[libfann] 80/242: API reference now includes all functions in the library. Began adding stuff from Steffen's report.
Christian Kastner
chrisk-guest at moszumanska.debian.org
Sat Oct 4 21:10:22 UTC 2014
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chrisk-guest pushed a commit to tag Version2_0_0
in repository libfann.
commit 90af5ea492abc102a90bfe6da1db4c6e90e182a5
Author: Evan Nemerson <evan at coeus-group.com>
Date: Mon Feb 16 09:50:45 2004 +0000
API reference now includes all functions in the library. Began adding stuff from Steffen's report.
---
doc/fann.xml | 1269 +++++++++++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 1206 insertions(+), 63 deletions(-)
diff --git a/doc/fann.xml b/doc/fann.xml
index 7261819..56ab4f5 100644
--- a/doc/fann.xml
+++ b/doc/fann.xml
@@ -1,7 +1,4 @@
<!-- $Id$ -->
-<!-- To compile this file, use
- jw -b html -o html fann.xml
- -->
<?xml version='1.0' encoding='ISO-8859-1' ?>
<!DOCTYPE book PUBLIC "-//OASIS//DTD DocBook XML V4.1.2//EN" "docbook/xml-dtd-4.1.2/docbookx.dtd">
<book>
@@ -194,12 +191,104 @@ int main()
</section>
</chapter>
- <chapter>
- <title>Artificial Neural Networks</title>
- <para>A short introduction to Artificial Neural Networks</para>
+ <chapter id="theory">
+ <title>Neural Network Theory</title>
+ <para>
+ This section will briefly explain the theory of neural networks (hereafter known
+ as NN) and artificial neural networks (hereafter known as ANN). For a more in depth
+ explanation of these concepts please consult the literature;
+ <xref linkend="bib.hassoun_1995" endterm="bib.hassoun_1995.abbrev"/> has good coverage
+ of most concepts of ANN and <xref linkend="bib.hertz_1991" endterm="bib.hertz_1991.abbrev"/>
+ describes the mathematics of ANN very thoroughly, while
+ <xref linkend="bib.anderson_1995" endterm="bib.anderson_1995.abbrev"/> has a more
+ psychological and physiological approach to NN and ANN. For the pragmatic I could recommend
+ <xref linkend="bib.tettamanzi_2001" endterm="bib.tettamanzi_2001.abbrev"/>, which has a short
+ and easily understandable introduction to NN and ANN.
+ </para>
- <section>
- <title>Training</title>
+ <section id="theory.neural_networks">
+ <title>Neural Networks</title>
+
+ <para>
+ The human brain is a highly complicated machine capable of solving very complex problems.
+ Although we have a good understanding of some of the basic operations that drive the brain,
+ we are still far from understanding everything there is to know about the brain.
+ </para>
+ <para>
+ In order to understand ANN, you will need to have a basic knowledge of how the internals of
+ the brain work. The brain is part of the central nervous system and consists of a very large
+ NN. The NN is actually quite complicated, but I will only include the details needed to
+ understand ANN, in order to simplify the explanation.
+ </para>
+ <para>
+ The NN is a network consisting of connected neurons. The center of the neuron is called the
+ nucleus. The nucleus is connected to other nucleuses by means of the dendrites and the axon.
+ This connection is called a synaptic connection.
+ </para>
+ <para>
+ The neuron can fire electric pulses through its synaptic connections, which is received at
+ the dendrites of other neurons.
+ </para>
+ <para>
+ When a neuron receives enough electric pulses through its dendrites, it activates and fires a
+ pulse through its axon, which is then received by other neurons. In this way information can
+ propagate through the NN. The synaptic connections change throughout the lifetime of a neuron
+ and the amount of incoming pulses needed to activate a neuron (the threshold) also change. This
+ behavior allows the NN to learn.
+ </para>
+ <para>
+ The human brain consists of around 10^11 neurons which are highly interconnected with around
+ 10^15 connections <xref linkend="bib.tettamanzi_2001" endterm="bib.tettamanzi_2001.abbrev"/>.
