[libfann] 180/242: test code for more activation functions

[Christian Kastner]

This is an automated email from the git hooks/post-receive script.

chrisk-guest pushed a commit to tag Version2_0_0
in repository libfann.

commit 8651008615bb86e37b62cc6089dbb25861cbd710
Author: Steffen Nissen <lukesky at diku.dk>
Date:   Mon May 30 20:25:13 2005 +0000

    test code for more activation functions
---
 benchmarks/datasets/mushroom.train | 16248 +++++++++++++++++------------------
 benchmarks/datasets/parity13.test  |  8192 +++++++++---------
 doc/Makefile                       |     1 +
 examples/cascade_train.c           |    55 +-
 src/fann.c                         |    16 +-
 src/fann_cascade.c                 |    83 +-
 src/fann_train.c                   |    34 +-
 src/fann_train_data.c              |    60 +
 src/include/fann.h                 |    13 +
 src/include/fann_activation.h      |    40 +-
 src/include/fann_internal.h        |     2 +-
 11 files changed, 12458 insertions(+), 12286 deletions(-)

diff --git a/benchmarks/datasets/mushroom.train b/benchmarks/datasets/mushroom.train
index 1374172..209981a 100644
--- a/benchmarks/datasets/mushroom.train
+++ b/benchmarks/datasets/mushroom.train
@@ -1,8125 +1,8125 @@
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-1 1 -1 -1 -1 1 -1 -1 1 1 1 1 1 0
+1 1 -1 -1 -1 1 -1 -1 1 1 1 1 1 -1
 -1 -1 1 -1 -1 1 -1 -1 1 1 1 1 1 1
-1 -1 1 -1 -1 1 -1 -1 1 1 1 1 1 0
--1 1 1 -1 -1 1 -1 -1 1 1 1 1 1 0
+1 -1 1 -1 -1 1 -1 -1 1 1 1 1 1 -1
+-1 1 1 -1 -1 1 -1 -1 1 1 1 1 1 -1
 1 1 1 -1 -1 1 -1 -1 1 1 1 1 1 1
 -1 -1 -1 1 -1 1 -1 -1 1 1 1 1 1 1
-1 -1 -1 1 -1 1 -1 -1 1 1 1 1 1 0
--1 1 -1 1 -1 1 -1 -1 1 1 1 1 1 0
+1 -1 -1 1 -1 1 -1 -1 1 1 1 1 1 -1
+-1 1 -1 1 -1 1 -1 -1 1 1 1 1 1 -1
 1 1 -1 1 -1 1 -1 -1 1 1 1 1 1 1
--1 -1 1 1 -1 1 -1 -1 1 1 1 1 1 0
+-1 -1 1 1 -1 1 -1 -1 1 1 1 1 1 -1
 1 -1 1 1 -1 1 -1 -1 1 1 1 1 1 1
 -1 1 1 1 -1 1 -1 -1 1 1 1 1 1 1
-1 1 1 1 -1 1 -1 -1 1 1 1 1 1 0
+1 1 1 1 -1 1 -1 -1 1 1 1 1 1 -1
 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 1 1
-1 -1 -1 -1 1 1 -1 -1 1 1 1 1 1 0
--1 1 -1 -1 1 1 -1 -1 1 1 1 1 1 0
+1 -1 -1 -1 1 1 -1 -1 1 1 1 1 1 -1
+-1 1 -1 -1 1 1 -1 -1 1 1 1 1 1 -1
 1 1 -1 -1 1 1 -1 -1 1 1 1 1 1 1
--1 -1 1 -1 1 1 -1 -1 1 1 1 1 1 0
+-1 -1 1 -1 1 1 -1 -1 1 1 1 1 1 -1
 1 -1 1 -1 1 1 -1 -1 1 1 1 1 1 1
 -1 1 1 -1 1 1 -1 -1 1 1 1 1 1 1
-1 1 1 -1 1 1 -1 -1 1 1 1 1 1 0
--1 -1 -1 1 1 1 -1 -1 1 1 1 1 1 0
+1 1 1 -1 1 1 -1 -1 1 1 1 1 1 -1
+-1 -1 -1 1 1 1 -1 -1 1 1 1 1 1 -1
 1 -1 -1 1 1 1 -1 -1 1 1 1 1 1 1
 -1 1 -1 1 1 1 -1 -1 1 1 1 1 1 1
-1 1 -1 1 1 1 -1 -1 1 1 1 1 1 0
+1 1 -1 1 1 1 -1 -1 1 1 1 1 1 -1
 -1 -1 1 1 1 1 -1 -1 1 1 1 1 1 1
-1 -1 1 1 1 1 -1 -1 1 1 1 1 1 0
--1 1 1 1 1 1 -1 -1 1 1 1 1 1 0
+1 -1 1 1 1 1 -1 -1 1 1 1 1 1 -1
+-1 1 1 1 1 1 -1 -1 1 1 1 1 1 -1
 1 1 1 1 1 1 -1 -1 1 1 1 1 1 1
--1 -1 -1 -1 -1 -1 1 -1 1 1 1 1 1 0
+-1 -1 -1 -1 -1 -1 1 -1 1 1 1 1 1 -1
 1 -1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1
 -1 1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1
-1 1 -1 -1 -1 -1 1 -1 1 1 1 1 1 0
+1 1 -1 -1 -1 -1 1 -1 1 1 1 1 1 -1
 -1 -1 1 -1 -1 -1 1 -1 1 1 1 1 1 1
-1 -1 1 -1 -1 -1 1 -1 1 1 1 1 1 0
--1 1 1 -1 -1 -1 1 -1 1 1 1 1 1 0
+1 -1 1 -1 -1 -1 1 -1 1 1 1 1 1 -1
+-1 1 1 -1 -1 -1 1 -1 1 1 1 1 1 -1
 1 1 1 -1 -1 -1 1 -1 1 1 1 1 1 1
 -1 -1 -1 1 -1 -1 1 -1 1 1 1 1 1 1
-1 -1 -1 1 -1 -1 1 -1 1 1 1 1 1 0
--1 1 -1 1 -1 -1 1 -1 1 1 1 1 1 0
+1 -1 -1 1 -1 -1 1 -1 1 1 1 1 1 -1
+-1 1 -1 1 -1 -1 1 -1 1 1 1 1 1 -1
 1 1 -1 1 -1 -1 1 -1 1 1 1 1 1 1
--1 -1 1 1 -1 -1 1 -1 1 1 1 1 1 0
+-1 -1 1 1 -1 -1 1 -1 1 1 1 1 1 -1
 1 -1 1 1 -1 -1 1 -1 1 1 1 1 1 1
 -1 1 1 1 -1 -1 1 -1 1 1 1 1 1 1
-1 1 1 1 -1 -1 1 -1 1 1 1 1 1 0
+1 1 1 1 -1 -1 1 -1 1 1 1 1 1 -1
 -1 -1 -1 -1 1 -1 1 -1 1 1 1 1 1 1
-1 -1 -1 -1 1 -1 1 -1 1 1 1 1 1 0
--1 1 -1 -1 1 -1 1 -1 1 1 1 1 1 0
+1 -1 -1 -1 1 -1 1 -1 1 1 1 1 1 -1
+-1 1 -1 -1 1 -1 1 -1 1 1 1 1 1 -1
 1 1 -1 -1 1 -1 1 -1 1 1 1 1 1 1
--1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 0
+-1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 -1
 1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1
 -1 1 1 -1 1 -1 1 -1 1 1 1 1 1 1
-1 1 1 -1 1 -1 1 -1 1 1 1 1 1 0
--1 -1 -1 1 1 -1 1 -1 1 1 1 1 1 0
+1 1 1 -1 1 -1 1 -1 1 1 1 1 1 -1
+-1 -1 -1 1 1 -1 1 -1 1 1 1 1 1 -1
 1 -1 -1 1 1 -1 1 -1 1 1 1 1 1 1
 -1 1 -1 1 1 -1 1 -1 1 1 1 1 1 1
-1 1 -1 1 1 -1 1 -1 1 1 1 1 1 0
+1 1 -1 1 1 -1 1 -1 1 1 1 1 1 -1
 -1 -1 1 1 1 -1 1 -1 1 1 1 1 1 1
-1 -1 1 1 1 -1 1 -1 1 1 1 1 1 0
--1 1 1 1 1 -1 1 -1 1 1 1 1 1 0
+1 -1 1 1 1 -1 1 -1 1 1 1 1 1 -1
+-1 1 1 1 1 -1 1 -1 1 1 1 1 1 -1
 1 1 1 1 1 -1 1 -1 1 1 1 1 1 1
 -1 -1 -1 -1 -1 1 1 -1 1 1 1 1 1 1
-1 -1 -1 -1 -1 1 1 -1 1 1 1 1 1 0
--1 1 -1 -1 -1 1 1 -1 1 1 1 1 1 0
+1 -1 -1 -1 -1 1 1 -1 1 1 1 1 1 -1
