[knn] by Fujinaga and MacMillan, initial commit

svn path=/trunk/externals/postlude/; revision=9446

5 files changed, 935 insertions, 0 deletions

diff --git a/knn/LICENSE b/knn/LICENSE
new file mode 100644
index 0000000..60549be
--- /dev/null
+++ b/knn/LICENSE
@@ -0,0 +1,340 @@
+		    GNU GENERAL PUBLIC LICENSE
+		       Version 2, June 1991
+
+ Copyright (C) 1989, 1991 Free Software Foundation, Inc.
+                       59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
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diff --git a/knn/README b/knn/README
new file mode 100644
index 0000000..c87f31c
--- /dev/null
+++ b/knn/README
@@ -0,0 +1,20 @@
+k-NN object for PD
+
+Introduction
+
+The k Nearest Neighbor algorithm is a simple and robust classification
+algorithm.  It compares an unknown feature vector to a database by
+calculating distance and assigns the classification of the k closest
+database entries to the unknown vector.  This implementation for PD
+takes inputs of PD lists for both learning and classification.
+Currently the length of the feature vectors and maximum number of
+classes is fixed at compile time.
+
+Feature Weighting
+
+This implementation also includes feature weighting to allow control
+over the relative importance of each feature.  This has been shown to
+provide significant improvements to the performance of the k-NN
+algorithm.
+
+Karl MacMillan <karlmac@peabody.jhu.edu>
\ No newline at end of file
diff --git a/knn/help-knn.pd b/knn/help-knn.pd
new file mode 100644
index 0000000..14b8e5c
--- /dev/null
+++ b/knn/help-knn.pd
@@ -0,0 +1,23 @@
+#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;
diff --git a/knn/knn.c b/knn/knn.c
new file mode 100644
index 0000000..592f982
--- /dev/null
+++ b/knn/knn.c
@@ -0,0 +1,475 @@
+/*
+ * Copyright (C) 2000 Ichiro Fujinaga and Karl MacMillan
+ *
+ * Minor updates and maintenance (2008) Jamie Bullock <jamie@postlude.co.uk>
+ *
+ *
+ * This program is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU General Public License
+ * as published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ * 
+ * You should have received a copy of the GNU General Public License
+ * along with this program; if not, write to the Free Software
+ * Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
+ */
+
+/* This is a simple KNN object for PureData */
+
+#include <stdio.h>
+#include <math.h>
+#include <stdlib.h>
+#include <errno.h>
+#include <string.h>
+#include <assert.h>
+#include <float.h>
+#include "m_pd.h"
+
+
+#undef N_FEATURES
+#define N_FEATURES 40 /* FIXME - this really needs to be in the file somehow */
+#define MAX_N_CLASSES 100  /* number of instruments */
+#define TRUE 1
+#define FALSE 0
+
+#define ABS(x) (((x) > 0) ? (x) : -(x))
+
+typedef struct _nn {
+   int index;			/* where in the feature db */
+   float dist;			/* distance from the unknown */
+} NN;
+
+typedef struct _feature {
+   unsigned long id;
+   unsigned long symbol_id;
+   float *feature;
+} FEATURE;
+
+static t_class *knn_class;	/* class pointer for PD */
+
+typedef struct _knn {
+   t_object x_obj;		/* object pointer for PD */
+   t_outlet *out;		/* this outputs the instrument class 
+				 * (a float) */
