source: cpp/frams/neuro/neurocls-f0-SDK-library.h @ 1090

Last change on this file since 1090 was 1043, checked in by Maciej Komosinski, 4 years ago

Fixed the quotation character
  • Property svn:eol-style set to native
File size: 10.4 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
[934]14
[975]15       
[109]16static ParamEntry NI_StdNeuron_tab[]={
17{"Neuron",1, 4 ,"N",},
18
19{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
20{"fo",1,0,"Force","f 0.0 999.0 0.04",},
21{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
22{"s",2,0,"State","f -1.0 1.0 0.0",},
23 
24{0,0,0,},};
[975]25addClass(new NeuroClass(NI_StdNeuron_tab,"Standard neuron",-1,1,0, NULL,false, 2, 3, 15));
[109]26
[975]27       
[109]28static ParamEntry NI_StdUNeuron_tab[]={
29{"Unipolar neuron [EXPERIMENTAL!]",1, 4 ,"Nu",},
30{"in",1,0,"Inertia","f 0.0 1.0 0.8",},
31{"fo",1,0,"Force","f 0.0 999.0 0.04",},
32{"si",1,0,"Sigmoid","f -99999.0 99999.0 2.0",},
33{"s",2,0,"State","f -1.0 1.0 0.0",},
34 
35{0,0,0,},};
[975]36addClass(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, 15));
[109]37
[975]38       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};   
[109]39static ParamEntry NI_Gyro_tab[]={
40{"Gyroscope",1, 0 ,"G",},
41
42
[952]43
[109]44 
45{0,0,0,},};
[975]46addClass(new NeuroClass(NI_Gyro_tab,"Tilt sensor.\nSignal is proportional to sin(angle) = most sensitive in horizontal orientation.\n0=the stick is horizontal\n+1/-1=the stick is vertical",0,1,2, Gyro_xy,false, 32, 1, 15));
[109]47
[975]48       static int GyroP_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};   
[952]49static ParamEntry NI_GyroP_tab[]={
[976]50{"Part Gyroscope",1, 2 ,"Gpart",},
[952]51
52
53{"ry",1,0,"rotation.y","f -6.282 6.282 0",},
54{"rz",1,0,"rotation.z","f -6.282 6.282 0",},
55 
56{0,0,0,},};
[975]57addClass(new NeuroClass(NI_GyroP_tab,"Tilt sensor. Signal is directly proportional to the tilt angle.\n0=the part X axis is horizontal\n+1/-1=the axis is vertical",0,1,1, GyroP_xy,false, 32, 3, 15));
[952]58
[975]59       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};   
[109]60static ParamEntry NI_Touch_tab[]={
[952]61{"Touch",1, 3 ,"T",},
[109]62
63
64{"r",1,0,"Range","f 0.0 1.0 1.0",},
[952]65{"ry",1,0,"rotation.y","f -6.282 6.282 0",},
66{"rz",1,0,"rotation.z","f -6.282 6.282 0",},
[109]67 
68{0,0,0,},};
[975]69addClass(new NeuroClass(NI_Touch_tab,"Touch and proximity sensor (Tc+Tp combined)\n-1=no contact\n0=just touching\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, Touch_xy,false, 32, 3, 15));
[109]70
[975]71       static int TouchC_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};   
[952]72static ParamEntry NI_TouchC_tab[]={
[976]73{"Touch contact",1, 0 ,"Tcontact",},
[952]74
75
76 
77{0,0,0,},};
[975]78addClass(new NeuroClass(NI_TouchC_tab,"Touch sensor.\n-1=no contact\n0=the Part is touching the obstacle\n>0=pressing, value depends on the force applied (not implemented in ODE mode)",0,1,1, TouchC_xy,false, 32, 3, 15));
[952]79
[975]80       static int TouchP_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};   
[952]81static ParamEntry NI_TouchP_tab[]={
[976]82{"Touch proximity",1, 3 ,"Tproximity",},
[952]83
84
85{"r",1,0,"Range","f 0.0 1.0 1.0",},
86{"ry",1,0,"rotation.y","f -6.282 6.282 0",},
87{"rz",1,0,"rotation.z","f -6.282 6.282 0",},
88 
89{0,0,0,},};
[1043]90addClass(new NeuroClass(NI_TouchP_tab,"Proximity sensor detecting obstacles along the X axis.\n-1=distance is \"r\" or more\n0=zero distance",0,1,1, TouchP_xy,false, 32, 3, 15));
[952]91
[975]92       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};   
[109]93static ParamEntry NI_Smell_tab[]={
94{"Smell",1, 0 ,"S",},
95
96
97 
98{0,0,0,},};
[975]99addClass(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, 15));
[109]100
[975]101       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};   
[109]102static ParamEntry NI_Const_tab[]={
103{"Constant",1, 0 ,"*",},
104
105
106 
107{0,0,0,},};
[975]108addClass(new NeuroClass(NI_Const_tab,"Constant value",0,1,0, Const_xy,false, 1, 3, 15));
[109]109
[975]110       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};   
[109]111static ParamEntry NI_BendMuscle_tab[]={
112{"Bend muscle",1, 2 ,"|",},
113
114
[932]115
