2 files changed, 666 insertions, 637 deletions

diff --git a/helps/ann_mlp-help.pd b/helps/ann_mlp-help.pd
index 6f59c27..37213ed 100644
--- a/helps/ann_mlp-help.pd
+++ b/helps/ann_mlp-help.pd
@@ -1,400 +1,415 @@
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-#X msg 93 128 create 3 2;

-#X msg 59 38 create 3 2 3 3 1 0.7;

-#X text 121 7 create with default values;

-#X text 236 38 specifying all;

-#X text 166 68 2 inputs 1 output;

-#X text 176 99 3 inputs 1 output;

-#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

-\, connection_rate \, learning_rate;

-#X connect 1 0 0 0;

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-#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;

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-#X connect 4 0 0 0;

-#X restore 107 180 pd run the net;

-#N canvas 0 0 619 610 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 265 255 690 335 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;

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-#X obj 52 230 outlet;

-#X msg 69 118 desired_error 0.01;

-#X msg 79 146 max_iterations 500000;

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-#X text 58 28 you can change training parameters. see FANN manual for

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-#X restore 151 179 pd training params;

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-#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

-;

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-#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;

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-#X connect 13 0 0 0;

-#X restore 128 258 pd other commands;

-#N canvas 0 0 653 513 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_mlp test.net];

-#X connect 1 0 0 0;

-#X connect 2 0 0 0;

-#X connect 3 0 0 0;

-#X connect 8 0 0 0;

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-#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 156 303 pd tips;

-#X text 270 353 an interface to fann classes (http://fann.sourceforge.net)

-;

-#X text 272 371 by Davide Morelli - info@davidemorelli.it;

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-#X restore 149 284 pd outputs;

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-#X obj 260 223 + 1;

-#X obj 239 257 nbx 5 14 -1e+037 1e+037 0 0 empty empty how_many_patterns

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-#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 259 72 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;

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-#X msg 260 36 100;

-#X msg 326 342 write test.txt cr;

-#X text 293 224 comment;

-#N canvas 262 68 647 603 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 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 connect 18 0 0 0;

-#X restore 86 42 pd build training file;

-#X msg 88 74 train-on-file test.txt;

-#X text 285 45 build a training file;

-#X text 287 74 train the nn with the training file;

-#X obj 56 139 outlet;

-#X connect 1 0 4 0;

-#X restore 79 103 pd train you net using a train file;

-#N canvas 120 72 892 558 train 0;

-#X obj 55 487 outlet;

-#X msg 60 31 train;

-#X text 126 33 1- set the train mode;

-#X text 192 120 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 190 464 run;

-#X obj 198 170 tgl 15 0 empty empty in1 0 -6 0 8 -262144 -1 -1 0 1

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-#X obj 228 170 tgl 15 0 empty empty in2 0 -6 0 8 -262144 -1 -1 0 1

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-1;

-#X obj 148 169 bng 15 250 50 0 empty empty train! 0 -6 0 8 -262144

--1 -1;

-#X text 312 160 set inputs and output value \, then send the list clicking

-on the "train!" bang;

-#X msg 316 261 create 2 1;

-#X text 227 464 3- when you are ready switch again to run mode before

-exiting;

-#X text 315 226 NOTE1: before training with this example you should

-have created a nn with 2 ins and 1 out with a command like:;

-#N canvas 255 158 517 436 autotrain 0;

-#X obj 89 286 outlet;

-#X obj 85 87 metro 10;

-#X obj 85 38 tgl 15 0 empty empty toggle_training 0 -6 0 8 -262144

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-#X obj 82 112 random 4;

-#X obj 83 138 sel 0 1 2 3;

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-#X obj 256 124 + 1;

-#X floatatom 226 149 8 0 0 0 - - -;

-#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 connect 9 0 11 0;

-#X connect 10 0 9 1;

-#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;

-#X connect 1 0 0 0;

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-#X connect 9 0 4 2;

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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;

-#X connect 1 0 0 0;

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-#X restore 115 119 pd train;

-#X text 190 118 train a nn;

-#X obj 103 345 print mse;

-#X obj 52 373 print out;

-#X obj 52 313 ann_mlp;

-#X text 9 2 ann_mlp: multi layer perceptrons neural networks in PD

-;

