X-Git-Url: http://git.indexdata.com/?p=pazpar2-moved-to-github.git;a=blobdiff_plain;f=src%2Frelevance.c;h=e484ca9b2ebf7120b4893ceba6116886f52f50e5;hp=75800a2fa40d98f00dce2bfa2dd55b5f348fe08d;hb=HEAD;hpb=41b3d6e76417912fda803543325e1edba21896ba

diff --git a/src/relevance.c b/src/relevance.c
index 75800a2..e484ca9 100644
--- a/src/relevance.c
+++ b/src/relevance.c
@@ -1,245 +1,718 @@
-/*
- * $Id: relevance.c,v 1.4 2007-01-08 12:43:41 adam Exp $
- */
+/* This file is part of Pazpar2.
+   Copyright (C) Index Data
 
-#include <ctype.h>
-#include <math.h>
-#include <stdlib.h>
+Pazpar2 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, or (at your option) any later
+version.
+
+Pazpar2 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., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
+
+*/
 
 #if HAVE_CONFIG_H
-#include <cconfig.h>
+#include <config.h>
 #endif
 
+#include <assert.h>
+#include <math.h>
+#include <stdlib.h>
+
 #include "relevance.h"
-#include "pazpar2.h"
+#include "session.h"
+#include "client.h"
+#include "settings.h"
+
+#ifdef WIN32
+#define log2(x) (log(x)/log(2))
+#endif
 
 struct relevance
 {
     int *doc_frequency_vec;
+    int *term_frequency_vec_tmp;
+    int *term_pos;
     int vec_len;
-    struct word_trie *wt;
+    struct word_entry *entries;
+    pp2_charset_token_t prt;
+    int rank_cluster;
+    double follow_factor;
+    double lead_decay;
+    int length_divide;
     NMEM nmem;
+    struct norm_client *norm;
+};
+
+struct word_entry {
+    const char *norm_str;
+    const char *display_str;
+    int termno;
+    char *ccl_field;
+    struct word_entry *next;
 };
 
-// We use this data structure to recognize terms in input records,
-// and map them to record term vectors for counting.
-struct word_trie
+// Structure to keep data for norm_client scores from one client
+struct norm_client
 {
-    struct
-    {
-        struct word_trie *child;
-        int termno;
-    } list[26];
+    int num; // number of the client
+    float max;
+    float min;
+    int count;
+    const char *native_score;
+    int scorefield;
+    float a,b; // Rn = a*R + b
+    struct client *client;
+    struct norm_client *next;
+    struct norm_record *records;
+};
+
+const int scorefield_none = -1;  // Do not normalize anything, use tf/idf as is
+  // This is the old behavior, and the default
+const int scorefield_internal = -2;  // use our tf/idf, but normalize it
+const int scorefield_position = -3;  // fake a score based on the position
+// Positive numbers indicate the field to be used for scoring.
+
+// A structure for each (sub)record. There is one list for each client
+struct norm_record
+{
+    struct record *record;
+    float score;
+    struct record_cluster *clust;
+    struct norm_record *next;
 };
 
-static struct word_trie *create_word_trie_node(NMEM nmem)
+// Find the norm_client entry for this client, or create one if not there
+struct norm_client *findnorm( struct relevance *rel, struct client* client)
 {
-    struct word_trie *res = nmem_malloc(nmem, sizeof(struct word_trie));
-    int i;
-    for (i = 0; i < 26; i++)
-    {
-        res->list[i].child = 0;
-        res->list[i].termno = -1;
+    struct norm_client *n = rel->norm;
+    struct session_database *sdb;
+    while (n) {
+        if (n->client == client )
+            return n;
+        n = n->next;
     }
-    return res;
+    n = nmem_malloc(rel->nmem, sizeof(struct norm_client) );
+    if ( rel->norm )
+        n->num = rel->norm->num +1;
+    else
+        n->num = 1;
+    n->count = 0;
+    n->max = 0.0;
+    n->min = 0.0;
+    n->client = client;
+    n->next = rel->norm;
+    rel->norm = n;
+    sdb = client_get_database(client);
+    n->native_score = session_setting_oneval(sdb, PZ_NATIVE_SCORE);
+    n->records = 0;
+    n->scorefield = scorefield_none;
+    yaz_log(YLOG_LOG,"Normalizing: Client %d uses '%s'", n->num, n->native_score );
+    if ( ! n->native_score  || ! *n->native_score )  // not specified
+        n->scorefield = scorefield_none; 
+    else if ( strcmp(n->native_score,"position") == 0 )
+        n->scorefield = scorefield_position;
+    else if ( strcmp(n->native_score,"internal") == 0 )
+        n->scorefield = scorefield_internal;
+    else
+    { // Get the field index for the score
+        struct session *se = client_get_session(client);
+        n->scorefield = conf_service_metadata_field_id(se->service, n->native_score);
+    }
+    yaz_log(YLOG_LOG,"Normalizing: Client %d uses '%s' = %d",
+            n->num, n->native_score, n->scorefield );
+    return n;
 }
 
