New parameter learning options

The main change here is in the parameter learning methods.  The parameters that are learned at first (i.e., if there is no evidence) are the distributions that are found directly in the data. I had to create or significantly modify several BNT files for this.

If there is evidence, the parameters are learned using a Dirichlet prior. This only required a couple of small changes to the BNW parameter learning files.

1 files changed, 11 insertions, 0 deletions

diff --git a/sourcecodes/header_batchsearch.inc b/sourcecodes/header_batchsearch.inc
index 95ff5687..4e142255 100644
--- a/sourcecodes/header_batchsearch.inc
+++ b/sourcecodes/header_batchsearch.inc
@@ -4,6 +4,17 @@
 <script language="JavaScript">
 <!-- hide
 
+function input_check(input) {
+     var valid_char = /^[a-zA-Z0-9/./-]+$/;
+     if(valid_char.test(input))
+      {
+       return true;
+      }
+     else
+      {
+       return false;
+      }
+}
 function demo_str()
 {
     with(window.document.key_search)