+   t_outlet *loo;		/* this is used for debugging - it outputs a 
+				 * feature vec */
+   FEATURE *feature_db;		/* the database of features */
+   FEATURE unknown;
+   int db_size;		/* size of the features db */
+   float mean[N_FEATURES];	/* for normalization */
+   float stdev[N_FEATURES];	/* for normalization */
+   int k;			/* number of k to use */
+   NN *knn;			/* array of nearest neighbors of length k */
+   float wt[N_FEATURES];	/* the feature weight vector */
+   int normalize;		/* flag for normalization */
+   int learn_mode;		/* flag for learn (i.e. add) mode */
+   int learn_symbol_id;		/* the symbol id to add if in learn mode */
+} t_knn;
+
+static void *knn_new(t_floatarg f);
+static int find_nn(t_knn * x, FEATURE * unknown, FEATURE * database,
+		   int size, float *weights, int k);
+static NN *k_nn_new(int k);
+static int majority(t_knn * x);
+static float k_nn(t_knn * x, int candidate, float dist);
+static float calc_dist(FEATURE * unknown, FEATURE * known, float *weights,
+		       float min_dist);
+static void get_timbre_features(t_knn * x, char *fname);
+static void normalize_database(t_knn * x, FEATURE * Features, int size);
+static void normalize(t_knn * x, FEATURE * f);
+static void add_feature(t_knn * x, float *features, int id);
+
+static void *knn_new(t_floatarg f)
+{
+   int i;
+   t_knn *x = (t_knn *) pd_new(knn_class);
+   x->out = outlet_new(&x->x_obj, &s_float);
+   x->loo = outlet_new(&x->x_obj, gensym("list"));
+
+   /* create the NN for the correct size of k */
+   if (f == 0)
+      x->k = 1;	/* default */
+   else
+      x->k = (int) f;
+   x->knn = k_nn_new(x->k);
+
+   /* create space for the unknown feature */
+   x->unknown.feature = (float *) calloc(sizeof(float), N_FEATURES);
+
+   x->db_size = 0;
+   x->feature_db = NULL;
+   x->normalize = FALSE;
+   x->learn_mode = FALSE;
+
+   for (i = 0; i < N_FEATURES; i++) {
+      x->wt[i] = 1;
+      x->mean[i] = 0;
+      x->stdev[i] = 1;
+   }
+   return (x);
+}
+
+static void knn_free(t_knn * x)
+{
+   free(x->knn);
+   free(x->feature_db);
+}
+
+static void knn_list(t_knn * x, t_symbol * s, int argc, t_atom * argv)
+{
+   int i;
+   int answer;
+   int id;
+   int add;
+   t_atom *a;
+   float f[N_FEATURES];  // Is this still used? 00/12/04 IF
+
+   /* read in the features from the PD list */
+   for (i = 0, a = argv; i < argc; i++, a++) {
+      if (a->a_type == A_FLOAT)
+	 f[i] = x->unknown.feature[i] = a->a_w.w_float;
+      else {
+	 f[i] = x->unknown.feature[i] = 0;
+	 post("KNN: error - wrong type for list elem %d %d", i,
+	      a->a_w.w_float);
+      if (isinf(x->unknown.feature[i]))
+	 f[i] = x->unknown.feature[i] = 0;
+      }
+   }
+
+   if (x->learn_mode == TRUE) {
+      add_feature(x, x->unknown.feature, x->learn_symbol_id);
+   } else if (x->feature_db != NULL) {
+      post("Looking for answer");
+      if (x->normalize == TRUE)
+	 normalize(x, &x->unknown);
+      answer =
+	  find_nn(x, &x->unknown, x->feature_db, x->db_size, x->wt, 1);
+post("ANSWER: %d", answer);
+      outlet_float(x->out, answer);
+   } else
+      post("No database loaded!");
+}
+
+static void add_feature(t_knn * x, float *features, int id)
+{
+   int i;