-static void word_trie_addterm(NMEM nmem, struct word_trie *n, const char *term, int num)
+
+// Add all records from a cluster into the list for that client, for normalizing later
+static void setup_norm_record( struct relevance *rel,  struct record_cluster *clust)
 {
-    while (*term) {
-        int c = tolower(*term);
-        if (c < 'a' || c > 'z')
-            term++;
+    struct record *record;
+    for (record = clust->records; record; record = record->next)
+    {
+        struct norm_client *norm = findnorm(rel, record->client);
+        struct norm_record *rp;
+        if ( norm->scorefield == scorefield_none)
+            break;  // not interested in normalizing this client
+        rp = nmem_malloc(rel->nmem, sizeof(struct norm_record) );
+        norm->count ++;
+        rp->next = norm->records;
+        norm->records = rp;
+        rp->clust = clust;
+        rp->record = record;
+        if ( norm->scorefield == scorefield_position )
+            rp->score = 1.0 / record->position;
+        else if ( norm->scorefield == scorefield_internal )
+            rp->score = clust->relevance_score; // the tf/idf for the whole cluster
+              // TODO - Get them for each record, merge later!
         else
         {
-            c -= 'a';
-            if (!*(++term))
-                n->list[c].termno = num;
-            else
+            struct record_metadata *md = record->metadata[norm->scorefield];
+            rp->score = md->data.fnumber;
+        }
+        yaz_log(YLOG_LOG,"Got score for %d/%d : %f ",
+                norm->num, record->position, rp->score );
+        record -> score = rp->score; 
+        if ( norm->count == 1 )
+        {
+            norm->max = rp->score;
+            norm->min = rp->score;
+        } else {
+            if ( rp->score > norm->max )
+                norm->max = rp->score;
+            if ( rp->score < norm->min )
+                norm->min = rp->score; 
+        }
+    }
+}
+
+// Calculate the squared sum of residuals, that is the difference from
+// normalized values to the target curve, which is 1/n 
+static double squaresum( struct norm_record *rp, double a, double b)
+{
+    double sum = 0.0;
+    for ( ; rp; rp = rp->next )
+    {
+        double target = 1.0 / rp->record->position;
+        double normscore = rp->score * a + b;
+        double diff = target - normscore;
+        sum += diff * diff;
+    }
+    return sum;
+}
+
+// For each client, normalize scores
+static void normalize_scores(struct relevance *rel)
+{
+    const int maxiterations = 1000;
+    const double enough = 100.0;  // sets the number of decimals we are happy with
+    const double stepchange = 0.5; // reduction of the step size when finding middle
+      // 0.5 sems to be magical, much better than 0.4 or 0.6
+    struct norm_client *norm;
+    for ( norm = rel->norm; norm; norm = norm->next )
+    {
+        yaz_log(YLOG_LOG,"Normalizing client %d: scorefield=%d count=%d range=%f %f = %f",
+                norm->num, norm->scorefield, norm->count, norm->min,
+                norm->max, norm->max-norm->min);
+        norm->a = 1.0; // default normalizing factors, no change
+        norm->b = 0.0;
+        if ( norm->scorefield != scorefield_none &&
+             norm->scorefield != scorefield_position )
+        { // have something to normalize
+            double range = norm->max - norm->min;
+            int it = 0;
+            double a,b;  // params to optimize
+            double as,bs; // step sizes
+            double chi;
+            char *branch = "?";
+            // initial guesses for the parameters
+            // Rmax = a * rmax + b    # want to be 1.0
+            // Rmin = a * rmin + b    # want to be 0.0
+            // Rmax - Rmin = a ( rmax - rmin )    # subtracting equations
+            // 1.0 - 0.0 = a ( rmax - rmin )
+            // a = 1 / range
+            // Rmin = a * rmin + b
+            // b = Rmin - a * rmin
+            //   = 0.0 - 1/range * rmin
+            //   = - rmin / range
+            
+            if ( range < 1e-6 ) // practically zero
+                range = norm->max;
+            a = 1.0 / range;
+            b = -1.0 * norm->min / range;
