source: cpp/frams/neuro/neurocls-f0-SDK-library.h @ 924

Last change on this file since 924 was 924, checked in by Maciej Komosinski, 10 days ago

Fixed flag value of SolidMuscle?

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File size: 8.3 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));
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));
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));
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));
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));
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));
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{"p",0,0,"power","f 0.01 1.0 0.25",},
80{"r",0,0,"bending range","f 0.0 1.0 1.0",},
81 
82{0,0,0,},};
83addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,false, 2+16+64+4));
84
85     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};   
86static ParamEntry NI_RotMuscle_tab[]={
87{"Rotation muscle",1, 1 ,"@",},
88
89
90{"p",0,0,"power","f 0.01 1.0 1.0",},
91 
92{0,0,0,},};
93addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,false, 2+16+128+4));
94
95     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};   
96static ParamEntry NI_SolidMuscle_tab[]={
97{"Muscle",1, 2 ,"M",},
98
99
100{"p",0,0,"power","f 0.01 1.0 1.0",},
101{"a",0,0,"axis","d 0 1 0",},
102 
103{0,0,0,},};
104addClass(new NeuroClass(NI_SolidMuscle_tab,"",1,0,2, SolidMuscle_xy,false, 16+4+512));
105
106     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};   
107static ParamEntry NI_Diff_tab[]={
108{"Differentiate",1, 0 ,"D",},
109
110 
111{0,0,0,},};
112addClass(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));
113
114     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};   
115static ParamEntry NI_FuzzyNeuro_tab[]={
116{"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
117
118{"ns",0,0,"number of fuzzy sets","d 1  ",},
119{"nr",0,0,"number of rules","d 1  ",},
120{"fs",0,0,"fuzzy sets","s 0 -1 ",},
121{"fr",0,0,"fuzzy rules","s 0 -1 ",},
122 
123{0,0,0,},};
124addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0));
125
126     
127static ParamEntry NI_Sticky_tab[]={
128{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
129
130 
131{0,0,0,},};
132addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16));
133
134     
135static ParamEntry NI_LinearMuscle_tab[]={
136{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
137
138{"p",0,0,"power","f 0.01 1.0 1.0",},
139 
140{0,0,0,},};
141addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16));
142
143     
144static ParamEntry NI_WaterDetect_tab[]={
145{"Water detector",1, 0 ,"Water",},
146
147 
148{0,0,0,},};
149addClass(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));
150
151     
152static ParamEntry NI_Energy_tab[]={
153{"Energy level",1, 0 ,"Energy",},
154
155 
156{0,0,0,},};
157addClass(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));
158
159     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};   
160static ParamEntry NI_Channelize_tab[]={
161{"Channelize",1, 0 ,"Ch",},
162
163 
164{0,0,0,},};
165addClass(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));
166
167     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};   
168static ParamEntry NI_ChMux_tab[]={
169{"Channel multiplexer",1, 0 ,"ChMux",},
170
171 
172{0,0,0,},};
173addClass(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));
174
175     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};   
176static ParamEntry NI_ChSel_tab[]={
177{"Channel selector",1, 1 ,"ChSel",},
178
179{"ch",0,0,"channel","d   ",},
180 
181{0,0,0,},};
182addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0));
183
184     
185static ParamEntry NI_Random_tab[]={
186{"Random noise",1, 0 ,"Rnd",},
187 
188{0,0,0,},};
189addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0));
190
191     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};   
192static ParamEntry NI_Sinus_tab[]={
193{"Sinus generator",1, 2 ,"Sin",},
194
195{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
196{"t",0,0,"time","f 0 6.283185307 0",},
197 
198{0,0,0,},};
199addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0));
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