From b0bc91a579c22b7dfe934095f28cc04211860f81 Mon Sep 17 00:00:00 2001 From: Davide Morelli Date: Wed, 18 May 2005 19:11:14 +0000 Subject: files moved in examples/ann_som folder svn path=/trunk/externals/ann/; revision=3021 --- examples/ann_som.pd | 114 ----------------------------- examples/test.som | 206 ---------------------------------------------------- 2 files changed, 320 deletions(-) delete mode 100644 examples/ann_som.pd delete mode 100644 examples/test.som (limited to 'examples') diff --git a/examples/ann_som.pd b/examples/ann_som.pd deleted file mode 100644 index 9f03979..0000000 --- a/examples/ann_som.pd +++ /dev/null @@ -1,114 +0,0 @@ -#N canvas 50 -127 640 687 10; -#X msg 131 495 print; -#X msg 132 528 new 5 8 8; -#X msg 127 99 init; -#X msg 128 274 train; -#X msg 129 296 test; -#X msg 128 387 write; -#X obj 70 559 ann_som 4 9 10; -#X msg 70 49 1 0 0 1; -#X msg 70 68 0 1 0 1; -#X msg 70 87 2 1 0 0; -#X msg 128 118 init 0.5; -#X msg 128 138 init 1 0.5 0 0.5; -#X text 234 101 init all weights with "0"; -#X text 235 120 init all weights with "0.5"; -#X text 235 137 init weights for each sensor; -#X msg 128 163 learn 1; -#X msg 128 197 learn 1 0.9 0.1; -#X text 226 163 set learning rate to 1; -#X msg 128 180 learn 0.5 0.999; -#X text 227 179 set learning rate to 0.5 and factor to 0.999; -#X text 227 197 set learning rate to 1 \, factor to 0.9 and offset to 0.1; -#X msg 128 214 neighbour 1; -#X msg 128 231 neighbour 0.5 0.999; -#X msg 128 248 neighbour 1 0.9 0.1; -#X text 248 215 set neighbourhood to 1; -#X text 249 231 set neighbourhoodto 0.5 and factor to 0.999; -#X text 249 249 set neighbourhood to 1 \, factor to 0.9 and offset to 0.1; -#X text 180 269 set som to "train" mode (learn from sensor-input and output winning neuron); -#X text 179 291 set som to "test" mode (output winning neuron for sensor-input \, but do not learn !); -#X msg 129 328 rule INSTAR; -#X msg 129 345 rule OUTSTAR; -#X msg 129 362 rule KOHONEN; -#X text 218 327 learn with IN-STAR rule; -#X text 219 345 learn with OUT-STAR rule; -#X text 219 362 learn with KOHONENrule; -#X msg 128 405 write mysom.som; -#X msg 129 429 read; -#X msg 129 447 read mysom.som; -#X text 156 68 present various data to the SOM; -#X text 203 495 for debugging; -#X text 207 530 create a new SOM with 8x8 neurons \, each having 5 sensors; -#X text 204 561 create a new SOM with 9x10 neurons \, each having 4 sensors; -#X floatatom 70 614 4 0 0; -#X text 113 618 winning neuron; -#N canvas 13 0 889 630 SOMs 0; -#X text 76 27 SOM :: Self-Organized Maps; -#X text 55 53 SOMs are "Artificial Neural Networks" \, that are trying to learn something about the data presented to them without a supervisor/teacher.; -#X text 59 118 in short:; -#X text 120 119 the neuron \, whose weight-configuration matches the presented data best is the winner (its number (counting from the lower-left corner) is sent to the output); -#X text 121 163 to match the data better the next time it is presented \, the weights of the winning neuron are adjusted.; -#X text 121 188 the weights of the neurons neighbouring the winner are adjusted to match the data too \, but not so strong as the winner's.; -#X text 121 276 lr(n+1)=lr(n)*factor; -#X text 275 277 learning_rate=lr+offset; -#X text 121 289 nb(n+1)=nb(n)*factor; -#X text 275 290 neighbourhood=nb+offset; -#X text 121 230 both neighbourhood and learning-rate (==amount of how much the weights of the winner (and \, proportional \, the weights of the neighbours) are adjusted) are decreasing recursively with time.; -#X text 119 319 thus you will sooner or (most of the time) later get a "brain map" \, where similar inputs will activate neurons in specifique regions (like there are regions for seeing and regions for hearing in our brains); -#X text 97 381 there are various rules \, how to re-adjust the weights of the neurons : in-star \, out-star and kohonen (maybe there are others \, but these i found in literature); -#X obj 607 220 +; -#X text 640 182 ...; -#X obj 579 185 * \$1; -#X obj 607 185 * \$2; -#X obj 670 185 * \$0; -#X obj 579 128 unpack 0 0 0 0 0; -#X text 602 111 n sensors; -#X text 705 186 weights 1 to n; -#X obj 579 90 inlet; -#X obj 607 288 outlet; -#X text 594 62 a neuron; -#X text 566 307 the neuron with the highest weighted sum; -#X text 567 318 matches best and is therefore the winner; -#X text 53 452 notes:; -#X text 101 453 each neuron of the SOM has n sensors. you have to present a list of n floats to the SOM to make it work; -#X text 102 482 you should init the weights for each sensor with the expected mean of the sensor values before you start training to get best and fastest results; -#X text 55 87 they were first proposed by the Finnish scientist T.Kohonen in the 80ies (i think).; -#X text 98 543 if you have no clue \, what's this all about \, maybe you do not need SOMs (which i doubt) or you should have a look at; -#X text 118 577 http://www.eas.asu.edu/~eee511; -#X text 118 591 http://www.cis.hut.fi/projects/ica; -#X connect 13 0 22 0; -#X connect 15 0 13 0; -#X connect 16 0 13 0; -#X connect 17 0 13 0; -#X connect 18 0 15 0; -#X connect 18 1 16 0; -#X connect 18 4 17 0; -#X connect 21 0 18 0; -#X restore 535 44 pd SOMs; -#X text 81 13 ann_som :: train and test Self-Organized Maps; -#X obj 73 660 ann_som test.som; -#X text 211 664 load a SOM-file; -#X connect 0 0 6 0; -#X connect 1 0 6 0; -#X connect 2 0 6 0; -#X connect 3 0 6 0; -#X connect 4 0 6 0; -#X connect 5 0 6 0; -#X connect 6 0 42 0; -#X connect 7 0 6 0; -#X connect 8 0 6 0; -#X connect 9 0 6 0; -#X connect 11 0 6 0; -#X connect 15 0 6 0; -#X connect 16 0 6 0; -#X connect 18 0 6 0; -#X connect 21 0 6 0; -#X connect 22 0 6 0; -#X connect 23 0 6 0; -#X connect 29 0 6 0; -#X connect 30 0 6 0; -#X connect 31 0 6 0; -#X connect 35 0 6 0; -#X connect 36 0 6 0; -#X connect 37 0 6 0; diff --git a/examples/test.som b/examples/test.som deleted file mode 100644 index f1074db..0000000 --- a/examples/test.som +++ 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