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SOM:
10 10 10 INSTAR
0.999999999999999 0.999999999000000 0.000000000000000
0.999999500000777 0.999999999666666 1.000000000000000
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externals/ann/examples/ann_td/example.pd deleted 100644 → 0
View file @ 8c96a1f7
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#X text 128 14 1- change to training mode;
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#X text 239 182 3- toggle on to start training and off to stop;
#X msg 170 282 run;
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#X text 224 182 3- run the nn switching this metro ON;
#X text 277 213 (note \, you must be in run mode);
#X msg 281 231 run;
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#X text 325 115 2- train on the fly in realtime;
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#X msg 249 279 save tdnn.net;
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#X text 363 301 remember that you can load it also passing filename
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#X text 72 203 2- open the subpatch and follow instructions;
#X text 15 5 TDNN implementation (Time Delay Neural Network) useful
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externals/ann/examples/ann_td/tdnn.net deleted 100644 → 0
View file @ 8c96a1f7
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externals/ann/examples/tutorial-ann_mlp.pd deleted 100644 → 0
View file @ 8c96a1f7
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#X text 39 59 input: mouse coord;
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#X text 141 13 let's create the ANN;
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#X text 81 231 optionally \, you can use the "a" key instead of the
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#X text 117 190 <-- toggle to train;
#X text 119 404 everytime the metro bangs you send a training pattern
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#X text 53 45 (for this to work you need to give focus to this window
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#X text 335 293 <-- check mouse position;
#X text 112 213 then move the mouse and press left button when you
are in the upper left part of the screen \, release button when you
are not in the upper left part of the screen;
#X text 293 357 <-- check button pressed/not pressed;
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#X text 166 68 2 inputs 1 output;
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#X text 189 128 3 inputs 2 output;
#X text 159 222 TIP:don't set the layers param too high;
#X text 158 179 params: num_input \, num_output \, num_layers \, num_neurons_hidden
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directory;
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is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
http://fann.sourceforge.net/html/r1996.html;
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#X text 144 217 like [ann_mlp test.net];
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#X text 223 179 run your net;
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#X text 41 19 TIPS;
#X text 41 56 inputs;
#X text 39 211 outputs;
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1 for each.;
#X restore 156 303 pd tips;
#X text 270 333 an interface to fann classes (http://fann.sourceforge.net)
;
#X text 272 351 by Davide Morelli - info@davidemorelli.it;
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#X text 67 432 please help me getting this patch more usable: - how
to add a line at the very beginning of a text file after i have filled
it? - how to manage inputs and outputs of different sized without forcing
the user to edit the patch?;
#X text 9 63 how to use: 1) modify [pd inputs] and [ps outputs] inserting
[r] objects to receive input data \, and modify [pack]s to handle the
right number of inputs 2) do the same with [pd outputs] 3) click on
reset 4) toggle ON and start collecting data 5) when you are ready
toggle OFF 6) edit [write filename cr( with the actual filename you
want for your training data (always keep the cr after the filename)
7) open the file with training data 8) add a line at the beginning
and write 3 integers: the 1st is the number of training patterns written
(see "how many patterns" number box) \, the 2nd is how many inputs
your ann has \, the 3th is how many outputs e.g. i collected 100 training
snapshots \, for a ann with 10 ins and 2 outs I write: 100 10 2 at
the very beginning of the file now the training file is ready and can
be read from nn via train-on-file command;
#X text 9 7 this tricky sub-patch is usefull to write a file to train
ann and is intended to be used with the nn external;
#X restore 25 16 pd README;
#X text 479 210 by davide morelli info@davidemorelli.it;
#X text 106 14 <--readme!;
#X text 242 283 <--edit here!;
#X text 142 308 <--edit here!;
#X text 429 86 usage: read [pd README] \, edit [pd inputs] and [pd
outputs] \, toggle on and record inputs and outputs \, toggle off when
ready \, write to a file \, edit the file adding a line at the beginning
(see REAMDE);
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#X text 113 36 <--train OR untile mse is low enough;
#X text 143 51 (you must be in train mode);
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#X restore 224 363 pd autotrain OR;
#X text 172 101 2a)- build a list with inputs and desired output;
#X text 336 291 NOTE2: while training the right outlet gives you the
mean square error after each training pattern. continue training until
mse is low enough.;
#X text 221 383 2b) use autotrain for the OR function;
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#X restore 68 50 pd train it on the fly;
#X text 62 5 there are 2 ways to train your net;
#X text 253 47 on the fly is simpler;
#X text 88 128 with a trainfile the net could be more accurate;
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#X text 149 118 train a nn;
#X obj 103 345 print mse;
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#X obj 52 313 ann_mlp;
#X text 9 2 ann_mlp: multi layer perceptrons neural networks in PD
;
#N canvas 405 166 494 332 META 0;
#X text 12 190 HELP_PATCH_AUTHORS "pd meta" information added by Jonathan
Wilkes for Pd version 0.42.;
#X text 12 25 LICENSE GPL v2;
#X text 12 5 KEYWORDS control;
#X text 12 45 DESCRIPTION multi layer perceptrons neural networks in
PD;
#X text 12 130 OUTLET_0;
#X text 12 150 OUTLET_1;
#X text 12 170 AUTHOR Davide Morelli - info@davidemorelli.it;
#X text 12 65 INLET_0 list create train filename save load setmode
FANN_TRAIN_INCREMENTAL FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP
desired_error max_iterations iterations_between_reports set_activation_function_output
;
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-----------------ann_mlp manual
by davide morelli - www.davidemorelli.it
-----------What is a neural network?