+ These neurons activates in parallel as an effect to internal and external sources. The brain is
+ connected to the rest of the nervous system, which allows it to receive information by means of
+ the five senses and also allows it to control the muscles.
+ </para>
+ </section>
+
+ <section id="theory.artificial_neural_networks">
+ <title>Artificial Neural Networks</title>
+ <para>
+ It is not possible (at the moment) to make an artificial brain, but it is possible to make
+ simplified artificial neurons and artificial neural networks. These ANNs can be made in many
+ different ways and can try to mimic the brain in many different ways.
+ </para>
+ <para>
+ ANNs are not intelligent, but they are good for recognizing patterns and making simple rules for
+ complex problems. They also have excellent training capabilities which is why they are often used
+ in artificial intelligence research.
+ </para>
+ <para>
+ ANNs are good at generalizing from a set of training data. E.g. this means an ANN given data about
+ a set of animals connected to a fact telling if they are mammals or not, is able to predict whether
+ an animal outside the original set is a mammal from its data. This is a very desirable feature of
+ ANNs, because you do not need to know the characteristics defining a mammal, the ANN will find out
+ by itself.
+ </para>
+ </section>
+
+ <section id="theory.training">
+ <title>Training an ANN</title>
+
+ <para>
+ When training an ANN with a set of input and output data, we wish to adjust the weights in the ANN,
+ to make the ANN give the same outputs as seen in the training data. On the other hand, we do not
+ want to make the ANN too specific, making it give precise results for the training data, but incorrect
+ results for all other data. When this happens, we say that the ANN has been over-fitted.
+ </para>
+ <para>
+ The training process can be seen as an optimization problem, where we wish to minimize the mean square
+ error of the entire set of training data. This problem can be solved in many different ways, ranging
+ from standard optimization heuristics like simulated annealing, through more special optimization
+ techniques like genetic algorithms to specialized gradient descent algorithms like backpropagation.
+ </para>
+ <para>
+ The most used algorithm is the backpropagation algorithm, but this algorithm has some limitations
+ concerning, the extent of adjustment to the weights in each iteration. This problem has been solved in
+ more advanced algorithms like RPROP
+ <xref linkend="bib.riedmiller_1993" endterm="bib.riedmiller_1993.abbrev"/> and quickprop
+ <xref linkend="bib.fahlman_1988" endterm="bib.fahlman_1988.abbrev"/>, but I will not elaborate further
+ on these algorithms.
+ </para>
</section>
</chapter>
@@ -355,7 +444,7 @@ int main()
</section>
<section id="api.sec.train_algo">
- <title>Training Algorithms</title>
+ <title>Training</title>
<refentry id="api.fann_train">
<refnamediv>
@@ -398,53 +487,638 @@ int main()
</refsect1>
</refentry>
- <refentry id="api.fann_get_MSE">
+ <refentry id="api.fann_get_MSE">
+ <refnamediv>
+ <refname>fann_get_MSE</refname>
+ <refpurpose>Return the mean square error of an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_MSE</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Reads the mean square error from the network.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_reset_MSE">
+ <refnamediv>
+ <refname>fann_reset_MSE</refname>
+ <refpurpose>Reset the mean square error of an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_reset_MSE</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Resets the mean square error from the network.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.train_data">
+ <title>Training Data</title>
+
+ <refentry id="api.fann_read_train_from_file">
+ <refnamediv>
+ <refname>fann_read_train_from_file</refname>
+ <refpurpose>Read training data from a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann_train_data *</type><methodname>fann_read_train_from_file</methodname>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_read_train_from_file</function> will load training data from a file.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_destroy_train">
+ <refnamediv>
+ <refname>fann_destroy_train</refname>
+ <refpurpose>Destroy training data.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_destroy_train_data</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>train_data</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Destroy the training data stored in <parameter>train_data</parameter>, freeing the associated memory.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_train_on_data">
+ <refnamediv>
+ <refname>fann_train_on_data</refname>
+ <refpurpose>Train an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_train_on_data</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>max_epochs</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>epochs_between_reports</parameter></methodparam>
+ <methodparam><type>float</type><parameter>desired_error</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Trains <parameter>ann</parameter> using <parameter>data</parameter> until <parameter>desired_error</parameter>
+ is reached, or until <parameter>max_epochs</parameter> is surpassed.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_train_on_data_callback">
+ <refnamediv>
+ <refname>fann_train_on_data_callback</refname>
+ <refpurpose>Train an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_train_on_data_callback</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>max_epochs</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>epochs_between_reports</parameter></methodparam>
+ <methodparam><type>float</type><parameter>desired_error</parameter></methodparam>
+ <methodparam><type>int</type><parameter>(*callback)(unsigned int epochs, float error)</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Trains <parameter>ann</parameter> using <parameter>data</parameter> until <parameter>desired_error</parameter>
+ is reached, or until <parameter>max_epochs</parameter> is surpassed.
