path: root/sci-mathematics/fann/files/fann-1.2.0-r1.patch

diff -N -u -r fann-1.2.0.orig/python/examples/mushroom.py fann-1.2.0.my/python/examples/mushroom.py
--- fann-1.2.0.orig/python/examples/mushroom.py	2004-07-24 01:36:04.000000000 +0200
+++ fann-1.2.0.my/python/examples/mushroom.py	2005-01-03 20:48:21.000000000 +0100
@@ -20,9 +20,9 @@
 
 # start training the network
 print "Training network"
-ann.set_activation_function_hidden(fann.FANN_SIGMOID_SYMMETRIC_STEPWISE)
-ann.set_activation_function_output(fann.FANN_SIGMOID_STEPWISE)
-ann.set_training_algorithm(fann.FANN_TRAIN_INCREMENTAL)
+ann.set_activation_function_hidden(fann.SIGMOID_SYMMETRIC_STEPWISE)
+ann.set_activation_function_output(fann.SIGMOID_STEPWISE)
+ann.set_training_algorithm(fann.TRAIN_INCREMENTAL)
 	
 ann.train_on_data(train_data, max_iterations, iterations_between_reports, desired_error)
 	
@@ -40,9 +40,3 @@
 print "Saving network"
 ann.save("mushroom_float.net")
 
-# blow it all up
-print "Cleaning up."
-ann.destroy()
-test_data.destroy()
-train_data.destroy()
-
diff -N -u -r fann-1.2.0.orig/python/examples/simple_train.py fann-1.2.0.my/python/examples/simple_train.py
--- fann-1.2.0.orig/python/examples/simple_train.py	2004-07-24 01:35:58.000000000 +0200
+++ fann-1.2.0.my/python/examples/simple_train.py	2005-01-03 20:48:01.000000000 +0100
@@ -12,9 +12,9 @@
 iterations_between_reports = 1000
 
 ann = fann.create(connection_rate, learning_rate, (num_input, num_neurons_hidden, num_output))
+ann.set_activation_function_output(fann.SIGMOID_SYMMETRIC_STEPWISE)
 
 ann.train_on_file("datasets/xor.data", max_iterations, iterations_between_reports, desired_error)
 
 ann.save("xor_float.net")
 
-ann.destroy()
diff -N -u -r fann-1.2.0.orig/python/fann_helper.c fann-1.2.0.my/python/fann_helper.c
--- fann-1.2.0.orig/python/fann_helper.c	2004-07-26 09:52:30.000000000 +0200
+++ fann-1.2.0.my/python/fann_helper.c	2005-01-03 22:13:09.000000000 +0100
@@ -68,3 +68,8 @@
 	return get_row_from_double_array(t->output, row, t->num_output);
 }
 
+
+int fann_is_NULL(struct fann *ann)
+{
+	return ann == NULL ? 1 : 0;
+}
diff -N -u -r fann-1.2.0.orig/python/fann.py fann-1.2.0.my/python/fann.py
--- fann-1.2.0.orig/python/fann.py	2004-07-26 09:46:04.000000000 +0200
+++ fann-1.2.0.my/python/fann.py	2005-01-03 22:32:49.000000000 +0100
@@ -21,30 +21,23 @@
 
 import libfann
 
-# Activation function
-FANN_LINEAR = 0
-FANN_THRESHOLD = 1
-FANN_THRESHOLD_SYMMETRIC = 2
-FANN_SIGMOID = 3
-FANN_SIGMOID_STEPWISE = 4                # default
-FANN_SIGMOID_SYMMETRIC = 5
-FANN_SIGMOID_SYMMETRIC_STEPWISE = 6
-FANN_GAUSSIAN = 7
-FANN_GAUSSIAN_STEPWISE = 8
-FANN_ELLIOT = 9                          # not implemented yet
-FANN_ELLIOT_SYMMETRIC = 10               # not implemented yet
-
-# Training algorithm
-FANN_TRAIN_INCREMENTAL = 0
-FANN_TRAIN_BATCH = 1
-FANN_TRAIN_RPROP = 2
-FANN_TRAIN_QUICKPROP = 3
+# import all FANN_ constants without FANN_ prefix
+for name, value in libfann.__dict__.iteritems():
+    if name.startswith('FANN_') and not name.endswith('_NAMES'):
+        globals()[name[5:]] = value
+del name, value
 
 class fann_class:
 
     def __init__(self, ann):
+        """
+        Never call this directly.
+        """
         self.__ann = ann
-    
+
+    def __del__(self):
+        libfann.fann_destroy(self.__ann)
+
     def get_native_object(self):
         return self.__train_data
 
