Fix calculations.

diff --git a/src/classifiers/bayes.c b/src/classifiers/bayes.c
index f3ad365585c00ac48a2e4474f57f9aa751f3748c..e701fe2a015fd641f8fd4410ab2df63c2f4aba36 100644 (file)
--- a/src/classifiers/bayes.c
+++ b/src/classifiers/bayes.c
@@ -62,6 +62,7 @@ struct bayes_callback_data {
        guint64                         processed_tokens;
        gsize                           max_tokens;
        double                         spam_probability;
+       double                         ham_probability;
 };
 
 static                          gboolean
@@ -170,6 +171,7 @@ bayes_classify_callback (gpointer key, gpointer value, gpointer data)
                spam_prob = spam_freq / (spam_freq + ham_freq);
                bayes_spam_prob = (0.5 + spam_prob * total_count) / (double)total_count;
                cd->spam_probability += log (bayes_spam_prob);
+               cd->ham_probability += log (1. - bayes_spam_prob);
                cd->processed_tokens ++;
        }
 
@@ -201,7 +203,7 @@ bayes_classify (struct classifier_ctx* ctx, statfile_pool_t *pool, GTree *input,
        gint                             nodes, i = 0, selected_st = -1, cnt;
        gint                             minnodes;
        guint64                          maxhits = 0;
-       double                          final_prob;
+       double                          final_prob, h, s;
        struct statfile                *st;
        stat_file_t                     *file;
        GList                           *cur;
@@ -237,6 +239,7 @@ bayes_classify (struct classifier_ctx* ctx, statfile_pool_t *pool, GTree *input,
 
        data.processed_tokens = 0;
        data.spam_probability = 0;
+       data.ham_probability = 0;
        data.total_ham = 0;
        data.total_spam = 0;
        if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "max_tokens")) != NULL) {
@@ -279,10 +282,12 @@ bayes_classify (struct classifier_ctx* ctx, statfile_pool_t *pool, GTree *input,
                final_prob = 0;
        }
        else {
-               final_prob = inv_chi_square (-2. * data.spam_probability, 2 * data.processed_tokens);
+               h = 1 - inv_chi_square (-2. * data.spam_probability, 2 * data.processed_tokens);
+               s = 1 - inv_chi_square (-2. * data.ham_probability, 2 * data.processed_tokens);
+               final_prob = (s + 1 - h) / 2.;
        }
 
-       if (final_prob > 0 && fabs (final_prob - 0.5) > 0.0001) {
+       if (final_prob > 0 && fabs (final_prob - 0.5) > 0.1) {
 
                sumbuf = memory_pool_alloc (task->task_pool, 32);
                for (i = 0; i < cnt; i ++) {