fann.xml

一个功能强大的神经网络分析程序

              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
            <function>fann_destroy</function> will destroy an artificial neural network, properly freeing all associate
	    memory.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_run">
        <refnamediv>
          <refname>fann_run</refname>
          <refpurpose>Run (execute) 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>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_randomize_weights">
        <refnamediv>
          <refname>fann_randomize_weights</refname>
          <refpurpose>Give each connection a random weight.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_randomize_weights</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
            <methodparam>
              <type>fann_type</type>
              <parameter>min_weight</parameter>
            </methodparam>
            <methodparam>
              <type>fann_type</type>
              <parameter>max_height</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Randomizes the weight of each connection in <parameter>ann</parameter>, effectively resetting the network.
	  </para>
	  <para>
	    See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
	    <link linkend="api.fann_init_weights"><function>fann_init_weights</function></link>
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_init_weights">
        <refnamediv>
          <refname>fann_init_weights</refname>
          <refpurpose>Initialize the weight of each connection.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_init_weights</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
            <methodparam>
              <type>struct fann_train_data *</type>
              <parameter>train_data</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    This function behaves similarly to <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>.
	    It will use the algorithm developed by Derrick Nguyen and Bernard Widrow
	    [<link linkend="bib.nguyen_1990" endterm="bib.nguyen_1990.abbrev" />] to set the weights in such a way as to speed up training.
	    This technique is not always successful, and in some cases can be <emphasis>less</emphasis> efficient than a purely random
	    initialization.
	  </para>
	  <para>
	    The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second
	    argument, <parameter>data</parameter>, which is the training data that will be used to train the network.
	  </para>
	  <para>
	    See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
	    <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>
	  </para>
          <para>This function appears in FANN &gt;= 1.1.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_print_connections">
        <refnamediv>
          <refname>fann_print_connections</refname>
          <refpurpose>Prints the connections of an ann.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_print_connections</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
            <function>fann_print_connections</function> will print the connections of the ann in a compact matrix, for easy viewing of the internals of the ann.
	  </para>
        <para>
	  The output from fann_print_connections on a small (2 2 1) network trained on the xor problem:
	  <literallayout class="monospaced" id="api.fann_print_connections.output">
Layer / Neuron 012345
L   1 / N    3 ddb...
L   1 / N    4 bbb...
L   2 / N    6 ...cda
	  </literallayout> This network have five real neurons and two bias neurons. This gives a total of seven neurons named from 0 to 6. The connections between these neurons can be seen in the matrix. <constant>"."</constant> is a place where there is no connection, while a character tells how strong the connection is on a scale from a-z. The two real neurons in the hidden layer (neuron <constant>3</constant> and <constant>4</constant> in layer <constant>1</constant>) has connection from the three neurons in the previous layer as is visible in the first two lines. The output neuron (<constant>6</constant>) has connections form the three neurons in the hidden layer <constant>3 - 5</constant> as is visible in the last line.
	</para>
	<para> To simplify the matrix output neurons is not visible as neurons that connections can come from, and input and bias neurons are not visible as neurons that connections can go to.
	</para>
          <para>This function appears in FANN &gt;= 1.2.0.</para>
        </refsect1>
      </refentry>
    </section>
    <section id="api.sec.io">
      <title 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>
          <para>This function appears in FANN &gt;= 1.0.0.</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_to_fixed</function> will attempt to save <parameter>ann</parameter> to the file located at
	    <parameter>configuration_file</parameter> as a fixed-point network.

	  </para>
	  <para>
	    This is useful for training a network in floating points,
	    and then later executing it in fixed point.
	  </para>
	  <para>
	    The function returns the bit position of the fix point, which
	    can be used to find out how accurate the fixed point network will be.
	    A high value indicates high precision, and a low value indicates low
	    precision.
	  </para>
	  <para>
	    A negative value indicates very low precision, and a very
	    strong possibility for overflow.
	    (the actual fix point will be set to 0, since a negative
	    fix point does not make sense).
	  </para>
	  <para>
	    Generally, a fix point lower than 6 is bad, and should be avoided.
	    The best way to avoid this, is to have less connections to each neuron,
	    or just less neurons in each layer.
	  </para>
	  <para>
	    The fixed point use of this network is only intended for use on machines that
	    have no floating point processor, like an iPAQ. On normal computers the floating
	    point version is actually faster.
	  </para>

	  <para>This function appears in FANN &gt;= 1.0.0.</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 network from a file.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
    </section>
    <section id="api.sec.train_algo">
      <title id="api.sec.train_algo.title">Training</title>
      <refentry id="api.fann_train">
        <refnamediv>
          <refname>fann_train</refname>
          <refpurpose>Train an ANN.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_train</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
            <methodparam>
              <type>fann_type *</type>
              <parameter>input</parameter>
            </methodparam>
            <methodparam>
              <type>fann_type *</type>
              <parameter>output</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    <function>fann_train</function> will train one iteration with a set of inputs, and a set of desired
	    outputs. The training will be done by the standard backpropagation algorithm.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_test">
        <refnamediv>
          <refname>fann_test</refname>
          <refpurpose>Tests an ANN.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>fann_type *</type>
            <methodname>fann_test</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>