apv.rar_robocode

package apv.nrlibj; import java.io.*; import java.lang.*; //import java.lang.Exception; import java.util.*; import java.util.zip.*; import robocode.*; /************************************************************************/ /* */ /* */ /* CLASS NNet */ /* */ /* */ /************************************************************************/ /** * This Class (the most important) defines a Neural Network object. * A NNet object contains a lot of computation nodes linked toghether in various * ways but organized in layers.<BR> * So, a NNet is an array of layers.<p> * The computing phase proceeds from the layer 0 to last layer.<BR> * For each layer:<BR> * each node is computed appling its "trf" function.<BR> * After that, before pass to next layer, again for each node is applied the * "out" node function. This function moves the output value from the out-buffer * to out variable and sincronize layer computation. * (for the EBP phase, if used, the process is reversed using err * variable).<p> * A Node can be defined by user or can be one of predefined node type (NodeLin * or NodeSigm). But a layer have same type nodes.<p> * A layer can have a buffer. This buffer is another layer. A layer can have just * one buffer but this buffer can have a buffer... in a chain way. * When a layer have a buffer, its output values are copied into the input * variable of destination layer nodes.<BR> * These values will be computed when this buffer will be computed. So, if this * buffer have a number &lt than the buffered layer these values will be considered * in next forward cycle as a memory, otherwise in the same computational cycle. * <PRE> * layer 0 layer 2 layer 3 * |--| |--| |--| * | |---------------->| |--------->| | * | | |-->| | | | * | | |--| | |--| |--| * |--| | |-| 在uffered * | | 奸ayer * layer 1|--| � * buffer of layer2 ^........� * * </PRE> * * @author D. Denaro * @version 5.0 , 2/2001 */ public class NNet { Layer lyr[]; NNet(){} /** * This constructor return a NNet with <TT>"nlayer"</TT> layers empty */ NNet(int nlayer){this.lyr=new Layer[nlayer];} /** * This constructor creates a NNet with tree layers * and a buffer memory (0,1,2,3) or without memory (0,1,2). * <BR>When <TT>"mem"</TT> = true, a buffer of hiden layer is created and this * memory layer is linked with the hiden layer (Elman context memory) * <BR>The weights of the net are randomicaly choosen with range <TT>"ra"</TT> - <TT>"rb"</TT> * <BR><TT>"nodeinp"</TT> is the number of first layer nodes, * <BR><TT>"nodehid"</TT> of hiden layer and * <BR><TT>"nodeout"</TT> of last layer. * <BR>The first layer nodes (layer 0) are liner node (NodeLin); hidden layer * and last layer nodes have sigmoid transfer function of activation (NodeSigm) */ public NNet(int nnodeinp,int nnodehid,int nnodeout,boolean fmem,float ra,float rb) {int i; String CnodeLin, CnodeSigm ;int nlyr; {CnodeLin="NodeLin";CnodeSigm="NodeSigm";} if (fmem) nlyr=4; else nlyr=3; lyr= new Layer[nlyr]; lyr[0]=new Layer(0,nnodeinp,CnodeLin); if (fmem) { lyr[1]=new Layer(1,nnodehid,CnodeSigm); lyr[2]=new Layer(2,nnodehid,CnodeSigm); lyr[2].mbuff=lyr[1]; lyr[3]=new Layer(3,nnodeout,CnodeSigm); } else { lyr[1]=new Layer(1,nnodehid,CnodeSigm); lyr[2]=new Layer(2,nnodeout,CnodeSigm); } if (fmem) {lyr[2].linkFromAll(lyr[0],ra,rb);lyr[2].linkFromAll(lyr[1],ra,rb);lyr[3].linkFromAll(lyr[2],ra,rb);} else {lyr[1].linkFromAll(lyr[0],ra,rb);lyr[2].linkFromAll(lyr[1],ra,rb);} } /** * This