To be shure you fully understand what is and why to use a ANN, read
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
-----------Why use a ANN?
Because they are useful in Pattern recognition, gesure recognition (patterns
over time), associative recall of data (images, sounds, etc), predictions
(e.g. time-series forecasting), complex data handling, etc..
--ANNs can handle noisy inputs:
if you trained your ANN that [1,1] -> 1
Then if you pass [1.1, 0.9] -> 1 probably..
--ANNs can be trained without writing code:
see ann/examples/ann_mlp_example2 (in CVS), you can teach the ANN to tell
you when all these balls are close together or still.. How could you do this
coding? You'd have to compute the distance of every ball from every other
ball, then sum all the distances and ... Very complex and difficult! With
ANN you simply teach when the balls are close and when they are not, you
don't have to write code at all, you just have to use pd.
-----------How to build a ANN?
INPUTS:
You must code your input data as a list of float.
E.g. If you want timbre recognition you must fft a signal then build a list
with fft's partial and feed ann_mlp with it
E.g. if you want midi chord recognition and you played A4 C5 E6 then use the
midi values of the notes of the chord to build a list with 3 integers (57 60
64)
Tip: inputs should be 0 centered
the example of chord recognition should not work well (hard to train)
because possible input values go from 30 to 90, you should remap them so
they go from -30 to 30
Notice how the inputs in ann/examples/ann_mlp_example2 go from -1 to 1
If you can't make inputs 0 centered they should at least start from 0
Tip: inputs should be normalized
If you have one input that goes from -10 to 10 and another input that goes
from -1 to 1 the first input will be more important than the second input
OUTPUTS:
Each "meaning" you want your ANN to detect should have its own output.
Notice ann/examples/ann_mlp_example2:
"Calm" and "chaos" have their outputs even if they are related.
I could have set only 1 output 0 for calm and 1 for chaos.
But having separated outputs I can see if my ANN has been trained well or
not, but also could be that a situation is neither calm nor chaotic, or
somehow calm AND chaotic..
-----------TRAINING ON THE FLY:
It is much easier to train the ANN on the fly rather than using a train
file.
To train on the fly you simply must pass a list with
[inputs + expected outputs(
If you have 3 inputs and 2 outputs then you must pass a list with 5 floats
E.g. You want to train a ANN for a simple logical function: OR
You build a ann_mlp with 2 inputs and 1 output
You set |train(
You pass lists like
|0 0 0( inputs are 0 0 output is 0
|1 0 1( inputs are 1 0 output is 1
|0 1 1( inputs are 0 1 output is 1
And so on.. Repeating until MSE is low enough
MSE tells you the general error the ANN currently has with the inputs and
outputs you are giving
When you are ready set |run(
And start passing lists with only inputs values
|0 0(
|1 0(
Etc..
The left outlet of ann_mlp will start sending lists of float, in this case a
list with only 1 float
-----------PUTTING IT TOGETHER:
1) Create a ANN passing ann_mlp a message with num_inputs and num_outputs
E.g. (the simple ann for logical function OR)
|create 2 1(
|
[ann_mlp]
2) set train mode
|train(
|
[ann_mlp]
3) train the ANN passing lists num_inputs+num_outputs long
|0 0 0(
|
[ann_mlp]
(repeat at will using different inputs until mse is low, see right outlet)
4) set run mode
|run(
|
[ann_mlp]
5) run the net passing lists num_inputs long, the left outlet will send a
list with the results
|0 0(
|
[ann_mlp]
When everything is fine you can save it to a file
|save filename(
|
[ann_mlp]
Can be loaded in 2 ways
|load filename(
|
[ann_mlp]
Or as argument
[ann_mlp filename]
-----------
For a more in-depth sight over technical issues:
http://fann.sourceforge.net/report/report.html
See fann manual for details on advanced params
(activation functions,training params, etc..)