+-1 1 -1 -1 -1 1 1 -1 1 1 1 1 1 -1
 1 1 -1 -1 -1 1 1 -1 1 1 1 1 1 1
--1 -1 1 -1 -1 1 1 -1 1 1 1 1 1 0
+-1 -1 1 -1 -1 1 1 -1 1 1 1 1 1 -1
 1 -1 1 -1 -1 1 1 -1 1 1 1 1 1 1
 -1 1 1 -1 -1 1 1 -1 1 1 1 1 1 1
-1 1 1 -1 -1 1 1 -1 1 1 1 1 1 0
--1 -1 -1 1 -1 1 1 -1 1 1 1 1 1 0
+1 1 1 -1 -1 1 1 -1 1 1 1 1 1 -1
+-1 -1 -1 1 -1 1 1 -1 1 1 1 1 1 -1
 1 -1 -1 1 -1 1 1 -1 1 1 1 1 1 1
 -1 1 -1 1 -1 1 1 -1 1 1 1 1 1 1
-1 1 -1 1 -1 1 1 -1 1 1 1 1 1 0
+1 1 -1 1 -1 1 1 -1 1 1 1 1 1 -1
 -1 -1 1 1 -1 1 1 -1 1 1 1 1 1 1
-1 -1 1 1 -1 1 1 -1 1 1 1 1 1 0
--1 1 1 1 -1 1 1 -1 1 1 1 1 1 0
+1 -1 1 1 -1 1 1 -1 1 1 1 1 1 -1
+-1 1 1 1 -1 1 1 -1 1 1 1 1 1 -1
 1 1 1 1 -1 1 1 -1 1 1 1 1 1 1
--1 -1 -1 -1 1 1 1 -1 1 1 1 1 1 0
+-1 -1 -1 -1 1 1 1 -1 1 1 1 1 1 -1
 1 -1 -1 -1 1 1 1 -1 1 1 1 1 1 1
 -1 1 -1 -1 1 1 1 -1 1 1 1 1 1 1
-1 1 -1 -1 1 1 1 -1 1 1 1 1 1 0
+1 1 -1 -1 1 1 1 -1 1 1 1 1 1 -1
 -1 -1 1 -1 1 1 1 -1 1 1 1 1 1 1
-1 -1 1 -1 1 1 1 -1 1 1 1 1 1 0
--1 1 1 -1 1 1 1 -1 1 1 1 1 1 0
+1 -1 1 -1 1 1 1 -1 1 1 1 1 1 -1
+-1 1 1 -1 1 1 1 -1 1 1 1 1 1 -1
 1 1 1 -1 1 1 1 -1 1 1 1 1 1 1
 -1 -1 -1 1 1 1 1 -1 1 1 1 1 1 1
-1 -1 -1 1 1 1 1 -1 1 1 1 1 1 0
--1 1 -1 1 1 1 1 -1 1 1 1 1 1 0
+1 -1 -1 1 1 1 1 -1 1 1 1 1 1 -1
+-1 1 -1 1 1 1 1 -1 1 1 1 1 1 -1
 1 1 -1 1 1 1 1 -1 1 1 1 1 1 1
--1 -1 1 1 1 1 1 -1 1 1 1 1 1 0
+-1 -1 1 1 1 1 1 -1 1 1 1 1 1 -1
 1 -1 1 1 1 1 1 -1 1 1 1 1 1 1
 -1 1 1 1 1 1 1 -1 1 1 1 1 1 1
-1 1 1 1 1 1 1 -1 1 1 1 1 1 0
--1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 0
+1 1 1 1 1 1 1 -1 1 1 1 1 1 -1
+-1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 -1
 1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1
 -1 1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1
-1 1 -1 -1 -1 -1 -1 1 1 1 1 1 1 0
+1 1 -1 -1 -1 -1 -1 1 1 1 1 1 1 -1
 -1 -1 1 -1 -1 -1 -1 1 1 1 1 1 1 1
-1 -1 1 -1 -1 -1 -1 1 1 1 1 1 1 0
--1 1 1 -1 -1 -1 -1 1 1 1 1 1 1 0
+1 -1 1 -1 -1 -1 -1 1 1 1 1 1 1 -1
+-1 1 1 -1 -1 -1 -1 1 1 1 1 1 1 -1
 1 1 1 -1 -1 -1 -1 1 1 1 1 1 1 1
 -1 -1 -1 1 -1 -1 -1 1 1 1 1 1 1 1
-1 -1 -1 1 -1 -1 -1 1 1 1 1 1 1 0
--1 1 -1 1 -1 -1 -1 1 1 1 1 1 1 0
+1 -1 -1 1 -1 -1 -1 1 1 1 1 1 1 -1
+-1 1 -1 1 -1 -1 -1 1 1 1 1 1 1 -1
 1 1 -1 1 -1 -1 -1 1 1 1 1 1 1 1
--1 -1 1 1 -1 -1 -1 1 1 1 1 1 1 0
+-1 -1 1 1 -1 -1 -1 1 1 1 1 1 1 -1
 1 -1 1 1 -1 -1 -1 1 1 1 1 1 1 1
 -1 1 1 1 -1 -1 -1 1 1 1 1 1 1 1
-1 1 1 1 -1 -1 -1 1 1 1 1 1 1 0
+1 1 1 1 -1 -1 -1 1 1 1 1 1 1 -1
 -1 -1 -1 -1 1 -1 -1 1 1 1 1 1 1 1
-1 -1 -1 -1 1 -1 -1 1 1 1 1 1 1 0
--1 1 -1 -1 1 -1 -1 1 1 1 1 1 1 0
+1 -1 -1 -1 1 -1 -1 1 1 1 1 1 1 -1
+-1 1 -1 -1 1 -1 -1 1 1 1 1 1 1 -1
 1 1 -1 -1 1 -1 -1 1 1 1 1 1 1 1
--1 -1 1 -1 1 -1 -1 1 1 1 1 1 1 0
+-1 -1 1 -1 1 -1 -1 1 1 1 1 1 1 -1
 1 -1 1 -1 1 -1 -1 1 1 1 1 1 1 1
 -1 1 1 -1 1 -1 -1 1 1 1 1 1 1 1
-1 1 1 -1 1 -1 -1 1 1 1 1 1 1 0
--1 -1 -1 1 1 -1 -1 1 1 1 1 1 1 0
+1 1 1 -1 1 -1 -1 1 1 1 1 1 1 -1
+-1 -1 -1 1 1 -1 -1 1 1 1 1 1 1 -1
 1 -1 -1 1 1 -1 -1 1 1 1 1 1 1 1
 -1 1 -1 1 1 -1 -1 1 1 1 1 1 1 1
-1 1 -1 1 1 -1 -1 1 1 1 1 1 1 0
+1 1 -1 1 1 -1 -1 1 1 1 1 1 1 -1
 -1 -1 1 1 1 -1 -1 1 1 1 1 1 1 1
-1 -1 1 1 1 -1 -1 1 1 1 1 1 1 0
--1 1 1 1 1 -1 -1 1 1 1 1 1 1 0
+1 -1 1 1 1 -1 -1 1 1 1 1 1 1 -1
+-1 1 1 1 1 -1 -1 1 1 1 1 1 1 -1
 1 1 1 1 1 -1 -1 1 1 1 1 1 1 1
 -1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1 1
-1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1 0
--1 1 -1 -1 -1 1 -1 1 1 1 1 1 1 0
+1 -1 -1 -1 -1 1 -1 1 1 1 1 1 1 -1
+-1 1 -1 -1 -1 1 -1 1 1 1 1 1 1 -1
 1 1 -1 -1 -1 1 -1 1 1 1 1 1 1 1
--1 -1 1 -1 -1 1 -1 1 1 1 1 1 1 0
+-1 -1 1 -1 -1 1 -1 1 1 1 1 1 1 -1
 1 -1 1 -1 -1 1 -1 1 1 1 1 1 1 1
 -1 1 1 -1 -1 1 -1 1 1 1 1 1 1 1
-1 1 1 -1 -1 1 -1 1 1 1 1 1 1 0
--1 -1 -1 1 -1 1 -1 1 1 1 1 1 1 0
+1 1 1 -1 -1 1 -1 1 1 1 1 1 1 -1
+-1 -1 -1 1 -1 1 -1 1 1 1 1 1 1 -1
 1 -1 -1 1 -1 1 -1 1 1 1 1 1 1 1
 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1 1
-1 1 -1 1 -1 1 -1 1 1 1 1 1 1 0
+1 1 -1 1 -1 1 -1 1 1 1 1 1 1 -1
 -1 -1 1 1 -1 1 -1 1 1 1 1 1 1 1
-1 -1 1 1 -1 1 -1 1 1 1 1 1 1 0
--1 1 1 1 -1 1 -1 1 1 1 1 1 1 0
+1 -1 1 1 -1 1 -1 1 1 1 1 1 1 -1
+-1 1 1 1 -1 1 -1 1 1 1 1 1 1 -1
 1 1 1 1 -1 1 -1 1 1 1 1 1 1 1
--1 -1 -1 -1 1 1 -1 1 1 1 1 1 1 0
+-1 -1 -1 -1 1 1 -1 1 1 1 1 1 1 -1
 1 -1 -1 -1 1 1 -1 1 1 1 1 1 1 1
 -1 1 -1 -1 1 1 -1 1 1 1 1 1 1 1
-1 1 -1 -1 1 1 -1 1 1 1 1 1 1 0
+1 1 -1 -1 1 1 -1 1 1 1 1 1 1 -1
 -1 -1 1 -1 1 1 -1 1 1 1 1 1 1 1
-1 -1 1 -1 1 1 -1 1 1 1 1 1 1 0
--1 1 1 -1 1 1 -1 1 1 1 1 1 1 0
+1 -1 1 -1 1 1 -1 1 1 1 1 1 1 -1
+-1 1 1 -1 1 1 -1 1 1 1 1 1 1 -1
 1 1 1 -1 1 1 -1 1 1 1 1 1 1 1
 -1 -1 -1 1 1 1 -1 1 1 1 1 1 1 1
-1 -1 -1 1 1 1 -1 1 1 1 1 1 1 0
--1 1 -1 1 1 1 -1 1 1 1 1 1 1 0
+1 -1 -1 1 1 1 -1 1 1 1 1 1 1 -1
+-1 1 -1 1 1 1 -1 1 1 1 1 1 1 -1
 1 1 -1 1 1 1 -1 1 1 1 1 1 1 1
--1 -1 1 1 1 1 -1 1 1 1 1 1 1 0
