[libfann] 77/242: Initial import of DocBook XML documentation.
Christian Kastner
chrisk-guest at moszumanska.debian.org
Sat Oct 4 21:10:21 UTC 2014
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 eeedcb64e65ccf16e5ea648a65ec72bdf52a9a17
Author: Evan Nemerson <evan at coeus-group.com>
Date: Sat Feb 14 11:05:33 2004 +0000
Initial import of DocBook XML documentation.
---
doc/fann.xml | 1002 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 1002 insertions(+)
diff --git a/doc/fann.xml b/doc/fann.xml
new file mode 100644
index 0000000..5678d02
--- /dev/null
+++ b/doc/fann.xml
@@ -0,0 +1,1002 @@
+<!-- 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>
+ <bookinfo id="bookinfo">
+ <title>Fast Artificial Neural Network Library</title>
+ <authorgroup id="authors">
+ <author>
+ <firstname>Steffen</firstname>
+ <surname>Nissen</surname>
+ </author>
+ <author>
+ <firstname>Evan</firstname>
+ <surname>Nemerson</surname>
+ </author>
+ </authorgroup>
+ <copyright>
+ <year>2004</year>
+ </copyright>
+ </bookinfo>
+
+ <chapter id="intro">
+ <title>Introduction</title>
+ <para>
+ fann - Fast Artificial Neural Network Library is written in ANSI C. The
+ library implements multilayer feedforward ANNs, up to 150 times faster
+ than other libraries. FANN supports execution in fixed point, for fast
+ execution on systems like the iPAQ.
+ </para>
+
+ <section id="intro.install">
+ <title>Installation</title>
+
+ <section id="intro.install.rpm">
+ <title>RPMs</title>
+ <para>
+ RPMs are a simple way to manage packages, and is used on many common
+ Linux distributions such as <ulink url="http://www.redhat.com">Red Hat</ulink>
+ and <ulink url="http://www.mandrake.com/">Mandrake</ulink>.
+ </para>
+ <para>
+ After downloading FANN, simply run (as root) the following command:
+ <command>rpm -ivh $PATH_TO_RPM</command>
+ </para>
+ </section>
+
+ <section id="intro.install.deb">
+ <title>DEBs</title>
+ <para>
+ Dunno- never used dpkg. Steffen?
+ </para>
+ </section>
+
+ <section id="intro.install.win32">
+ <title>Windows</title>
+ <para>
+ Instructions for Borland & VC++
+ </para>
+ </section>
+
+ <section id="intro.install.src">
+ <title>Compiling from source</title>
+ <para>
+ Compiling FANN from source code entails the standard GNU autotools technique. First,
+ configure the package as you want it by typing (in the FANN directory), <command>
+ ./configure</command> If you need help choosing the options you would like to use,
+ try <command>./configure --help</command>
+ </para>
+ <para>
+ Next, you have to actually compile the library. To do this, simply type <command>make
+ </command>
+ </para>
+ <para>
+ Finally, to install the library, type <command>make install</command> Odds are you will
+ have to be root to install, so you may need to <command>su</command> to root before installing.
+ Please remember to log out of the root account immediately after <command>make install
+ </command> finishes.
+ </para>
+ </section>
+ </section>
+
+ <section id="intro.start">
+ <title>Getting Started</title>
+ <para>
+ An ANN is normally run in two different modes, a training mode and an execution mode.
+ Although it is possible to do this in the same program, I will recommend doing it in two
+ different programs.
+ </para>
+ <para>
+ There are several reasons to why it is usually a good idea to write the training and
+ execution in two different programs, but the most obvious is the fact that a typical ANN
+ system is only trained once, while it is executed many times.
+ </para>
+ <section id="intro.start.train">
+ <title>Training</title>
+ <para>
+ The following is a simple program which trains an ANN with a data set and then saves the
+ ANN to a file.