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+#N canvas 1 53 717 456 12;
+#N canvas 160 189 627 328 creation 0;
+#X obj 52 235 outlet;
+#X msg 49 10 create;
+#X msg 72 68 create 2 1;
+#X msg 81 97 create 3 1;
+#X msg 93 128 create 3 2;
+#X msg 59 38 create 3 2 3 3 1 0.7;
+#X text 121 7 create with default values;
+#X text 236 38 specifying all;
+#X text 166 68 2 inputs 1 output;
+#X text 176 99 3 inputs 1 output;
+#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
+\, connection_rate \, learning_rate;
+#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 5 0 0 0;
+#X restore 52 68 pd creation examples;
+#N canvas 83 141 728 356 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 86 180 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 113 201 690 335 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 34 162 698 395 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;
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+#X connect 5 0 0 0;
+#X connect 6 0 0 0;
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+#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 107 258 pd other commands;
+#N canvas 2 82 653 513 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_mlp test.net];
+#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 97 218 pd save the net;
+#X text 229 66 create a nn;
+#X text 223 179 run your net;
+#X text 237 215 save your net;
+#N canvas 2 82 712 542 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 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;
+#N canvas 146 200 580 411 train 0;
+#X obj 32 241 outlet;
+#N canvas 0 0 458 308 train 0;
+#N canvas 8 48 990 509 build 0;
+#X obj 65 417 textfile;
+#X msg 190 337 clear;
+#N canvas 0 0 462 312 alternate 0;
+#X obj 103 117 + 1;
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+#X obj 70 171 sel 0 1;
+#X obj 70 146 mod 2;
+#X msg 95 90 0;
+#X obj 68 31 inlet;
+#X obj 140 40 inlet;
+#X obj 140 63 bang;
+#X obj 68 55 bang;
+#X obj 65 205 outlet;
+#X obj 125 206 outlet;
+#X text 59 6 bang;
+#X text 139 18 reset to 0 without bang;
+#X connect 0 0 1 1;
+#X connect 1 0 0 0;
+#X connect 1 0 3 0;
+#X connect 2 0 9 0;
+#X connect 2 1 10 0;
+#X connect 3 0 2 0;
+#X connect 4 0 1 1;
+#X connect 5 0 8 0;
+#X connect 6 0 7 0;
+#X connect 7 0 4 0;
+#X connect 8 0 1 0;
+#X restore 58 227 pd alternate;
+#X obj 24 81 bng 15 250 50 0 empty empty write-once 0 -6 0 8 -262144
+-1 -1;
+#X obj 341 183 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
+-1;
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+#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+37 1e+37 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 259 72 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 260 16 loadbang;
+#X msg 260 36 100;
+#X msg 326 342 write test.txt cr;
+#X text 293 224 comment;
+#N canvas 262 68 647 603 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 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);
+#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;
+#X restore 86 42 pd build training file;
+#X msg 88 74 train-on-file test.txt;
+#X text 285 45 build a training file;
+#X text 287 74 train the nn with the training file;
+#X obj 56 139 outlet;
+#X connect 1 0 4 0;
+#X restore 79 103 pd train you net using a train file;
+#N canvas 120 72 892 558 train 0;
+#X obj 55 487 outlet;
+#X msg 60 31 train;
+#X text 126 33 1- set the train mode;
+#X text 192 120 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 190 464 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 312 160 set inputs and output value \, then send the list clicking
+on the "train!" bang;
+#X msg 316 261 create 2 1;
+#X text 227 464 3- when you are ready switch again to run mode before
+exiting;
+#X text 315 226 NOTE1: before training with this example you should
+have created a nn with 2 ins and 1 out with a command like:;
+#N canvas 255 158 517 436 autotrain 0;
+#X obj 89 286 outlet;
+#X obj 85 87 metro 10;
+#X obj 85 38 tgl 15 0 empty empty toggle_training 0 -6 0 8 -262144
+-1 -1 0 1;
+#X msg 101 192 0 0 0;
+#X msg 126 215 0 1 1;
+#X msg 82 168 1 0 1;
+#X msg 150 244 1 1 1;
+#X obj 82 112 random 4;
+#X obj 83 138 sel 0 1 2 3;
+#X obj 226 125 f 0;
+#X obj 256 124 + 1;
+#X floatatom 226 149 8 0 0 0 - - -;
+#X text 113 36 <--train OR untile mse is low enough;
+#X text 143 51 (you must be in train mode);
+#X connect 1 0 7 0;
+#X connect 1 0 9 0;
+#X connect 2 0 1 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 8 0;
+#X connect 8 0 5 0;
+#X connect 8 1 3 0;
+#X connect 8 2 4 0;
+#X connect 8 3 6 0;
+#X connect 9 0 10 0;
+#X connect 9 0 11 0;
+#X connect 10 0 9 1;
+#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;
+#X connect 1 0 0 0;
+#X connect 4 0 6 0;
+#X connect 5 0 0 0;
+#X connect 6 1 5 0;
+#X connect 6 2 5 1;
+#X connect 6 3 5 2;
+#X connect 7 0 0 0;
+#X connect 8 0 4 1;
+#X connect 9 0 4 2;
+#X connect 10 0 4 3;
+#X connect 11 0 4 0;
+#X connect 13 0 0 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 88 128 with a trainfile the net could be more accurate;
+#X connect 1 0 0 0;
+#X connect 2 0 0 0;
+#X restore 74 119 pd train;
+#X text 149 118 train a nn;
+#X obj 103 345 print mse;
+#X obj 52 373 print out;
+#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
+;
+#X restore 646 402 pd META;
+#X connect 0 0 14 0;
+#X connect 1 0 14 0;
+#X connect 2 0 14 0;
+#X connect 3 0 14 0;
+#X connect 10 0 14 0;
+#X connect 14 0 13 0;
+#X connect 14 1 12 0;
diff --git a/helps/ann_td-help.pd b/helps/ann_td-help.pd
index 997e19e..50d7b9a 100644
--- a/helps/ann_td-help.pd
+++ b/helps/ann_td-help.pd
@@ -1,237 +1,251 @@
-#N canvas 117 9 858 468 12;

-#N canvas 445 134 647 348 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 218 152 650 413 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 136 60 728 356 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 0 0 619 610 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 265 255 690 335 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 371 92 698 395 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 371 92 694 391 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 0 0 665 525 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 227 183 580 411 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;

-#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;

+#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;