[109]116{"p",0,0,"power","f 0.01 1.0 0.25",},
117{"r",0,0,"bending range","f 0.0 1.0 1.0",},
118 
119{0,0,0,},};
[975]120addClass(new NeuroClass(NI_BendMuscle_tab,"",1,0,2, BendMuscle_xy,false, 2+16+64+4, 1, 15));
[109]121
[975]122       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};   
[109]123static ParamEntry NI_RotMuscle_tab[]={
124{"Rotation muscle",1, 1 ,"@",},
125
126
[932]127
[109]128{"p",0,0,"power","f 0.01 1.0 1.0",},
129 
130{0,0,0,},};
[975]131addClass(new NeuroClass(NI_RotMuscle_tab,"",1,0,2, RotMuscle_xy,false, 2+16+128+4, 1, 15));
[109]132
[975]133       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};   
[921]134static ParamEntry NI_SolidMuscle_tab[]={
[946]135{"Muscle for solids",1, 2 ,"M",},
[921]136
137
[932]138
[975]139
[921]140{"p",0,0,"power","f 0.01 1.0 1.0",},
141{"a",0,0,"axis","d 0 1 0",},
142 
143{0,0,0,},};
[975]144addClass(new NeuroClass(NI_SolidMuscle_tab,"",1,0,2, SolidMuscle_xy,false, 16+4+512, 2, 4+8));
[921]145
[975]146       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};   
[109]147static ParamEntry NI_Diff_tab[]={
148{"Differentiate",1, 0 ,"D",},
149
150 
151{0,0,0,},};
[975]152addClass(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, 15));
[109]153
[975]154       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};   
[109]155static ParamEntry NI_FuzzyNeuro_tab[]={
156{"Fuzzy system [EXPERIMENTAL!]",1, 4 ,"Fuzzy",},
157
158{"ns",0,0,"number of fuzzy sets","d 1  ",},
159{"nr",0,0,"number of rules","d 1  ",},
[419]160{"fs",0,0,"fuzzy sets","s 0 -1 ",},
161{"fr",0,0,"fuzzy rules","s 0 -1 ",},
[109]162 
163{0,0,0,},};
[975]164addClass(new NeuroClass(NI_FuzzyNeuro_tab,"Refer to publications to learn more about this neuron.",-1,1,0, FuzzyNeuro_xy,false, 0, 3, 15));
[109]165
[975]166       
[109]167static ParamEntry NI_Sticky_tab[]={
168{"Sticky [EXPERIMENTAL!]",1, 0 ,"Sti",},
169
[932]170
[109]171 
172{0,0,0,},};
[975]173addClass(new NeuroClass(NI_Sticky_tab,"",1,0,1, NULL,false, 16, 1, 15));
[109]174
[975]175       
[109]176static ParamEntry NI_LinearMuscle_tab[]={
177{"Linear muscle [EXPERIMENTAL!]",1, 1 ,"LMu",},
178
[932]179
[109]180{"p",0,0,"power","f 0.01 1.0 1.0",},
181 
182{0,0,0,},};
[975]183addClass(new NeuroClass(NI_LinearMuscle_tab,"",1,0,2, NULL,false, 16+256, 1, 15));
[109]184
[975]185       
[109]186static ParamEntry NI_WaterDetect_tab[]={
187{"Water detector",1, 0 ,"Water",},
188
189 
190{0,0,0,},};
[975]191addClass(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, 15));
[109]192
[975]193       
[109]194static ParamEntry NI_Energy_tab[]={
195{"Energy level",1, 0 ,"Energy",},
196
197 
198{0,0,0,},};
[975]199addClass(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, 15));
[109]200
[975]201       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};   
[109]202static ParamEntry NI_Channelize_tab[]={
203{"Channelize",1, 0 ,"Ch",},
204
205 
206{0,0,0,},};
[975]207addClass(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, 15));
[109]208
[975]209       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};   
[109]210static ParamEntry NI_ChMux_tab[]={
211{"Channel multiplexer",1, 0 ,"ChMux",},
212
213 
214{0,0,0,},};
[975]215addClass(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, 15));
[109]216
[975]217       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};   
[109]218static ParamEntry NI_ChSel_tab[]={
219{"Channel selector",1, 1 ,"ChSel",},
220
221{"ch",0,0,"channel","d   ",},
222 
223{0,0,0,},};
[975]224addClass(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, 15));
[109]225
[975]226       
[109]227static ParamEntry NI_Random_tab[]={
228{"Random noise",1, 0 ,"Rnd",},
229 
230{0,0,0,},};
[975]231addClass(new NeuroClass(NI_Random_tab,"Generates random noise (subsequent random values in the range of -1..+1)",0,1,0, NULL,false, 0, 3, 15));
[109]232
[975]233       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};   
[109]234static ParamEntry NI_Sinus_tab[]={
235{"Sinus generator",1, 2 ,"Sin",},
236
237{"f0",0,0,"base frequency","f -1.0 1.0 0.06283185307",},
238{"t",0,0,"time","f 0 6.283185307 0",},
239 
240{0,0,0,},};
[975]241addClass(new NeuroClass(NI_Sinus_tab,"Output frequency = f0+input",1,1,0, Sinus_xy,false, 0, 3, 15));
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