source: cpp/frams/neuro/neurocls-f0-GDK-library.h @ 109

Last change on this file since 109 was 109, checked in by sz, 10 years ago

source reorganization (see README)
new feature added: part/joint shapes (see frams/_demos/part_shapes.cpp)

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File size: 7.7 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, NULL,false, 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, NULL,false, 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,false, 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,false, 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,false, 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,false, 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,false, 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,false, 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,false, 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,false, 0));
110
111     
112static ParamEntry NI_Sticky_tab[]={
113{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
114
115 
116{0,0,0,},};
117addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16));
118
119     
120static ParamEntry NI_LinearMuscle_tab[]={
121{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
122
123{"p",0,0,"power","f 0.01 1.0 1.0",},
124 
125{0,0,0,},};
126addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16));
127
128     
129static ParamEntry NI_WaterDetect_tab[]={
130{"Water detector",1, 0 ,"Water",},
131
132 
133{0,0,0,},};
134addClass(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, NULL,false, 32));
135
136     
137static ParamEntry NI_Energy_tab[]={
138{"Energy level",1, 0 ,"Energy",},
139
140 
141{0,0,0,},};
142addClass(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, NULL,false, 32));
143
144     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};   
145static ParamEntry NI_Channelize_tab[]={
146{"Channelize",1, 0 ,"Ch",},
147
148 
149{0,0,0,},};
150addClass(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,false, 0));
151
152     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};   
153static ParamEntry NI_ChMux_tab[]={
154{"Channel multiplexer",1, 0 ,"ChMux",},
155
156 
157{0,0,0,},};
158addClass(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,false, 0));
159
160     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};   
161static ParamEntry NI_ChSel_tab[]={
162{"Channel selector",1, 1 ,"ChSel",},
163
164{"ch",0,0,"channel","d   ",},
165 
166{0,0,0,},};
167addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0));
168
169     
170static ParamEntry NI_Random_tab[]={
171{"Random noise",1, 0 ,"Rnd",},
172 
173{0,0,0,},};
174addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0));
175
176     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};   
177static ParamEntry NI_Sinus_tab[]={
178{"Sinus generator",1, 2 ,"Sin",},
179
180{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
181{"t",0,0,"time","f 0 6.283185307 0",},
182 
183{0,0,0,},};
184addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0));
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