#N canvas 552 184 590 372 10;
#X obj 73 247 knn;
#X msg 142 125 normal 0;
#X msg 142 145 normal 1;
#X msg 144 200 learn 0;
#X msg 143 179 learn 1 \$1;
#X msg 145 226 save databasename;
#X msg 145 247 read databasename;
#X msg 141 101 readweights weightfilename;
#X floatatom 73 281 4 0 0;
#X text 38 30 k-NN Object This is an implementation of the k Nearest Neighbor algorithm that can be used to classify objects by a feature vector.;
#X text 318 102 Read feature weight table;
#X text 217 134 Turn on and off normalization - normalization should be on when reading a file and off when training;
#X text 233 181 Turn learning mode on and off - the second argument to 'learn 1' is number of the class for the input.;
#X text 281 236 read and save the database as a textfile;
#X connect 0 0 8 0;
#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 connect 6 0 0 0;
#X connect 7 0 0 0;