http://fann.sourceforge.net/
-----------
questions and suggestions to info(a)davidemorelli.it
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#X obj 24 81 bng 15 250 50 0 empty empty write-once 0 -6 0 8 -262144
-1 -1;
#X obj 506 106 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
-1;
#N canvas 0 0 466 316 inputs 0;
#X obj 61 153 pack s f f;
#X obj 63 200 pack f f;
#X obj 61 176 unpack s f f;
#X msg 66 223 add \$1 \$2;
#X obj 66 257 outlet;
#X text 120 258 to textfile;
#X obj 24 42 inlet;
#X text 23 22 bang;
#X text 66 77 here go the inputs;
#X obj 94 52 r input1;
#X obj 163 52 r input2;
#X connect 0 0 2 0;
#X connect 1 0 3 0;
#X connect 2 1 1 0;
#X connect 2 2 1 1;
#X connect 3 0 4 0;
#X connect 6 0 0 0;
#X connect 9 0 0 1;
#X connect 10 0 0 2;
#X restore 58 306 pd inputs;
#N canvas 0 0 466 316 outputs 0;
#X obj 61 153 pack s f f;
#X obj 63 200 pack f f;
#X obj 61 176 unpack s f f;
#X msg 66 223 add \$1 \$2;
#X obj 66 257 outlet;
#X text 120 258 to textfile;
#X obj 24 42 inlet;
#X text 23 22 bang;
#X text 66 77 here go the outputs;
#X obj 91 51 r output1;
#X obj 166 51 r output2;
#X connect 0 0 2 0;
#X connect 1 0 3 0;
#X connect 2 1 1 0;
#X connect 2 2 1 1;
#X connect 3 0 4 0;
#X connect 6 0 0 0;
#X connect 9 0 0 1;
#X connect 10 0 0 2;
#X restore 149 284 pd outputs;
#X obj 230 223 f 0;
#X obj 260 223 + 1;
#X obj 239 257 nbx 5 14 -1e+037 1e+037 0 0 empty empty how_many_patterns
0 -6 0 10 -262144 -1 -1 0 256;
#X text 156 406 todo: write header (a line at the beginning of file
with 3 int: how many tests \, num_input \, num_output);
#X obj 122 190 delay 50;
#X obj 115 159 metro 100;
#X floatatom 346 70 5 100 5000 2 msec_between_snapshots - -;
#X obj 127 80 tgl 15 0 empty empty toggle_on-off 0 -6 0 8 -262144 -1
-1 0 1;
#X obj 219 189 / 2;
#X obj 347 14 loadbang;
#X msg 347 34 100;
#X msg 385 314 write test.txt cr;
#X text 293 224 comment;
#N canvas 262 68 639 595 README 0;
#X text 67 432 please help me getting this patch more usable: - how
to add a line at the very beginning of a text file after i have filled
it? - how to manage inputs and outputs of different sized without forcing
the user to edit the patch?;
#X text 9 7 this tricky patch is usefull to write a file to train ann
and is intended to be used with the nn external;
#X text 9 63 how to use: 1) modify [pd inputs] and [ps outputs] inserting
[r] objects to receive input data \, and modify [pack]s to handle the
right number of inputs 2) do the same with [pd outputs] 3) click on
reset 4) toggle ON and start collecting data 5) when you are ready
toggle OFF 6) edit [write filename cr( with the actual filename you
want for your training data (always keep the cr after the filename)
7) open the file with training data 8) add a line at the beginning
and write 3 integers: the 1st is the number of training patterns written
(see "how many patterns" number box) \, the 2nd is how many inputs
your ann has \, the 3th is how many outputs e.g. i collected 100 training
snapshots \, for a ann with 10 ins and 2 outs I write: 100 10 2 at
the very beginning of the file now the training file is ready and can
be read from nn via train-on-file command;
#X restore 25 16 pd README;
#X text 479 210 by davide morelli info@davidemorelli.it;
#X text 106 14 <--readme!;
#X text 242 283 <--edit here!;
#X text 142 308 <--edit here!;
#X connect 1 0 0 0;
#X connect 2 0 5 0;
#X connect 2 1 6 0;
#X connect 2 1 7 0;
#X connect 3 0 11 0;
#X connect 3 0 2 0;
#X connect 4 0 2 1;
#X connect 4 0 1 0;
#X connect 5 0 0 0;
#X connect 6 0 0 0;
#X connect 7 0 8 0;
#X connect 7 0 9 0;
#X connect 8 0 7 1;
#X connect 11 0 2 0;
#X connect 12 0 11 0;
#X connect 12 0 2 0;
#X connect 13 0 12 1;
#X connect 13 0 15 0;
#X connect 14 0 12 0;
#X connect 15 0 11 1;
#X connect 16 0 17 0;
#X connect 17 0 13 0;
#X connect 18 0 0 0;
externals/ann/helps/ann_td-help.pd deleted 100644 → 0
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#N canvas 1 53 858 468 12;
#N canvas 376 163 647 348 creation 0;
#X obj 52 235 outlet;
#X text 246 38 specifying all;
#X text 159 216 TIP:don't set the layers param too high;
#X msg 49 10 create 2 1 5;
#X text 175 6 create with 2 inputs \, 1 output and 5 frames;
#X msg 59 38 create 2 1 5 3 3 1 0.7;
#X text 159 179 params: num_input \, num_output \, frames \, num_layers
\, num_neurons_hidden \, connection_rate \, learning_rate;
#N canvas 219 181 650 413 what 0;
#X text 37 134 you pass [0 0.1] to ann_tdnn;
#X text 34 152 internally now there is this array: [0 0.1 0 0 0 0]
;
#X text 38 196 next input is [0.2 1];
#X text 36 211 internally now there is this array: [0.2 1 0 0.1 0 0]
;
#X text 37 255 next input is [0.3 0.4];
#X text 35 270 internally now there is this array: [0.3 0.4 0.2 1 0
0.1];
#X text 36 317 next input is [0.7 0];
#X text 34 332 internally now there is this array: [0.7 0 0.3 0.4 0.2
1];
#X text 35 168 a normal ann_mlp is run with this inputs;