+
+   if (x->feature_db == NULL)
+      x->feature_db = (FEATURE *) calloc(sizeof(FEATURE), 1);
+   else
+      x->feature_db = (FEATURE *) realloc(x->feature_db, sizeof(FEATURE) *
+					  (x->db_size + 1));
+   assert(x->feature_db != NULL);
+
+   x->feature_db[x->db_size].feature =
+       (float *) calloc(sizeof(float), N_FEATURES);
+   x->feature_db[x->db_size].symbol_id = id;
+   for (i = 0; i < N_FEATURES; i++)
+      x->feature_db[x->db_size].feature[i] = features[i];
+   x->db_size++;
+   if (x->learn_mode == TRUE)
+      post("Added entry for instrument %d - db now has %d entries.",
+	   id, x->db_size);
+}
+
+static void knn_loo(t_knn * x, float f)
+{
+   int i;
+   t_atom at[N_FEATURES];
+
+   if ((int) f >= 0 && (int) f <= x->db_size - 1) {
+      for (i = 0; i < N_FEATURES; i++) {
+	 SETFLOAT(&at[i], x->feature_db[(int) f].feature[i]);
+      }
+      outlet_list(x->loo, 0, N_FEATURES, at);
+      outlet_float(x->out, x->feature_db[(int) f].symbol_id);
+
+      post("KNN: output instrument %d", x->feature_db[(int) f].symbol_id);
+   } else
+      post("Index out of range");
+}
+
+static void knn_set_normal(t_knn * x, float f)
+{
+   if ((int) f == FALSE)
+      x->normalize = FALSE;
+   else if ((int) f == TRUE)
+      x->normalize = TRUE;
+   post("set normalize %d", x->normalize);
+}
+
+static void knn_set_learn(t_knn * x, float mode, float symbol)
+{
+   if ((int) mode == TRUE) {
+      x->learn_mode = TRUE;
+      x->learn_symbol_id = (int) symbol;
+      post("Now in learn mode for instrument %d", (int) symbol);
+   } else {
+      x->learn_mode = FALSE;
+      post("Learn mode deactivated");
+   }
+}
+
+static void knn_free_db(t_knn * x)
+{
+   int i;
+   for (i = 0; i < x->db_size; i++) {
+      free(x->feature_db[i].feature);
+   }
+   free(x->feature_db);
+   x->db_size = 0;
+   x->feature_db = NULL;
+   post("Database cleared");
+}
+
+static void knn_save(t_knn * x, t_symbol * s)
+{
+   FILE *fp;
+   int i, j;
+
+   fp = fopen(s->s_name, "w");
+   if (fp == NULL) {
+      post("Error saving file %s", strerror(errno));
+      return;
+   }
+
+   for (i = 0; i < x->db_size; i++) {
+      fprintf(fp, "%i", (int) x->feature_db[i].symbol_id);
+
+      for (j = 0; j < N_FEATURES; j++) {
+	 fprintf(fp, " %g", x->feature_db[i].feature[j]);
+      }
+      fprintf(fp, "\n");
+   }
+   fclose(fp);
+   post("File saved to %s", s->s_name);
+
+}
+
+static void knn_read(t_knn * x, t_symbol * s)
+{
+   if (x->feature_db != NULL)
+      knn_free_db(x);
+   get_timbre_features(x, s->s_name);
+}
+
+static void knn_read_wt(t_knn * x, t_symbol * s)
+{
+   FILE *fp;
+   int ret, count = 0;
+
+   fp = fopen(s->s_name, "r");
+   if (fp == NULL) {
+      post("Error reading weights %s", strerror(errno));
+      return;
+   }
+   printf("Reading weights from %s . . . ", s->s_name);
+   while (1) {
+      ret = fscanf(fp, "%g", &x->wt[count]);
+      if (ret != 1)
+	 break;
+      count++;
+      if (count > N_FEATURES) {
+	 post("too many weights!");
+	 return;
+      }
+   }
+   fclose(fp); /* JB-070505 - close the file handle */
+   printf("done\n");
+}
+
+void knn_setup(void)
+{
+   knn_class = class_new(gensym("knn"), (t_newmethod) knn_new,
