Revision: 9446 http://pure-data.svn.sourceforge.net/pure-data/?rev=9446&view=rev Author: postlude Date: 2008-02-16 03:22:24 -0800 (Sat, 16 Feb 2008)

Log Message: ----------- [knn] by Fujinaga and MacMillan, initial commit

Added Paths: ----------- trunk/externals/postlude/knn/ trunk/externals/postlude/knn/LICENSE trunk/externals/postlude/knn/README trunk/externals/postlude/knn/help-knn.pd trunk/externals/postlude/knn/knn.c trunk/externals/postlude/knn/makefile

Added: trunk/externals/postlude/knn/LICENSE =================================================================== --- trunk/externals/postlude/knn/LICENSE (rev 0) +++ trunk/externals/postlude/knn/LICENSE 2008-02-16 11:22:24 UTC (rev 9446) @@ -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 + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. By contrast, the GNU General Public +License is intended to guarantee your freedom to share and change free +software--to make sure the software is free for all its users. This +General Public License applies to most of the Free Software +Foundation's software and to any other program whose authors commit to +using it. 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Added: trunk/externals/postlude/knn/README =================================================================== --- trunk/externals/postlude/knn/README (rev 0) +++ trunk/externals/postlude/knn/README 2008-02-16 11:22:24 UTC (rev 9446) @@ -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

Added: trunk/externals/postlude/knn/help-knn.pd =================================================================== --- trunk/externals/postlude/knn/help-knn.pd (rev 0) +++ trunk/externals/postlude/knn/help-knn.pd 2008-02-16 11:22:24 UTC (rev 9446) @@ -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;

Added: trunk/externals/postlude/knn/knn.c =================================================================== --- trunk/externals/postlude/knn/knn.c (rev 0) +++ trunk/externals/postlude/knn/knn.c 2008-02-16 11:22:24 UTC (rev 9446) @@ -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); +}

Added: trunk/externals/postlude/knn/makefile =================================================================== --- trunk/externals/postlude/knn/makefile (rev 0) +++ trunk/externals/postlude/knn/makefile 2008-02-16 11:22:24 UTC (rev 9446) @@ -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

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