+            // b = fabs(norm->min) / range;
+            as = a / 10;
+            bs = fabs(b) / 10;
+            chi = squaresum( norm->records, a,b);
+            yaz_log(YLOG_LOG,"Initial done: it=%d: a=%f / %f  b=%f / %f  chi = %f",
+                        0, a, as, b, bs, chi );
+            while (it++ < maxiterations)  // safeguard against things not converging
             {
-                if (!n->list[c].child)
+                double aplus = squaresum(norm->records, a+as, b);
+                double aminus= squaresum(norm->records, a-as, b);
+                double bplus = squaresum(norm->records, a, b+bs);
+                double bminus= squaresum(norm->records, a, b-bs);
+                double prevchi = chi;
+                if ( aplus < chi && aplus < aminus && aplus < bplus && aplus < bminus)
+                {
+                    a = a + as;
+                    chi = aplus;
+                    as = as * (1.0 + stepchange);
+                    branch = "aplus ";
+                }
+                else if ( aminus < chi && aminus < aplus && aminus < bplus && aminus < bminus)
+                {
+                    a = a - as;
+                    chi = aminus;
+                    as = as * (1.0 + stepchange);
+                    branch = "aminus";
+                }
+                else if ( bplus < chi && bplus < aplus && bplus < aminus && bplus < bminus)
                 {
-                    struct word_trie *new = create_word_trie_node(nmem);
-                    n->list[c].child = new;
+                    b = b + bs;
+                    chi = bplus;
+                    bs = bs * (1.0 + stepchange);
+                    branch = "bplus ";
+                }
+                else if ( bminus < chi && bminus < aplus && bminus < bplus && bminus < aminus)
+                {
+                    b = b - bs;
+                    chi = bminus;
+                    branch = "bminus";
+                    bs = bs * (1.0+stepchange);
+                }
+                else
+                { // a,b is the best so far, adjust one step size
+                  // which one? The one that has the greatest effect to chi
+                  // That is, the average of plus and minus is further away from chi
+                    double adif = 0.5 * ( aplus + aminus ) - prevchi;
+                    double bdif = 0.5 * ( bplus + bminus ) - prevchi;
+                    if ( fabs(adif) > fabs(bdif) )
+                    {
+                        as = as * ( 1.0 - stepchange);
+                        branch = "step a";
+                    }
+                    else
+                    {
+                        bs = bs * ( 1.0 - stepchange);
+                        branch = "step b";
+                    }
+                }
+                yaz_log(YLOG_LOG,"Fitting %s it=%d: a=%g %g  b=%g %g  chi=%g ap=%g am=%g, bp=%g bm=%g p=%g",
+                    branch, it, a, as, b, bs, chi,
+                    aplus, aminus, bplus, bminus, prevchi );
+                norm->a = a;
+                norm->b = b;
+                if ( fabs(as) * enough < fabs(a) &&
+                     fabs(bs) * enough < fabs(b) ) {
+                    break;  // not changing much any more
+
                 }
-                word_trie_addterm(nmem, n->list[c].child, term, num);
             }
-            break;
+            yaz_log(YLOG_LOG,"Fitting done: it=%d: a=%g / %g  b=%g / %g  chi = %g",
+                        it-1, a, as, b, bs, chi );
         }
-    }
+
+        if ( norm->scorefield != scorefield_none )
+        { // distribute the normalized scores to the records
+            struct norm_record *nr = norm->records;
+            for ( ; nr ; nr = nr->next ) {
+                double r = nr->score;
+                r = norm->a * r + norm -> b;
+                nr->clust->relevance_score = 10000 * r;
+                nr->record->score = r;
+                yaz_log(YLOG_LOG,"Normalized %f * %f + %f = %f",
+                        nr->score, norm->a, norm->b, r );
+                // TODO - This keeps overwriting the cluster score in random order!
+                // Need to merge results better               
+            }
+        }
+    } // client loop
 }
 
