source: cpp/gdk/neurocls-library.h @ 80

Last change on this file since 80 was 80, checked in by Maciej Komosinski, 11 years ago
  • new properties in Parts and Joints: visual red, green, blue, thickness
  • updated list of Neurons and their properties
  • Property svn:eol-style set to native
File size: 9.0 KB
Line 
1
2// do not edit - generated automatically from "f0.def"
3// (to be included in "neurolibrary.cpp")
4
5
6
7
8
9
10     
11static ParamEntry NI_StdNeuron_tab[]={
12{"Neuron",1, 4 ,"N",},
13
14{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
15{"fo",1,0,"Force","f 0.0 999.0 0.04",},
16{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
17{"s",2,0,"State","f -1.0 1.0 0.0",},
18 
19{0,0,0,},};
20addClass(new NeuroClass(NI_StdNeuron_tab,"Standard neuron",-1,1,0, 0,2));
21
22     
23static ParamEntry NI_StdUNeuron_tab[]={
24{"Unipolar neuron [EXPERIMENTAL!]",1, 4 ,"Nu",},
25{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
26{"fo",1,0,"Force","f 0.0 999.0 0.04",},
27{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
28{"s",2,0,"State","f -1.0 1.0 0.0",},
29 
30{0,0,0,},};
31addClass(new NeuroClass(NI_StdUNeuron_tab,"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, 0,0));
32
33     static int Gyro_xy[]={83,8,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,12,43,24,48,24,48,19,38,19,38,24,43,24,43,54,48,54,48,64,43,69,38,64,38,54,43,54,5,63,69,58,74,48,79,38,79,28,74,23,69,1,43,79,43,74,1,23,69,26,66,1,63,69,60,66,1,55,76,53,73,1,31,75,33,72};   
34static ParamEntry NI_Gyro_tab[]={
35{"Gyroscope",1, 0 ,"G",},
36
37
38 
39{0,0,0,},};
40addClass(new NeuroClass(NI_Gyro_tab,"Equilibrium sensor.\n0=the stick is horizontal\n+1/-1=the stick is vertical",0,1,2, Gyro_xy,0,32));
41
42     static int Touch_xy[]={43,2,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,11,75,50,65,50,60,55,55,45,50,55,45,45,40,50,35,50,30,45,25,50,30,55,35,50};   
43static ParamEntry NI_Touch_tab[]={
44{"Touch",1, 1 ,"T",},
45
46
47{"r",1,0,"Range","f 0.0 1.0 1.0",},
48 
49{0,0,0,},};
50addClass(new NeuroClass(NI_Touch_tab,"Touch sensor.\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied",0,1,1, Touch_xy,0,32));
51
52     static int Smell_xy[]={64,5,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,3,10,40,15,45,15,55,10,60,5,20,30,25,35,30,45,30,55,25,65,20,70,4,15,35,20,40,22,50,20,60,15,65,5,75,50,50,50,45,45,40,50,45,55,50,50};   
53static ParamEntry NI_Smell_tab[]={
54{"Smell",1, 0 ,"S",},
55
56
57 
58{0,0,0,},};
59addClass(new NeuroClass(NI_Smell_tab,"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, Smell_xy,0,32));
60
61     static int Const_xy[]={29,4,4,26,27,26,73,73,73,73,27,26,27,1,73,50,100,50,1,56,68,46,68,2,41,47,51,32,51,68};   
62static ParamEntry NI_Const_tab[]={
63{"Constant",1, 0 ,"*",},
64
65
66 
67{0,0,0,},};
68addClass(new NeuroClass(NI_Const_tab,"Constant value",0,1,0, Const_xy,0,1));
69
70     static int BendMuscle_xy[]={63,6,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,3,65,56,49,29,57,24,72,50,4,68,53,70,53,70,55,68,55,68,53,5,50,21,60,15,70,14,79,15,87,20,81,10,1,86,20,77,21};   
71static ParamEntry NI_BendMuscle_tab[]={
72{"Bend muscle",1, 2 ,"|",},
73
74
75{"p",0,0,"power","f 0.01 1.0 0.25",},
76{"r",0,0,"bending range","f 0.0 1.0 1.0",},
77 
78{0,0,0,},};
79addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,0,2+16+64+4));
80
81     static int RotMuscle_xy[]={62,5,5,25,40,35,40,45,50,35,60,25,60,25,40,4,65,85,65,50,75,50,75,85,65,85,1,69,10,77,17,10,59,15,57,17,57,22,60,26,69,27,78,26,82,21,82,16,79,12,69,10,80,6,3,65,50,65,20,75,20,75,50};   
82static ParamEntry NI_RotMuscle_tab[]={
83{"Rotation muscle",1, 1 ,"@",},
84
85
86{"p",0,0,"power","f 0.01 1.0 1.0",},
87 
88{0,0,0,},};
89addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,0,2+16+128+4));
90
91     static int Diff_xy[]={24,3,3,25,0,25,100,75,50,25,0,1,75,50,100,50,3,44,42,51,57,36,57,44,42};   
92static ParamEntry NI_Diff_tab[]={
93{"Differentiate",1, 0 ,"D",},
94
95 
96{0,0,0,},};
97addClass(new NeuroClass(NI_Diff_tab,"Calculate the difference between the current and previous input value. Multiple inputs are aggregated with respect to their weights",-1,1,0, Diff_xy,0,0));
98
99     static int FuzzyNeuro_xy[]={44,5,2,30,65,37,37,44,65,3,37,65,44,37,51,37,58,65,2,51,65,58,37,65,65,6,100,50,70,50,70,25,25,10,25,90,70,75,70,50,1,70,65,25,65};   
100static ParamEntry NI_FuzzyNeuro_tab[]={
101{"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
102
