128 lines
4.4 KiB
MQL5
128 lines
4.4 KiB
MQL5
//+------------------------------------------------------------------+
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//| BOSSNoiseSweep.mq5|
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//| MMQ — Muhammad Minhas Qamar |
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//| www.mql5.com/en/articles/23491 |
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//+------------------------------------------------------------------+
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#property copyright "MMQ — Muhammad Minhas Qamar"
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#property link "https://www.mql5.com/en/articles/23491"
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#property description "Sweeps the retained-coefficient count against a rising noise level on a trend-vs-range task and prints an accuracy grid."
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#property version "1.00"
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#property strict
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#include <BOSS\BOSS.mqh>
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input int Alphabet = 4;
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input int Window = 24;
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input int SeriesLen = 240;
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//+------------------------------------------------------------------+
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//| Trending shape: drift plus a small oscillation and noise. |
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//+------------------------------------------------------------------+
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void MakeTrend(double &out[],int len,double noise)
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{
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ArrayResize(out,len);
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for(int i=0;i<len;i++)
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out[i]=0.02*i+0.5*MathSin(2.0*M_PI*i/Window)+noise*(MathRand()/32767.0-0.5);
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}
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//+------------------------------------------------------------------+
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//| Ranging shape: clean oscillation about a flat mean plus noise. |
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//+------------------------------------------------------------------+
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void MakeRange(double &out[],int len,double noise)
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{
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ArrayResize(out,len);
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for(int i=0;i<len;i++)
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out[i]=MathSin(2.0*M_PI*i/Window)+noise*(MathRand()/32767.0-0.5);
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}
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//+------------------------------------------------------------------+
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//| Build a broad window pool (both shapes) for MCB fitting. |
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//+------------------------------------------------------------------+
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void BuildWindows(double &windows[],int &count,int win,double noise)
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{
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int per=150;
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count=per*2;
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ArrayResize(windows,count*win);
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double s[];
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int idx=0;
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for(int r=0;r<per;r++)
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{
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MakeTrend(s,win,noise);
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for(int i=0;i<win;i++)
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windows[idx*win+i]=s[i];
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idx++;
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MakeRange(s,win,noise);
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for(int i=0;i<win;i++)
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windows[idx*win+i]=s[i];
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idx++;
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}
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}
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//+------------------------------------------------------------------+
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//| Accuracy of a trend/range classifier at a given query noise. |
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//| Trains one clean prototype per class, then classifies fresh |
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//| noisy series of both shapes and returns overall accuracy. |
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//+------------------------------------------------------------------+
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double AccuracyAt(int word_len,double train_noise,double query_noise)
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{
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double windows[];
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int nwin;
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BuildWindows(windows,nwin,Window,train_noise);
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CBossClassifier clf;
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if(!clf.Build(word_len,Alphabet,windows,nwin,Window))
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return -1.0;
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double s[];
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MakeTrend(s,SeriesLen,train_noise);
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clf.AddExample(s,SeriesLen,"trend");
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MakeRange(s,SeriesLen,train_noise);
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clf.AddExample(s,SeriesLen,"range");
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int correct=0, trials=40, total=0;
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double bd;
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for(int t=0;t<trials;t++)
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{
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MakeTrend(s,SeriesLen,query_noise);
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if(clf.Classify(s,SeriesLen,bd)=="trend")
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correct++;
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total++;
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MakeRange(s,SeriesLen,query_noise);
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if(clf.Classify(s,SeriesLen,bd)=="range")
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correct++;
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total++;
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}
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return 100.0*correct/total;
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}
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//+------------------------------------------------------------------+
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//| Print the WordLen x noise accuracy grid. |
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//+------------------------------------------------------------------+
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void OnStart()
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{
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MathSrand(20260712);
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int wls[3] = {2,3,4};
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double noises[5]= {0.1,0.3,0.5,1.0,1.5};
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//--- header row
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string hdr=" WordLen |";
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for(int n=0;n<5;n++)
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hdr+=StringFormat(" noise=%.1f",noises[n]);
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Print("=== BOSS accuracy: retained coefficients vs query noise ===");
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Print(" (trained at low noise 0.07; trend-vs-range; chance = 50%)");
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Print(hdr);
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for(int w=0;w<3;w++)
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{
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string row=StringFormat(" %2d (%dL) |",wls[w],wls[w]*2);
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for(int n=0;n<5;n++)
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{
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double acc=AccuracyAt(wls[w],0.07,noises[n]);
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row+=StringFormat(" %5.0f%%",acc);
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}
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Print(row);
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}
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Print("=== sweep complete ===");
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}
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//+------------------------------------------------------------------+
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