| [109] | 1 | CLASS(Model,f0_model,m) |
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| 2 | GROUP(Properties) |
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| 3 | GROUP(Visual) |
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| 4 | PROP(se,0,1024,startenergy,f,,,,startenergy) |
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| 5 | PROP(Vstyle,1,0,vis_style,s,0,-1,,vis_style) |
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| 6 | ENDCLASS |
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| 7 | |
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| 8 | CLASS(Part,f0_part,p) |
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| 9 | GROUP(Geometry) |
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| 10 | GROUP(Other properties) |
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| 11 | GROUP(Visual) |
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| 12 | PROP(x,0,1024,position.x,f,,,,p.x) |
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| 13 | PROP(y,0,1024,position.y,f,,,,p.y) |
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| 14 | PROP(z,0,1024,position.z,f,,,,p.z) |
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| 15 | XPROP(m,1,0,mass,f,0.1,999.0,1.0,mass) |
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| 16 | PROP(sh,1,0,shape,d,0,3,0,shape) |
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| 17 | PROP(s,1,0,size,f,0.1,10.0,1.0,size) |
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| 18 | PROP(sx,1,0,scale.x,f,0.001,1000.0,1.0,scale.x) |
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| 19 | PROP(sy,1,0,scale.y,f,0.001,1000.0,1.0,scale.y) |
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| 20 | PROP(sz,1,0,scale.z,f,0.001,1000.0,1.0,scale.z) |
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| 21 | XPROP(dn,1,0,density,f,0.2,5.0,1.0,density) |
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| 22 | XPROP(fr,1,0,friction,f,0.0,4.0,0.4,friction) |
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| 23 | XPROP(ing,1,0,ingestion,f,0.0,1.0,0.25,ingest) |
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| 24 | XPROP(as,1,0,assimilation,f,0.0,1.0,0.25,assim) |
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| 25 | PROP(rx,0,0,rot.x,f,,,,rot.x) |
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| 26 | PROP(ry,0,1024,rot.y,f,,,,rot.y) |
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| 27 | PROP(rz,0,1024,rot.z,f,,,,rot.z) |
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| 28 | PROP(i,1,0,`info',s,,,,info) |
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| 29 | PROP(Vstyle,2,0,vis_style,s,0,-1,part,vis_style) |
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| 30 | XPROP(vs,2,0,visual thickness,f,0.05,0.7,0.2,vsize) |
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| 31 | XPROP(vr,2,0,red component,f,0.0,1.0,0.5,vcolor.x) |
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| 32 | XPROP(vg,2,1024,green component,f,0.0,1.0,0.5,vcolor.y) |
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| 33 | XPROP(vb,2,1024,blue component,f,0.0,1.0,0.5,vcolor.z) |
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| 34 | ENDCLASS |
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| 35 | |
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| 36 | CLASS(Joint,f0_joint,j) |
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| 37 | GROUP(Connections) |
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| 38 | GROUP(Geometry) |
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| 39 | GROUP(Other properties) |
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| 40 | GROUP(Visual) |
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| 41 | PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno) |
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| 42 | PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno) |
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| 43 | PROP(rx,1,0,rotation.x,f,,,,rot.x) |
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| 44 | PROP(ry,1,1024,rotation.y,f,,,,rot.y) |
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| 45 | PROP(rz,1,1024,rotation.z,f,,,,rot.z) |
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| 46 | PROP(dx,1,0,delta.x,f,-2,2,0,d.x) |
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| 47 | PROP(dy,1,1024,delta.y,f,-2,2,0,d.y) |
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| 48 | PROP(dz,1,1024,delta.z,f,-2,2,0,d.z) |
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| 49 | PROP(sh,1,0,shape,d,0,1,0,shape) |
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| 50 | XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif) |
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| 51 | XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif) |
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| 52 | PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina) |
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| 53 | PROP(i,2,0,`info',s,,,,info) |
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| 54 | PROP(Vstyle,3,0,vis_style,s,0,-1,joint,vis_style) |
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| 55 | XPROP(vr,3,0,red component,f,0.0,1.0,0.5,vcolor.x) |
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| 56 | XPROP(vg,3,1024,green component,f,0.0,1.0,0.5,vcolor.y) |
