PLearn 0.1
MinusScalarVariable.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: MinusScalarVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "MinusScalarVariable.h"
00044 #include "SumVariable.h"
00045 #include "Var_operators.h"
00046 //#include "Var_utils.h"
00047 
00048 namespace PLearn {
00049 using namespace std;
00050 
00051 
00054 PLEARN_IMPLEMENT_OBJECT(MinusScalarVariable,
00055                         "ONE LINE DESCR",
00056                         "NO HELP");
00057 
00058 MinusScalarVariable::MinusScalarVariable(Variable* input1, Variable* input2)
00059     : inherited(input1, input2, input1->length(), input1->width())
00060 {
00061     build_();
00062 }
00063 
00064 void
00065 MinusScalarVariable::build()
00066 {
00067     inherited::build();
00068     build_();
00069 }
00070 
00071 void
00072 MinusScalarVariable::build_()
00073 {
00074     if(input2 && !input2->isScalar())
00075         PLERROR("IN MinusScalarVariable: input2 is not a scalar");
00076 }
00077 
00078 void MinusScalarVariable::recomputeSize(int& l, int& w) const
00079 {
00080     if (input1) {
00081         l = input1->length();
00082         w = input1->width();
00083     } else
00084         l = w = 0;
00085 }
00086 
00087 void MinusScalarVariable::fprop()
00088 {
00089     real scal = input2->valuedata[0];
00090     for(int k=0; k<nelems(); k++)
00091         valuedata[k] = input1->valuedata[k] - scal;
00092 }
00093 
00094 
00095 void MinusScalarVariable::bprop()
00096 {
00097     for(int k=0; k<nelems(); k++)
00098     {
00099         input1->gradientdata[k] += gradientdata[k];
00100         input2->gradientdata[0] -= gradientdata[k];
00101     }
00102 }
00103 
00104 
00105 void MinusScalarVariable::bbprop()
00106 {
00107     if (input1->diaghessian.length()==0)
00108         input1->resizeDiagHessian();
00109     if (input2->diaghessian.length()==0)
00110         input2->resizeDiagHessian();
00111     for(int k=0; k<nelems(); k++)
00112     {
00113         input1->diaghessiandata[k] += diaghessiandata[k];
00114         input2->diaghessiandata[0] -= diaghessiandata[k];
00115     }
00116 }
00117 
00118 
00119 void MinusScalarVariable::symbolicBprop()
00120 {
00121     input1->accg(g);
00122     input2->accg(-sum(g));
00123 }
00124 
00125 
00126 // R(x1-x2) = R(x1)-R(x2)
00127 void MinusScalarVariable::rfprop()
00128 {
00129     if (rValue.length()==0) resizeRValue();
00130     real rscal = input2->rvaluedata[0];
00131     for(int k=0; k<nelems(); k++)
00132         rvaluedata[k] = input1->rvaluedata[k] - rscal;
00133 }
00134 
00135 
00136 
00137 } // end of namespace PLearn
00138 
00139 
00140 /*
00141   Local Variables:
00142   mode:c++
00143   c-basic-offset:4
00144   c-file-style:"stroustrup"
00145   c-file-offsets:((innamespace . 0)(inline-open . 0))
00146   indent-tabs-mode:nil
00147   fill-column:79
00148   End:
00149 */
00150 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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