+			 (t_method) knn_free, sizeof(t_knn), 0, A_DEFFLOAT,
+			 0);
+   class_addmethod(knn_class, (t_method) knn_loo, gensym("loo"), A_FLOAT,
+		   0);
+   class_addmethod(knn_class, (t_method) knn_set_normal, gensym("normal"),
+		   A_FLOAT, 0);
+   class_addmethod(knn_class, (t_method) knn_set_learn, gensym("learn"),
+		   A_FLOAT, A_DEFFLOAT, 0);
+   class_addmethod(knn_class, (t_method) knn_save, gensym("save"),
+		   A_SYMBOL, 0);
+   class_addmethod(knn_class, (t_method) knn_read, gensym("read"),
+		   A_SYMBOL, 0);
+   class_addmethod(knn_class, (t_method) knn_free_db, gensym("clear"), 0);
+   class_addmethod(knn_class, (t_method) knn_read_wt,
+		   gensym("readweights"), A_SYMBOL, 0);
+   class_addlist(knn_class, knn_list);
+}
+
+static int find_nn(t_knn * x, FEATURE * unknown, FEATURE * database,
+		   int size, float *weights, int k)
+{
+   int i, min = 1;
+   float dist = 0, min_dist = 9e100;	// A BIG number
+
+   for (i = 0; i < x->k; i++) {
+      x->knn[i].index = 0;
+      x->knn[i].dist = 9e10;
+   }
+
+   for (i = 0; i < x->db_size; i++) {
+
+      dist = calc_dist(unknown, &database[i], weights, min_dist);
+      if (dist < min_dist) {
+	 min_dist = dist;
+	 min = i;
+	 min_dist = k_nn(x, min, min_dist);
+      }
+   }
+
+   min = majority(x);
+
+   return min;
+}
+
+static NN *k_nn_new(int k)
+{
+   int i;
+   NN *nn;
+
+   nn = (NN *) malloc(sizeof(NN) * k);
+   assert(nn != NULL);
+   for (i = 0; i < k; i++) {
+      nn[i].index = -1;
+      nn[i].dist = FLT_MAX;
+   }
+   return (nn);
+}
+
+
+static int majority(t_knn * x)
+{
+/* returns class number. NB: no tie breaker */
+   int i, max_i = 0, max = 0;
+   int classes[MAX_N_CLASSES];
+
+   for (i = 0; i < MAX_N_CLASSES; i++)
+      classes[i] = 0;
+
+   for (i = 0; i < x->k; i++)
+      classes[x->feature_db[x->knn[i].index].symbol_id] += 1;
+
+   for (i = 0; i < MAX_N_CLASSES; i++) {
+      if (classes[i] > max) {
+	 max = classes[i];
+	 max_i = i;
+      }
+   }
+   return (max_i);
+}
+
+static float k_nn(t_knn * x, int candidate, float dist)
+{
+   int i, insert;
+
+   for (i = 0; i < x->k; i++) {
+      if (dist < x->knn[i].dist)
+	 break;
+   }
+   if (i < x->k) {
+      insert = i;
+
+      for (i = x->k - 1; i > insert; i--)
+	 x->knn[i] = x->knn[i - 1];
+
+      x->knn[insert].index = candidate;
+      x->knn[insert].dist = dist;
+   }
+   return (x->knn[x->k - 1].dist);
+}
+
+static float calc_dist(FEATURE * unknown, FEATURE * known, float *weights,
+		       float min_dist)
+{
+   float dist = 0;
+   int i;
+
+   for (i = 0; i < N_FEATURES && dist < min_dist; i++) {
+      /* Euclidean w/o the sqrt */
+      dist += (weights[i]) *
+	  (unknown->feature[i] - known->feature[i]) *
+	  (unknown->feature[i] - known->feature[i]);
+
+   }
+   return ((float) dist);
+}
+
+static void normalize_database(t_knn * x, FEATURE * Features, int size)
+{
+
+   int i, k;
+   float sum, sum2;
+
+   for (k = 0; k < N_FEATURES; k++) {
+      sum = 0;
+      sum2 = 0;
+      for (i = 0; i < size; i++) {
+	 sum += Features[i].feature[k];
+	 sum2 += Features[i].feature[k] * Features[i].feature[k];
+      }
+      x->mean[k] = sum / size;
+      x->stdev[k] = sqrt((size * sum2 - sum * sum) / (size * (size - 1)));
