PLearn 0.1
Var_utils.cc
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00001 // -*- C++ -*-
00002 
00003 // PLearn (A C++ Machine Learning Library)
00004 // Copyright (C) 1998 Pascal Vincent
00005 // Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
00006 // Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
00007 // Copyright (C) 2002 Xiangdong Wang, Christian Dorion
00008 
00009 // Redistribution and use in source and binary forms, with or without
00010 // modification, are permitted provided that the following conditions are met:
00011 // 
00012 //  1. Redistributions of source code must retain the above copyright
00013 //     notice, this list of conditions and the following disclaimer.
00014 // 
00015 //  2. Redistributions in binary form must reproduce the above copyright
00016 //     notice, this list of conditions and the following disclaimer in the
00017 //     documentation and/or other materials provided with the distribution.
00018 // 
00019 //  3. The name of the authors may not be used to endorse or promote
00020 //     products derived from this software without specific prior written
00021 //     permission.
00022 // 
00023 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00024 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00025 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00026 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00027 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00028 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00029 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00030 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00031 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00032 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00033 // 
00034 // This file is part of the PLearn library. For more information on the PLearn
00035 // library, go to the PLearn Web site at www.plearn.org
00036 
00037 
00038 /* *******************************************************      
00039  * $Id: Var_utils.cc 4412 2005-11-02 19:00:20Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "AbsVariable.h"
00044 #include "ColumnIndexVariable.h"
00045 #include "ExpVariable.h"
00046 #include "LogVariable.h"
00047 #include "PLogPVariable.h"
00048 #include "PowVariable.h"
00049 #include "SumVariable.h"
00050 #include "Var_utils.h"
00051 //#include "Var_all.h"
00052 #include "Var_operators.h"
00053 
00054 namespace PLearn {
00055 using namespace std;
00056 
00057 Var mean(Var v)
00058 { return sum(v)/real(v->nelems()); }
00059 
00060 Var neg_log_pi(Var p, Var index)
00061 {
00062     if(index->isScalar())  return -log(p[index]);
00063     else return -log(matrixIndex(p,index));
00064 }
00065 
00066 Var softmax(Var input, int index)
00067 { 
00068     return 1.0/sum(exp(input-input[index])); 
00069 }
00070 
00071 Var pownorm(Var input, real n)
00072 {
00073     if(fast_exact_is_equal(n, 2.0))
00074         return sum(square(input));
00075     else if(fast_exact_is_equal(n, 1.0))
00076         return sum(abs(input));
00077     else
00078         return sum(pow(abs(input),n));
00079 }
00080 
00081 Var norm(Var input, real n)
00082 {
00083     if(fast_exact_is_equal(n,2.0))
00084         return sqrt(sum(square(input)));
00085     else if(fast_exact_is_equal(n, 1.0))
00086         return sum(abs(input));
00087     else
00088         return pow(sum(pow(abs(input),n)),1.0/n);
00089 }
00090 
00091 Var entropy(Var v, bool normalize)
00092 {
00093     if(normalize)
00094     {
00095         Var absx = abs(v);
00096         Var normalized = absx/sum(absx);
00097         return sum(plogp(normalized))*(-1.0/pl_log(2.0));
00098     }
00099     else
00100         return sum(plogp(v))*(-1.0/pl_log(2.0));
00101 }
00102 
00103 Var distance(Var input1, Var input2, real n)
00104 { return norm(input1-input2, n); }
00105 
00106 Var powdistance(Var input1, Var input2, real n)
00107 { return pownorm(input1-input2, n); }
00108 
00109 
00110 
00111 } // end of namespace PLearn
00112 
00113 
00114 /*
00115   Local Variables:
00116   mode:c++
00117   c-basic-offset:4
00118   c-file-style:"stroustrup"
00119   c-file-offsets:((innamespace . 0)(inline-open . 0))
00120   indent-tabs-mode:nil
00121   fill-column:79
00122   End:
00123 */
00124 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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