6 files changed, 631 insertions, 0 deletions
diff --git a/examples/ann_mlp_example4/gen_trainfile-help.pd b/examples/ann_mlp_example4/gen_trainfile-help.pd
new file mode 100755
index 0000000..6c3f5bc
--- /dev/null
+++ b/examples/ann_mlp_example4/gen_trainfile-help.pd
@@ -0,0 +1,39 @@
+#N canvas 711 130 586 632 10;
+#X text 198 23 ::::_gen_trainfile_::::;
+#X text 46 68 This abstraction generates a trainig file for ann_mlp
+and ann_td;
+#X obj 351 443 gen_trainfile;
+#X msg 437 154 test.txt;
+#X text 313 153 1) set filename:;
+#X msg 394 215 4 2 1;
+#X text 249 217 2) set file header:;
+#X text 183 241 4 = nr. of training datasets;
+#X text 184 255 2 = inputs of the neural net;
+#X text 184 269 1 = output of the neural net;
+#X msg 150 393 0 0 0;
+#X msg 197 393 0 1 1;
+#X msg 247 393 1 0 1;
+#X msg 293 393 1 1 0;
+#X text 46 116 Example:;
+#X text 36 311 3) send training data (first inputs \, then output)
+;
+#X text 58 327 because you have now 4 training datasets you;
+#X text 57 342 must pass 4 lists !!!;
+#X text 162 374 a;
+#X text 209 374 b;
+#X text 259 375 c;
+#X text 302 374 d;
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+-1;
+#X text 332 502 file is ready:;
+#X text 238 468 added datasets:;
+#X text 150 573 (c) 2005 \, Georg Holzmann <grh@mur.at>;
+#X connect 2 0 22 0;
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+#X connect 3 0 2 2;
+#X connect 5 0 2 1;
+#X connect 10 0 2 0;
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+#X connect 13 0 2 0;
diff --git a/examples/ann_mlp_example4/gen_trainfile.pd b/examples/ann_mlp_example4/gen_trainfile.pd
new file mode 100755
index 0000000..0f134e4
--- /dev/null
+++ b/examples/ann_mlp_example4/gen_trainfile.pd
@@ -0,0 +1,180 @@
+#N canvas 60 0 578 726 10;
+#X obj 60 466 inlet;
+#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;
+#X obj 230 467 inlet;
+#X text 229 448 header;
+#X text 40 169 INLETS:;
+#X obj 419 466 inlet;
+#X text 410 447 filename;
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+#X obj 30 29 inlet;
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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;
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+#X obj 181 206 s \$0-ok_for_new_datasets;
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+#X text 89 74 bang;
+#X text 248 125 modulo;
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+#X obj 20 22 inlet;
+#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;
+#X obj 20 141 sel 1;
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+#X restore 289 75 pd WARNINGS;
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+#X restore 60 495 pd write_datasets;
+#X text 39 118 NEEDED EXTERNALS: zexy and iemlib (http://pd.iem.at)
+;
+#N canvas 832 484 389 329 write_file 0;
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+#X text 40 137 HOW TO Use it: (see also help file);
+#X obj 60 561 outlet;
+#X obj 222 563 outlet;
+#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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diff --git a/examples/ann_mlp_example4/multidim_net.pd b/examples/ann_mlp_example4/multidim_net.pd
new file mode 100755
index 0000000..41acb5f
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+++ b/examples/ann_mlp_example4/multidim_net.pd
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+#X msg 183 119 print;
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+#X text 37 27 add new datasets to the vector:;
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+#X text 91 404 2) step through all;
+#X text 85 108 1) create net;
+#X text 106 151 a) get nr. of inputs:;
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+#X msg 122 183 get 0;
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+#X text 297 151 b) get nr. of outputs:;
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+#X text 65 601 3) train the net on it;
+#X msg 202 512 tmp/trainfile.dat;
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+#X msg 122 314 create \$1 \$2 3 \$3 1 0.7;
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+#X msg 364 583 set_activation_function_hidden FANN_LINEAR;
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+#X text 37 178 generate new net and train it on the datasets:;
+#X text 35 263 the neural net:;
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+is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
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+#X connect 31 0 21 0;
+#X connect 31 0 22 0;
+#X connect 31 0 23 0;
+#X connect 31 0 24 0;
+#X connect 31 0 25 0;
+#X connect 31 0 26 0;
+#X connect 31 0 27 0;
+#X connect 32 0 17 1;
+#X connect 43 0 6 0;
+#X connect 44 0 4 0;
diff --git a/examples/ann_mlp_example4/test.txt b/examples/ann_mlp_example4/test.txt
new file mode 100755
index 0000000..c5cdf2b
--- /dev/null
+++ b/examples/ann_mlp_example4/test.txt
@@ -0,0 +1,7 @@
+3 2 2
+4 9
+2 1
+5 3
+2 1
+2 4
+0 1
diff --git a/examples/ann_mlp_example4/trainfile.dat b/examples/ann_mlp_example4/trainfile.dat
new file mode 100755
index 0000000..a053f7d
--- /dev/null
+++ b/examples/ann_mlp_example4/trainfile.dat
@@ -0,0 +1,9 @@
+4 2 4
+-1 -1
+1 0 0 0
+1 -1
+0 1 0 0
+-1 1
+0 0 1 0
+1 1
+0 0 0 1
diff --git a/examples/ann_mlp_example4/trainfile2.dat b/examples/ann_mlp_example4/trainfile2.dat
new file mode 100755
index 0000000..0d3eea7
--- /dev/null
+++ b/examples/ann_mlp_example4/trainfile2.dat
@@ -0,0 +1,9 @@
+4 2 24
+-0.5248 -0.4752
+-0.690478 -0.328445 0.787627 0.457685 0.785165 -0.609361 2.73531 -0.871147 -0.654311 -0.979694 -0.782768 -0.476533 -0.880441 -0.970331 -0.694041 -0.926005 -0.116742 -0.992307 -0.907731 0.220775 -0.719765 -1 1 -0.103331
+-0.6039 0.6138
+-0.107354 -0.437122 0.571922 0.56767 -0.0480328 -0.0436363 0.015686 -0.937416 -0.623195 -0.99371 -0.854111 -0.385827 -0.714651 -0.989703 -0.90756 -0.996025 -0.992405 -1 -1 0.405901 -0.0118679 -1 1 -0.0133747
+0.5545 0.6039
+-0.272313 -0.036396 0.248606 0.722645 -0.00976638 -0.905512 0.210309 -0.969341 -0.664621 -0.99696 -0.904754 -0.325938 -0.930082 -0.99386 -0.956322 -0.917093 0.0444865 -0.998078 -0.987068 0.468123 0.836558 -1 1 -0.024074
+0.5446 -0.5643
+0.480176 0.343487 0.0527416 0.789494 -0.45131 0.074421 0.647385 -0.942653 -0.760625 -0.993795 -0.840143 -0.820498 -0.926746 -0.992776 -0.938142 -0.830174 -0.155173 -1 -1 0.204588 -0.0142726 -1 1 -0.0610782