+-1 -1 1 1 1 1 -1 1 1 1 1 1 1 -1
 1 -1 1 1 1 1 -1 1 1 1 1 1 1 1
 -1 1 1 1 1 1 -1 1 1 1 1 1 1 1
-1 1 1 1 1 1 -1 1 1 1 1 1 1 0
+1 1 1 1 1 1 -1 1 1 1 1 1 1 -1
 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1
-1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 0
--1 1 -1 -1 -1 -1 1 1 1 1 1 1 1 0
+1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 -1
+-1 1 -1 -1 -1 -1 1 1 1 1 1 1 1 -1
 1 1 -1 -1 -1 -1 1 1 1 1 1 1 1 1
--1 -1 1 -1 -1 -1 1 1 1 1 1 1 1 0
+-1 -1 1 -1 -1 -1 1 1 1 1 1 1 1 -1
 1 -1 1 -1 -1 -1 1 1 1 1 1 1 1 1
 -1 1 1 -1 -1 -1 1 1 1 1 1 1 1 1
-1 1 1 -1 -1 -1 1 1 1 1 1 1 1 0
--1 -1 -1 1 -1 -1 1 1 1 1 1 1 1 0
+1 1 1 -1 -1 -1 1 1 1 1 1 1 1 -1
+-1 -1 -1 1 -1 -1 1 1 1 1 1 1 1 -1
 1 -1 -1 1 -1 -1 1 1 1 1 1 1 1 1
 -1 1 -1 1 -1 -1 1 1 1 1 1 1 1 1
-1 1 -1 1 -1 -1 1 1 1 1 1 1 1 0
+1 1 -1 1 -1 -1 1 1 1 1 1 1 1 -1
 -1 -1 1 1 -1 -1 1 1 1 1 1 1 1 1
-1 -1 1 1 -1 -1 1 1 1 1 1 1 1 0
--1 1 1 1 -1 -1 1 1 1 1 1 1 1 0
+1 -1 1 1 -1 -1 1 1 1 1 1 1 1 -1
+-1 1 1 1 -1 -1 1 1 1 1 1 1 1 -1
 1 1 1 1 -1 -1 1 1 1 1 1 1 1 1
--1 -1 -1 -1 1 -1 1 1 1 1 1 1 1 0
+-1 -1 -1 -1 1 -1 1 1 1 1 1 1 1 -1
 1 -1 -1 -1 1 -1 1 1 1 1 1 1 1 1
 -1 1 -1 -1 1 -1 1 1 1 1 1 1 1 1
-1 1 -1 -1 1 -1 1 1 1 1 1 1 1 0
+1 1 -1 -1 1 -1 1 1 1 1 1 1 1 -1
 -1 -1 1 -1 1 -1 1 1 1 1 1 1 1 1
-1 -1 1 -1 1 -1 1 1 1 1 1 1 1 0
--1 1 1 -1 1 -1 1 1 1 1 1 1 1 0
+1 -1 1 -1 1 -1 1 1 1 1 1 1 1 -1
+-1 1 1 -1 1 -1 1 1 1 1 1 1 1 -1
 1 1 1 -1 1 -1 1 1 1 1 1 1 1 1
 -1 -1 -1 1 1 -1 1 1 1 1 1 1 1 1
-1 -1 -1 1 1 -1 1 1 1 1 1 1 1 0
--1 1 -1 1 1 -1 1 1 1 1 1 1 1 0
+1 -1 -1 1 1 -1 1 1 1 1 1 1 1 -1
+-1 1 -1 1 1 -1 1 1 1 1 1 1 1 -1
 1 1 -1 1 1 -1 1 1 1 1 1 1 1 1
--1 -1 1 1 1 -1 1 1 1 1 1 1 1 0
+-1 -1 1 1 1 -1 1 1 1 1 1 1 1 -1
 1 -1 1 1 1 -1 1 1 1 1 1 1 1 1
 -1 1 1 1 1 -1 1 1 1 1 1 1 1 1
-1 1 1 1 1 -1 1 1 1 1 1 1 1 0
--1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 0
+1 1 1 1 1 -1 1 1 1 1 1 1 1 -1
+-1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 -1
 1 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1
 -1 1 -1 -1 -1 1 1 1 1 1 1 1 1 1
-1 1 -1 -1 -1 1 1 1 1 1 1 1 1 0
+1 1 -1 -1 -1 1 1 1 1 1 1 1 1 -1
 -1 -1 1 -1 -1 1 1 1 1 1 1 1 1 1
-1 -1 1 -1 -1 1 1 1 1 1 1 1 1 0
--1 1 1 -1 -1 1 1 1 1 1 1 1 1 0
+1 -1 1 -1 -1 1 1 1 1 1 1 1 1 -1
+-1 1 1 -1 -1 1 1 1 1 1 1 1 1 -1
 1 1 1 -1 -1 1 1 1 1 1 1 1 1 1
 -1 -1 -1 1 -1 1 1 1 1 1 1 1 1 1
-1 -1 -1 1 -1 1 1 1 1 1 1 1 1 0
--1 1 -1 1 -1 1 1 1 1 1 1 1 1 0
+1 -1 -1 1 -1 1 1 1 1 1 1 1 1 -1
+-1 1 -1 1 -1 1 1 1 1 1 1 1 1 -1
 1 1 -1 1 -1 1 1 1 1 1 1 1 1 1
--1 -1 1 1 -1 1 1 1 1 1 1 1 1 0
+-1 -1 1 1 -1 1 1 1 1 1 1 1 1 -1
 1 -1 1 1 -1 1 1 1 1 1 1 1 1 1
 -1 1 1 1 -1 1 1 1 1 1 1 1 1 1
-1 1 1 1 -1 1 1 1 1 1 1 1 1 0
+1 1 1 1 -1 1 1 1 1 1 1 1 1 -1
 -1 -1 -1 -1 1 1 1 1 1 1 1 1 1 1
-1 -1 -1 -1 1 1 1 1 1 1 1 1 1 0
--1 1 -1 -1 1 1 1 1 1 1 1 1 1 0
+1 -1 -1 -1 1 1 1 1 1 1 1 1 1 -1
+-1 1 -1 -1 1 1 1 1 1 1 1 1 1 -1
 1 1 -1 -1 1 1 1 1 1 1 1 1 1 1
--1 -1 1 -1 1 1 1 1 1 1 1 1 1 0
+-1 -1 1 -1 1 1 1 1 1 1 1 1 1 -1
 1 -1 1 -1 1 1 1 1 1 1 1 1 1 1
 -1 1 1 -1 1 1 1 1 1 1 1 1 1 1
-1 1 1 -1 1 1 1 1 1 1 1 1 1 0
--1 -1 -1 1 1 1 1 1 1 1 1 1 1 0
+1 1 1 -1 1 1 1 1 1 1 1 1 1 -1
+-1 -1 -1 1 1 1 1 1 1 1 1 1 1 -1
 1 -1 -1 1 1 1 1 1 1 1 1 1 1 1
 -1 1 -1 1 1 1 1 1 1 1 1 1 1 1
-1 1 -1 1 1 1 1 1 1 1 1 1 1 0
+1 1 -1 1 1 1 1 1 1 1 1 1 1 -1
 -1 -1 1 1 1 1 1 1 1 1 1 1 1 1
-1 -1 1 1 1 1 1 1 1 1 1 1 1 0
--1 1 1 1 1 1 1 1 1 1 1 1 1 0
+1 -1 1 1 1 1 1 1 1 1 1 1 1 -1
+-1 1 1 1 1 1 1 1 1 1 1 1 1 -1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1
diff --git a/doc/Makefile b/doc/Makefile
index d07a68b..add3556 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -31,6 +31,7 @@ txt: fann.xml Makefile
 sf: pdf html html-single
 	perl -p -i -e 's/><\/BODY/> <br><div><A href="http:\/\/sourceforge.net"> <IMG src="http:\/\/sourceforge.net\/sflogo.php?group_id=93562&type=5" width="210" height="62" border="0" alt="SourceForge.net Logo" \/><\/A><\/div><br> <\/body/' fann.html
 	(cd html && perl -p -i -e 's/><\/BODY/> <br><div><A href="http:\/\/sourceforge.net"> <IMG src="http:\/\/sourceforge.net\/sflogo.php?group_id=93562&type=5" width="210" height="62" border="0" alt="SourceForge.net Logo" \/><\/A><\/div><br> <\/body/' *.html)
+	(cd html && perl -p -i -e 's/<TABLE/<center><script type="text\/javascript"><!--\ngoogle_ad_client = "pub-7943942007953059"\n;google_ad_width = 728\n;google_ad_height = 90\n;google_ad_format = "728x90_as"\n;google_ad_type = "text_image"\n;google_ad_channel =""\n;google_page_url = document.location\n;google_color_border = "336699"\n;google_color_bg = "FFFFFF"\n;google_color_link = "0000FF"\n;google_color_url = "008000"\n;google_color_text = "000000"\n;\/\/--><\/script><script type="text\ [...]
 	tar czf sf.tgz *.gif *.html *.php style.css fann.pdf html
 
