source: cpp/frams/neuro/neurocls-f0-SDK-library.h @ 932

Last change on this file since 932 was 932, checked in by Maciej Komosinski, 4 years ago

Neuron classes now have a property (a bit field) that says whether each neuron class supports model shape BALL_AND_STICK, SOLIDS, or both

  • Property svn:eol-style set to native
File size: 8.4 KB
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1// This file is a part of Framsticks SDK.  http://www.framsticks.com/
2// Copyright (C) 1999-2015  Maciej Komosinski and Szymon Ulatowski.
3// See LICENSE.txt for details.
4
5
6// do not edit - generated automatically from "f0.def"
7// (to be included in "neurolibrary.cpp")
8
9
10
11
12
13
14     
15static ParamEntry NI_StdNeuron_tab[]={
16{"Neuron",1, 4 ,"N",},
17
18{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
19{"fo",1,0,"Force","f 0.0 999.0 0.04",},
20{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
21{"s",2,0,"State","f -1.0 1.0 0.0",},
22 
23{0,0,0,},};
24addClass(new NeuroClass(NI_StdNeuron_tab,"Standard neuron",-1,1,0, NULL,false, 2, 3));
25
26     
27static ParamEntry NI_StdUNeuron_tab[]={
28{"Unipolar neuron [EXPERIMENTAL!]",1, 4 ,"Nu",},
29{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
30{"fo",1,0,"Force","f 0.0 999.0 0.04",},
31{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
32{"s",2,0,"State","f -1.0 1.0 0.0",},
33 
34{0,0,0,},};
35addClass(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, 3));
36
37      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};   
38static ParamEntry NI_Gyro_tab[]={
39{"Gyroscope",1, 0 ,"G",},
40
41
42 
43{0,0,0,},};
44addClass(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, 3));
45
46      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};   
47static ParamEntry NI_Touch_tab[]={
48{"Touch",1, 1 ,"T",},
49
50
51{"r",1,0,"Range","f 0.0 1.0 1.0",},
52 
53{0,0,0,},};
54addClass(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, 3));
55
56      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};   
57static ParamEntry NI_Smell_tab[]={
58{"Smell",1, 0 ,"S",},
59
60
61 
62{0,0,0,},};
63addClass(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, 3));
64
65      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};   
66static ParamEntry NI_Const_tab[]={
67{"Constant",1, 0 ,"*",},
68
69
70 
71{0,0,0,},};
72addClass(new NeuroClass(NI_Const_tab,"Constant value",0,1,0, Const_xy,false, 1, 3));
73
74      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};   
75static ParamEntry NI_BendMuscle_tab[]={
76{"Bend muscle",1, 2 ,"|",},
77
78
79
80{"p",0,0,"power","f 0.01 1.0 0.25",},
81{"r",0,0,"bending range","f 0.0 1.0 1.0",},
82 
83{0,0,0,},};
84addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,false, 2+16+64+4, 1));
85
86      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};   
87static ParamEntry NI_RotMuscle_tab[]={
88{"Rotation muscle",1, 1 ,"@",},
89
90
91
92{"p",0,0,"power","f 0.01 1.0 1.0",},
93 
94{0,0,0,},};
95addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,false, 2+16+128+4, 1));
96
97      static int SolidMuscle_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};   
98static ParamEntry NI_SolidMuscle_tab[]={
99{"Muscle",1, 2 ,"M",},
100
101
102
103{"p",0,0,"power","f 0.01 1.0 1.0",},
104{"a",0,0,"axis","d 0 1 0",},
105 
106{0,0,0,},};
107addClass(new NeuroClass(NI_SolidMuscle_tab,"",1,0,2, SolidMuscle_xy,false, 16+4+512, 2));
108
109      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};   
110static ParamEntry NI_Diff_tab[]={
111{"Differentiate",1, 0 ,"D",},
112
113 
114{0,0,0,},};
115addClass(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, 3));
116
117      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};   
118static ParamEntry NI_FuzzyNeuro_tab[]={
119{"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
120
121{"ns",0,0,"number of fuzzy sets","d 1  ",},
122{"nr",0,0,"number of rules","d 1  ",},
123{"fs",0,0,"fuzzy sets","s 0 -1 ",},
124{"fr",0,0,"fuzzy rules","s 0 -1 ",},
125 
126{0,0,0,},};
127addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0, 3));
128
129     
130static ParamEntry NI_Sticky_tab[]={
131{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
132
133
134 
135{0,0,0,},};
136addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16, 1));
137
138     
139static ParamEntry NI_LinearMuscle_tab[]={
140{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
141
142
143{"p",0,0,"power","f 0.01 1.0 1.0",},
144 
145{0,0,0,},};
146addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16+256, 1));
147
148     
149static ParamEntry NI_WaterDetect_tab[]={
150{"Water detector",1, 0 ,"Water",},
151
152 
153{0,0,0,},};
154addClass(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, 3));
155
156     
157static ParamEntry NI_Energy_tab[]={
158{"Energy level",1, 0 ,"Energy",},
159
160 
161{0,0,0,},};
162addClass(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, 3));
163
164      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};   
165static ParamEntry NI_Channelize_tab[]={
166{"Channelize",1, 0 ,"Ch",},
167
168 
169{0,0,0,},};
170addClass(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, 3));
171
172      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};   
173static ParamEntry NI_ChMux_tab[]={
174{"Channel multiplexer",1, 0 ,"ChMux",},
175
176 
177{0,0,0,},};
178addClass(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, 3));
179
180      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};   
181static ParamEntry NI_ChSel_tab[]={
182{"Channel selector",1, 1 ,"ChSel",},
183
184{"ch",0,0,"channel","d   ",},
185 
186{0,0,0,},};
187addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0, 3));
188
189     
190static ParamEntry NI_Random_tab[]={
191{"Random noise",1, 0 ,"Rnd",},
192 
193{0,0,0,},};
194addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0, 3));
195
196      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};   
197static ParamEntry NI_Sinus_tab[]={
198{"Sinus generator",1, 2 ,"Sin",},
199
200{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
201{"t",0,0,"time","f 0 6.283185307 0",},
202 
203{0,0,0,},};
204addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0, 3));
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