#X text 38 225 a normal ann_mlp is run with this inputs;
#X text 33 284 a normal ann_mlp is run with this inputs;
#X text 33 347 a normal ann_mlp is run with this inputs;
#X text 12 139 1);
#X text 14 197 2);
#X text 15 258 3);
#X text 13 319 4);
#X text 33 4 this implementation od tdnn is simply a normal ann_mlp
with num_input*frame inputs and num_output outputs. ann_tdnn simply
helps managing the delay \, frames and buffers.;
#X text 65 385 ...and so on...;
#X text 34 64 frames can be seen as the delay feedback: how many times
an input is internally held in the input array;
#X text 35 104 eg: 2 inputs 3 frames = internally 6 inputs;
#X restore 155 109 pd what frames are?;
#X connect 3 0 0 0;
#X connect 5 0 0 0;
#X restore 93 68 pd creation examples;
#N canvas 137 89 728 356 run 0;
#X obj 90 219 outlet;
#X msg 123 69 0 1;
#X msg 124 92 1 0;
#X msg 125 115 1 1;
#X msg 126 140 0 0;
#X text 40 17 now you can run your nn passing it a list with inputs
;
#X text 169 70 send a list of data and watch the console for output
;
#X text 39 35 the output is sent as a list of float;
#X text 184 134 these inputs are good for a nn like the one in example1
directory;
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 4 0 0 0;
#X restore 107 180 pd run the net;
#N canvas 1 53 619 610 other 0;
#X obj 43 401 outlet;
#X msg 102 37 train;
#X msg 103 63 run;
#X msg 152 37 setmode 0;
#X msg 153 63 setmode 1;
#X text 249 40 set training/running mode;
#X text 247 63 training mode currently not implemented;
#N canvas 266 284 690 335 training 0;
#X obj 71 288 outlet;
#X msg 82 195 FANN_TRAIN_INCREMENTAL;
#X msg 82 216 FANN_TRAIN_BATCH;
#X msg 81 238 FANN_TRAIN_RPROP;
#X msg 81 258 FANN_TRAIN_QUICKPROP;
#X text 40 28 you can set the training algorithm simply sending a message
with the name of the algorithm chosen. possible values are: FANN_TRAIN_INCREMENTAL
FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP the default
is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
http://fann.sourceforge.net/html/r1996.html;
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 4 0 0 0;
#X restore 150 153 pd training algorithm;
#X text 360 175 some advanced param;
#N canvas 325 121 698 395 training 0;
#X obj 52 230 outlet;
#X msg 69 118 desired_error 0.01;
#X msg 79 146 max_iterations 500000;
#X msg 90 178 iterations_between_reports 1000;
#X text 58 28 you can change training parameters. see FANN manual for
details (http://fann.sourceforge.net);
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X restore 151 179 pd training params;
#N canvas 329 121 694 391 activation 0;
#X obj 49 335 outlet;
#X text 40 28 you can set ti output activation algorithm passing a
message to nn. see the FANN manual for description of the algorithms
;
#X msg 69 118 set_activation_function_output FANN_THRESHOLD;
#X msg 83 139 set_activation_function_output FANN_THRESHOLD_SYMMETRIC
;
#X msg 95 163 set_activation_function_output FANN_LINEAR;
#X msg 98 184 set_activation_function_output FANN_SIGMOID;
#X msg 106 206 set_activation_function_output FANN_SIGMOID_STEPWISE
;
#X msg 108 233 set_activation_function_output FANN_SIGMOID_SYMMETRIC
;
#X msg 115 256 set_activation_function_output FANN_SIGMOID_SYMMETRIC_STEPWISE
;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 4 0 0 0;
#X connect 5 0 0 0;
#X connect 6 0 0 0;
#X connect 7 0 0 0;
#X connect 8 0 0 0;
#X restore 150 203 pd activation algorithm;
#X msg 151 287 details;
#X text 229 285 details on the current nn;
#X msg 145 333 help;
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 4 0 0 0;
#X connect 7 0 0 0;
#X connect 9 0 0 0;
#X connect 10 0 0 0;
#X connect 11 0 0 0;
#X connect 13 0 0 0;
#X restore 128 258 pd other commands;
#N canvas 1 53 665 525 save 0;
#X obj 39 264 outlet;
#X msg 64 20 filename test.net;
#X msg 66 46 save;
#X msg 82 103 load;
#X text 221 19 set the filename;
#X text 214 42 save the net to the file;
#X text 138 104 you can reload it too;
#X text 144 182 nn can be loaded from a file at creation time simply
passing the filename as argument;
#X msg 68 71 save test.net;
#X msg 93 130 load test.net;
#X text 144 217 like [ann_td num_inputs frames filename];
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 8 0 0 0;
#X connect 9 0 0 0;
#X restore 118 218 pd save the net;
#X text 270 66 create a nn;
#X text 244 179 run your net;
#X text 258 215 save your net;
#N canvas 0 0 712 542 tips 0;
#X text 51 84 for better performances inputs value should be normalized
\, all input should have the same range (if one input has a larger
range it will be more "important"). the range of each input should
be 0 centered. so [-1 \, 1] is good [-2 \, 2] is good \, [0 \, 1] not
so good [1 \, 2] is bad. the range sould not be too small ([-0.1 \,
0.1] is bad).;
#X text 41 19 TIPS;
#X text 41 56 inputs;
#X text 39 211 outputs;
#X text 50 235 each class of outputs should have its own output value:
don't use the same output for 2 meanings \, use 2 outputs intead \,