103{"ns",0,0,"number of fuzzy sets","d 1  ",},
104{"nr",0,0,"number of rules","d 1  ",},
105{"fs",0,0,"fuzzy sets","s 0 -1 0",},
106{"fr",0,0,"fuzzy rules","s 0 -1 0",},
107 
108{0,0,0,},};
109addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,0,0));
110
111     static int VectorEye_xy[]={122,11,7,100,50,90,50,90,40,70,40,80,50,70,60,90,60,90,50,14,70,50,65,40,60,35,45,30,30,30,15,35,10,40,5,50,10,60,15,65,30,70,45,70,60,65,65,60,70,50,8,38,67,28,62,23,52,28,42,38,37,48,42,53,52,48,62,38,67,4,33,52,38,47,43,52,38,57,33,52,5,70,50,60,40,45,35,30,35,15,40,5,50,1,53,33,58,22,1,62,36,68,26,1,45,30,47,19,1,35,30,35,19,1,27,31,24,20,1,18,34,12,24};   
112static ParamEntry NI_VectorEye_tab[]={
113{"Vector Eye [EXPERIMENTAL!]",1, 9 ,"VEye",},
114
115{"tx",0,0,"target.x","f   ",},
116{"ty",0,0,"target.y","f   ",},
117{"tz",0,0,"target.z","f   ",},
118{"ts",0,0,"target shape","s 0 -1 0",},
119{"p",0,0,"perspective","f 0.1 10.0 1.0",},
120{"s",0,0,"scale","f 0.1 100.0 1.0",},
121{"h",0,0,"show hidden lines","d 0 1 0",},
122{"o",0,0,"output lines count (each line needs four channels)","d 0 99 0",},
123{"d",0,0,"debug","d 0 1 0",},
124 
125{0,0,0,},};
126addClass(new NeuroClass(NI_VectorEye_tab,"Refer to publications to learn more about this neuron.",1,1,1, VectorEye_xy,0,0));
127
128     
129static ParamEntry NI_VisualMotorNeuron_tab[]={
130{"Visual-Motor Cortex [EXPERIMENTAL!]",1, 3 ,"VMotor",},
131{"noIF",0,0,"number of basic features","d   ",},
132{"noDim",0,0,"number of degrees of freedom","d   ",},
133{"params",0,0,"parameters","s   ",},
134 
135{0,0,0,},};
136addClass(new NeuroClass(NI_VisualMotorNeuron_tab,"Must be connected to the VEye and properly set up. Refer to publications to learn more about this neuron.",-1,1,0, 0,0));
137
138     
139static ParamEntry NI_Sticky_tab[]={
140{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
141
142 
143{0,0,0,},};
144addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, 0,16));
145
146     
147static ParamEntry NI_LinearMuscle_tab[]={
148{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
149
150{"p",0,0,"power","f 0.01 1.0 1.0",},
151 
152{0,0,0,},};
153addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, 0,16));
154
155     
156static ParamEntry NI_WaterDetect_tab[]={
157{"Water detector",1, 0 ,"Water",},
158
159 
160{0,0,0,},};
161addClass(new NeuroClass(NI_WaterDetect_tab,"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, 0,32));
162
163     
164static ParamEntry NI_Energy_tab[]={
165{"Energy level",1, 0 ,"Energy",},
166
167 
168{0,0,0,},};
169addClass(new NeuroClass(NI_Energy_tab,"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, 0,32));
170
171     static int Channelize_xy[]={57,10,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,1,30,80,55,50,1,30,20,55,50,1,30,35,55,50,1,30,45,55,50,1,30,55,55,50,1,61,53,65,47,1,30,65,55,50};   
172static ParamEntry NI_Channelize_tab[]={
173{"Channelize",1, 0 ,"Ch",},
174
175 
176{0,0,0,},};
177addClass(new NeuroClass(NI_Channelize_tab,"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, Channelize_xy,0,0));
178
179     static int ChMux_xy[]={52,7,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,3,30,67,45,67,45,50,50,50,1,35,70,39,64,2,30,33,53,33,53,48};   
180static ParamEntry NI_ChMux_tab[]={
181{"Channel multiplexer",1, 0 ,"ChMux",},
182
183 
184{0,0,0,},};
185addClass(new NeuroClass(NI_ChMux_tab,"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, ChMux_xy,0,0));
186
187     static int ChSel_xy[]={41,6,4,25,0,25,100,75,70,75,30,25,0,1,75,50,100,50,1,70,50,55,50,3,50,55,55,50,50,45,50,55,1,30,50,50,50,1,35,53,39,47};   
188static ParamEntry NI_ChSel_tab[]={
189{"Channel selector",1, 1 ,"ChSel",},
190
191{"ch",0,0,"channel","d   ",},
192 
193{0,0,0,},};
194addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,0,0));
195
196     
197static ParamEntry NI_Random_tab[]={
198{"Random noise",1, 0 ,"Rnd",},
199 
200{0,0,0,},};
201addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, 0,0));
202
203     static int Sinus_xy[]={46,3,12,75,50,71,37,62,28,50,25,37,28,28,37,25,50,28,62,37,71,50,75,62,71,71,62,75,50,1,75,50,100,50,5,35,50,40,35,45,35,55,65,60,65,65,50};   
204static ParamEntry NI_Sinus_tab[]={
205{"Sinus generator",1, 2 ,"Sin",},
206
207{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
208{"t",0,0,"time","f 0 6.283185307 0",},
209 
210{0,0,0,},};
211addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,0,0));
212
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