+ </para>
+ <para>
+ This function behaves identically to <link linkend="api.fann_train_on_data"><function>fann_train_on_data</function></link>,
+ except that <function>fann_train_on_data_callback</function> allows you to specify a function to be called every
+ <parameter>epochs_between_reports</parameter> instead of using the default reporting mechanism.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_train_on_file">
+ <refnamediv>
+ <refname>fann_train_on_file</refname>
+ <refpurpose>Train an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_train_on_file</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>max_epochs</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>epochs_between_reports</parameter></methodparam>
+ <methodparam><type>float</type><parameter>desired_error</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Trains <parameter>ann</parameter> using the data in <parameter>filename</parameter> until
+ <parameter>desired_error</parameter> is reached, or until <parameter>max_epochs</parameter>
+ is surpassed.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_train_on_file_callback">
+ <refnamediv>
+ <refname>fann_train_on_file_callback</refname>
+ <refpurpose>Train an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_train_on_file_callback</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>max_epochs</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>epochs_between_reports</parameter></methodparam>
+ <methodparam><type>float</type><parameter>desired_error</parameter></methodparam>
+ <methodparam><type>int</type><parameter>(*callback)(unsigned int epochs, float error)</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Trains <parameter>ann</parameter> using the data in <parameter>filename</parameter> until
+ <parameter>desired_error</parameter> is reached, or until <parameter>max_epochs</parameter>
+ is surpassed.
+ </para>
+ <para>
+ This function behaves identically to <link linkend="api.fann_train_on_file"><function>fann_train_on_file</function></link>,
+ except that <function>fann_train_on_file_callback</function> allows you to specify a function to be called every
+ <parameter>epochs_between_reports</parameter> instead of using the default reporting mechanism.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_shuffle_train_data">
+ <refnamediv>
+ <refname>fann_shuffle_train_data</refname>
+ <refpurpose>Shuffle the training data.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_shuffle_train_data</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_shuffle_train_data</function> will randomize the order of the training data
+ contained in <parameter>data</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_merge_train_data">
+ <refnamediv>
+ <refname>fann_merge_train_data</refname>
+ <refpurpose>Merge two sets of training data.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann_train_data *</type><methodname>fann_merge_train_data</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>data1</parameter></methodparam>
+ <methodparam><type>struct fann_train_data *</type><parameter>data2</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_merge_train_data</function> will return a single set of training data which
+ contains all data from <parameter>data1</parameter> and <parameter>data2</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_duplicate_train_data">
+ <refnamediv>
+ <refname>fann_duplicate_train_data</refname>
+ <refpurpose>Copies a set of training data.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann_train_data *</type><methodname>fann_duplicate_train_data</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_duplicate_train_data</function> will return a copy of <parameter>data</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.options">
+ <title>Options</title>
+
+ <refentry id="api.fann_get_learning_rate">
+ <refnamediv>
+ <refname>fann_get_learning_rate</refname>
+ <refpurpose>Retrieve learning rate from a network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_learning_rate</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the learning rate for a given network.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_learning_rate">
+ <refnamediv>
+ <refname>fann_set_learning_rate</refname>
+ <refpurpose>Set a network's learning rate.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type></type><methodname>fann_set_learning_rate</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>float</type><parameter>learning_rate</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Set the learning rate of a network.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_activation_function_hidden">
+ <refnamediv>
+ <refname>fann_get_activation_function_hidden</refname>
+ <refpurpose>Get the activation function of the hidden layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_activation_function_hidden</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the activation function of the hidden layer.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_activation_function_hidden">
+ <refnamediv>
+ <refname>fann_set_activation_function_hidden</refname>
+ <refpurpose>Set the activation function for the hidden layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type></type><methodname>fann_set_activation_function_hidden</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>activation_function</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Set the activation function of the hidden layer to <parameter>activation_function></parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_activation_function_output">
+ <refnamediv>
+ <refname>fann_get_activation_function_output</refname>