1 for each.;
#X restore 167 285 pd tips;
#X text 272 371 an interface to fann classes (http://fann.sourceforge.net)
;
#X text 274 389 by Davide Morelli - info@davidemorelli.it;
#N canvas 228 212 580 411 train 0;
#X obj 32 241 outlet;
#N canvas 100 44 892 558 train 0;
#X obj 57 397 outlet;
#X msg 60 31 train;
#X text 126 33 1- set the train mode;
#X text 116 81 2- build a list with inputs and desired output;
#X text 139 101 be shure you provide the correct numbers of inputs
and outputs;
#X obj 168 202 pack s f f f;
#X obj 197 248 pack f f f;
#X obj 168 225 unpack s f f f;
#X msg 192 374 run;
#X obj 198 170 tgl 15 0 empty empty in1 0 -6 0 8 -262144 -1 -1 0 1
;
#X obj 228 170 tgl 15 0 empty empty in2 0 -6 0 8 -262144 -1 -1 0 1
;
#X obj 259 170 tgl 15 0 empty empty output 0 -6 0 8 -262144 -1 -1 0
1;
#X obj 148 169 bng 15 250 50 0 empty empty train! 0 -6 0 8 -262144
-1 -1;
#X text 299 183 set inputs and output value \, then send the list clicking
on the "train!" bang;
#X text 229 374 3- when you are ready switch again to run mode before
exiting;
#X text 311 308 NOTE2: while training the right outlet gives you the
mean square error after each training pattern.;
#X msg 316 278 create 2 1 5;
#X text 315 226 NOTE1: before training with this example you should
have created a nn with 2 ins and 1 out and 5 frames with a command
like:;
#X connect 1 0 0 0;
#X connect 5 0 7 0;
#X connect 6 0 0 0;
#X connect 7 1 6 0;
#X connect 7 2 6 1;
#X connect 7 3 6 2;
#X connect 8 0 0 0;
#X connect 9 0 5 1;
#X connect 10 0 5 2;
#X connect 11 0 5 3;
#X connect 12 0 5 0;
#X connect 16 0 0 0;
#X restore 68 50 pd train it on the fly;
#X text 62 5 there are 2 ways to train your net;
#X text 253 47 on the fly is simpler;
#X text 86 128 with a trainfile the net could be more accurate;
#X msg 89 149 train-on-file test.txt;
#X connect 1 0 0 0;
#X connect 5 0 0 0;
#X restore 115 119 pd train;
#X text 190 118 train a nn;
#X obj 113 360 print mse;
#X obj 54 391 print out;
#X text 150 315 2 args needed: num_inputs and frames;
#X text 148 331 see [pd creation examples] for details;
#X obj 33 319 ann_td 2 5;
#X text 9 2 ann_td: time delay neural networks in pd;
#N canvas 406 195 494 332 META 0;
#X text 12 210 HELP_PATCH_AUTHORS "pd meta" information added by Jonathan
Wilkes for Pd version 0.42.;
#X text 12 25 LICENSE GPL v2;
#X text 12 5 KEYWORDS control;
#X text 12 150 OUTLET_0;
#X text 12 170 OUTLET_1;
#X text 12 190 AUTHOR Davide Morelli - info@davidemorelli.it;
#X text 12 45 DESCRIPTION time delay neural networks in pd;
#X text 12 65 INLET_0 list create train train-on-file filename save
load setmode run FANN_TRAIN_INCREMENTAL FANN_TRAIN_BATCH FANN_TRAIN_RPROP
FANN_TRAIN_QUICKPROP desired_error max_iterations iterations_between_reports
set_activation_function_output;
#X restore 734 408 pd META;
#X connect 0 0 16 0;
#X connect 1 0 16 0;
#X connect 2 0 16 0;
#X connect 3 0 16 0;
#X connect 10 0 16 0;
#X connect 16 0 13 0;
#X connect 16 1 12 0;
externals/ann/src/ann.c deleted 100644 → 0
View file @ 8c96a1f7
/* ...this is an externals for comouting Aritficial Neural Networks...
thikn aboiut this
0201:forum::für::umläute:2001
*/
#include "ann.h"
//#include "ann_som.c"
//#include "ann_mlp.c"
//#include "ann_td.c"
typedef struct ann
{
t_object t_ob;
} t_ann;
t_class *ann_class;
/* do a little help thing */
static void ann_help(void)
{
post("\n\n...this is the ann external "VERSION"..\n");
post("self-organized maps"
"\n\tann_som"
"");
post("\n(l) forum::für::umläute 2001\n"
"this software is under the GnuGPL that is provided with these files");
}
void *ann_new(void)
{
t_ann *x = (t_ann *)pd_new(ann_class);
return (void *)x;
}
void ann_som_setup(void);
void ann_mlp_setup(void);
void ann_td_setup(void);
/*
waiting to be released in near future:
ANN_SOM : self organized maps
ANN_PERCEPTRON : perceptrons
ANN_MLP : multilayer perceptrons
waiting to be realeased sometimes
ANN_RBF : radial basis functions
*/
void ann_setup(void)
{
ann_som_setup();
ann_mlp_setup();
ann_td_setup();
/* ************************************** */
post("\n\t................................");
post("\t...artificial neural networks...");
post("\t..........version "VERSION"..........");
post("\t....forum::für::umläute 2001....");
post("\t....send me a 'help' message....");
post("\t................................\n");
ann_class = class_new(gensym("ann"), ann_new, 0, sizeof(t_ann), 0, 0);
class_addmethod(ann_class, ann_help, gensym("help"), 0);
}
externals/ann/src/ann.h deleted 100644 → 0
View file @ 8c96a1f7
/* ********************************************** */
/* the ANN external */
/* ********************************************** */
/* forum::für::umläute */
/* ********************************************** */
#ifndef INCLUDE_ANN_H__
#define INCLUDE_ANN_H__
#include "m_pd.h"
/* to beautify the logo make sure to make the VERSION-info 4 chars long */
#define VERSION "0.1."