+ <example>
+ <title>Simple training example</title>
+ <programlisting>
+<![CDATA[
+#include "fann.h"
+
+int main()
+{
+ const float connection_rate = 1;
+ const float learning_rate = 0.7;
+ const unsigned int num_input = 2;
+ const unsigned int num_output = 1;
+ const unsigned int num_layers = 3;
+ const unsigned int num_neurons_hidden = 4;
+ const float desired_error = 0.0001;
+ const unsigned int max_iterations = 500000;
+ const unsigned int iterations_between_reports = 1000;
+
+ struct fann *ann = fann_create(connection_rate, learning_rate, num_layers,
+ num_input, num_neurons_hidden, num_output);
+
+ fann_train_on_file(ann, "xor.data", max_iterations,
+ iterations_between_reports, desired_error);
+
+ fann_save(ann, "xor_float.net");
+
+ fann_destroy(ann);
+
+ return 0;
+}
+]]>
+ </programlisting>
+ </example>
+ </para>
+ <para>
+ The file xor.data, used to train the xor function:
+ <literallayout>
+4 2 1
+0 0
+0
+0 1
+1
+1 0
+1
+1 1
+0
+ </literallayout>The first line consists of three numbers:
+ The first is the number of training pairs in the file, the
+ second is the number of inputs and the third is the number
+ of outputs. The rest of the file is the actual training data,
+ consisting of one line with inputs, one with outputs etc.
+ </para>
+ <para>
+ TODO: Link up functions to API reference.
+ </para>
+ </section>
+
+ <section id="intro.start.execution">
+ <title>Execution</title>
+ <para>
+ The following example shows a simple program which executes a single
+ input on the ANN. The program introduces two new functions which were
+ not used in the traiining procedure, as well as the fann_type type.
+ <example>
+ <title>Simple training example</title>
+ <programlisting>
+<![CDATA[
+#include <stdio.h>
+#include "floatfann.h"
+
+int main()
+{
+ fann_type *calc_out;
+ fann_type input[2];
+
+ struct fann *ann = fann_create_from_file("xor_float.net");
+
+ input[0] = 0;
+ input[1] = 1;
+ calc_out = fann_run(ann, input);
+
+ printf("xor test (%f,%f) -> %f\n",
+ input[0], input[1], *calc_out);
+
+ fann_destroy(ann);
+ return 0;
+}
+]]>
+ </programlisting>
+ </example>
+ </para>
+ </section>
+ </section>
+ </chapter>
+
+ <chapter>
+ <title>Artificial Neural Networks</title>
+ <para>A short introduction to Artificial Neural Networks</para>
+
+ <section>
+ <title>Training</title>
+ </section>
+ </chapter>
+
+ <chapter id="api">
+ <title>API Reference</title>
+ <para>
+ This is a list of all functions in FANN.
+ </para>
+
+ <section id="api.sec.create_destroy">
+ <title>Creation and Destruction</title>
+
+ <refentry id="api.fann_create">
+ <refnamediv>
+ <refname>fann_create</refname>
+ <refpurpose>Save an artificial neural network to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann *</type><methodname>fann_create</methodname>
+ <methodparam><type>float</type><parameter>connection_rate</parameter></methodparam>
+ <methodparam><type>float</type><parameter>learning_rate</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>num_layers</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>...</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_create</function> will create a new artificial neural network, and return
+ a pointer to it.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_create_array">
+ <refnamediv>
+ <refname>fann_create_array</refname>
+ <refpurpose>Save an artificial neural network to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann *</type><methodname>fann_create_array</methodname>
+ <methodparam><type>float</type><parameter>connection_rate</parameter></methodparam>
+ <methodparam><type>float</type><parameter>learning_rate</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>num_layers</parameter></methodparam>
+ <methodparam><type>unsigned int *</type><parameter>neurons_per_layer</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_create_array</function> will create a new artificial neural network, and return
+ a pointer to it. It is the same as <function>fann_create</function>, only it accepts an array
+ as its final parameter instead of variable arguments.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_destroy">
+ <refnamediv>
+ <refname>fann_destroy</refname>
+ <refpurpose>Destroy an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_destroy</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_destroy</function> will destroy an artificial neural network, properly
+ freeing all associated memory.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_run">
+ <refnamediv>
+ <refname>fann_run</refname>
+ <refpurpose>Run an ANN.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>fann_type *</type><methodname>fann_run</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>fann_type *</type><parameter>input</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_run</function> will run <parameter>input</parameter> through
