Update README.md

src/rescore/lua/README.md

@@ -4,25 +4,25 @@ Run corpus_test.lua to test emails and generate log. These logs can be used for
 
 Example:
 
-`lua corpus_test.lua -a path/to/ham/dir -s path/to/spam/dir -o results.log`
+`rspamadm lua corpus_test.lua -a -a -a path/to/ham/dir -a -s -a path/to/spam/dir -a -o -a results.log`
 
 Use -h option to get more info about usage.
 
 Log file consists of one line per email. It contains actual email_type, score, action, symbols in that order.
 
 # STATISTICS
 
-Use statistics.lua to infer useful information from the log file generated in previous step. For generating statistics specify spam threshold score using -t. Feed in log file using input redirection.
+Use statistics.lua to infer useful information from the log file generated in previous step. Use -t option to specify spam threshold.
 
 ### Example:
 
 `rspamadm lua statistics.lua -a path/to/log/file -a -t -a 15`
 
 Use `rspamadm lua statistics.lua -a -h` to get more info about usage
 
-Statistics contains two different information - File stats and symbol stats.
+Statistics contains two different information - Corpus statistics and Symbols statistics
 
-### File stats:
+### Corpus Statistics
 
 **Number of emails**: Number of emails read from log  
 **Number of spam**: Number of spam emails read from log  
@@ -33,7 +33,7 @@ Statistics contains two different information - File stats and symbol stats.
 **False negative rate**: Percentage of spam emails that were falsely classified as ham  
 **Overall Accuracy**: Overall accuracy of classification
 
-### Symbol stats:
+### Symbol Statistics
 
 Each line presents statistics about a symbol read from the log.  
 
@@ -46,12 +46,20 @@ Each line presents statistics about a symbol read from the log.
 
 # Rescoring
 
-Use rescore.lua on logs generated from corpus-test to find optimal symbol scores using perceptron.
+Use rescore.lua on logs generated from corpus-test to find optimal symbol scores using perceptron. Use -o option to dump new scores in json format.
 
 ### Example:
 
 	rspamadm lua -a -l -a path/to/log/dir -a --diff -a -o -a new.scores
 
+
+# Example usage
+
+1. Collect ham and spam messages and store them in /ham and /spam directories respectively.
+2. Run `rspamadm lua corpus_test.lua -a -a -a path/to/ham/dir -a -s -a path/to/spam/dir -a -o -a results.log`
+3. Make a directory for logs files `mkdir logs`
+4. Move log files into logs directory `mv results.log logs/`
+5. Run `rspamadm lua -a -l -a logs -a --diff -a -o -a new.scores`