#endif
externals/ann/src/ann_mlp.c deleted 100644 → 0
View file @ 8c96a1f7
/* ann_mlp : Neural Networks for PD
by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it
this software is simply an interface for FANN classes
http://fann.sourceforge.net/
FANN is obviously needed for compilation
USE 1.2 VERSION ONLY
this software is licensed under the GNU General Public License
*/
/*
hacked by Georg Holzmann for some additional methods, bug fixes, ...
2005, grh@mur.at
*/
#include <stdio.h>
#include <string.h>
#include "m_pd.h"
#include "fann.h"
#ifndef VERSION
#define VERSION "0.2"
#endif
#ifndef __DATE__
#define __DATE__ ""
#endif
#define TRAIN 0
#define RUN 1
static t_class *ann_mlp_class;
typedef struct _ann_mlp {
t_object x_obj;
struct fann *ann;
int mode; // 0 = training, 1 = running
t_symbol *filename; // name of the file where this ann is saved
t_symbol *filenametrain; // name of the file with training data
float desired_error;
unsigned int max_iterations;
unsigned int iterations_between_reports;
fann_type *input; // grh: storage for input
t_atom *output; // grh: storage for output (t_atom)
fann_type *out_float; // grh: storage for output (fann_type)
t_canvas *x_canvas;
t_outlet *l_out, *f_out;
} t_ann_mlp;
// allocation
static void ann_mlp_allocate_storage(t_ann_mlp *x)
{
unsigned int i;
if(!x->ann)
return;
x->input = (fann_type *)getbytes(x->ann->num_input*sizeof(fann_type));
x->output = (t_atom *)getbytes(x->ann->num_output*sizeof(t_atom));
x->out_float = (fann_type *)getbytes(x->ann->num_output*sizeof(fann_type));
// init storage with zeros
for (i=0; i<x->ann->num_input; i++)
x->input[i]=0;
for (i=0; i<x->ann->num_output; i++)
{
SETFLOAT(x->output+i, 0);
x->out_float[i]=0;
}
}
// deallocation
static void ann_mlp_free(t_ann_mlp *x)
{
if(!x->ann)
return;
freebytes(x->input, x->ann->num_input * sizeof(fann_type));
freebytes(x->output, x->ann->num_output * sizeof(t_atom));
freebytes(x->out_float, x->ann->num_output * sizeof(fann_type));
fann_destroy(x->ann);
}
static void ann_mlp_help(t_ann_mlp *x)
{
post("");
post("ann_mlp: neural nets for PD");
post("ann_mlp:Davide Morelli - info@davidemorelli.it - (c)2005");
post("ann_mlp:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");
post("ann_mlp:main commands: create, filename, load, save, train-on-file, run");
post("ann_mlp:see help-nn.pd for details on commands and usage");
post("ann_mlp:this is an interface to FANN");
}
static void ann_mlp_createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int num_input = 2;
unsigned int num_output = 1;
unsigned int num_layers = 3;
unsigned int *neurons_per_layer = NULL;
int activated=0;
int i, count_args = 0;
float connection_rate = 1;
float learning_rate = (float)0.7;
// okay, start parsing init args ...
if (argc > count_args++)
num_input = atom_getint(argv++);
if (argc > count_args++)
num_output = atom_getint(argv++);
if (argc > count_args++)
{
int hidden=0;
num_layers = atom_getint(argv++);
hidden = num_layers-2;
neurons_per_layer = (unsigned int *)getbytes(num_layers*sizeof(unsigned int));
neurons_per_layer[0] = num_input;
// make standard initialization (if there are too few init args)
for (i=1; i<hidden+1; i++)
neurons_per_layer[i] = 3;
// now check init args
for (i=1; i<hidden+1; i++)
{
if (argc > count_args++)
neurons_per_layer[i] = atom_getint(argv++);
}
neurons_per_layer[num_layers-1] = num_output;
activated=1;
}
if (argc > count_args++)
connection_rate = atom_getfloat(argv++);
if (argc > count_args++)
learning_rate = atom_getfloat(argv++);
// make one hidden layer as standard, if there were too few init args
if(!activated)
{
neurons_per_layer = (unsigned int *)getbytes(3*sizeof(unsigned int));
neurons_per_layer[0] = num_input;
neurons_per_layer[1] = 3;
neurons_per_layer[2] = num_output;
}
// ... end of parsing init args
if(x->ann)
ann_mlp_free(x);
x->ann = fann_create_array(connection_rate, learning_rate, num_layers, neurons_per_layer);
// deallocate helper array
freebytes(neurons_per_layer, num_layers * sizeof(unsigned int));
if(!x->ann)
{
error("error creating the ann");
return;
}
ann_mlp_allocate_storage(x);
fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);
// set error log to stdout, so that you see it in the pd console
//fann_set_error_log((struct fann_error*)x->ann, stdout);
// unfortunately this doesn't work ... but it should do in a similar way !!