 spell:
diff --git a/examples/cascade_train.c b/examples/cascade_train.c
index 66c963b..5be2657 100644
--- a/examples/cascade_train.c
+++ b/examples/cascade_train.c
@@ -24,10 +24,10 @@ Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 int main()
 {
 	const float learning_rate = (const float)0.7;
-	const float desired_error = (const float)0.001;
-	unsigned int max_out_epochs = 100;
-	unsigned int max_cand_epochs = 100;
-	unsigned int max_neurons = 40;
+	const float desired_error = (const float)0.005;
+	unsigned int max_out_epochs = 150;
+	unsigned int max_cand_epochs = 150;
+	unsigned int max_neurons = 30;
 	unsigned int neurons_between_reports = 1;
 	struct fann *ann;
 	struct fann_train_data *train_data, *test_data;
@@ -40,26 +40,59 @@ int main()
 	/* this is in range -1 to 1 */
 	/*
 	*/
-	
-	train_data = fann_read_train_from_file("../benchmarks/datasets/parity4.train");
-	test_data = fann_read_train_from_file("../benchmarks/datasets/parity4.test");
-	
+		
+	train_data = fann_read_train_from_file("../benchmarks/datasets/mushroom.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/mushroom.train");
+
 	train_data = fann_read_train_from_file("xor.data");
 	test_data = fann_read_train_from_file("xor.data");
 