-#define raw_char(c) (((c) >= 'a' && (c) <= 'z') ? (c) - 'a' : -1)
 
-static int word_trie_match(struct word_trie *t, const char *word, int *skipped)
+static struct word_entry *word_entry_match(struct relevance *r,
+                                           const char *norm_str,
+                                           const char *rank, int *weight)
 {
-    int c = raw_char(tolower(*word));
+    int i = 1;
+    struct word_entry *entries = r->entries;
+    for (; entries; entries = entries->next, i++)
+    {
+        if (*norm_str && !strcmp(norm_str, entries->norm_str))
+        {
+            const char *cp = 0;
+            int no_read = 0;
+            sscanf(rank, "%d%n", weight, &no_read);
+            rank += no_read;
+            while (*rank == ' ')
+                rank++;
+            if (no_read > 0 && (cp = strchr(rank, ' ')))
+            {
+                if ((cp - rank) == strlen(entries->ccl_field) &&
+                    memcmp(entries->ccl_field, rank, cp - rank) == 0)
+                    *weight = atoi(cp + 1);
+            }
+            return entries;
+        }
+    }
+    return 0;
+}
 
-    if (!*word)
-        return 0;
+int relevance_snippet(struct relevance *r,
+                      const char *words, const char *name,
+                      WRBUF w_snippet)
+{
+    int no = 0;
+    const char *norm_str;
+    int highlight = 0;
 
-    word++;
-    (*skipped)++;
-    if (!*word || raw_char(*word) < 0)
+    pp2_charset_token_first(r->prt, words, 0);
+    while ((norm_str = pp2_charset_token_next(r->prt)))
     {
-        if (t->list[c].termno > 0)
-            return t->list[c].termno;
+        size_t org_start, org_len;
+        struct word_entry *entries = r->entries;
+        int i;
+
+        pp2_get_org(r->prt, &org_start, &org_len);
+        for (; entries; entries = entries->next, i++)
+        {
+            if (*norm_str && !strcmp(norm_str, entries->norm_str))
+                break;
+        }
+        if (entries)
+        {
+            if (!highlight)
+            {
+                highlight = 1;
+                wrbuf_puts(w_snippet, "<match>");
+                no++;
+            }
+        }
         else
-            return 0;
+        {
+            if (highlight)
+            {
+                highlight = 0;
+                wrbuf_puts(w_snippet, "</match>");
+            }
+        }
+        wrbuf_xmlputs_n(w_snippet, words + org_start, org_len);
     }
-    else
+    if (highlight)
+        wrbuf_puts(w_snippet, "</match>");
+    if (no)
+    {
+        yaz_log(YLOG_DEBUG, "SNIPPET match: %s", wrbuf_cstr(w_snippet));
+    }
+    return no;
+}
+
+void relevance_countwords(struct relevance *r, struct record_cluster *cluster,
+                          const char *words, const char *rank,
+                          const char *name)
+{
+    int *w = r->term_frequency_vec_tmp;
+    const char *norm_str;
+    int i, length = 0;
+    double lead_decay = r->lead_decay;
+    struct word_entry *e;
+    WRBUF wr = cluster->relevance_explain1;
+    int printed_about_field = 0;
+
+    pp2_charset_token_first(r->prt, words, 0);
+    for (e = r->entries, i = 1; i < r->vec_len; i++, e = e->next)
     {
-        if (t->list[c].child)
+        w[i] = 0;
+        r->term_pos[i] = 0;
+    }
+
+    assert(rank);
+    while ((norm_str = pp2_charset_token_next(r->prt)))
+    {
+        int local_weight = 0;
+        e = word_entry_match(r, norm_str, rank, &local_weight);
+        if (e)
         {
-            return word_trie_match(t->list[c].child, word, skipped);
+            int res = e->termno;
+            int j;
+
+            if (!printed_about_field)
+            {
+                printed_about_field = 1;
+                wrbuf_printf(wr, "field=%s content=", name);
+                if (strlen(words) > 50)
+                {
+                    wrbuf_xmlputs_n(wr, words, 49);
+                    wrbuf_puts(wr, " ...");
+                }
+                else
+                    wrbuf_xmlputs(wr, words);
+                wrbuf_puts(wr, ";\n");
+            }
+            assert(res < r->vec_len);
+            w[res] += local_weight / (1 + log2(1 + lead_decay * length));
+            wrbuf_printf(wr, "%s: w[%d] += w(%d) / "
+                         "(1+log2(1+lead_decay(%f) * length(%d)));\n",
+                         e->display_str, res, local_weight, lead_decay, length);
+            j = res - 1;
+            if (j > 0 && r->term_pos[j])
+            {
+                int d = length + 1 - r->term_pos[j];
+                wrbuf_printf(wr, "%s: w[%d] += w[%d](%d) * follow(%f) / "
+                             "(1+log2(d(%d));\n",
+                             e->display_str, res, res, w[res],
+                             r->follow_factor, d);
+                w[res] += w[res] * r->follow_factor / (1 + log2(d));
+            }
+            for (j = 0; j < r->vec_len; j++)
+                r->term_pos[j] = j < res ? 0 : length + 1;
         }
-        else
-            return 0;
+        length++;
     }
 