+ <refpurpose>Get the activation function of the output layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_activation_function_output</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the activation function of the output layer.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_activation_function_output">
+ <refnamediv>
+ <refname>fann_set_activation_function_output</refname>
+ <refpurpose>Set the activation function for the output layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_function_output</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>activation_function</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Set the activation function of the output layer to <parameter>activation_function></parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_activation_hidden_steepness">
+ <refnamediv>
+ <refname>fann_get_activation_hidden_steepness</refname>
+ <refpurpose>Retrieve the steepness of the activation function of the hidden layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>fann_type</type><methodname>fann_get_activation_hidden_steepness</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the steepness of the activation function of the hidden layers.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_activation_hidden_steepness">
+ <refnamediv>
+ <refname>fann_set_activation_hidden_steepness</refname>
+ <refpurpose>Set the steepness of the activation function of the hidden layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_hidden_steepness</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>fann_type</type><parameter>steepness</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Set the steepness of the activation function of thie hidden layers of
+ <parameter>ann</parameter> to <parameter>steepness</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_activation_output_steepness">
+ <refnamediv>
+ <refname>fann_get_activation_output_steepness</refname>
+ <refpurpose>Retrieve the steepness of the activation function of the hidden layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>fann_type</type><methodname>fann_get_activation_output_steepness</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the steepness of the activation function of the hidden layers.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_activation_output_steepness">
+ <refnamediv>
+ <refname>fann_set_activation_output_steepness</refname>
+ <refpurpose>Set the steepness of the activation function of the hidden layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_output_steepness</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>fann_type</type><parameter>steepness</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Set the steepness of the activation function of thie hidden layers of
+ <parameter>ann</parameter> to <parameter>steepness</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_num_input">
+ <refnamediv>
+ <refname>fann_get_num_input</refname>
+ <refpurpose>Get the number of neurons in the input layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_num_input</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the number of neurons in the input layer of <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_num_output">
+ <refnamediv>
+ <refname>fann_get_num_output</refname>
+ <refpurpose>Get number of neurons in the output layer.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_num_output</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the number of neurons in the output layer of <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_total_neurons">
+ <refnamediv>
+ <refname>fann_get_total_neurons</refname>
+ <refpurpose>Get the total number of neurons in a network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_total_neurons</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the total number of neurons in <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_total_connections">
+ <refnamediv>
+ <refname>fann_get_total_connections</refname>
+ <refpurpose>Get the total number of connections in a network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_total_connections</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the total number of connections in <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_decimal_point">
+ <refnamediv>
+ <refname>fann_get_decimal_point</refname>
+ <refpurpose>Get the position of the decimal point.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_decimal_point</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the position of the decimal point in <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_multiplier">
+ <refnamediv>
+ <refname>fann_get_multiplier</refname>
+ <refpurpose>Get the multiplier.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type></type><methodname>fann_get_multiplier</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Return the multiplier that fix point data in <parameter>ann</parameter> is
+ multiplied with.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.errors">
+ <title>Error Handling</title>
+
+ <refentry id="api.fann_get_errno">
+ <refnamediv>
+ <refname>fann_get_errno</refname>
+ <refpurpose>Return the numerical representation of the last error.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>unsigned int</type><methodname>fann_get_errno</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Returns the numerical representation of the last error. The error codes are defined
+ in <!-- What do I put this in??? -->fann_errno.h<!-- /confusion -->.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_get_errstr">
+ <refnamediv>
+ <refname>fann_get_errstr</refname>
+ <refpurpose>Return the last error.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>char *</type><methodname>fann_get_errstr</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Returns the last error.