+	train_data = fann_read_train_from_file("../benchmarks/datasets/parity13.test");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/parity13.test");
+
+	train_data = fann_read_train_from_file("../benchmarks/datasets/robot.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/robot.test");
+
+	train_data = fann_read_train_from_file("../benchmarks/datasets/pumadyn-32fm.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/pumadyn-32fm.test");
+
+	train_data = fann_read_train_from_file("../benchmarks/datasets/gene.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/gene.test");
+	
+	train_data = fann_read_train_from_file("../benchmarks/datasets/thyroid.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/thyroid.test");
+
+	train_data = fann_read_train_from_file("../benchmarks/datasets/soybean.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/soybean.test");
+
 	train_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral2.train");
 	test_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral2.test");
+
+	train_data = fann_read_train_from_file("../benchmarks/datasets/parity8.train");
+	test_data = fann_read_train_from_file("../benchmarks/datasets/parity8.test");
+
+	fann_scale_train_data(train_data, -1, 1);
+	fann_scale_train_data(test_data, -1, 1);
 	
 	printf("Creating network.\n");
 
 	ann = fann_create_shortcut(learning_rate, 2, train_data->num_input, train_data->num_output);
 	
-	fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);
-	fann_set_activation_steepness_hidden(ann, 1);
+	fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
+	fann_set_activation_steepness_hidden(ann, 0.5);
 	fann_set_activation_steepness_output(ann, 1);
-	fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
+	fann_set_activation_function_hidden(ann, FANN_GAUSSIAN_SYMMETRIC);
 	fann_set_activation_function_output(ann, FANN_LINEAR);
 	fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
+	fann_set_rprop_increase_factor(ann, 1.2);
+	fann_set_rprop_decrease_factor(ann, 0.5);
+	fann_set_rprop_delta_min(ann, 0.0);
+	fann_set_rprop_delta_max(ann, 50.0);
+
+	ann->cascade_change_fraction = 0.01;
+	ann->cascade_stagnation_epochs = 12;
+	ann->cascade_num_candidates = 10;
+	ann->cascade_weight_multiplier = 0.5;
 	
 	fann_print_parameters(ann);
 	/*fann_print_connections(ann);*/
diff --git a/src/fann.c b/src/fann.c
index b9db649..ea2b78a 100644
--- a/src/fann.c
+++ b/src/fann.c
@@ -659,6 +659,18 @@ FANN_EXTERNAL fann_type * FANN_API fann_run(struct fann *ann, fann_type *input)
 				case FANN_THRESHOLD_SYMMETRIC:
 					neuron_it->value = (fann_type)((neuron_sum < 0) ? -1 : 1);
 					break;
+				case FANN_GAUSSIAN:
+					neuron_it->value = (fann_type)fann_gaussian(steepness, neuron_sum);
+					break;
+				case FANN_GAUSSIAN_SYMMETRIC:
+					neuron_it->value = (fann_type)fann_gaussian_symmetric(steepness, neuron_sum);
+					break;
+				case FANN_ELLIOT:
+					neuron_it->value = (fann_type)fann_elliot(steepness, neuron_sum);
+					break;
+				case FANN_ELLIOT_SYMMETRIC:
+					neuron_it->value = (fann_type)fann_elliot_symmetric(steepness, neuron_sum);			
+					break;
 				default:
 					fann_error((struct fann_error *)ann, FANN_E_CANT_USE_ACTIVATION);
 			}
@@ -867,9 +879,9 @@ struct fann * fann_allocate_structure(float learning_rate, unsigned int num_laye
 
 	/* variables used for cascade correlation (reasonable defaults) */
 	ann->cascade_change_fraction = 0.01;
-	ann->cascade_stagnation_epochs = 8;
+	ann->cascade_stagnation_epochs = 12;
 	ann->cascade_num_candidates = 8;
-	ann->cascade_weight_multiplier = 1.0;
+	ann->cascade_weight_multiplier = 0.5;
 	ann->cascade_candidate_limit = 1000.0;
 	ann->cascade_candidate_scores = NULL;
 
diff --git a/src/fann_cascade.c b/src/fann_cascade.c
index 5d94af1..435db7a 100644
--- a/src/fann_cascade.c
+++ b/src/fann_cascade.c
@@ -23,6 +23,7 @@
 #ifndef FIXEDFANN
 
 #define CASCADE_DEBUG
+/* #define CASCADE_DEBUG_FULL */
 
 void fann_cascadetrain_on_data_callback(struct fann *ann, struct fann_train_data *data, float desired_error, int (*callback)(unsigned int epochs, float error), unsigned int max_out_epochs, unsigned int max_cand_epochs, unsigned int max_neurons, unsigned int neurons_between_reports);
 
@@ -136,13 +137,13 @@ void fann_cascadetrain_on_data_callback(struct fann *ann, struct fann_train_data
 	for(i = 1; i <= max_neurons; i++){
 		/* DEBUG TODO to print out connections */
 		fann_set_shortcut_connections(ann); /* update connections before printout */
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 		fann_print_connections(ann);
 		fann_print_connections_raw(ann);
 #endif
 
 		/* train output neurons */
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 		printf("training outputs\n");
 #endif
 		total_epochs += fann_train_outputs(ann, data, desired_error, max_out_epochs);
@@ -159,7 +160,7 @@ void fann_cascadetrain_on_data_callback(struct fann *ann, struct fann_train_data
 				|| i == 1
 				|| error < desired_error)){
 			if (callback == NULL) {
-				printf("Neurons     %6d. Current error: %.6f. Epochs %6d. Bit fail %d.\n", i, error, total_epochs, ann->num_bit_fail);
+				printf("Neurons     %6d. Current error: %.6f. Total error: %.6f. Epochs %6d. Bit fail %d.\n", i, error, ann->MSE_value, total_epochs, ann->num_bit_fail);
 			} else if((*callback)(i, error) == -1){
 				/* you can break the training by returning -1 */
 				break;
@@ -168,7 +169,7 @@ void fann_cascadetrain_on_data_callback(struct fann *ann, struct fann_train_data
 		
 		/* DEBUG TODO to print out connections */
 		fann_set_shortcut_connections(ann); /* update connections before printout */
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 		fann_print_connections(ann);
 		fann_print_connections_raw(ann);
 #endif
@@ -183,25 +184,23 @@ void fann_cascadetrain_on_data_callback(struct fann *ann, struct fann_train_data
 		}
 		
 		/* train new candidates */
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 		printf("training candidates\n");
 #endif
 		total_epochs += fann_train_candidates(ann, data, max_cand_epochs);
 
 		/* DEBUG TODO to print out connections */
 		fann_set_shortcut_connections(ann); /* update connections before printout */
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 		fann_print_connections(ann);
 		fann_print_connections_raw(ann);
 #endif
 
 		/* this installs the best candidate */
-#ifdef CASCADE_DEBUG
-		printf("install candidate error (%f)\n", fann_test_data(ann, data));
-#endif
 		fann_install_candidate(ann);
+
 #ifdef CASCADE_DEBUG
-		printf("candidate installed error (%f)\n", fann_test_data(ann, data));
+		printf("Sum error after candidate install: %.6f\n", fann_test_data(ann, data));
 #endif
 	}
 
@@ -287,8 +286,11 @@ float fann_train_outputs_epoch(struct fann *ann, struct fann_train_data *data)
 	/* TODO this should actually use the algorithm selected by
 	   ann->training_algorithm
 	*/
-	fann_update_weights_irpropm(ann, (ann->last_layer-1)->first_neuron->first_con, ann->total_connections);
-	/*fann_update_weights_quickprop(ann, data->num_data, (ann->last_layer-1)->first_neuron->first_con, ann->total_connections);*/
+	if(ann->training_algorithm == FANN_TRAIN_RPROP){
+		fann_update_weights_irpropm(ann, (ann->last_layer-1)->first_neuron->first_con, ann->total_connections);
+	} else {
+		fann_update_weights_quickprop(ann, data->num_data, (ann->last_layer-1)->first_neuron->first_con, ann->total_connections);
+	}
 
 	return fann_get_MSE(ann);
 }
@@ -362,7 +364,7 @@ int fann_reallocate_neurons(struct fann *ann, unsigned int total_neurons)
 	if(neurons != ann->first_layer->first_neuron){
 		/* Then the memory has moved, also move the pointers */
 
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL
 		printf("Moving neuron pointers\n");
 #endif
 
@@ -451,7 +453,7 @@ int fann_initialize_candidates(struct fann *ann)
 