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| 57 | XPROP(vb,3,1024,blue component,f,0.0,1.0,0.5,vcolor.z) |
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| 58 | ENDCLASS |
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| 59 | |
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| 60 | CLASS(Joint,f0_nodeltajoint,j,NOXML) |
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| 61 | GROUP(Connections) |
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| 62 | GROUP(Geometry) |
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| 63 | GROUP(Other properties) |
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| 64 | GROUP(Visual) |
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| 65 | PROP(p1,0,1024,`part1 ref#',d,-1,999999,-1,p1_refno) |
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| 66 | PROP(p2,0,1024,`part2 ref#',d,-1,999999,-1,p2_refno) |
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| 67 | PROP(sh,1,0,shape,d,0,1,0,shape) |
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| 68 | XPROP(stif,2,0,stiffness,f,0.0,1.0,1.0,stif) |
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| 69 | XPROP(rotstif,2,0,rotation stiffness,f,0.0,1.0,1.0,rotstif) |
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| 70 | PROP(stam,2,0,stamina,f,0.0,1.0,0.25,stamina) |
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| 71 | PROP(i,2,0,`info',s,,,,info) |
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| 72 | PROP(Vstyle,3,0,vis_style,s,0,-1,joint,vis_style) |
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| 73 | XPROP(vr,3,0,red component,f,0.0,1.0,0.5,vcolor.x) |
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| 74 | XPROP(vg,3,1024,green component,f,0.0,1.0,0.5,vcolor.y) |
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| 75 | XPROP(vb,3,1024,blue component,f,0.0,1.0,0.5,vcolor.z) |
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| 76 | ENDCLASS |
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| 77 | |
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| 78 | CLASS(Neuro,f0_neuro,n) |
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| 79 | GROUP(Connections) |
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| 80 | GROUP(Other) |
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| 81 | GROUP(Visual) |
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| 82 | PROP(p,0,0,`part ref#',d,-1,999999,-1,part_refno) |
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| 83 | PROP(j,0,0,`joint ref#',d,-1,999999,-1,joint_refno) |
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| 84 | PROP(d,1,0,item details,s,,,N,details,GETSET) |
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| 85 | PROP(i,1,0,`info',s,,,,info) |
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| 86 | PROP(Vstyle,2,0,vis_style,s,0,-1,neuro,vis_style) |
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| 87 | PROP(getInputCount,0,1+2,`input count',d,,,,inputCount,GETONLY) |
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| 88 | PROP(getInputNeuroDef,0,1+2,`get input neuron',p oNeuroDef(d),,,,p_getInputNeuroDef,PROCEDURE) |
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| 89 | PROP(getInputNeuroIndex,0,1+2,`get input neuron index',p d(d),,,,p_getInputNeuroIndex,PROCEDURE) |
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| 90 | PROP(getInputWeight,0,1+2,`get input weight',p f(d),,,,p_getInputWeight,PROCEDURE) |
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| 91 | PROP(classObject,0,1+2,`neuron class',o NeuroClass,,,,classObject,GETONLY) |
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| 92 | ENDCLASS |
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| 93 | |
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| 94 | CLASS(NeuroConn,f0_neuroconn,c) |
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| 95 | GROUP(Connection) |
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| 96 | GROUP(Other) |
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| 97 | PROP(n1,0,1024,`this neuro ref#',d,-1,999999,-1,n1_refno) |
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| 98 | PROP(n2,0,1024,`connected neuro ref#',d,-1,999999,-1,n2_refno) |
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| 99 | PROP(w,0,1024,weight,f,-999999,999999,1.0,weight) |
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| 100 | PROP(i,1,0,`info',s,,,,info) |
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| 101 | ENDCLASS |
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| 102 | |
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| 103 | NEUROCLASS(StdNeuron,N,Neuron,`Standard neuron',-1,1,0) |
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| 104 | VISUALHINTS(DontShowClass) |
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| 105 | NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia) |
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| 106 | NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force) |
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| 107 | NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo) |
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| 108 | NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate) |
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| 109 | ENDNEUROCLASS |
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| 110 | |
