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#X text 95 228 <nr of tests> <nr of inputs> <nr of outputs>;
#X text 98 248 nr of tests = with how many datasets;
#X text 197 262 should be trained;
#X text 97 282 nr of inputs = inputs of the neural net;
#X text 98 304 nr of outputs = outputs of the neural net;
#X text 100 380 <input1> <input2> ... <output1> <output2> ...;
#X obj 418 542 textfile;
#X text 59 190 1) set filename at 3.inlet;
#X text 59 210 2) set the following list at 2 inlet:;
#X text 61 334 3) pass the datasets as a list (one after the other)
into 1.inlet \, one list contains the input and desired output values
\, like:;
#X text 50 443 datalist;
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#X text 229 448 header;
#X text 40 169 INLETS:;
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#X text 410 447 filename;
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#X obj 30 256 s \$0-textfile;
#X msg 74 97 clear;
#X msg 52 135 rewind;
#X msg 30 165 add \$1 \$2 \$3;
#X obj 170 79 unpack f f f;
#X obj 170 111 s \$0-size;
#X obj 209 138 s \$0-ins;
#X obj 181 206 s \$0-ok_for_new_datasets;
#X msg 181 180 1;
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#X obj 273 340 s \$0-textfile;
#X obj 221 277 iem_append;
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#X obj 20 114 == 0;
#X obj 36 54 r \$0-ok_for_new_datasets;
#X obj 20 206 print gen_trainfile_WARNING;
#X obj 20 81 f 0;
#X msg 20 178 file is full or create new header;
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#X text 39 118 NEEDED EXTERNALS: zexy and iemlib (http://pd.iem.at)
;
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#X text 40 137 HOW TO Use it: (see also help file);
#X obj 60 561 outlet;
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#X text 48 583 written datasets;
#X text 216 585 file is ready and written (bang);
#X text 134 658 (c) 2005 \, Georg Holzmann <grh@mur.at>;
#X text 72 72 This abstraction generates a trainig file for ann_mlp
and ann_td.;
#X text 192 25 ::::_gen_trainfile_::::;
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