source: cpp/frams/neuro/neurocls-f0-SDK-library.h @ 343

Last change on this file since 343 was 288, checked in by Maciej Komosinski, 10 years ago

GDK->SDK, gdk_test.cpp -> genomanipulation.cpp
  • Property svn:eol-style set to native
File size: 7.9 KB
RevLine 
[286]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.
[109]4
[271]5
[109]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 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};   
96static ParamEntry NI_Diff_tab[]={
97{"Differentiate",1, 0 ,"D",},
98
99 
100{0,0,0,},};
101addClass(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));
102
103     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};   
104static ParamEntry NI_FuzzyNeuro_tab[]={
105{"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
106
107{"ns",0,0,"number of fuzzy sets","d 1  ",},
108{"nr",0,0,"number of rules","d 1  ",},
109{"fs",0,0,"fuzzy sets","s 0 -1 0",},
110{"fr",0,0,"fuzzy rules","s 0 -1 0",},
111 
112{0,0,0,},};
113addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0));
114
115     
116static ParamEntry NI_Sticky_tab[]={
117{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
118
119 
120{0,0,0,},};
121addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16));
122
123     
124static ParamEntry NI_LinearMuscle_tab[]={
125{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
126
127{"p",0,0,"power","f 0.01 1.0 1.0",},
128 
129{0,0,0,},};
130addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16));
131
132     
133static ParamEntry NI_WaterDetect_tab[]={
134{"Water detector",1, 0 ,"Water",},
135
136 
137{0,0,0,},};
138addClass(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));
139
140     
141static ParamEntry NI_Energy_tab[]={
142{"Energy level",1, 0 ,"Energy",},
143
144 
145{0,0,0,},};
146addClass(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));
147
148     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};   
149static ParamEntry NI_Channelize_tab[]={
150{"Channelize",1, 0 ,"Ch",},
151
152 
153{0,0,0,},};
154addClass(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));
155
156     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};   
157static ParamEntry NI_ChMux_tab[]={
158{"Channel multiplexer",1, 0 ,"ChMux",},
159
160 
161{0,0,0,},};
162addClass(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));
163
164     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};   
165static ParamEntry NI_ChSel_tab[]={
166{"Channel selector",1, 1 ,"ChSel",},
167
168{"ch",0,0,"channel","d   ",},
169 
170{0,0,0,},};
171addClass(new NeuroClass(NI_ChSel_tab,"Outputs a single channel (selected by the \"ch\" parameter) from multichannel input",1,1,0, ChSel_xy,false, 0));
172
173     
174static ParamEntry NI_Random_tab[]={
175{"Random noise",1, 0 ,"Rnd",},
176 
177{0,0,0,},};
178addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0));
179
180     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};   
181static ParamEntry NI_Sinus_tab[]={
182{"Sinus generator",1, 2 ,"Sin",},
183
184{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
185{"t",0,0,"time","f 0 6.283185307 0",},
186 
187{0,0,0,},};
188addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0));
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