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| 111 | NEUROCLASS(StdUNeuron,Nu,`Unipolar neuron [EXPERIMENTAL!]',`Works like standard neuron (N) but the output value is scaled to 0...+1 instead of -1...+1.\nHaving 0 as one of the saturation states should help in \"gate circuits\", where input signal is passed through or blocked depending on the other singal.',-1,1,0) |
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| 112 | NEUROPROP(in,1,0,Inertia,f,0.0,1.0,0.8,inertia) |
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| 113 | NEUROPROP(fo,1,0,Force,f,0.0,999.0,0.04,force) |
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| 114 | NEUROPROP(si,1,0,Sigmoid,f,-99999.0,99999.0,2.0,sigmo) |
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| 115 | NEUROPROP(s,2,0,State,f,-1.0,1.0,0.0,newstate) |
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| 116 | ENDNEUROCLASS |
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| 117 | |
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| 118 | NEUROCLASS(Gyro,G,Gyroscope,`Equilibrium sensor.\n0=the stick is horizontal\n+1/-1=the stick is vertical',0,1,2) |
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| 119 | VISUALHINTS(ReceptorClass) |
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| 120 | SYMBOL(`8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72') |
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| 121 | ENDNEUROCLASS |
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| 122 | |
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| 123 | NEUROCLASS(Touch,T,Touch,`Touch sensor.\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied',0,1,1) |
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| 124 | VISUALHINTS(ReceptorClass) |
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| 125 | SYMBOL(`2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50') |
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| 126 | NEUROPROP(r,1,0,Range,f,0.0,1.0,1.0,range) |
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| 127 | ENDNEUROCLASS |
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| 128 | |
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| 129 | NEUROCLASS(Smell,S,Smell,`Smell sensor. Aggregated \"smell of energy\" experienced from all energy objects (creatures and food pieces).\nClose objects have bigger influence than the distant ones: for each energy source, its partial feeling is proportional to its energy/(distance^2)',0,1,1) |
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| 130 | VISUALHINTS(ReceptorClass) |
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| 131 | SYMBOL(`5,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,3,10,40,15,45,15,55,10,60,5,20,30,25,35,30,45,30,55,25,65,20,70,4,15,35,20,40,22,50,20,60,15,65,5,75,50,50,50,45,45,40,50,45,55,50,50') |
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| 132 | ENDNEUROCLASS |
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| 133 | |
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| 134 | NEUROCLASS(Const,*,Constant,Constant value,0,1,0) |
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| 135 | VISUALHINTS(Invisible) |
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| 136 | SYMBOL(`4,4,26,27,26,73,73,73,73,27,26,27,1,73,50,100,50,1,56,68,46,68,2,41,47,51,32,51,68') |
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| 137 | ENDNEUROCLASS |
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| 138 | |
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| 139 | NEUROCLASS(BendMuscle,|,Bend muscle,,1,0,2) |
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| 140 | VISUALHINTS(DontShowClass+EffectorClass+V1BendMuscle+AtFirstPart) |
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| 141 | SYMBOL(`6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21') |
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| 142 | NEUROPROP(p,0,0,power,f,0.01,1.0,0.25,power) |
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| 143 | NEUROPROP(r,0,0,bending range,f,0.0,1.0,1.0,bendrange) |
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| 144 | ENDNEUROCLASS |
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| 145 | |
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| 146 | NEUROCLASS(RotMuscle,@,Rotation muscle,,1,0,2) |
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| 147 | VISUALHINTS(DontShowClass+EffectorClass+V1RotMuscle+AtFirstPart) |
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| 148 | SYMBOL(`5,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,1,69,10,77,17,10,59,15,57,17,57,22,60,26,69,27,78,26,82,21,82,16,79,12,69,10,80,6,3,65,50,65,20,75,20,75,50') |
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| 149 | NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power) |
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| 150 | ENDNEUROCLASS |
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| 151 | |
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| 152 | NEUROCLASS(Diff,D,Differentiate,Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights,-1,1,0) |
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| 153 | SYMBOL(`3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42') |
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| 154 | ENDNEUROCLASS |
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| 155 | |
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| 156 | NEUROCLASS(FuzzyNeuro,Fuzzy,Fuzzy system [EXPERIMENTAL!],Refer to publications to learn more about this neuron.,-1,1,0) |