+ </para>
+ <para>
+ Note: This will reset the network's error- any subsequent calls to
+ <function>fann_get_errno</function> or <function>fann_get_errstr</function>
+ will yield 0 and NULL, respectively.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_reset_errno">
+ <refnamediv>
+ <refname>fann_reset_errno</refname>
+ <refpurpose>Reset the last error number.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_reset_errno</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Reset the last error number.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_reset_errstr">
+ <refnamediv>
+ <refname>fann_reset_errstr</refname>
+ <refpurpose>Reset the last error string.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_reset_errstr</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Reset the last error string.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_set_error_log">
<refnamediv>
- <refname>fann_get_MSE</refname>
- <refpurpose>Return the mean square error of an ANN.</refpurpose>
+ <refname>fann_set_error_log</refname>
+ <refpurpose>Set the error log to a file descriptor.</refpurpose>
</refnamediv>
<refsect1>
<title>Description</title>
- <methodsynopsis>
- <type>float</type><methodname>fann_get_MSE</methodname>
- <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
- </methodsynopsis>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_error_log</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>FILE *</type><parameter>log</parameter></methodparam>
+ </methodsynopsis>
<para>
- Reads the mean square error from the network.
+ Set the error log to <parameter>log</parameter>.
+ </para>
+ <para>
+ The error log defaults to stderr.
</para>
</refsect1>
</refentry>
- <refentry id="api.fann_reset_MSE">
+ <refentry id="api.fann_print_error">
<refnamediv>
- <refname>fann_reset_MSE</refname>
- <refpurpose>Reset the mean square error of an ANN.</refpurpose>
+ <refname>fann_print_error</refname>
+ <refpurpose>Print the last error to the error log.</refpurpose>
</refnamediv>
<refsect1>
<title>Description</title>
- <methodsynopsis>
- <type>void</type><methodname>fann_reset_MSE</methodname>
- <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
- </methodsynopsis>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_print_error_log</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
<para>
- Resets the mean square error from the network.
+ Prints the network's last error to the error log.
+ </para>
+ <para>
+ The error log defaults to stderr.
</para>
</refsect1>
</refentry>
</section>
- <section id="api.sec.train_data">
- <title>Training Data</title>
- </section>
-
- <section id="api.sec.options">
- <title>Options</title>
- </section>
-
- <section id="api.sec.errors">
- <title>Error Handling</title>
- </section>
-
<section id="api.sec.internal">
<title>Internal Functions</title>
<section id="api.sec.create_destroy.internal">
@@ -530,46 +1204,189 @@ int main()
</refsect1>
</refentry>
</section>
+
+ <section id="api.sec.train_data.internal">
+ <title>Training Data</title>
+
+ <refentry id="api.fann_save_train_internal">
+ <refnamediv>
+ <refname>fann_save_train_internal</refname>
+ <refpurpose>Save training data to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_save_train_internal</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>save_as_fixed</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>decimal_point</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Saves the data in <parameter>data</parameter> to <parameter>filename</parameter>.
+ <parameter>save_as_fixed</parameter> is either TRUE or FALSE.
+ <parameter>decimal_point</parameter> tells FANN where the decimal point may be if using
+ fixed point math. (Right?)
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_save_train_internal_fd">
+ <refnamediv>
+ <refname>fann_save_train_internal_fd</refname>
+ <refpurpose>Save training data to a file descriptor.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_save_train_internal_fd</methodname>
+ <methodparam><type>struct fann_train_data *</type><parameter>data</parameter></methodparam>
+ <methodparam><type>FILE *</type><parameter>file</parameter></methodparam>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>save_as_fixed</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>decimal_point</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Saves the data in <parameter>data</parameter> to <parameter>file</parameter>.