 	/* Now randomize the weights and zero out the arrays that needs zeroing out.
 	 */
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL
 	printf("random cand weight [%d ... %d]\n", first_candidate_connection, num_connections-1);
 #endif
 	if(ann->training_algorithm == FANN_TRAIN_RPROP){
@@ -461,6 +463,7 @@ int fann_initialize_candidates(struct fann *ann)
 	}
 	for(i = first_candidate_connection; i < num_connections; i++){
 		ann->weights[i] = fann_random_weight();
+		/*ann->weights[i] = fann_rand(-0.25,0.25);*/
 		ann->train_slopes[i] = 0;
 		ann->prev_steps[i] = 0;
 		ann->prev_train_slopes[i] = initial_slope;
@@ -496,8 +499,9 @@ int fann_train_candidates(struct fann *ann, struct fann_train_data *data, unsign
 		if ((best_cand_score > target_cand_score) ||
 			(best_cand_score < backslide_cand_score))
 		{
-#ifdef CASCADE_DEBUG	
-			printf("best_cand_score=%f, target_cand_score=%f, backslide_cand_score=%f, stagnation=%d\n", best_cand_score, target_cand_score, backslide_cand_score, stagnation);
+#ifdef CASCADE_DEBUG_FULL
+			printf("Best candidate score %f, real score: %f\n", ann->MSE_value-best_cand_score, best_cand_score);
+			/* printf("best_cand_score=%f, target_cand_score=%f, backslide_cand_score=%f, stagnation=%d\n", best_cand_score, target_cand_score, backslide_cand_score, stagnation);*/
 #endif
 
 			target_cand_score = best_cand_score * (1.0 + ann->cascade_change_fraction);
@@ -508,10 +512,16 @@ int fann_train_candidates(struct fann *ann, struct fann_train_data *data, unsign
 		/* No improvement in allotted period, so quit */
 		if (i >= stagnation)
 		{
+#ifdef CASCADE_DEBUG
+			printf("Stagnation with %d epochs, best candidate score %f, real score: %f\n", i+1, ann->MSE_value-best_cand_score, best_cand_score);
+#endif
 			return i+1;
 		}
 	}
 
+#ifdef CASCADE_DEBUG
+	printf("Max epochs %d reached, best candidate score %f, real score: %f\n", max_epochs, ann->MSE_value-best_cand_score, best_cand_score);
+#endif
 	return max_epochs;
 }
 
@@ -571,7 +581,7 @@ void fann_update_candidate_slopes(struct fann *ann)
 		cand_it->value = activation;
 		
 		derived = fann_activation_derived(ann->activation_function_hidden,
-			ann->activation_steepness_hidden, activation);
+			ann->activation_steepness_hidden, activation, cand_sum);
 
 		/* The output weights is located right after the input weights in
 		   the weight array.
@@ -581,16 +591,16 @@ void fann_update_candidate_slopes(struct fann *ann)
 		cand_out_slopes = ann->train_slopes + cand_it->first_con + num_connections;
 		for(j = 0; j < num_output; j++){
 			diff = (activation * cand_out_weights[j]) - output_train_errors[j];
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 			/* printf("diff = %f = (%f * %f) - %f;\n", diff, activation, cand_out_weights[j], output_train_errors[j]); */
 #endif
 			cand_out_slopes[j] -= 2.0 * diff * activation;
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 			/* printf("cand_out_slopes[%d] <= %f += %f * %f;\n", j, cand_out_slopes[j], diff, activation); */
 #endif
 			error_value += diff * cand_out_weights[j];
 			cand_score -= (diff * diff);
-#ifdef CASCADE_DEBUG
+#ifdef CASCADE_DEBUG_FULL
 			/* printf("cand_score[%d][%d] = %f -= (%f * %f)\n", cand_it - first_cand, j, cand_score, diff, diff); */
 
 			printf("cand[%d]: error=%f, activation=%f, diff=%f, slope=%f\n", cand_it - first_cand, output_train_errors[j], (activation * cand_out_weights[j]), diff, -2.0 * diff * activation);
@@ -612,7 +622,12 @@ void fann_update_candidate_weights(struct fann *ann, unsigned int num_data)
 	struct fann_neuron *first_cand = (ann->last_layer-1)->last_neuron + 1; /* there is an empty neuron between the actual neurons and the candidate neuron */
 	struct fann_neuron *last_cand = first_cand + ann->cascade_num_candidates-1;
 
-	fann_update_weights_quickprop(ann, num_data, first_cand->first_con, last_cand->last_con+ann->num_output);
+	/**/
+	if(ann->training_algorithm == FANN_TRAIN_RPROP){
+		fann_update_weights_irpropm(ann, first_cand->first_con, last_cand->last_con+ann->num_output);
+	}else{
+		fann_update_weights_quickprop(ann, num_data, first_cand->first_con, last_cand->last_con+ann->num_output);
+	}
 }
 
 float fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data)
@@ -667,7 +682,9 @@ float fann_train_candidates_epoch(struct fann *ann, struct fann_train_data *data
 	}
 
 	ann->cascade_best_candidate = ann->total_neurons + best_candidate + 1;
+#ifdef CASCADE_DEBUG_FULL
 	printf("Best candidate[%d]: with score %f, real score: %f\n", best_candidate, ann->MSE_value-best_score, best_score);
+#endif
 	
 	return best_score;
 }
@@ -699,7 +716,7 @@ struct fann_layer *fann_add_layer(struct fann *ann, struct fann_layer *layer)
 	ann->first_layer = layers;
 	ann->last_layer = layers + num_layers;
 
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 	printf("add layer at pos %d\n", layer_pos);
 #endif
 	
@@ -747,7 +764,7 @@ void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
 
 	/* first move the pointers to neurons in the layer structs */
 	for(layer_it = ann->last_layer-1; layer_it != layer; layer_it--){
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 		printf("move neuron pointers in layer %d, first(%d -> %d), last(%d -> %d)\n",
 			layer_it - ann->first_layer,
 			layer_it->first_neuron - ann->first_layer->first_neuron,
@@ -765,7 +782,7 @@ void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
 	/* this is the place that should hold the new neuron */
 	neuron_place = layer->last_neuron-1;
 
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 	printf("num_connections_in=%d, num_connections_out=%d\n", num_connections_in, num_connections_out);
 #endif
 
@@ -777,18 +794,18 @@ void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
 	/* move the actual output neurons and the indexes to the connection arrays */
 	for(neuron_it = (ann->last_layer-1)->last_neuron-1;
 		neuron_it != neuron_place; neuron_it--){
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 		printf("move neuron %d -> %d\n", neuron_it - ann->first_layer->first_neuron -1,
 			neuron_it - ann->first_layer->first_neuron);
 #endif
 		*neuron_it = *(neuron_it-1);
 
 		/* move the weights */
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 		printf("move weight[%d ... %d] -> weight[%d ... %d]\n", neuron_it->first_con, neuron_it->last_con-1, neuron_it->first_con + num_connections_move - 1, neuron_it->last_con + num_connections_move - 2);
 #endif
 		for(i = neuron_it->last_con - 1; i >= (int)neuron_it->first_con; i--){
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 			printf("move weight[%d] = weight[%d]\n", i + num_connections_move - 1, i);
 #endif
 			ann->weights[i + num_connections_move - 1] = ann->weights[i];
@@ -800,11 +817,7 @@ void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
 		neuron_it->first_con += num_connections_move;
 
 		/* set the new weight to the newly allocated neuron */
-#ifdef CASCADE_DEBUG	
-		printf("cadidate output weight set to weight[%d] = weight[%d] = %f %f\n", neuron_it->last_con-1, candidate_output_weight, -(ann->weights[candidate_output_weight]), (ann->weights[candidate_output_weight]));
-#endif
-
-		ann->weights[neuron_it->last_con-1] = -(ann->weights[candidate_output_weight]) * ann->cascade_weight_multiplier;
+		ann->weights[neuron_it->last_con-1] = (ann->weights[candidate_output_weight]) * ann->cascade_weight_multiplier;
 		candidate_output_weight++;
 	}
 