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| 157 | SYMBOL(`5,2,30,65,37,37,44,65,3,37,65,44,37,51,37,58,65,2,51,65,58,37,65,65,6,100,50,70,50,70,25,25,10,25,90,70,75,70,50,1,70,65,25,65') |
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| 158 | NEUROPROP(ns,0,0,number of fuzzy sets,d,1,,,fuzzySetsNr) |
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| 159 | NEUROPROP(nr,0,0,number of rules,d,1,,,rulesNr) |
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| 160 | NEUROPROP(fs,0,0,fuzzy sets,s,0,-1,0,fuzzySetString) |
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| 161 | NEUROPROP(fr,0,0,fuzzy rules,s,0,-1,0,fuzzyRulesString) |
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| 162 | ENDNEUROCLASS |
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| 163 | |
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| 164 | NEUROCLASS(Sticky,Sti,Sticky [EXPERIMENTAL!],,1,0,1) |
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| 165 | VISUALHINTS(EffectorClass) |
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| 166 | ENDNEUROCLASS |
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| 167 | |
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| 168 | NEUROCLASS(LinearMuscle,LMu,Linear muscle [EXPERIMENTAL!],,1,0,2) |
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| 169 | VISUALHINTS(EffectorClass) |
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| 170 | NEUROPROP(p,0,0,power,f,0.01,1.0,1.0,power) |
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| 171 | ENDNEUROCLASS |
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| 172 | |
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| 173 | NEUROCLASS(WaterDetect,Water,Water detector,`Output signal:\n0=on or above water surface\n1=under water (deeper than 1)\n0..1=in the transient area just below water surface',0,1,1) |
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| 174 | VISUALHINTS(ReceptorClass) |
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| 175 | ENDNEUROCLASS |
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| 176 | |
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| 177 | NEUROCLASS(Energy,Energy,Energy level,`The current energy level divided by the initial energy level.\nUsually falls from initial 1.0 down to 0.0 and then the creature dies. It can rise above 1.0 if enough food is ingested',0,1,0) |
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| 178 | VISUALHINTS(ReceptorClass) |
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| 179 | ENDNEUROCLASS |
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| 180 | |
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| 181 | NEUROCLASS(Channelize,Ch,Channelize,`Combines all input signals into a single multichannel output; Note: ChSel and ChMux are the only neurons which support multiple channels. Other neurons discard everything except the first channel.',-1,1,0) |
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| 182 | SYMBOL(`10,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,1,30,80,55,50,1,30,20,55,50,1,30,35,55,50,1,30,45,55,50,1,30,55,55,50,1,61,53,65,47,1,30,65,55,50') |
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| 183 | ENDNEUROCLASS |
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| 184 | |
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| 185 | NEUROCLASS(ChMux,ChMux,Channel multiplexer,`Outputs the selected channel from the second (multichannel) input. The first input is used as the selector value (-1=select first channel, .., 1=last channel)',2,1,0) |
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| 186 | SYMBOL(`7,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,3,30,67,45,67,45,50,50,50,1,35,70,39,64,2,30,33,53,33,53,48') |
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| 187 | ENDNEUROCLASS |
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| 188 | |
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| 189 | NEUROCLASS(ChSel,ChSel,Channel selector,`Outputs a single channel (selected by the \"ch\" parameter) from multichannel input',1,1,0) |
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| 190 | SYMBOL(`6,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,1,30,50,50,50,1,35,53,39,47') |
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| 191 | NEUROPROP(ch,0,0,channel,d,,,,ch) |
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| 192 | ENDNEUROCLASS |
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| 193 | |
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| 194 | NEUROCLASS(Random,Rnd,Random noise,`Generates random noise (subsequent random values in the range of -1..+1)',0,1,0) |
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| 195 | ENDNEUROCLASS |
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| 196 | |
|---|
| 197 | NEUROCLASS(Sinus,Sin,Sinus generator,`Output frequency = f0+input',1,1,0) |
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| 198 | SYMBOL(`3,12,75,50,71,37,62,28,50,25,37,28,28,37,25,50,28,62,37,71,50,75,62,71,71,62,75,50,1,75,50,100,50,5,35,50,40,35,45,35,55,65,60,65,65,50') |
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| 199 | NEUROPROP(f0,0,0,base frequency,f,-1.0,1.0,0.06283185307,f0) |
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| 200 | NEUROPROP(t,0,0,time,f,0,6.283185307,0,t) |
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| 201 | ENDNEUROCLASS |
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