+ <parameter>ann</parameter>, returning an array of outputs, the number of which
+ being equal to the number of neurons in the output layer.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.io">
+ <title>Input/Output</title>
+
+ <refentry id="api.fann_save">
+ <refnamediv>
+ <refname>fann_save</refname>
+ <refpurpose>Save an ANN to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_save</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>const char *</type><parameter>configuration_file</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_save</function> will attempt to save <parameter>ann</parameter>
+ to the file located at <parameter>configuration_file</parameter>
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_save_to_fixed">
+ <refnamediv>
+ <refname>fann_save_to_fixed</refname>
+ <refpurpose>Save an ANN to a fixed-point file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_save_to_fixed</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>const char *</type><parameter>configuration_file</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_save_fixed</function> will attempt to save <parameter>ann</parameter>
+ to the file located at <parameter>configuration_file</parameter> as a fixed-point netowrk.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="api.fann_create_from_file">
+ <refnamediv>
+ <refname>fann_create_from_file</refname>
+ <refpurpose>Load an ANN from a file..</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann *</type><methodname>fann_create_from_file</methodname>
+ <methodparam><type>const char *</type><parameter>configuration_file</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_create_from_file</function> will attempt to load an artificial neural netowrk
+ from a file.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.train_algo">
+ <title>Training Algorithms</title>
+ </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>
+ <title>Internal Functions</title>
+ <section id="api.sec.create_destroy.internal">
+ <title>Creation And Destruction</title>
+ <refentry id="api.fann_allocate_structure">
+ <refnamediv>
+ <refname>fann_allocate_structure</refname>
+ <refpurpose>Allocate the core elements of a <type>struct fann</type>.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>struct fann *</type><methodname>fann_allocate_structure</methodname>
+ <methodparam><type>float</type><parameter>learning_rate</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>num_layers</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_allocate_structure</function> is used internally to create a
+ <type>struct fann</type>.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+
+ <section id="api.sec.io.internal">
+ <title>Input/Output</title>
+ <refentry id="api.fann_save_internal">
+ <refnamediv>
+ <refname>fann_save_internal</refname>
+ <refpurpose>Save an ANN to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_save_internal</methodname>
+ <methodparam><type>struct fann *</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>const char *</type><parameter>configuration_file</parameter></methodparam>
+ <methodparam><type>unsigned int</type><parameter>save_as_fixed</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_allocate_structure</function> is used internally to create a
+ <type>struct fann</type>.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+ </section>
+ </chapter>
+
+ <chapter id="php">
+ <title>PHP Extension</title>
+ <para>
+ These functions allow you to interact with the FANN library from PHP.
+ </para>
+ <para>
+ This extension requires the
+ <ulink url="http://fann.sf.net/">FANN</ulink> library,
+ version 1.0.6 or later.
+ </para>
+ <para>
+ The following activation functions are supported:
+ <itemizedlist>
+ <listitem><simpara>FANN_SIGMOID</simpara></listitem>
+ <listitem><simpara>FANN_THRESHOLD</simpara></listitem>
+ <listitem><simpara>FANN_SIGMOID_STEPWISE</simpara></listitem>
+ </itemizedlist>
+ </para>
+
+ <section id="php.api">
+ <title>API Reference</title>
+ <refentry id="function.fann_create">
+ <refnamediv>
+ <refname>fann_create</refname>
+ <refpurpose>Creates an artificial neural network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>mixed</type><methodname>fann_create</methodname>
+ <methodparam><type>array</type><parameter>data</parameter></methodparam>
+ <methodparam choice="opt"><type>float</type><parameter>connection_rate</parameter></methodparam>
+ <methodparam choice="opt"><type>float</type><parameter>learning_rate</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_create</function> will create an artificial neural
+ network using the data given.