@@ -813,19 +826,19 @@ void fann_add_candidate_neuron(struct fann *ann, struct fann_layer *layer)
 	neuron_place->sum = 0;
 	neuron_place->last_con = (neuron_place+1)->first_con;
 	neuron_place->first_con = neuron_place->last_con - num_connections_in;
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 	printf("neuron[%d] = weights[%d ... %d]\n", neuron_place - ann->first_layer->first_neuron, neuron_place->first_con, neuron_place->last_con-1);
 #endif
 
 	candidate_con = candidate->first_con;
 	/* initialize the input weights at random */
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL	
 	printf("move cand weights[%d ... %d] -> [%d ... %d]\n", candidate_con, candidate_con + num_connections_in-1, neuron_place->first_con, neuron_place->last_con-1);
 #endif
 	
 	for(i = 0; i < num_connections_in; i++){
 		ann->weights[i + neuron_place->first_con] = ann->weights[i + candidate_con];
-#ifdef CASCADE_DEBUG	
+#ifdef CASCADE_DEBUG_FULL
 		printf("move weights[%d] -> weights[%d] (%f)\n", i + candidate_con, i + neuron_place->first_con, ann->weights[i + neuron_place->first_con]);
 #endif
 	}
diff --git a/src/fann_train.c b/src/fann_train.c
index f9f17db..8ee931f 100644
--- a/src/fann_train.c
+++ b/src/fann_train.c
@@ -34,19 +34,25 @@
    and a steepness
 */
 fann_type fann_activation_derived(unsigned int activation_function,
-	fann_type steepness, fann_type value)
+	fann_type steepness, fann_type value, fann_type sum)
 {
 	switch(activation_function){
 		case FANN_LINEAR:
 			return (fann_type)fann_linear_derive(steepness, value);
 		case FANN_SIGMOID:
 		case FANN_SIGMOID_STEPWISE:
-			value = fann_clip(value, 0.01f, 0.99f);
 			return (fann_type)fann_sigmoid_derive(steepness, value);
 		case FANN_SIGMOID_SYMMETRIC:
 		case FANN_SIGMOID_SYMMETRIC_STEPWISE:
-			value = fann_clip(value, -0.98f, 0.98f);
 			return (fann_type)fann_sigmoid_symmetric_derive(steepness, value);
+		case FANN_GAUSSIAN:
+			return (fann_type)fann_gaussian_derive(steepness, value, sum);
+		case FANN_GAUSSIAN_SYMMETRIC:
+			return (fann_type)fann_gaussian_symmetric_derive(steepness, value, sum);
+		case FANN_ELLIOT:
+			return (fann_type)fann_elliot_derive(steepness, value, sum);
+		case FANN_ELLIOT_SYMMETRIC:
+			return (fann_type)fann_elliot_symmetric_derive(steepness, value, sum);			
 		default:
 			return 0;
 	}
@@ -120,6 +126,14 @@ fann_type fann_activation(struct fann *ann, unsigned int is_output_layer,
 			return (fann_type)((value < 0) ? 0 : 1);
 		case FANN_THRESHOLD_SYMMETRIC:
 			return (fann_type)((value < 0) ? -1 : 1);
+		case FANN_GAUSSIAN:
+			return (fann_type)fann_gaussian(steepness, value);
+		case FANN_GAUSSIAN_SYMMETRIC:
+			return (fann_type)fann_gaussian_symmetric(steepness, value);
+		case FANN_ELLIOT:
+			return (fann_type)fann_elliot(steepness, value);
+		case FANN_ELLIOT_SYMMETRIC:
+			return (fann_type)fann_elliot_symmetric(steepness, value);			
 		default:
 			fann_error((struct fann_error *)ann, FANN_E_CANT_USE_ACTIVATION);
 			return 0;
@@ -277,7 +291,7 @@ void fann_compute_MSE(struct fann *ann, fann_type *desired_output)
 		}
 	
 		*error_it = fann_activation_derived(ann->activation_function_output,
-			ann->activation_steepness_output, neuron_value) * neuron_diff;
+			ann->activation_steepness_output, neuron_value, last_layer_begin->sum) * neuron_diff;
 		
 		desired_output++;
 		error_it++;
@@ -348,7 +362,14 @@ void fann_backpropagate_MSE(struct fann *ann)
 		/* then calculate the actual errors in the previous layer */
 		error_prev_layer = error_begin + ((layer_it-1)->first_neuron - first_neuron);
 		last_neuron = (layer_it-1)->last_neuron;
-		
+
+		for(neuron_it = (layer_it-1)->first_neuron;
+			neuron_it != last_neuron; neuron_it++){
+			neuron_value = neuron_it->value;
+			*error_prev_layer *= fann_activation(ann, 0, neuron_value);
+		}
+
+		/*
 		switch(ann->activation_function_hidden){
 			case FANN_LINEAR:
 				for(neuron_it = (layer_it-1)->first_neuron;
@@ -363,7 +384,6 @@ void fann_backpropagate_MSE(struct fann *ann)
 				for(neuron_it = (layer_it-1)->first_neuron;
 					neuron_it != last_neuron; neuron_it++){
 					neuron_value = neuron_it->value;
-					neuron_value = fann_clip(neuron_value, 0.01f, 0.99f);
 					*error_prev_layer *= (fann_type)fann_sigmoid_derive(activation_steepness_hidden, neuron_value);
 					error_prev_layer++;
 				}
@@ -373,7 +393,6 @@ void fann_backpropagate_MSE(struct fann *ann)
 				for(neuron_it = (layer_it-1)->first_neuron;
 					neuron_it != last_neuron; neuron_it++){
 					neuron_value = neuron_it->value;
-					neuron_value = fann_clip(neuron_value, -0.98f, 0.98f);
 					*error_prev_layer *= (fann_type)fann_sigmoid_symmetric_derive(activation_steepness_hidden, neuron_value);
 					error_prev_layer++;
 				}
@@ -382,6 +401,7 @@ void fann_backpropagate_MSE(struct fann *ann)
 				fann_error((struct fann_error *)ann, FANN_E_CANT_TRAIN_ACTIVATION);
 				return;
 		}
+		*/
 	}
 }
 
diff --git a/src/fann_train_data.c b/src/fann_train_data.c
index 77cc376..74dff55 100644
--- a/src/fann_train_data.c
+++ b/src/fann_train_data.c
@@ -285,6 +285,64 @@ FANN_EXTERNAL void FANN_API fann_shuffle_train_data(struct fann_train_data *trai
 	}
 }
 
+/* INTERNAL FUNCTION
+   Scales data to a specific range
+ */
+void fann_scale_data(fann_type **data, unsigned int num_data, unsigned int num_elem, fann_type new_min, fann_type new_max) {
+	unsigned int dat, elem;
+	fann_type old_min, old_max, temp, old_span, new_span, factor;
+	old_min = old_max = data[0][0];
+
+	/* first calculate min and max */
+	for (dat = 0 ; dat < num_data ; dat++ ) {
+		for ( elem = 0 ; elem < num_elem ; elem++ ) {
+			temp = data[dat][elem];
+			if(temp < old_min)
+				old_min = temp;
+			else if(temp > old_max)
+				old_max = temp;
+		}
+	}
+
+	old_span = old_max - old_min;
+	new_span = new_max - new_min;
+	factor = new_span / old_span;
+
+	for (dat = 0 ; dat < num_data ; dat++ ) {
+		for ( elem = 0 ; elem < num_elem ; elem++ ) {
+			temp = (data[dat][elem] - old_min) * factor + new_min;
+			if(temp < new_min){
+				data[dat][elem] = new_min;
+				printf("error %f < %f\n", temp, new_min);
+			} else if(temp > new_max){
+				data[dat][elem] = new_max;
+				printf("error %f > %f\n", temp, new_max);
+			} else {
+				data[dat][elem] = temp;
+			}
+		}
+	}
+}
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) {
+	fann_scale_data(train_data->input, train_data->num_data, train_data->num_input, new_min, new_max);
+}
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) {
+	fann_scale_data(train_data->output, train_data->num_data, train_data->num_output, new_min, new_max);
+}
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max) {
+	fann_scale_data(train_data->input, train_data->num_data, train_data->num_input, new_min, new_max);
+	fann_scale_data(train_data->output, train_data->num_data, train_data->num_output, new_min, new_max);
+}
+
 /* merges training data into a single struct.
  */
 FANN_EXTERNAL struct fann_train_data * FANN_API fann_merge_train_data(struct fann_train_data *data1, struct fann_train_data *data2) {
@@ -366,6 +424,8 @@ FANN_EXTERNAL struct fann_train_data * FANN_API fann_duplicate_train_data(struct
 	return dest;
 }
 