@@ -54,13 +47,6 @@
         """
         return libfann.fann_run(self.__ann, input)
 
-    def destroy(self):
-        """ 
-        Destructs the entire network.
-        Be sure to call this function after finished using the network.
-        """
-        libfann.fann_destroy(self.__ann)
-
     def randomize_weights(self, min_weight, max_weight):
         """
         Randomize weights (from the beginning the weights are random between -0.1 and 0.1)
@@ -198,31 +184,31 @@
         """
         libfann.fann_set_activation_function_output(self.__ann, activation_function)
 
-    def get_activation_hidden_steepness(self):
+    def get_activation_steepness_hidden(self):
         """
         Get the steepness parameter for the sigmoid function used in the hidden layers.
         """
-        return libfann.get_activation_hidden_steepness(self.__ann)
+        return libfann.get_activation_steepness_hidden(self.__ann)
 
-    def set_activation_hidden_steepness(self, steepness):
+    def set_activation_steepness_hidden(self, steepness):
         """
         Set the steepness of the sigmoid function used in the hidden layers.
         Only usefull if sigmoid function is used in the hidden layers (default 0.5).
         """
-        libfann.fann_set_activation_hidden_steepness(self.__ann, steepness)
+        libfann.fann_set_activation_steepness_hidden(self.__ann, steepness)
 
-    def get_activation_output_steepness(self):
+    def get_activation_steepness_output(self):
         """
         Get the steepness parameter for the sigmoid function used in the output layer.
         """
-        return libfann.fann_get_activation_output_steepness(self.__ann)
+        return libfann.fann_get_activation_steepness_output(self.__ann)
 
-    def set_activation_output_steepness(self, steepness):
+    def set_activation_steepness_output(self, steepness):
         """
         Set the steepness of the sigmoid function used in the output layer.
         Only usefull if sigmoid function is used in the output layer (default 0.5).
         """
-        libfann.fann_set_activation_output_steepness(self.__ann, steepness)
+        libfann.fann_set_activation_steepness_output(self.__ann, steepness)
 
     def train_on_data(self, data, max_epochs, epochs_between_reports, desired_error):
         """
@@ -269,7 +255,12 @@
 class train_class:
 
     def __init__(self, train_data):
+        """
+        Never call this directly.
+        """
         self.__train_data = train_data
+    def __del__(self):
+        libfann.fann_destroy_train(self.__train_data)
 
     def get_native_object(self):
         return self.__train_data
@@ -289,13 +280,6 @@
     def get_output(self, index):
 	return libfann.get_train_data_output(self.__train_data, index);
 
-    def destroy(self):
-        """
-        Destructs the training data
-        Be sure to call this function after finished using the training data.
-        """
-        libfann.fann_destroy_train(self.__train_data)
-
     def shuffle(self):
         """
         Shuffles training data, randomizing the order
@@ -317,12 +301,10 @@
 
     def merge(self, other):
         """
-        Merges training data into a single struct
+        Merges training data into a new struct
         """
         outcome = libfann.fann_merge_train_data(self.__train_data, other.get_native_object())
-        self.destroy()
-        self.__train_data = outcome
-        return self
+        return train_class(outcome)
 
     def duplicate(self):
         """
@@ -345,6 +327,8 @@
     When running the network, the bias nodes always emits 1
     """
     ann = libfann.fann_create_array(connection_rate, learning_rate, len(layers), layers)
+    if libfann.fann_is_NULL(ann):
+        return None # probably won't happen
     return fann_class(ann)
 