+ </para>
+ <para>
+ If the first parameter is an array, <function>fann_create</function>
+ will use the data and structure of the array, as well as
+ <parameter>connection_rate</parameter> and
+ <parameter>learning_rate</parameter>.
+ </para>
+ <para>
+ If <function>fann_create</function> is called with a sole string argument,
+ it will attempt to load an ANN created with <function>fann_save</function>
+ from the file at <parameter>filename</parameter>.
+ </para>
+ <para>
+ <function>fann_create</function> will return the artificial neural network
+ on success, or FALSE if it fails.
+ </para>
+ <para>
+ <example>
+ <title><function>fann_create</function> from scratch</title>
+ <programlisting role="php">
+<![CDATA[
+<?php
+$ann = fann_create(
+ /* Layers. In this case, three layers-
+ * two input neurons, 4 neurons on a
+ * hidden layer, and one output neuron. */
+ array(2, 4, 1),
+ 1.0,
+ 0.7);
+?>
+]]>
+ </programlisting>
+ </example>
+ </para>
+ <para>
+ <example>
+ <title><function>fann_create</function> loading from a file</title>
+ <programlisting role="php">
+<![CDATA[
+<?php
+$ann = fann_create("http://www.example.com/ann.net");
+);
+?>
+]]>
+ </programlisting>
+ </example>
+ </para>
+ <para>
+ See also <function>fann_save</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_train">
+ <refnamediv>
+ <refname>fann_train</refname>
+ <refpurpose>Train an artificial neural network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>bool</type><methodname>fann_train</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>mixed</type><parameter>data</parameter></methodparam>
+ <methodparam><type>int</type><parameter>max_iterations</parameter></methodparam>
+ <methodparam><type>double</type><parameter>desired_error</parameter></methodparam>
+ <methodparam choice="opt"><type>int</type><parameter>iterations_between_reports</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_train</function> will train <parameter>ann</parameter> on
+ the data supplied, returning TRUE on success or FALSE on failure.
+ </para>
+ <para>
+ Resources is anrtificial neural network returned by <function>fann_create</function>.
+ </para>
+ <para>
+ <parameter>data</parameter> must be either an array of training data, or
+ the URI of a properly formatted training file.
+ </para>
+ <para>
+ <function>fann_train</function> will continue training until
+ <parameter>desired_error</parameter> is reached, or
+ <parameter>max_iterations</parameter> is exceeded.
+ </para>
+ <para>
+ If <parameter>iterations_between_reports</parameter> is set,
+ <function>fann_create</function> will output a short progress
+ report every <parameter>iterations_between_reports</parameter>.
+ Default is 0 (meaning no reports).
+ </para>
+ <para>
+ <example>
+ <title><function>fann_create</function> from training data</title>
+ <programlisting role="php">
+<![CDATA[
+<?php
+$ann = fann_create(array(2, 4, 1), 1.0, 0.7);
+if ( fann_train($ann,
+ array(
+ array(
+ array(0,0), /* Input(s) */
+ array(0) /* Output(s) */
+ ),
+ array(
+ array(0,1), /* Input(s) */
+ array(1) /* Output(s) */
+ ),
+ array(
+ array(1,0), /* Input(s) */
+ array(1) /* Output(s) */
+ ),
+ array(array(1,1), /* Input(s) */
+ array(0) /* Output(s) */
+ )
+ ),
+ 100000,
+ 0.00001,
+ 1000) == FALSE) {
+ exit('Could not train $ann.');
+}
+?>
+]]>
+ </programlisting>
+ </example>
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_save">
+ <refnamediv>
+ <refname>fann_save</refname>
+ <refpurpose>Save an artificial neural network to a file.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>bool</type><methodname>fann_save</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>string</type><parameter>filename</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_save</function> will save <parameter>ann</parameter> to
+ <parameter>filename</parameter>, returning TRUE on success or FALSE on failure.
