fann.xml

一个功能强大的神经网络分析程序

            </methodparam>
            <methodparam>
              <type>fann_type *</type>
              <parameter>input</parameter>
            </methodparam>
            <methodparam>
              <type>fann_type *</type>
              <parameter>desired_output</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Test with a set of inputs, and a set of desired outputs. This operation updates the mean square error,
            but does not change the network in any way.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <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. This value is calculated during training or testing, and can therefore sometimes be a bit off if the weights have been changed since the last calculation of the value.</para>
          <para>This function appears in FANN &gt;= 1.1.0. (before this
	  <link linkend="api.fann_get_error"><function>fann_get_error</function></link> is used)</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>
          <para>
	    This function appears in FANN &gt;= 1.1.0. (before this
	    <link linkend="api.fann_reset_error"><function>fann_reset_error</function></link> is used)
	  </para>
        </refsect1>
      </refentry>
    </section>
    <section id="api.sec.train_data">
      <title 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.
	    The file should be formatted in the following way:
	  </para>
          <programlisting>
<![CDATA[
   num_train_data num_input num_output
   inputdata seperated by space
   outputdata seperated by space

   .
   .
   .
   
   inputdata seperated by space
   outputdata seperated by space
]]>	  
          </programlisting>
	  <para>
	    An example of a <link linkend="file_contents.xor.data">properly formatted file</link> is
	    provided in the Introduction.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_save_train">
        <refnamediv>
          <refname>fann_save_train</refname>
          <refpurpose>Save training data.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_save_train</methodname>
            <methodparam>
              <type>struct data *</type>
              <parameter>train_data</parameter>
            </methodparam>
            <methodparam>
              <type>FILE *</type>
              <parameter>filename</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Save <parameter>train_data</parameter> to <parameter>filename</parameter>.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_save_train_to_fixed">
        <refnamediv>
          <refname>fann_save_train_to_fixed</refname>
          <refpurpose>Save training data as fixed point.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>void</type>
            <methodname>fann_save_to_fixed</methodname>
            <methodparam>
              <type>struct data *</type>
              <parameter>train_data</parameter>
            </methodparam>
            <methodparam>
              <type>FILE *</type>
              <parameter>filename</parameter>
            </methodparam>
            <methodparam>
              <type>unsigned int</type>
              <parameter>decimal_point</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Save <parameter>train_data</parameter> as fixed point to <parameter>filename</parameter>.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</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>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_train_epoch">
        <refnamediv>
          <refname>fann_train_epoch</refname>
          <refpurpose>Trains one epoch.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>float</type>
            <methodname>fann_train_epoch</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
            <methodparam>
              <type>struct fann_train_data *</type>
              <parameter>data</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Train one epoch with the training data stored in <parameter>data</parameter>. One epoch is 
	    where all of the training data is considered exactly once.
	  </para>
	  <para>
	    This function returns the MSE error as it is calculated either before or during the actual training.
	    This is not the actual MSE after the training epoch, but since calculating this will require to go 
	    through the entire training set once more, it is more than adequate to use this value during training.
	  </para>
	  <para>
	    The training algorithm used by this function is chosen by the 
	    <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link> 
	    function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
	  </para>
          <para>This function appears in FANN &gt;= 1.2.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_test_data">
        <refnamediv>
          <refname>fann_test_data</refname>
          <refpurpose>Calculates the mean square error for a set of data.</refpurpose>
        </refnamediv>
        <refsect1>
          <title>Description</title>
          <methodsynopsis>
            <type>float</type>
            <methodname>fann_test_data</methodname>
            <methodparam>
              <type>struct fann *</type>
              <parameter>ann</parameter>
            </methodparam>
            <methodparam>
              <type>struct fann_train_data *</type>
              <parameter>data</parameter>
            </methodparam>
          </methodsynopsis>
          <para>
	    Calculates the mean square error for a set of data.
	  </para>
          <para>This function appears in FANN &gt;= 1.2.0.</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>
	  <para>
	    The training algorithm used by this function is chosen by the 
	    <link linkend="api.fann_set_training_algorithm"><function>fann_set_training_algorithm</function></link> 
	    function. The default training algorithm is <link linkend="api.sec.constants.training"><constant>FANN_TRAIN_RPROP</constant></link>.
	  </para>
          <para>This function appears in FANN &gt;= 1.0.0.</para>
        </refsect1>
      </refentry>
      <refentry id="api.fann_train_on_data_callback">
        <refnamediv>
          <refname>fann_train_on_data_callback</refname>
          <refp