packages/AForge.Math.2.2.5/lib/AForge.Math.xml

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<?xml version="1.0"?>
<doc>
    <assembly>
        <name>AForge.Math</name>
    </assembly>
    <members>
        <member name="T:AForge.Math.Tools">
            <summary>
            Set of tool functions.
            </summary>
            
            <remarks>The class contains different utility functions.</remarks>
            
        </member>
        <member name="M:AForge.Math.Tools.Pow2(System.Int32)">
            <summary>
            Calculates power of 2.
            </summary>
            
            <param name="power">Power to raise in.</param>
            
            <returns>Returns specified power of 2 in the case if power is in the range of
            [0, 30]. Otherwise returns 0.</returns>
            
        </member>
        <member name="M:AForge.Math.Tools.IsPowerOf2(System.Int32)">
            <summary>
            Checks if the specified integer is power of 2.
            </summary>
            
            <param name="x">Integer number to check.</param>
            
            <returns>Returns <b>true</b> if the specified number is power of 2.
            Otherwise returns <b>false</b>.</returns>
            
        </member>
        <member name="M:AForge.Math.Tools.Log2(System.Int32)">
            <summary>
            Get base of binary logarithm.
            </summary>
            
            <param name="x">Source integer number.</param>
            
            <returns>Power of the number (base of binary logarithm).</returns>
            
        </member>
        <member name="T:AForge.Math.Metrics.IDistance">
             <summary>
             Interface for distance metric algorithms.
             </summary>
             
             <remarks><para>The interface defines a set of methods implemented
             by distance metric algorithms. These algorithms typically take a set of points and return a 
             distance measure of the x and y coordinates. In this case, the points are represented by two vectors.</para>
             
             <para>Distance metric algorithms are used in many machine learning algorithms e.g K-nearest neighbor
             and K-means clustering.</para>
            
             <para>For additional details about distance metrics, documentation of the
             particular algorithms should be studied.</para>