+ </para>
+ <para>
+ See also <function>fann_create</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_run">
+ <refnamediv>
+ <refname>fann_run</refname>
+ <refpurpose>Run an artificial neural network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>mixed</type><methodname>fann_run</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>array</type><parameter>input</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_run</function> will run <parameter>input</parameter> through
+ <parameter>ann</parameter>, returning an an ouput array on success or FALSE
+ on failure.
+ </para>
+ <para>
+ <example>
+ <title><function>fann_run</function> Example</title>
+ <programlisting role="php">
+<![CDATA[
+<?php
+if ( ($ann = fann_create("http://www.example.com/ann.net")) == FALSE )
+ exit("Could not create ANN.");
+if ( fann_train($ann, "http://www.example.com/train.data", 100000, 0.00001) == FALSE )
+ exit("Could not train ANN.");
+
+if ( ($output = fann_run($ann, array(0, 1))) == FALSE )
+ exit("Could not run ANN.");
+else
+ print_r($output);
+?>
+]]>
+ </programlisting>
+ </example>
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_randomize_weights">
+ <refnamediv>
+ <refname>fann_randomize_weights</refname>
+ <refpurpose>Randomize the weights of the neurons in the network.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_save</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam choice="opt"><type>float</type><parameter>minimum</parameter></methodparam>
+ <methodparam choice="opt"><type>float</type><parameter>maximum</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_randomize_weights</function> will randomize the weights of all neurons in
+ <parameter>ann</parameter>, effectively resetting the network.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_MSE">
+ <refnamediv>
+ <refname>fann_get_MSE</refname>
+ <refpurpose>Get the mean squared error.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_MSE</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_MSE</function> will return the mean squared error (MSE) of
+ <parameter>ann</parameter>, or 0 if it is unavailable.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_num_input">
+ <refnamediv>
+ <refname>fann_get_num_input</refname>
+ <refpurpose>Get the number of input neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_num_input</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_num_input</function> will return the number of input neurons in
+ <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_get_num_output</function>, <function>fann_get_total_neurons</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_num_output">
+ <refnamediv>
+ <refname>fann_get_num_output</refname>
+ <refpurpose>Get the number of output neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_num_output</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_num_output</function> will return the number of output neurons in
+ <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_get_num_input</function>, <function>fann_get_total_neurons</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_total_neurons">
+ <refnamediv>
+ <refname>fann_get_total_neurons</refname>
+ <refpurpose>Get the total number of neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_total_neurons</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_total_neurons</function> will return the total number of neurons in
+ <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_get_num_input</function>, <function>fann_get_num_output</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_total_connections">
+ <refnamediv>
+ <refname>fann_get_total_connections</refname>
+ <refpurpose>Get the total number of connections.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_total_connections</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_total_connections</function> will return the total number of connections in
+ <parameter>ann</parameter>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_learning_rate">
+ <refnamediv>
+ <refname>fann_get_learning_rate</refname>
+ <refpurpose>Get the learning rate.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_learning_rate</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_learning_rate</function> will return the learning rate of
+ <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_learning_rate</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_activation_function_hidden">
+ <refnamediv>
+ <refname>fann_get_activation_function_hidden</refname>
+ <refpurpose>Get the activation function of the hidden neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_activation_function_hidden</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_activation_function_hidden</function> will return the activation function
+ for the hidden neurons in <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_activation_function_hidden</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_activation_function_output">
+ <refnamediv>
+ <refname>fann_get_activation_function_output</refname>
+ <refpurpose>Get the activation function of the output neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>int</type><methodname>fann_get_activation_function_output</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_activation_function_output</function> will return the activation function
+ for the output neurons in <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_activation_function_output</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_activation_hidden_steepness">
+ <refnamediv>
+ <refname>fann_get_activation_hidden_steepness</refname>
+ <refpurpose>Get the steepness of the activation function for the hidden neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_activation_hidden_steepness</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_activation_hidden_steepness</function> will return the steepness of the
+ activation function for the hidden neurons in <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_activation_function_hidden_steepness</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_get_activation_output_steepness">
+ <refnamediv>
+ <refname>fann_get_activation_output_steepness</refname>
+ <refpurpose>Get the steepness of the activation function for the output neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_get_activation_output_steepness</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_get_activation_output_steepness</function> will return the steepness of the
+ activation function for the output neurons in <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_activation_output_steepness</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_set_learning_rate">
+ <refnamediv>
+ <refname>fann_set_learning_rate</refname>
+ <refpurpose>Set the learning rate.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>float</type><methodname>fann_set_learning_rate</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_set_learning_rate</function> will return the learning rate of
+ <parameter>ann</parameter>.
