python bindings patch by Martin Renold (official mailing list). http://old.homeip.net/martin/fann-python-bindings.patch

2 files changed, 418 insertions, 0 deletions

diff --git a/sci-mathematics/fann/files/digest-fann-1.2.0-r1 b/sci-mathematics/fann/files/digest-fann-1.2.0-r1
new file mode 100644
index 000000000000..24559c72c665
--- /dev/null
+++ b/sci-mathematics/fann/files/digest-fann-1.2.0-r1
@@ -0,0 +1 @@
+MD5 d655f82d4a47e4b697b0083fdaa78c71 fann-1.2.0.tar.bz2 2082660
diff --git a/sci-mathematics/fann/files/fann-1.2.0-r1.patch b/sci-mathematics/fann/files/fann-1.2.0-r1.patch
new file mode 100644
index 000000000000..12aae86dad7e
--- /dev/null
+++ b/sci-mathematics/fann/files/fann-1.2.0-r1.patch
@@ -0,0 +1,417 @@
+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']
++    )
++