+ <parameter>save_as_fixed</parameter> is either TRUE or FALSE.
+ <parameter>decimal_point</parameter> tells FANN where the decimal point may be if using
+ fixed point math. (Right?)
+ </para>
+ <para>
+ <parameter>filename</parameter> is used for debugging output only.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_read_train_from_fd">
+ <refnamediv>
+ <refname>fann_read_train_from_fd</refname>
+ <refpurpose>Read training data from a file descriptor.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann_train_data *</type><methodname>fann_read_train_from_file</methodname>
+ <methodparam><type>FILE *</type><parameter>file</parameter></methodparam>
+ <methodparam><type>char *</type><parameter>filename</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_read_train_from_file</function> will load training data from the file
+ descriptor <parameter>file</parameter>.
+ </para>
+ <para>
+ <parameter>filename</parameter> is used for debugging output only.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.io.errors">
+ <title>Error Handling</title>
+
+ <refentry id="api.fann_error">
+ <refnamediv>
+ <refname>fann_error</refname>
+ <refpurpose>Throw an internal error.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_error</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>errno</parameter></methodparam>
+ <methodparam><parameter>...</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ This will set the network's error to correspond to <parameter>errno</parameter>.
+ The variable arguments depend (both in type and quantity) on <parameter>errno</parameter>.
+ Possible <parameter>errno</parameter> values are defined in fann_errno.h.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.options.internal">
+ <title>Options</title>
+
+ <refentry id="api.fann_update_stepwise_hidden">
+ <refnamediv>
+ <refname>fann_update_stepwise_hidden</refname>
+ <refpurpose>Adjust the stepwise function in the hidden layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_update_stepwise_hidden</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Update the stepwise function in the hidden layers of <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_update_stepwise_output">
+ <refnamediv>
+ <refname>fann_update_stepwise_output</refname>
+ <refpurpose>Adjust the stepwise functions in the output layers.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_update_stepwise_output</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ Update the stepwise function in the output layers of <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
</section>
<section id="api.sec.deprecated">
<title>Deprecated Functions</title>
- <refentry id="api.fann_get_error">
- <refnamediv>
- <refname>fann_get_error</refname>
- <refpurpose>Return the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
+ <section id="api.sec.error.deprecated">
+ <title>Error Handling</title>
+
+ <refentry id="api.fann_get_error">
+ <refnamediv>
+ <refname>fann_get_error</refname>
+ <refpurpose>Return the mean square error of an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
<methodsynopsis>
<type>float</type><methodname>fann_get_error</methodname>
<methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
</methodsynopsis>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_get_MSE"><function>fann_get_MSE</function></link> instead.
- </para>
- </refsect1>
- </refentry>
+ <para>
+ This function is deprecated and will be removed in a future version. Use
+ <link linkend="api.fann_get_MSE"><function>fann_get_MSE</function></link> instead.
+ </para>
+ </refsect1>
+ </refentry>
- <refentry id="api.fann_reset_error">
- <refnamediv>
- <refname>fann_get_error</refname>
- <refpurpose>Reset the mean square error of an ANN.</refpurpose>
- </refnamediv>
- <refsect1>
- <title>Description</title>
+ <refentry id="api.fann_reset_error">
+ <refnamediv>
+ <refname>fann_get_error</refname>
+ <refpurpose>Reset the mean square error of an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
<methodsynopsis>
<type>void</type><methodname>fann_reset_error</methodname>
<methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
</methodsynopsis>
- <para>
- This function is deprecated and will be removed in a future version. Use
- <link linkend="api.fann_reset_MSE"><function>fann_reset_MSE</function></link> instead.