constructor creates a NNet with tree layers (0,1,3) * and a buffer memory or not. But the * the weights of the net are randomicaly choosen with default range */ public NNet(int nnodeinp,int nnodehid,int nnodeout,boolean fmem) {this(nnodeinp,nnodehid,nnodeout,fmem,NrPop.ra,NrPop.rb);} /** * This constructor creates a simple NNet with tree layers (0,1,3) without context * memory. Default range for random choosen weights */ public NNet(int nnodeinp,int nnodehid,int nnodeout) {this(nnodeinp,nnodehid,nnodeout,false,NrPop.ra,NrPop.rb);} /** * This constructor creates a network in a most flexible way. * The string array <TT>"descrrec"</TT> contains the records that describes the network * population in a simple predefined laguage: * <PRE> * 3 records types * * <B> * layer=n [tnode=n,[m] nname=xxxx... copytoml=n] </B> * meens: definition of layer number "n" with "n" total-node (or n x m nodes * if is a bidimesional layer) and with a buffer realised by layer "n" * The node type is defined by the name (Ex. NodeLin, NodeSigm) * <B> * linktype=[xxx...] fromlayer=n[(h[,k])] tolayer=n[(h[,k])] [ value=na,[nb]] </B> * meens: link "all" to all (default) or "one" to one from layer number "n" to * layer number "n". As default link is realized between all layers * node, but it is possible to describe a link for a sub-set of nodes * from node "h" to node "k". As default weights are randomically chosen * in default range, but it is possible define a different range between * "na" and "nb". If only "na" is present, that meens all weights = "na" * <B> * biasval=na[,nb] oflayer=n[(h[,k])] </B> * meens: as previous kind of record but for bias values * * example of descrrec[] contents (each record = one array element) * * layer=0 tnode=2 nname=NodeLin * layer=1 tnode=2 nname=NodeSigm * layer=2 tnode=1 nname=NodeSigm * linktype=all fromlayer=0 tolayer=1 * linktype=all fromlayer=1 tolayer=2 * </PRE> * it defines a network with 3 layers and 2,2,1 nodes (for XOR problem for instance) * NodeLin is a kind of node that reproduce the input value into the output variable * NodeSigm is a sigmoid node. */ public NNet(String descrrec[]) { this(descrrec,0);} /** * This constructor is similar to NNet(String descrrec[]) but read net * description from a file. (for an external NN definition) */ public NNet(String namefile) {this(NrPop.fileToStrArray(namefile),0);} NNet(String descrrec[],int s) { int i,k,ct; StringTokenizer tok; String t,val,ThisC,Pack; String a,b,c; ThisC=this.getClass().getName(); Pack=ThisC.substring(0,ThisC.lastIndexOf('.')+1); int maxlayer=-1; int nlayer=0,tnode=0,tnodey=0,layerbuff=-1; String nname=""; int ltype=0,lfrom=-1,lto=-1,fnodes=-1,fnodee=-1,tnodes=-1,tnodee=-1; float vala=NrPop.ra,valb=NrPop.rb; int blayer=-1,bnodes=-1,bnodee=-1; float bvaluea=0,bvalueb=0; for (i=s;i<descrrec.length;i++) { tok=new StringTokenizer(descrrec[i]); if (tok.countTokens()<1) continue; t=tok.nextToken(); if (t.startsWith("layer=")) { val=t.substring(t.indexOf('='));a=NrPop.values(val,1); try {nlayer=Integer.parseInt(a);} catch (NumberFormatException e){NNError.err("Layer Number missed at rec "+i);} if (nlayer>maxlayer) maxlayer=nlayer; } if (t.startsWith("net=")) break; } lyr= new Layer[maxlayer+1]; for (i=s;i<descrrec.length;i++) { tok=new StringTokenizer(descrrec[i]); if (tok.countTokens()<1) continue; t=tok.nextToken(); if (t.startsWith("layer=")) {ct=tok.countTokens(); val=t.substring(t.indexOf('='));a=NrPop.values(val,1); try {n