+
+      if (x->stdev[k] < 0.00001)
+	 x->stdev[k] = 0.00001;
+      if (x->normalize == TRUE) {
+	 for (i = 0; i < size; i++)
+	    Features[i].feature[k] =
+		(Features[i].feature[k] - x->mean[k]) / x->stdev[k];
+      }
+   }
+}
+
+static void normalize(t_knn * x, FEATURE * f)
+{
+   int i;
+
+   for (i = 0; i < N_FEATURES; i++)
+      f->feature[i] = (f->feature[i] - x->mean[i]) / x->stdev[i];
+}
+
+
+void get_timbre_features(t_knn * x, char *fname)
+{
+   FILE *fp;
+   int i, j, ret, instr = 0;
+   float val;
+   static char last_name[20], name[20];
+   float features[N_FEATURES];
+   int id = 0;
+   int lastpitch = 0;
+
+   printf("Reading %s . . . ", fname);
+
+   fp = fopen(fname, "r");
+   if (fp == NULL) {
+      post("\nError opening file - %s", strerror(errno));
+      return;
+   }
+   while (1) {
+      ret = fscanf(fp, "%d", &instr);
+
+      if (ret != 1)
+	 break;
+
+      for (i = 0; i < N_FEATURES; i++) {
+	 fscanf(fp, "%g ", &features[i]);
+         if (isinf(features[i]))
+           features[i] = 0.0;
+      }
+
+      add_feature(x, features, instr);
+   }
+   printf("done.\nDatabase contains %d entries.\n", x->db_size);
+   fclose(fp);
+   normalize_database(x, x->feature_db, x->db_size);
+}
diff --git a/knn/makefile b/knn/makefile
new file mode 100644
index 0000000..f21322c
--- /dev/null
+++ b/knn/makefile
@@ -0,0 +1,77 @@
+NAME=knn
+CSYM=knn
+
+current: pd_linux
+
+# ----------------------- NT -----------------------
+
+pd_nt: $(NAME).dll
+
+.SUFFIXES: .dll
+
+PDNTCFLAGS = /W3 /WX /DNT /DPD /nologo
+VC="C:\Program Files\Microsoft Visual Studio\Vc98"
+
+PDNTINCLUDE = /I. /I\tcl\include /I\ftp\pd\src /I$(VC)\include
+
+PDNTLDIR = $(VC)\lib
+PDNTLIB = $(PDNTLDIR)\libc.lib \
+	$(PDNTLDIR)\oldnames.lib \
+	$(PDNTLDIR)\kernel32.lib \
+	\ftp\pd\bin\pd.lib 
+
+.c.dll:
+	cl $(PDNTCFLAGS) $(PDNTINCLUDE) /c $*.c
+	link /dll /export:$(CSYM)_setup $*.obj $(PDNTLIB)
+
+# ----------------------- IRIX 5.x -----------------------
+
+pd_irix5: $(NAME).pd_irix5
+
+.SUFFIXES: .pd_irix5
+
+SGICFLAGS5 = -o32 -DPD -DUNIX -DIRIX -O2
+
+SGIINCLUDE =  -I../../src
+
+.c.pd_irix5:
+	cc $(SGICFLAGS5) $(SGIINCLUDE) -o $*.o -c $*.c
+	ld -elf -shared -rdata_shared -o $*.pd_irix5 $*.o
+	rm $*.o
+
+# ----------------------- IRIX 6.x -----------------------
+
+pd_irix6: $(NAME).pd_irix6
+
+.SUFFIXES: .pd_irix6
+
+SGICFLAGS6 = -n32 -DPD -DUNIX -DIRIX -DN32 -woff 1080,1064,1185 \
+	-OPT:roundoff=3 -OPT:IEEE_arithmetic=3 -OPT:cray_ivdep=true \
+	-Ofast=ip32
+
+.c.pd_irix6:
+	cc $(SGICFLAGS6) $(SGIINCLUDE) -o $*.o -c $*.c
+	ld -n32 -shared -rdata_shared -o $*.pd_irix6 $*.o
+	rm $*.o
+
+# ----------------------- LINUX i386 -----------------------
+
+pd_linux: $(NAME).pd_linux
+
+.SUFFIXES: .pd_linux
+
+LINUXCFLAGS = -DPD -O2 -funroll-loops -fomit-frame-pointer \
+    -Wall -W -Wshadow -Wstrict-prototypes \
+    -Wno-unused -Wno-parentheses -Wno-switch
+
+LINUXINCLUDE =  -I/usr/lib/pd/include/
+
+.c.pd_linux:
+	cc $(LINUXCFLAGS) $(LINUXINCLUDE) -g -o $*.o -c $*.c
+	ld -export_dynamic  -shared -o $*.pd_linux $*.o -lc -lm
+	strip --strip-unneeded $*.pd_linux
+	rm $*.o
+	cp $*.pd_linux ../.
+
+clean:
+	rm -f *.o *.pd_* so_locations