winnow.c

使用具有增量学习的监控式学习方法。包括几个不同的分类算法。

/* Copyright (C) 2001-2002  Mikael Ylikoski
 * See the accompanying file "README" for the full copyright notice */
/**
 * @file
 * Winnow learning algorithm.
 *
 * @author  Mikael Ylikoski
 * @date    2001-2002
 */

#include <math.h>
#include <stdlib.h>
#include "multi.h"
#include "utility.h"
#include "vector.h"
#include "winnow.h"


/**
 * Winnow global data
 */
typedef struct {
    float a;		/**< Promotion rate a > 1 */
    float b;		/**< Demotion rate 0 < b < 1*/
    float f;		/**< Bias term */
} winnow_db;


/**
 * Winnow class
 */
typedef struct {
    vector *w;		/**< Weight vector */
} winnow_class;


void *
winnow_new_db (const char *opts) {
    winnow_db *wdb;

    wdb = my_malloc (sizeof(winnow_class));
    wdb->a = 2;
    wdb->b = wdb->a;
    wdb->f = 10;

    return wdb;
}


void *
winnow_new_class (void) {
    winnow_class *wc;

    wc = my_malloc (sizeof(winnow_class));
    wc->w = vector_new (10);
    if (!wc->w) {
	free (wc);
	return NULL;
    }

    return wc;
}


void *
winnow_copy (void *class) {
    winnow_class *wc;
    winnow_class *nc;

    wc = (winnow_class *)class;
    nc = my_malloc (sizeof(winnow_class));
    nc->w = vector_copy (wc->w);
    if (!nc->w) {
	free (nc);
	return NULL;
    }

    return nc;
}


/**
 * Free memory used by winnow class.
 */
void
winnow_free (void *class) {
    winnow_class *wc;

    wc = (winnow_class *)class;
    vector_free (wc->w);
    free (wc);
}


/**
 * Winnow classifier function.
 */
double
winnow_classify (void *db, void *data, vector *v) {
    winnow_class *wc;
    winnow_db *wdb;

    wc = (winnow_class *)data;
    wdb = (winnow_db *)db;
    return vector_dot_product_nonzero (wc->w, v, 1) - wdb->f;
}


/**
 * Winnow learning function.
 */
int
winnow_learn (void *db, void *data, vector *v, int class) {
    double d;
    winnow_class *wc;
    winnow_db *wdb;

    wc = (winnow_class *)data;
    wdb = (winnow_db *)db;
    d = (class + 1) * 0.5 - winnow_classify (db, data, v);
    //printf ("::  %f  ", d);

    if (class > 0)
	d = pow (wdb->a, d);
    else
	d = pow (wdb->b, d);
    //printf ("%f\n", d);
    vector_exp_mul (wc->w, v, d);

    //vector_set_min_value (wc->w, wdb->min);

    return 0;
}


void *
winnow_load_db (FILE *file) {
    int i;
    winnow_db *ndb;

    ndb = my_malloc (sizeof(winnow_db));
    i = fscanf (file, "a %f\n", &ndb->a);
    i = fscanf (file, "b %f\n", &ndb->b);
    i = fscanf (file, "bias %f\n", &ndb->f);

    return ndb;
}


void *
winnow_load_class (FILE *file) {
    winnow_class *ncl;

    ncl = my_malloc (sizeof(winnow_class));
    fscanf (file, "weights ");
    ncl->w = vector_load (file);
    if (!ncl->w) {
	free (ncl);
	return NULL;
    }
    fscanf (file, "\n");

    return ncl;
}


int
winnow_save_db (FILE *file, void *db) {
    winnow_db *ndb;

    ndb = (winnow_db *)db;
    fprintf (file, "a %f\n", ndb->a);
    fprintf (file, "b %f\n", ndb->b);
    fprintf (file, "bias %f\n", ndb->f);

    return 0;
}


int
winnow_save_class (FILE *file, void *data) {
    winnow_class *ncl;

    ncl = (winnow_class *)data;
    fprintf (file, "weights ");
    vector_save (ncl->w, file);
    fprintf (file, "\n");

    return 0;
}


/**
 * Keep cygwin happy.
 */
int
main (void) {
    return 0;
}


/**
 * Winnow classifier name.
 */
const char *my_classifier_name = "Winnow";


/**
 * Winnow classifier functions.
 */
const multi_functions my_functions = {
    .new_db = winnow_new_db,
    .new = winnow_new_class,
    .copy = winnow_copy,
    .free = winnow_free,
    .learn = winnow_learn,
    .classify = winnow_classify,
    .load_db = winnow_load_db,
    .load_class = winnow_load_class,
    .save_db = winnow_save_db,
    .save_class = winnow_save_class,
    .option = OPTION_BINARY | OPTION_MISTAKE
};