--- /dev/null
+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);\r
+ }\r
+ \r
++\r
++int fann_is_NULL(struct fann *ann)\r
++{\r
++ return ann == NULL ? 1 : 0;\r
++}\r
+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"\r
+ %}\r
+ \r
+-%typemap(in) fann_type[ANY] {\r
++%define CHECKED_FLOAT_ARRAY(typemap_name, expected_length)\r
++%typemap(in) typemap_name {\r
+ int i;\r
+ if (!PySequence_Check($input)) {\r
+ PyErr_SetString(PyExc_ValueError,"Expected a sequence");\r
+- return NULL;\r
++ SWIG_fail;\r
+ }\r
+- if (PySequence_Length($input) == 0) {\r
+- PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements");\r
+- return NULL;\r
++ if (PySequence_Length($input) != expected_length) {\r
++ PyErr_SetString(PyExc_ValueError,"Sequence has wrong length");\r
++ SWIG_fail;\r
+ }\r
+ $1 = (float *) malloc(PySequence_Length($input)*sizeof(float));\r
+ for (i = 0; i < PySequence_Length($input); i++) {\r
+@@ -24,20 +25,29 @@
+ $1[i] = (float) PyFloat_AsDouble(o);\r
+ } else {\r
+ PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers"); \r
+- return NULL;\r
++ Py_DECREF(o);\r
++ SWIG_fail;\r
+ }\r
++ Py_DECREF(o);\r
+ }\r
+ }\r
++%typemap(freearg) typemap_name {\r
++ if ($1) free($1);\r
++}\r
++%enddef\r
++\r
++CHECKED_FLOAT_ARRAY(fann_type *input, arg1->num_input)\r
++CHECKED_FLOAT_ARRAY(fann_type *desired_output, arg1->num_output)\r
+ \r
+ %typemap(in) int[ANY] {\r
+ int i;\r
+ if (!PySequence_Check($input)) {\r
+ PyErr_SetString(PyExc_ValueError,"Expected a sequence");\r
+- return NULL;\r
++ SWIG_fail;\r
+ }\r
+ if (PySequence_Length($input) == 0) {\r
+ PyErr_SetString(PyExc_ValueError,"Size mismatch. Expected some elements");\r
+- return NULL;\r
++ SWIG_fail;\r
+ }\r
+ $1 = (unsigned int *) malloc(PySequence_Length($input)*sizeof(unsigned int));\r
+ for (i = 0; i < PySequence_Length($input); i++) {\r
+@@ -46,37 +56,41 @@
+ $1[i] = (int) PyInt_AsLong(o);\r
+ } else {\r
+ PyErr_SetString(PyExc_ValueError,"Sequence elements must be numbers"); \r
+- return NULL;\r
++ Py_DECREF(o);\r
++ SWIG_fail;\r
+ }\r
++ Py_DECREF(o);\r
+ }\r
+ }\r
+-\r
+-%typemap(freearg) fann_type* {\r
++%typemap(freearg) int[ANY] {\r
+ if ($1) free($1);\r
+ }\r
++%apply int[ANY] {int *, unsigned int*};\r
++\r
++typedef double fann_type; \r
+ \r
+ %typemap(out) PyObject* {\r
+ $result = $1;\r
+ }\r
+ \r
+-%apply fann_type[ANY] {fann_type *};\r
+-%apply int[ANY] {int *, unsigned int*};\r
++// create_array is used instead\r
++%ignore fann_create;\r
++%ignore fann_create_shortcut;\r
+ \r
+-#define FANN_INCLUDE\r
+-%varargs(10,int n = 0) fann_create;\r
+ %rename(fann_run_old) fann_run;\r
+ %rename(fann_run) fann_run2;\r
+ \r
+ %rename(fann_test_old) fann_test;\r
+ %rename(fann_test) fann_test2;\r
+ \r
++#define FANN_INCLUDE\r
+ %include "../src/include/fann.h"\r
+ %include "../src/include/fann_data.h"\r
++%include "../src/include/fann_activation.h"\r
+ \r
+ // Helper functions\r
+ PyObject* fann_run2(struct fann *ann, fann_type *input);\r
+ PyObject* fann_test2(struct fann *ann, fann_type *input, fann_type *desired_output);\r
+ PyObject* get_train_data_input(struct fann_train_data *ann, int row);\r
+ PyObject* get_train_data_output(struct fann_train_data *ann, int row);\r
+-\r
+-\r
++int fann_is_NULL(struct fann *ann);\r
+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 @@
+ """\r
+ override default distutils install_data, so we can copy\r
+ files directly, without splitting into modules, scripts,\r
+- packages, and extensions."\r
++ packages, and extensions.\r
+ """\r
+ def run(self):\r
+ # need to change self.install_dir to the actual library dir\r
+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']
++ )
++
projects / gentoo.git / commitdiff