-}
+    for (e = r->entries, i = 1; i < r->vec_len; i++, e = e->next)
+    {
+        if (length == 0 || w[i] == 0)
+            continue;
+        wrbuf_printf(wr, "%s: tf[%d] += w[%d](%d)", e->display_str, i, i, w[i]);
+        switch (r->length_divide)
+        {
+        case 0:
+            cluster->term_frequency_vecf[i] += (double) w[i];
+            break;
+        case 1:
+            wrbuf_printf(wr, " / log2(1+length(%d))", length);
+            cluster->term_frequency_vecf[i] +=
+                (double) w[i] / log2(1 + length);
+            break;
+        case 2:
+            wrbuf_printf(wr, " / length(%d)", length);
+            cluster->term_frequency_vecf[i] += (double) w[i] / length;
+        }
+        cluster->term_frequency_vec[i] += w[i];
+        wrbuf_printf(wr, " (%f);\n", cluster->term_frequency_vecf[i]);
+    }
 
+    cluster->term_frequency_vec[0] += length;
+}
 
-static struct word_trie *build_word_trie(NMEM nmem, const char **terms)
+static void pull_terms(struct relevance *res, struct ccl_rpn_node *n)
 {
-    struct word_trie *res = create_word_trie_node(nmem);
-    const char **p;
+    char **words;
+    int numwords;
+    char *ccl_field;
     int i;
 
-    for (i = 1, p = terms; *p; p++, i++)
-        word_trie_addterm(nmem, res, *p, i);
-    return res;
+    switch (n->kind)
+    {
+    case CCL_RPN_AND:
+    case CCL_RPN_OR:
+    case CCL_RPN_NOT:
+    case CCL_RPN_PROX:
+        pull_terms(res, n->u.p[0]);
+        pull_terms(res, n->u.p[1]);
+        break;
+    case CCL_RPN_TERM:
+        nmem_strsplit(res->nmem, " ", n->u.t.term, &words, &numwords);
+        for (i = 0; i < numwords; i++)
+        {
+            const char *norm_str;
+
+            ccl_field = nmem_strdup_null(res->nmem, n->u.t.qual);
+
+            pp2_charset_token_first(res->prt, words[i], 0);
+            while ((norm_str = pp2_charset_token_next(res->prt)))
+            {
+                struct word_entry **e = &res->entries;
+                while (*e)
+                    e = &(*e)->next;
+                *e = nmem_malloc(res->nmem, sizeof(**e));
+                (*e)->norm_str = nmem_strdup(res->nmem, norm_str);
+                (*e)->ccl_field = ccl_field;
+                (*e)->termno = res->vec_len++;
+                (*e)->display_str = nmem_strdup(res->nmem, words[i]);
+                (*e)->next = 0;
+            }
+        }
+        break;
+    default:
+        break;
+    }
+}
+void relevance_clear(struct relevance *r)
+{
+    if (r)
+    {
+        int i;
+        for (i = 0; i < r->vec_len; i++)
+            r->doc_frequency_vec[i] = 0;
+    }
 }
 
-struct relevance *relevance_create(NMEM nmem, const char **terms, int numrecs)
+struct relevance *relevance_create_ccl(pp2_charset_fact_t pft,
+                                       struct ccl_rpn_node *query,
+                                       int rank_cluster,
+                                       double follow_factor, double lead_decay,
+                                       int length_divide)
 {
-    struct relevance *res = nmem_malloc(nmem, sizeof(struct relevance));
-    const char **p;
-    int i;
+    NMEM nmem = nmem_create();
+    struct relevance *res = nmem_malloc(nmem, sizeof(*res));
 