- </para>
- </refsect1>
- </refentry>
+ <para>
+ This function is deprecated and will be removed in a future version. Use
+ <link linkend="api.fann_reset_MSE"><function>fann_reset_MSE</function></link> instead.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
</section>
</chapter>
@@ -1136,6 +1953,332 @@ else
</refentry>
</section>
</chapter>
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+ <firstname>S.</firstname>
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+ </author>
+ <author>
+ <firstname>S.</firstname>
+ <surname>Larsen</surname>
+ </author>
+ <author>
+ <firstname>S.</firstname>
+ <surname>Jensen</surname>
+ </author>
+ <pubdate>2003</pubdate>
+ <title>Real-time image processing of an iPAQ based robot (iBOT)</title>
+ <releaseinfo><ulink url="http://www.hamster.dk/~purple/robot/iBOT/report.pdf">http://www.hamster.dk/~purple/robot/iBOT/report.pdf</ulink></releaseinfo>
+ </biblioentry>
+
+ <biblioentry id="bib.OSDN_2003">
+ <abbrev id="bib.OSDN_2003.abbrev">[OSDN, 2003]</abbrev>
+ <pubdate>2003</pubdate>
+ <title>SourceForge.net</title>
+ <releaseinfo><ulink url="http://sourceforge.net/">http://sourceforge.net/</ulink></releaseinfo>
+ </biblioentry>
+
+ <biblioentry id="bib.pendleton_1993">
+ <abbrev id="bib.pendleton_1993.abbrev">[Pendleton, 1993]</abbrev>
+ <author>
+ <firstname>R.C.</firstname>
+ <surname>Pendleton</surname>
+ </author>
+ <pubdate>1993</pubdate>
+ <title>Doing it Fast</title>
+ <releaseinfo><ulink url="http://www.gameprogrammer.com/4-fixed.html">http://www.gameprogrammer.com/4-fixed.html</ulink></releaseinfo>
+ </biblioentry>
+
+ <biblioentry id="bib.prechelt_1994">
+ <abbrev id="bib.prechelt_1994.abbrev">[Prechelt, 1994]</abbrev>
+ <author>
+ <firstname>L.</firstname>
+ <surname>Prechelt</surname>
+ </author>
+ <pubdate>1994</pubdate>
+ <title>Proben1</title>
+ <subtitle>A set of neural network benchmark problems and benchmarking rules</subtitle>
+ </biblioentry>
+
+ <biblioentry id="bib.riedmiller_1993">
+ <abbrev id="bib.riedmiller_1993.abbrev">[Riedmiller and Braun, 1993]</abbrev>
+ <author>
+ <firstname></firstname>
+ <surname>Riedmiller</surname>
+ </author>
+ <author>
+ <firstname></firstname>
+ <surname>Braun</surname>
+ </author>
+ <pubdate>1993</pubdate>
+ <title>A direct adaptive method for faster backpropagation learning: The RPROP algorithm</title>
+ <pagenums>586-591</pagenums>
+ <releaseinfo><ulink url="http://citeseer.nj.nec.com/riedmiller93direct.html">http://citeseer.nj.nec.com/riedmiller93direct.html</ulink></releaseinfo>
+ </biblioentry>
+
+<!-- TODO:
+
+Sarle, 2002
+Sarle, W. S. (2002).
+Neural network faq.
+
+ftp://ftp.sas.com/pub/neural/FAQ2.html#A_binary.
+
+Software, 2002
+Software, W. (2002).
+Ann++.
+
+http://savannah.nongnu.org/projects/annpp/.
+
+Tettamanzi and Tomassini, 2001
+Tettamanzi, A. and Tomassini, M. (2001).
+Soft Computing.
+Springer-Verlag.
+
+van Rossum, 2003
+van Rossum, P. (2003).
+Lightweight neural network.
+
+http://lwneuralnet.sourceforge.net/.
+
+van Waveren, 2001
+van Waveren, J. P. (2001).
+The quake III arena bot.
+
+http://www.kbs.twi.tudelft.nl/Publications/MSc/2001-VanWaveren-MSc.html.
+
+Zell, 2003
+Zell, A. (2003).
+Stuttgart neural network simulator.
+
+http://www-ra.informatik.uni-tuebingen.de/SNNS/.
+-->
+ </bibliography>
</book>
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