of) slope of skill vs variance of noise public static double noiseSlope; //********************************8** // Instance parameters // Two colormaps are generated for color-coding the nodes in GUI public static ColorMap centerColorMap; public static ColorMap edgeColorMap; public static final int colorMapSize = 16; public static final int colorMapMax = colorMapSize - 1; private int id; // uniquely ids node public Color myColor; // used to draw color in GUI public String myPajekColor = "Red"; // color to appear in Pajek output public double reputation; // current wealth or stored reputation public double skill; // skill parameter //public int numVotes; // the number of vote rounds //public double success; // most recent success /////////////////////////////////////////////////////////////// // constructor // Inputs: NetworkDrawable drawable, to draw in GUI // double skill, the skill parameter public CustomNode (NetworkDrawable drawable, double skill) { super(drawable); id = nextID++; // set id and iterate to next this.skill = skill; reputation = 0.5; // Generalize. Currently hard-coded if (guiModel != null) setNodeEdgeColorFromSkill(); //numVotes = 0; //success = 0; } /////////////////////////////////////////////////////////// // getProbedProperties // Required to implement CustomProbeable // Inputs: none // Outputs: array of strings holding parameter names public String[] getProbedProperties(){ return new String[] {"id", "reputation", "skill"}; } ////////////////////////////////////////////////////////// // step // Input: none // Output: none // (Can be) Called by main model to have nodes execute a single step public void step(){ } ////////////////////////////////////////////////////////// // setNodeColorFromReputation // Input: maxReputation, the highest reputation from all agents // Output: none // Sets the node color to the colormap leval depending on reputation public void setNodeColorFromReputation(double maxReputation){ int i = (int) Math.round(colorMapMax*(reputation)/ (maxReputation)); this.setColor(centerColorMap.getColor(i)); } ////////////////////////////////////////////////////////// // setNodeEdgeColorFromSkill // Input: none // Output: none // Sets the node edge color to the colormap level depending on skill public void setNodeEdgeColorFromSkill(){ int i = (int) Math.round(colorMapMax*skill); this.setBorderColor(edgeColorMap.getColor(i)); this.setBorderWidth(3); } /////////////////////////////////////////////////////// // getDegree // Input: none // Output: int degree // Calculates the degree of the node (assuming symmetric) public int getDegree(){ return (this.getOutNodes()).size(); } /////////////////////////////////////////////////////// // formOpinion // Input: double success, the focus' real skill // Output: double vote, the voter's estimate of focus' skill public double formOpinion(double fSkill, double prevOp){ double op = fSkill + model.getNormalDouble(0, getNoiseFromSkill(this.skill)); op = histWeight*prevOp + (1-histWeight)*op; return op; } //////////////////////////////////////////////////////// // voteAll public void voteAll(){ ArrayList edgeList = this.getOutEdges(); double opinion = 0; int vote = 0; for (int i = 0; i < edgeList.size(); i++){ CustomEdge edge = (CustomEdge) edgeList.get(i); CustomNode node = (CustomNode) edge.getTo(); opinion = formOpinion(node.getSkill(), edge.getOpinion()); edge.setOpinion(opinion); if (opinion > 0.5) vote = 1; else vote = 0; edge.setVote(vote); } } //////////////////////////////////////////////////////// // calcReputation // Input: none // Ouput: none public void calcReputation(){ ArrayList edgeList = this.getInEdges(); double repSum = 0; double normSum = 0; for (int i = 0; i < edgeList.size(); i++){ CustomEdge edge = (CustomEdge) edgeList.get(i); CustomNode node = (CustomNode) edge.getFrom(); if (voteType ==0){ repSum += edge.getVote(); normSum ++; } else if (voteType ==1){ repSum += edge.getVote() * node.getReputation(); normSum += node.getReputation(); } } reputation = repSum/normSum; } //*************************************************************** // Getters and Setters public int getID() {return id;} public void setMyPajekColor(String i) {myPajekColor = i;} public String getMyPajekColor() {return myPajekColor;} public double getReputation() {return reputation;} public void setReputation(double i) {reputation = i;} public double getSkill() {return skill;} public void setSkill(double i) {skill = i;} //public double getSuccess() {return success;} //public void setSuccess(double i) {success = i;} //*************************************************************** // Static Methods public static void setModel(RepMod m) {model = m;} ////////////////////////////////////////////////////////// // setUpNodeDrawing // Input: GUIModel m, a pointer to the GUIModel // Output: none public static void setUpNodeDrawing (GUIModel m) { guiModel = m; centerColorMap = new ColorMap(); edgeColorMap = new ColorMap(); double minColor = 0.1; for (int i = 0; i < colorMapSize; i++){ double fracColor = minColor + ( (1.0-minColor)*i/colorMapMax ); centerColorMap.mapColor(i, 0, 0, fracColor); edgeColorMap.mapColor(i, 0, fracColor, 0); } } /////////////////////////////////////////////////////////// // resetNextID // Inputs: none // Outputs: none // Resets nextID. Called when the model is reset, and a // new set of nodes is generated public static void resetNextID(){ nextID = 0; } /////////////////////////////////////////////////////////// // getNoiseFromSkill // Inputs: double skill // Outputs: double, the noise variance public static double getNoiseFromSkill(double skill){ return (1-skill)*noiseSlope; } public static int getVoteType() {return voteType;} public static void setVoteType(int i) {voteType = i;} public static double getHistWeight() {return histWeight;} public static void setHistWeight(double i) {histWeight = i;} public static double getNoiseSlope() {return noiseSlope;} public static void setNoiseSlope(double i) {noiseSlope = i;} }