post("created ann with:");
post("num_input = %i", num_input);
post("num_output = %i", num_output);
post("num_layers = %i", num_layers);
post("connection_rate = %f", connection_rate);
post("learning_rate = %f", learning_rate);
}
static void ann_mlp_print_status(t_ann_mlp *x)
{
if (x->mode == TRAIN)
post("nn:training");
else
post("nn:running");
}
static void ann_mlp_train(t_ann_mlp *x)
{
x->mode=TRAIN;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
fann_reset_MSE(x->ann);
ann_mlp_print_status(x);
}
static void ann_mlp_run(t_ann_mlp *x)
{
x->mode=RUN;
ann_mlp_print_status(x);
}
static void ann_mlp_set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
if (argc<1)
{
error("usage: setmode 0/1: 0 for training, 1 for running");
}
else
{
x->mode = atom_getint(argv++);
ann_mlp_print_status(x);
}
}
static void ann_mlp_train_on_file(t_ann_mlp *x, t_symbol *s)
{
// make correct path
char patcher_path[MAXPDSTRING];
char filename[MAXPDSTRING];
if (x->ann == 0)
{
error("ann not initialized");
return;
}
// make correct path
canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
sys_bashfilename(patcher_path, filename);
x->filenametrain = gensym(filename);
if(!x->filenametrain)
return;
post("nn: starting training on file %s, please be patient and wait ... (it could take severeal minutes to complete training)", x->filenametrain->s_name);
fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,
x->iterations_between_reports, x->desired_error);
post("ann_mlp: finished training on file %s", x->filenametrain->s_name);
}
static void ann_mlp_set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
float desired_error = (float)0.001;
if (0<argc)
{
desired_error = atom_getfloat(argv);
x->desired_error = desired_error;
post("nn:desired_error set to %f", x->desired_error);
} else
{
error("you must pass me a float");
}
}
static void ann_mlp_set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int max_iterations = 500000;
if (argc>0)
{
max_iterations = atom_getint(argv);
x->max_iterations = max_iterations;
post("nn:max_iterations set to %i", x->max_iterations);
} else
{
error("you must pass me an int");
}
}
static void ann_mlp_set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int iterations_between_reports = 1000;
if (argc>0)
{
iterations_between_reports = atom_getint(argv);
x->iterations_between_reports = iterations_between_reports;
post("nn:iterations_between_reports set to %i", x->iterations_between_reports);
} else
{
error("you must pass me an int");
}
}
// run the ann using floats in list passed to the inlet as input values
// and send result to outlet as list of float
static void ann_mlp_run_the_net(t_ann_mlp *x, t_symbol *sl, unsigned int argc, t_atom *argv)
{
unsigned int i=0;
fann_type *calc_out;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if(argc < x->ann->num_input)
{
error("ann_mlp: too few input values!!");
return;
}
// fill input array with actual data sent to inlet
for (i=0;i<x->ann->num_input;i++)
{
x->input[i] = atom_getfloat(argv++);
}
// run the ann
calc_out = fann_run(x->ann, x->input);
// fill the output array with result from ann
for (i=0;i<x->ann->num_output;i++)
SETFLOAT(x->output+i, calc_out[i]);
// send output array to outlet
outlet_anything(x->l_out, gensym("list"),
x->ann->num_output, x->output);
}
static void ann_mlp_train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
int i=0;
int quantiINs, quantiOUTs;
float mse;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
quantiINs = x->ann->num_input;
quantiOUTs = x->ann->num_output;
if ((quantiINs + quantiOUTs)>argc)
{
error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");
return;
}
// fill input array with actual data sent to inlet
for (i=0;i<quantiINs;i++)
x->input[i] = atom_getfloat(argv++);
for (i=0;i<quantiOUTs;i++)
x->out_float[i] = atom_getfloat(argv++);
//fann_reset_MSE(x->ann);
fann_train(x->ann, x->input, x->out_float);
mse = fann_get_MSE(x->ann);
outlet_float(x->f_out, mse);
}
static void ann_mlp_manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
if (x->mode)
ann_mlp_run_the_net(x, sl, argc, argv);
else
{
ann_mlp_train_on_the_fly(x, sl, argc, argv);
}
}
static void ann_mlp_set_filename(t_ann_mlp *x, t_symbol *s)
{
// make correct path
char patcher_path[MAXPDSTRING];
char filename[MAXPDSTRING];
if(!s)
return;
// make correct path
canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
sys_bashfilename(patcher_path, filename);
x->filename = gensym(filename);
}
static void ann_mlp_load_ann_from_file(t_ann_mlp *x, t_symbol *s)
{
ann_mlp_set_filename(x,s);
if(!x->filename)
{
error("ann: no filename !!!");
return;
}
// deallocate storage
if(x->ann)
ann_mlp_free(x);
x->ann = fann_create_from_file(x->filename->s_name);
if (x->ann == 0)
error("error opening %s", x->filename->s_name);
else
post("nn:ann loaded fom file %s", x->filename->s_name);
// allocate storage
ann_mlp_allocate_storage(x);
}
static void ann_mlp_save_ann_to_file(t_ann_mlp *x, t_symbol *s)
{
ann_mlp_set_filename(x,s);
if(!x->filename)
{
error("ann: no filename !!!");
return;
}
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_save(x->ann, x->filename->s_name);
post("nn:ann saved in file %s", x->filename->s_name);
}
}
// functions for training algo:
static void ann_mlp_set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);
post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");
}
}
static void ann_mlp_set_FANN_TRAIN_BATCH(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);
post("nn:training algorithm set to FANN_TRAIN_BATCH");
}
}
static void ann_mlp_set_FANN_TRAIN_RPROP(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);
post("nn:training algorithm set to FANN_TRAIN_RPROP");
}
}
static void ann_mlp_set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);
post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");
}
}
static void ann_mlp_set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_symbol *parametro = 0;
int funzione = 0;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if (argc>0) {
parametro = atom_gensym(argv);
if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
funzione = FANN_THRESHOLD;
if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
funzione = FANN_THRESHOLD_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
funzione = FANN_LINEAR;
if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
funzione = FANN_SIGMOID;
if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