 def create_from_file(filename):
@@ -352,6 +336,8 @@
     Constructs a backpropagation neural network from a configuration file.
     """
     ann = libfann.fann_create_from_file(filename)
+    if libfann.fann_is_NULL(ann):
+        raise IOError, "Could not load ann from file '%s'" + filename
     return fann_class(ann)
 
 def read_train_from_file(filename):
diff -N -u -r fann-1.2.0.orig/python/libfann.i fann-1.2.0.my/python/libfann.i
--- fann-1.2.0.orig/python/libfann.i	2004-07-20 00:21:20.000000000 +0200
+++ fann-1.2.0.my/python/libfann.i	2005-01-03 22:58:56.000000000 +0100
@@ -7,15 +7,16 @@
 #include "../src/include/fann.h"
 %}
 
-%typemap(in) fann_type[ANY] {
+%define CHECKED_FLOAT_ARRAY(typemap_name, expected_length)
+%typemap(in) typemap_name {
   int i;
   if (!PySequence_Check($input)) {
     PyErr_SetString(PyExc_ValueError,"Expected a sequence");
-    return NULL;
+    SWIG_fail;
   }
-  if (PySequence_Length($input) == 0) {
-    PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements");
-    return NULL;
+  if (PySequence_Length($input) != expected_length) {
+    PyErr_SetString(PyExc_ValueError,"Sequence has wrong length");
+    SWIG_fail;
   }
   $1 = (float *) malloc(PySequence_Length($input)*sizeof(float));
   for (i = 0; i < PySequence_Length($input); i++) {
@@ -24,20 +25,29 @@
       $1[i] = (float) PyFloat_AsDouble(o);
     } else {
       PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers");      
-      return NULL;
+      Py_DECREF(o);
+      SWIG_fail;
     }
+    Py_DECREF(o);
   }
 }
+%typemap(freearg) typemap_name {
+   if ($1) free($1);
+}
+%enddef
+
+CHECKED_FLOAT_ARRAY(fann_type *input, arg1->num_input)
+CHECKED_FLOAT_ARRAY(fann_type *desired_output, arg1->num_output)
 
 %typemap(in) int[ANY] {
   int i;
   if (!PySequence_Check($input)) {
     PyErr_SetString(PyExc_ValueError,"Expected a sequence");
-    return NULL;
+    SWIG_fail;
   }
   if (PySequence_Length($input) == 0) {
     PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements");
-    return NULL;
+    SWIG_fail;
   }
   $1 = (unsigned int *) malloc(PySequence_Length($input)*sizeof(unsigned int));
   for (i = 0; i < PySequence_Length($input); i++) {
@@ -46,37 +56,41 @@
       $1[i] = (int) PyInt_AsLong(o);
     } else {
       PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers");      
-      return NULL;
+      Py_DECREF(o);
+      SWIG_fail;
     }
+    Py_DECREF(o);
   }
 }
-
-%typemap(freearg) fann_type* {
+%typemap(freearg) int[ANY] {
    if ($1) free($1);
 }
+%apply int[ANY] {int *, unsigned int*};
+
+typedef double fann_type; 
 
 %typemap(out) PyObject* {
   $result = $1;
 }
 
-%apply fann_type[ANY] {fann_type *};
-%apply int[ANY] {int *, unsigned int*};
+// create_array is used instead
+%ignore fann_create;
+%ignore fann_create_shortcut;
 
-#define FANN_INCLUDE
-%varargs(10,int n = 0) fann_create;
 %rename(fann_run_old) fann_run;
 %rename(fann_run) fann_run2;
 
 %rename(fann_test_old) fann_test;
 %rename(fann_test) fann_test2;
 
+#define FANN_INCLUDE
 %include "../src/include/fann.h"
 %include "../src/include/fann_data.h"
+%include "../src/include/fann_activation.h"
 