-    for (p = terms, i = 0; *p; p++, i++)
-        ;
-    res->vec_len = ++i;
-    res->doc_frequency_vec = nmem_malloc(nmem, res->vec_len * sizeof(int));
-    bzero(res->doc_frequency_vec, res->vec_len * sizeof(int));
     res->nmem = nmem;
-    res->wt = build_word_trie(nmem, terms);
+    res->entries = 0;
+    res->vec_len = 1;
+    res->rank_cluster = rank_cluster;
+    res->follow_factor = follow_factor;
+    res->lead_decay = lead_decay;
+    res->length_divide = length_divide;
+    res->norm = 0;
+    res->prt = pp2_charset_token_create(pft, "relevance");
+
+    pull_terms(res, query);
+
+    res->doc_frequency_vec = nmem_malloc(nmem, res->vec_len * sizeof(int));
+
+    // worker array
+    res->term_frequency_vec_tmp =
+        nmem_malloc(res->nmem,
+                    res->vec_len * sizeof(*res->term_frequency_vec_tmp));
+
+    res->term_pos =
+        nmem_malloc(res->nmem, res->vec_len * sizeof(*res->term_pos));
+
+    relevance_clear(res);
     return res;
 }
 
-void relevance_newrec(struct relevance *r, struct record *rec)
+void relevance_destroy(struct relevance **rp)
 {
-    if (!rec->term_frequency_vec)
+    if (*rp)
     {
-        rec->term_frequency_vec = nmem_malloc(r->nmem, r->vec_len * sizeof(int));
-        bzero(rec->term_frequency_vec, r->vec_len * sizeof(int));
+        pp2_charset_token_destroy((*rp)->prt);
+        nmem_destroy((*rp)->nmem);
+        *rp = 0;
     }
 }
 
+void relevance_mergerec(struct relevance *r, struct record_cluster *dst,
+                        const struct record_cluster *src)
+{
+    int i;
 
-// FIXME. The definition of a word is crude here.. should support
-// some form of localization mechanism?
-void relevance_countwords(struct relevance *r, struct record *head,
-        const char *words, int multiplier)
+    for (i = 0; i < r->vec_len; i++)
+        dst->term_frequency_vec[i] += src->term_frequency_vec[i];
+
+    for (i = 0; i < r->vec_len; i++)
+        dst->term_frequency_vecf[i] += src->term_frequency_vecf[i];
+}
+
+void relevance_newrec(struct relevance *r, struct record_cluster *rec)
 {
-    while (*words)
-    {
-        char c;
-        int res;
-        int skipped;
-        while (*words && (c = raw_char(tolower(*words))) < 0)
-            words++;
-        if (!*words)
-            return;
-        skipped = 0;
-        if ((res = word_trie_match(r->wt, words, &skipped)))
-        {
-            words += skipped;
-            head->term_frequency_vec[res] += multiplier;
-        }
-        else
-        {
-            while (*words && (c = raw_char(tolower(*words))) >= 0)
-                words++;
-        }
-        head->term_frequency_vec[0]++;
-    }
+    int i;
+
+    // term frequency [1,..] . [0] is total length of all fields
+    rec->term_frequency_vec =
+        nmem_malloc(r->nmem,
+                    r->vec_len * sizeof(*rec->term_frequency_vec));
+    for (i = 0; i < r->vec_len; i++)
+        rec->term_frequency_vec[i] = 0;
+
+    // term frequency divided by length of field [1,...]
+    rec->term_frequency_vecf =
+        nmem_malloc(r->nmem,
+                    r->vec_len * sizeof(*rec->term_frequency_vecf));
+    for (i = 0; i < r->vec_len; i++)
+        rec->term_frequency_vecf[i] = 0.0;
 }
 
-void relevance_donerecord(struct relevance *r, struct record *head)
+void relevance_donerecord(struct relevance *r, struct record_cluster *cluster)
 {
     int i;
 
     for (i = 1; i < r->vec_len; i++)
-        if (head->term_frequency_vec[i] > 0)
+        if (cluster->term_frequency_vec[i] > 0)
             r->doc_frequency_vec[i]++;
 
     r->doc_frequency_vec[0]++;
 }
 
-#ifdef FLOAT_REL
-static int comp(const void *p1, const void *p2)
-{
-    float res;
-    struct record **r1 = (struct record **) p1;
-    struct record **r2 = (struct record **) p2;
-    res = (*r2)->relevance - (*r1)->relevance;
-    if (res > 0)
-        return 1;
-    else if (res < 0)
-        return -1;
-    else
-        return 0;
-}
-#else
-static int comp(const void *p1, const void *p2)
-{
-    struct record **r1 = (struct record **) p1;
-    struct record **r2 = (struct record **) p2;
-    return (*r2)->relevance - (*r1)->relevance;
-}
-#endif
 
