Changeset 972 for cpp/frams/_demos/neuro_test.cpp

Timestamp:

07/03/20 00:32:23 (4 years ago)

Author:

Maciej Komosinski

Message:

• separate "0" and "0s" formats (for SHAPE_BALL_AND_STICK and SHAPE_SOLIDS, respectively)
• converting to format list (Geno::F0_FORMAT_LIST = "0,0s")
• (optional) declaring Model as SHAPE_BALL_AND_STICK or SHAPE_SOLIDS (or SHAPE_UNKNOWN)

File:

• cpp/frams/_demos/neuro_test.cpp (modified) (3 diffs)

cpp/frams/_demos/neuro_test.cpp

@@ -1 +1 @@
 // This file is a part of Framsticks SDK.  http://www.framsticks.com/
-// Copyright (C) 1999-2015  Maciej Komosinski and Szymon Ulatowski.
+// Copyright (C) 1999-2020  Maciej Komosinski and Szymon Ulatowski.
 // See LICENSE.txt for details.
 
@@ -18 +18 @@
 #ifndef SDK_WITHOUT_FRAMS
 #include <frams/mech/creatmechobj.h>
-int CreatMechObject::modeltags_id=0;
-int CreatMechObject::mechtags_id=0;
+int CreatMechObject::modeltags_id = 0;
+int CreatMechObject::mechtags_id = 0;
 #endif
 
-ParamEntry creature_paramtab[]={0};
+ParamEntry creature_paramtab[] = { 0 };
 
 #ifdef VEYETEST
@@ -33 +33 @@
 #define LEARNINGSTEPS 50
 
-void veyeStep(Model &m,int step)
+void veyeStep(Model &m, int step)
 {
-  static float angle=0;
+        static float angle = 0;
 
-  NeuroNetImpl::getImpl(m.getNeuro(N_Mode))->setState(step>=LEARNINGSTEPS); //0 (learning) or 1 (normal)
+        NeuroNetImpl::getImpl(m.getNeuro(N_Mode))->setState(step >= LEARNINGSTEPS); //0 (learning) or 1 (normal)
 
-        NeuroImpl *ni=NeuroNetImpl::getImpl(m.getNeuro(N_VEye));
-  ((NI_VectorEye*)ni)->relpos.y=0;
-  ((NI_VectorEye*)ni)->relpos.z=0;
-  if (NeuroNetImpl::getImpl(m.getNeuro(N_Mode))->getNewState()<0.5)
-  { //learning
-    ((NI_VectorEye*)ni)->relpos.x=5.0*sin(2*M_PI*step/LEARNINGSTEPS);
-  }
-  else
-  { //VMotor controls location of VEye
-    angle+=NeuroNetImpl::getImpl(m.getNeuro(N_VMotor))->getState();
-    angle=fmod((double)angle,M_PI*2.0);
-    ((NI_VectorEye*)ni)->relpos.x=5*sin(angle);
-  }
+        NeuroImpl *ni = NeuroNetImpl::getImpl(m.getNeuro(N_VEye));
+        ((NI_VectorEye*)ni)->relpos.y = 0;
+        ((NI_VectorEye*)ni)->relpos.z = 0;
+        if (NeuroNetImpl::getImpl(m.getNeuro(N_Mode))->getNewState() < 0.5)
+        { //learning
+                ((NI_VectorEye*)ni)->relpos.x = 5.0 * sin(2 * M_PI * step / LEARNINGSTEPS);
+        }
+        else
+        { //VMotor controls location of VEye
+                angle += NeuroNetImpl::getImpl(m.getNeuro(N_VMotor))->getState();
+                angle = fmod((double)angle, M_PI * 2.0);
+                ((NI_VectorEye*)ni)->relpos.x = 5 * sin(angle);
+        }
 
-  NeuroNetImpl::getImpl(m.getNeuro(N_Fitness))->setState(angle); //wymaga poprawy
-  //oraz trzeba przemyslec kolejnosc get/set'ow neuronow zeby sygnal sie dobrze propagowal.
+        NeuroNetImpl::getImpl(m.getNeuro(N_Fitness))->setState(angle); //wymaga poprawy
+        //oraz trzeba przemyslec kolejnosc get/set'ow neuronow zeby sygnal sie dobrze propagowal.
 }
 #endif
 
-int main(int argc,char*argv[])
+int main(int argc, char*argv[])
 {
-LoggerToStdout messages_to_stdout(LoggerBase::Enable);
-PreconfiguredGenetics genetics;
+        LoggerToStdout messages_to_stdout(LoggerBase::Enable);
+        PreconfiguredGenetics genetics;
 