 // Helper functions
 PyObject* fann_run2(struct fann *ann, fann_type *input);
 PyObject* fann_test2(struct fann *ann, fann_type *input, fann_type *desired_output);
 PyObject* get_train_data_input(struct fann_train_data *ann, int row);
 PyObject* get_train_data_output(struct fann_train_data *ann, int row);
-
-
+int fann_is_NULL(struct fann *ann);
diff -N -u -r fann-1.2.0.orig/python/makefile.gnu fann-1.2.0.my/python/makefile.gnu
--- fann-1.2.0.orig/python/makefile.gnu	2004-10-09 13:56:57.000000000 +0200
+++ fann-1.2.0.my/python/makefile.gnu	2005-01-02 16:52:23.000000000 +0100
@@ -1,5 +1,7 @@
 # This makefile was written to compile a distribution of pyfann for
 # GNU platforms (cygwin included.)
+#
+# This is NOT needed for Linux/Unix, use setup_unix.py instead.
 
 TARGETS = _libfann.dll
 
diff -N -u -r fann-1.2.0.orig/python/README fann-1.2.0.my/python/README
--- fann-1.2.0.orig/python/README	2004-08-06 10:54:13.000000000 +0200
+++ fann-1.2.0.my/python/README	2005-01-02 17:00:28.000000000 +0100
@@ -1,6 +1,9 @@
 This python binding is provided by Vincenzo Di Massa <hawk.it@tiscalinet.it>
 and Gil Megidish <gil@megidish.net>
 
+Instructions for Windows:
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
 MAKE
 Make sure to make the fann library first. You are required to have
 swig and python development files installed. After you compiled the
@@ -24,3 +27,17 @@
 
 USAGE
 Just import fann.
+
+Instructions for Unix/Linux:
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+First build and install the fann library. Then run:
+
+./setup_unix.py build
+./setup_unix.py install
+
+Install alone will work too, if you run it twice (a small bug).
+The examples/ (not installed) should work now after you copy the datasets:
+
+mkdir examples/datasets
+cp ../examples/xor.data ../benchmarks/datasets/mushroom.* examples/datasets/
diff -N -u -r fann-1.2.0.orig/python/setup.py fann-1.2.0.my/python/setup.py
--- fann-1.2.0.orig/python/setup.py	2004-07-26 09:56:59.000000000 +0200
+++ fann-1.2.0.my/python/setup.py	2005-01-02 16:49:45.000000000 +0100
@@ -22,7 +22,7 @@
     """
     override default distutils install_data, so we can copy
     files directly, without splitting into modules, scripts,
-    packages, and extensions."
+    packages, and extensions.
     """
     def run(self):
         # need to change self.install_dir to the actual library dir
diff -N -u -r fann-1.2.0.orig/python/setup_unix.py fann-1.2.0.my/python/setup_unix.py
--- fann-1.2.0.orig/python/setup_unix.py	1970-01-01 01:00:00.000000000 +0100
+++ fann-1.2.0.my/python/setup_unix.py	2005-01-02 16:27:17.000000000 +0100
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+from distutils.core import setup, Extension
+#from glob import glob
+
+VERSION='1.2.0'
+
+LONG_DESCRIPTION="""\
+Fast Artificial Neural Network Library implements multilayer
+artificial neural networks with support for both fully connected
+and sparsely connected networks. It includes a framework for easy 
+handling of training data sets. It is easy to use, versatile, well 
+documented, and fast. 
+"""
+
+module1 = Extension(
+    '_libfann', 
+    sources = ['libfann.i', 'fann_helper.c'], 
+    libraries = ['fann'],
+    #extra_objects = glob('../src/fann*.o'),
+    )
+
+setup(
+    name='pyfann',
+    version=VERSION,
+    description='Fast Artificial Neural Network Library (fann)',
+    long_description=LONG_DESCRIPTION,
+    author='Steffen Nissen',
+    author_email='lukesky@diku.dk',
+    maintainer='Gil Megidish',
+    maintainer_email='gil@megidish.net',
+    url='http://sourceforge.net/projects/fann/',
+    license='GNU LESSER GENERAL PUBLIC LICENSE (LGPL)',
+    platforms='UNIX',
+
+    ext_modules = [module1],
+    py_modules = ['libfann', 'fann']
+    )
+