+
+
 /* INTERNAL FUNCTION
    Reads training data from a file descriptor.
  */
diff --git a/src/include/fann.h b/src/include/fann.h
index 0ce0382..3f420f3 100644
--- a/src/include/fann.h
+++ b/src/include/fann.h
@@ -307,6 +307,19 @@ FANN_EXTERNAL void FANN_API fann_train_on_file_callback(struct fann *ann, char *
  */
 FANN_EXTERNAL void FANN_API fann_shuffle_train_data(struct fann_train_data *train_data);
 
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_input_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max);
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_output_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max);
+
+/* Scales the inputs in the training data to the specified range
+ */
+FANN_EXTERNAL void FANN_API fann_scale_train_data(struct fann_train_data *train_data, fann_type new_min, fann_type new_max);
+	
 /* merges training data into a single struct.
  */
 FANN_EXTERNAL struct fann_train_data * FANN_API fann_merge_train_data(struct fann_train_data *data1, struct fann_train_data *data2);
diff --git a/src/include/fann_activation.h b/src/include/fann_activation.h
index c609fa6..c6d04f8 100644
--- a/src/include/fann_activation.h
+++ b/src/include/fann_activation.h
@@ -77,7 +77,7 @@ enum {
 	*/
 	FANN_SIGMOID_SYMMETRIC_STEPWISE,
 
-	/* Gausian activation function.
+	/* Gaussian activation function.
 	   0 when x = -inf, 1 when x = 0 and 0 when x = inf
 	   span: 0 < y < 1
 	   y = exp(-x*s*x*s)
@@ -85,6 +85,14 @@ enum {
 	*/
 	FANN_GAUSSIAN,
 
+	/* Symmetric gaussian activation function.
+	   -1 when x = -inf, 1 when x = 0 and 0 when x = inf
+	   span: -1 < y < 1
+	   y = exp(-x*s*x*s)*2-1
+	   d = -4*x*y*s
+	*/
+	FANN_GAUSSIAN_SYMMETRIC,
+
 	/* Stepwise linear approximation to gaussian.
 	   Faster than gaussian but a bit less precise.
 	   NOT implemented yet.
@@ -95,7 +103,6 @@ enum {
 	   span: 0 < y < 1
 	   y = ((x*s) / 2) / (1 + |x*s|) + 0.5
 	   d = s*1/(2*(1+|x|)*(1+|x|))
-	   NOT implemented yet.
 	*/
 	FANN_ELLIOT,
 
@@ -103,7 +110,6 @@ enum {
 	   span: -1 < y < 1   
 	   y = (x*s) / (1 + |x*s|)
 	   d = s*1/((1+|x|)*(1+|x|))
-	   NOT implemented yet.
 	*/
 	FANN_ELLIOT_SYMMETRIC
 };
@@ -117,6 +123,7 @@ static char const * const FANN_ACTIVATION_NAMES[] = {
 	"FANN_SIGMOID_SYMMETRIC",
 	"FANN_SIGMOID_SYMMETRIC_STEPWISE",
 	"FANN_GAUSSIAN",
+	"FANN_GAUSSIAN_SYMMETRIC",
 	"FANN_GAUSSIAN_STEPWISE",
 	"FANN_ELLIOT",
 	"FANN_ELLIOT_SYMMETRIC"
@@ -129,22 +136,35 @@ static char const * const FANN_ACTIVATION_NAMES[] = {
 
 /* defines used for the stepwise linear functions */
 
-#define fann_linear_func(v1, r1, v2, r2, value) ((((r2-r1) * (value-v1))/(v2-v1)) + r1)
-#define fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, min, max, value) (value < v5 ? (value < v3 ? (value < v2 ? (value < v1 ? min : fann_linear_func(v1, r1, v2, r2, value)) : fann_linear_func(v2, r2, v3, r3, value)) : (value < v4 ? fann_linear_func(v3, r3, v4, r4, value) : fann_linear_func(v4, r4, v5, r5, value))) : (value < v6 ? fann_linear_func(v5, r5, v6, r6, value) : max))
+#define fann_linear_func(v1, r1, v2, r2, sum) ((((r2-r1) * (sum-v1))/(v2-v1)) + r1)
+#define fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, min, max, sum) (sum < v5 ? (sum < v3 ? (sum < v2 ? (sum < v1 ? min : fann_linear_func(v1, r1, v2, r2, sum)) : fann_linear_func(v2, r2, v3, r3, sum)) : (sum < v4 ? fann_linear_func(v3, r3, v4, r4, sum) : fann_linear_func(v4, r4, v5, r5, sum))) : (sum < v6 ? fann_linear_func(v5, r5, v6, r6, sum) : max))
 
 /* FANN_LINEAR */
-#define fann_linear(steepness, value) fann_mult(steepness, value)
+#define fann_linear(steepness, sum) fann_mult(steepness, sum)
 #define fann_linear_derive(steepness, value) (steepness)
 
 /* FANN_SIGMOID */
-#define fann_sigmoid(steepness, value) (1.0f/(1.0f + exp(-2.0f * steepness * value)))
-#define fann_sigmoid_derive(steepness, value) (2.0f * steepness * value * (1.0f - value)) /* the plus is a trick to the derived function, to avoid getting stuck on flat spots */
+#define fann_sigmoid(steepness, sum) (1.0f/(1.0f + exp(-2.0f * steepness * sum)))
+#define fann_sigmoid_derive(steepness, value) (2.0f * steepness * value * (1.0f - value))
 
 /* FANN_SIGMOID_SYMMETRIC */
-#define fann_sigmoid_symmetric(steepness, value) (2.0f/(1.0f + exp(-2.0f * steepness * value)) - 1.0f)
+#define fann_sigmoid_symmetric(steepness, sum) (2.0f/(1.0f + exp(-2.0f * steepness * sum)) - 1.0f)
 #define fann_sigmoid_symmetric_derive(steepness, value) steepness * (1.0f - (value*value))
 
 /* FANN_GAUSSIAN */
-#define fann_gaussian(steepness, value) (exp(-value * steepness * value * steepness))
+#define fann_gaussian(steepness, sum) (exp(-sum * steepness * sum * steepness))
+#define fann_gaussian_derive(steepness, value, sum) (-2.0f * sum * value * steepness)
+
+/* FANN_GAUSSIAN_SYMMETRIC */
+#define fann_gaussian_symmetric(steepness, sum) ((exp(-sum * steepness * sum * steepness)*2.0)-1.0)
+#define fann_gaussian_symmetric_derive(steepness, value, sum) (-4.0f * sum * value * steepness)
+
+/* FANN_ELLIOT */
+#define fann_elliot(steepness, sum) (((sum * steepness) / 2.0f) / (1.0f + abs(sum * steepness)) + 0.5f)
+#define fann_elliot_derive(steepness, value, sum) (steepness * 1.0f / (2.0f * (1.0f + abs(sum)) * (1.0f + abs(sum))))
+
+/* FANN_ELLIOT_SYMMETRIC */
+#define fann_elliot_symmetric(steepness, sum) ((sum * steepness) / (1.0f + abs(sum * steepness)))
+#define fann_elliot_symmetric_derive(steepness, value, sum) (steepness * 1.0f / ((1.0f + abs(sum)) * (1.0f + abs(sum))))
 
 #endif
diff --git a/src/include/fann_internal.h b/src/include/fann_internal.h
index 1f54ce0..63ce38c 100644
--- a/src/include/fann_internal.h
+++ b/src/include/fann_internal.h
@@ -72,7 +72,7 @@ fann_type fann_activation(struct fann *ann, unsigned int is_output_layer,
 	fann_type value);
 
 fann_type fann_activation_derived(unsigned int activation_function,
-	fann_type steepness, fann_type value);
+	fann_type steepness, fann_type value, fann_type sum);
 
 /* called fann_max, in order to not interferre with predefined versions of max */
 #define fann_max(x, y) (((x) > (y)) ? (x) : (y))

-- 
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