-// Prepare for a relevance-sorted read of up to num entries
+
+// Prepare for a relevance-sorted read
 void relevance_prepare_read(struct relevance *rel, struct reclist *reclist)
 {
     int i;
     float *idfvec = xmalloc(rel->vec_len * sizeof(float));
 
+    reclist_enter(reclist);
+
     // Calculate document frequency vector for each term.
     for (i = 1; i < rel->vec_len; i++)
     {
         if (!rel->doc_frequency_vec[i])
             idfvec[i] = 0;
         else
-            idfvec[i] = log((float) rel->doc_frequency_vec[0] / rel->doc_frequency_vec[i]);
+        {
+            /* add one to nominator idf(t,D) to ensure a value > 0 */
+            idfvec[i] = log((float) (1 + rel->doc_frequency_vec[0]) /
+                            rel->doc_frequency_vec[i]);
+        }
     }
-    // Calculate relevance for each document
-    for (i = 0; i < reclist->num_records; i++)
+    // Calculate relevance for each document (cluster)
+    while (1)
     {
-        int t;
-        struct record *rec = reclist->flatlist[i];
-        float relevance;
-        relevance = 0;
-        for (t = 1; t < rel->vec_len; t++)
+        int relevance = 0;
+        WRBUF w;
+        struct word_entry *e = rel->entries;
+        struct record_cluster *rec = reclist_read_record(reclist);
+        if (!rec)
+            break;
+        w = rec->relevance_explain2;
+        wrbuf_rewind(w);
+        wrbuf_puts(w, "relevance = 0;\n");
+        for (i = 1; i < rel->vec_len; i++)
         {
-            float termfreq;
-            if (!rec->term_frequency_vec[0])
-                break;
-            termfreq = (float) rec->term_frequency_vec[t] / rec->term_frequency_vec[0];
-            relevance += termfreq * idfvec[t];
+            float termfreq = (float) rec->term_frequency_vecf[i];
+            int add = 100000 * termfreq * idfvec[i];
+
+            wrbuf_printf(w, "idf[%d] = log(((1 + total(%d))/termoccur(%d));\n",
+                         i, rel->doc_frequency_vec[0],
+                         rel->doc_frequency_vec[i]);
+            wrbuf_printf(w, "%s: relevance += 100000 * tf[%d](%f) * "
+                         "idf[%d](%f) (%d);\n",
+                         e->display_str, i, termfreq, i, idfvec[i], add);
+            relevance += add;
+            e = e->next;
         }
-        rec->relevance = (int) (relevance * 100000);
-    }
-    qsort(reclist->flatlist, reclist->num_records, sizeof(struct record*), comp);
-    reclist->pointer = 0;
+        if (!rel->rank_cluster)
+        {
+            struct record *record;
+            int cluster_size = 0;
+
+            for (record = rec->records; record; record = record->next)
+                cluster_size++;
+
+            wrbuf_printf(w, "score = relevance(%d)/cluster_size(%d);\n",
+                         relevance, cluster_size);
+            relevance /= cluster_size;
+        }
+        else
+        {
+            wrbuf_printf(w, "score = relevance(%d);\n", relevance);
+        }
+        rec->relevance_score = relevance;
+
+        // Build the normalizing structures
+        // List of (sub)records for each target
+        setup_norm_record( rel, rec );
+
+    } // cluster loop
+
+    normalize_scores(rel);
+
+    // TODO - Calculate the cluster scores from individual records
+    // At the moment the record scoring puts one of them in the cluster...
+    reclist_rewind(reclist);
+
+    reclist_leave(reclist);
     xfree(idfvec);
+
 }
 
 /*
  * Local variables:
  * c-basic-offset: 4
+ * c-file-style: "Stroustrup"
  * indent-tabs-mode: nil
  * End:
  * vim: shiftwidth=4 tabstop=8 expandtab
  */
+