funzione = FANN_SIGMOID_STEPWISE;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
funzione = FANN_SIGMOID_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
funzione = FANN_GAUSSIAN;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
funzione = FANN_GAUSSIAN_STEPWISE;
if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
funzione = FANN_ELLIOT;
if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
funzione = FANN_ELLIOT_SYMMETRIC;
fann_set_activation_function_output(x->ann, funzione);
} else
{
error("you must specify the activation function");
}
post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
}
static void ann_mlp_set_activation_function_hidden(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_symbol *parametro = 0;
int funzione = 0;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if (argc>0) {
parametro = atom_gensym(argv);
if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
funzione = FANN_THRESHOLD;
if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
funzione = FANN_THRESHOLD_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
funzione = FANN_LINEAR;
if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
funzione = FANN_SIGMOID;
if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
funzione = FANN_SIGMOID_STEPWISE;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
funzione = FANN_SIGMOID_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
funzione = FANN_GAUSSIAN;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
funzione = FANN_GAUSSIAN_STEPWISE;
if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
funzione = FANN_ELLIOT;
if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
funzione = FANN_ELLIOT_SYMMETRIC;
fann_set_activation_function_hidden(x->ann, funzione);
} else
{
error("you must specify the activation function");
}
post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
}
static void ann_mlp_randomize_weights(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_float min = -1;
t_float max = 1;
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
if (argc>0)
min = atom_getfloat(argv++);
if (argc>1)
max = atom_getfloat(argv++);
fann_randomize_weights(x->ann, min, max);
}
static void ann_mlp_learnrate(t_ann_mlp *x, t_float f)
{
int learnrate = 0;
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
learnrate = (f<0) ? 0 : f;
fann_set_learning_rate(x->ann, learnrate);
}
static void ann_mlp_set_activation_steepness_hidden(t_ann_mlp *x, t_float f)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_set_activation_steepness_hidden(x->ann, f);
}
static void ann_mlp_set_activation_steepness_output(t_ann_mlp *x, t_float f)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_set_activation_steepness_output(x->ann, f);
}
void fann_set_activation_steepness_hidden(struct fann * ann, fann_type steepness);
static void ann_mlp_print_ann_details(t_ann_mlp *x)
{
if (x->ann == 0)
{
post("ann_mlp:ann is not initialized");
} else
{
post("follows a description of the current ann:");
post("num_input=%i", x->ann->num_input);
post("num_output=%i", x->ann->num_output);
post("learning_rate=%f", x->ann->learning_rate);
post("connection_rate=%f", x->ann->connection_rate);
post("total_neurons=%i", x->ann->total_neurons);
post("total_connections=%i", x->ann->total_connections);
post("last error=%i", x->ann->errstr);
if (x->filename == 0)
{
post("ann_mlp:filename not set");
} else
{
post("filename=%s", x->filename->s_name);
}
}
}
static void ann_mlp_print_ann_print(t_ann_mlp *x)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_print_connections(x->ann);
fann_print_parameters(x->ann);
}
static void *ann_mlp_new(t_symbol *s, int argc, t_atom *argv)
{
t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class);
x->l_out = outlet_new(&x->x_obj, &s_list);
x->f_out = outlet_new(&x->x_obj, &s_float);
x->desired_error = (float)0.001;
x->max_iterations = 500000;
x->iterations_between_reports = 1000;
x->mode=RUN;
x->x_canvas = canvas_getcurrent();
x->filename = NULL;
x->filenametrain = NULL;
x->ann = NULL;
x->input = NULL;
x->output = NULL;
x->out_float = NULL;
if (argc>0) {
x->filename = atom_gensym(argv);
ann_mlp_load_ann_from_file(x, NULL);
}
return (void *)x;
}
void ann_mlp_setup(void) {
post("");
post("ann_mlp: multilayer perceptron for PD");
post("version: "VERSION"");
post("compiled: "__DATE__);
post("author: Davide Morelli");
post("contact: info@davidemorelli.it www.davidemorelli.it");
ann_mlp_class = class_new(gensym("ann_mlp"),
(t_newmethod)ann_mlp_new,
(t_method)ann_mlp_free, sizeof(t_ann_mlp),
CLASS_DEFAULT, A_GIMME, 0);
// general..
class_addmethod(ann_mlp_class, (t_method)ann_mlp_help, gensym("help"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_createFann, gensym("create"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_train, gensym("train"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_run, gensym("run"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_mode, gensym("setmode"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_train_on_file, gensym("train-on-file"), A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_manage_list, gensym("data"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_filename, gensym("filename"), A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_load_ann_from_file, gensym("load"),A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_save_ann_to_file, gensym("save"),A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_details, gensym("details"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_print, gensym("print"), 0);
// change training parameters
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_desired_error, gensym("desired_error"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_learnrate, gensym("learnrate"), A_FLOAT, 0);
// change training and activation algorithms
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_hidden, gensym("set_activation_function_hidden"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_hidden, gensym("set_activation_steepness_hidden"), A_FLOAT, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_output, gensym("set_activation_steepness_output"), A_FLOAT, 0);
// initialization:
class_addmethod(ann_mlp_class, (t_method)ann_mlp_randomize_weights, gensym("randomize_weights"),A_GIMME, 0);
// the most important one: running the ann
class_addlist(ann_mlp_class, (t_method)ann_mlp_manage_list);
}