-if (argc<=1)
+        if (argc <= 1)
         {
                 puts("Parameters: <genotype> [number of simulation steps]");
-          return 10;
+                return 10;
         }
-SString gen(argv[1]);
-if (!strcmp(gen.c_str(),"-"))
+        SString gen(argv[1]);
+        if (!strcmp(gen.c_str(), "-"))
         {
-        gen=0;
-        StdioFILEDontClose in(stdin);
-        loadSString(&in,gen);
+                gen = 0;
+                StdioFILEDontClose in(stdin);
+                loadSString(&in, gen);
         }
-Geno g(gen);
-if (!g.isValid()) {puts("invalid genotype");return 5;}
-Model m(g);
-if (!m.getNeuroCount()) {puts("no neural network");return 1;}
-printf("%d neurons,",m.getNeuroCount());
-NeuroFactory neurofac;
-neurofac.setStandardImplementation();
-NeuroNetConfig nn_config(&neurofac);
-NeuroNetImpl *nn=new NeuroNetImpl(m,nn_config);
-int i; Neuro *n;
-if (!nn->getErrorCount()) printf(" no errors\n");
-else
+        Geno g(gen);
+        if (!g.isValid()) { puts("invalid genotype"); return 5; }
+        Model m(g, Model::SHAPE_UNKNOWN);
+        if (!m.getNeuroCount()) { puts("no neural network"); return 1; }
+        printf("%d neurons,", m.getNeuroCount());
+        NeuroFactory neurofac;
+        neurofac.setStandardImplementation();
+        NeuroNetConfig nn_config(&neurofac);
+        NeuroNetImpl *nn = new NeuroNetImpl(m, nn_config);
+        int i; Neuro *n;
+        if (!nn->getErrorCount()) printf(" no errors\n");
+        else
         {
-        printf(" %d errors:",nn->getErrorCount());
-        int no_impl=0; SString no_impl_names;
-        int init_err=0; SString init_err_names;
-        for(i=0;i<m.getNeuroCount();i++)
+                printf(" %d errors:", nn->getErrorCount());
+                int no_impl = 0; SString no_impl_names;
+                int init_err = 0; SString init_err_names;
+                for (i = 0; i < m.getNeuroCount(); i++)
                 {
-                n=m.getNeuro(i);
-                NeuroImpl *ni=NeuroNetImpl::getImpl(n);
-                if (!ni)
+                        n = m.getNeuro(i);
+                        NeuroImpl *ni = NeuroNetImpl::getImpl(n);
+                        if (!ni)
                         {
-                        if (no_impl) no_impl_names+=',';
-                        no_impl_names+=SString::sprintf("#%d.%s",i,n->getClassName().c_str());
-                        no_impl++;
+                                if (no_impl) no_impl_names += ',';
+                                no_impl_names += SString::sprintf("#%d.%s", i, n->getClassName().c_str());
+                                no_impl++;
                         }
-                else if (ni->status==NeuroImpl::InitError)
+                        else if (ni->status == NeuroImpl::InitError)
                         {
-                        if (init_err) init_err_names+=',';
-                        init_err_names+=SString::sprintf("#%d.%s",i,n->getClassName().c_str());
-                        init_err++;
+                                if (init_err) init_err_names += ',';
+                                init_err_names += SString::sprintf("#%d.%s", i, n->getClassName().c_str());
+                                init_err++;
                         }
                 }
+                printf("\n");
+                if (no_impl) printf("%d x missing implementation (%s)\n", no_impl, no_impl_names.c_str());
+                if (init_err) printf("%d x failed initialization (%s)\n", init_err, init_err_names.c_str());
+        }
+        int steps = 1;
+        if (argc > 2) steps = atol(argv[2]);
+        int st;
+        printf("step");
+        for (i = 0; i < m.getNeuroCount(); i++)
+        {
+                n = m.getNeuro(i);
+                printf("\t#%d.%s", i, n->getClassName().c_str());
+        }
         printf("\n");
-        if (no_impl) printf("%d x missing implementation (%s)\n",no_impl,no_impl_names.c_str());
-        if (init_err) printf("%d x failed initialization (%s)\n",init_err,init_err_names.c_str());
-        }
-int steps=1;
-if (argc>2) steps=atol(argv[2]);
-int st;
-printf("step");
-for(i=0;i<m.getNeuroCount();i++)
-        {
-        n=m.getNeuro(i);
-        printf("\t#%d.%s",i,n->getClassName().c_str());
-        }
-printf("\n");
-for(st=0;st<=steps;st++)
+        for (st = 0; st <= steps; st++)
         {
 #ifdef VEYETEST
-  veyeStep(m,st);
+                veyeStep(m, st);
 #endif
-        printf("%d",st);
-        for(i=0;i<m.getNeuroCount();i++)
+                printf("%d", st);
+                for (i = 0; i < m.getNeuroCount(); i++)
                 {
-                n=m.getNeuro(i);
-                printf("\t%g",n->state);
+                        n = m.getNeuro(i);
+                        printf("\t%g", n->state);
                 }
-        printf("\n");
-        nn->simulateNeuroNet();
+                printf("\n");
+                nn->simulateNeuroNet();
         }
-neurofac.freeImplementation();
+        neurofac.freeImplementation();
 }