+ </para>
+ <para>
+ See also <function>fann_set_learning_rate</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_set_activation_function_hidden">
+ <refnamediv>
+ <refname>fann_set_activation_function_hidden</refname>
+ <refpurpose>Set the activation function for the hidden neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_function_hidden</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>int</type><parameter>activation_function</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_set_activation_function_hidden</function> sets the activation function
+ for the hidden neurons to <parameter>activation_function</parameter>, which must be one
+ of the supported activation functions.
+ </para>
+ <para>
+ See also <function>fann_get_activation_function_hidden</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_set_activation_function_output">
+ <refnamediv>
+ <refname>fann_set_activation_function_output</refname>
+ <refpurpose>Set the activation function for the output neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_function_output</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>int</type><parameter>activation_function</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_set_activation_function_output</function> sets the activation function
+ for the output neurons to <parameter>activation_function</parameter>, which must be one
+ of the supported activation functions.
+ </para>
+ <para>
+ See also <function>fann_get_activation_function_output</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_set_activation_hidden_steepness">
+ <refnamediv>
+ <refname>fann_set_activation_hidden_steepness</refname>
+ <refpurpose>Set the steepness of the activation function for the hidden neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_hidden_steepness</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>float</type><parameter>steepness</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_set_activation_hidden_steepness</function> sets the steepness of the
+ activation function hidden neurons to <parameter>steepness</parameter>.
+ </para>
+ <para>
+ See also <function>fann_get_activation_hidden_steepness</function>.
+ </para>
+ </refsect1>
+ </refentry>
+
+ <refentry id="function.fann_set_activation_output_steepness">
+ <refnamediv>
+ <refname>fann_set_activation_output_steepness</refname>
+ <refpurpose>Set the steepness of the activation function for the output neurons.</refpurpose>
+ </refnamediv>
+ <refsect1>
+ <title>Description</title>
+ <methodsynopsis>
+ <type>void</type><methodname>fann_set_activation_output_steepness</methodname>
+ <methodparam><type>resource</type><parameter>ann</parameter></methodparam>
+ <methodparam><type>float</type><parameter>steepness</parameter></methodparam>
+ </methodsynopsis>
+ <para>
+ <function>fann_set_activation_output_steepness</function> sets the steepness of the
+ activation function output neurons to <parameter>steepness</parameter>.
+ </para>
+ <para>
+ See also <function>fann_get_activation_output_steepness</function>.
+ </para>
+ </refsect1>
+ </refentry>
+ </section>
+ </chapter>
+</book>
+
+<!-- Keep this comment at the end of the file
+Local variables:
+mode: sgml
+sgml-omittag:t
+sgml-shorttag:t
+sgml-minimize-attributes:nil
+sgml-always-quote-attributes:t
+sgml-indent-step:1
+sgml-indent-data:t
+sgml-parent-document:nil
+sgml-default-dtd-file:"../../manual.ced"
+sgml-exposed-tags:nil
+sgml-local-catalogs:nil
+sgml-local-ecat-files:nil
+End:
+-->
+
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