PLearn 0.1
TransposedDoubleProductVariable.cc
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00001 // -*- C++ -*-
00002 
00003 // TransposedDoubleProductVariable.cc
00004 //
00005 // Copyright (C) 2007 Simon Lemieux, Pascal Vincent
00006 //
00007 // Redistribution and use in source and binary forms, with or without
00008 // modification, are permitted provided that the following conditions are met:
00009 //
00010 //  1. Redistributions of source code must retain the above copyright
00011 //     notice, this list of conditions and the following disclaimer.
00012 //
00013 //  2. Redistributions in binary form must reproduce the above copyright
00014 //     notice, this list of conditions and the following disclaimer in the
00015 //     documentation and/or other materials provided with the distribution.
00016 //
00017 //  3. The name of the authors may not be used to endorse or promote
00018 //     products derived from this software without specific prior written
00019 //     permission.
00020 //
00021 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00022 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00023 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00024 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00025 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00026 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00027 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00028 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00029 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00030 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00031 //
00032 // This file is part of the PLearn library. For more information on the PLearn
00033 // library, go to the PLearn Web site at www.plearn.org
00034 
00035 // Authors: Simon Lemieux, Pascal Vincent
00036 
00040 #include "TransposedDoubleProductVariable.h"
00041 
00042 namespace PLearn {
00043 using namespace std;
00044 
00047 PLEARN_IMPLEMENT_OBJECT(
00048     TransposedDoubleProductVariable,
00049     "Let H, W and M be the inputs and nw the length of W. Then output(n,k) = sum_i{ sum_j { W(i,k)*M(j,k)*H(n,i+j*nw) } }",
00050     "MULTI LINE\nHELP FOR USERS"
00051     );
00052 
00053 TransposedDoubleProductVariable::TransposedDoubleProductVariable()
00054 {}
00055 
00056 TransposedDoubleProductVariable::TransposedDoubleProductVariable(Var h, Var w, Var m)
00057     : inherited(h & w & m, h.length(), w.width())
00058 {
00059     build_();
00060 }
00061 
00062 
00063 void TransposedDoubleProductVariable::recomputeSize(int& l, int& w) const
00064 {    
00065         if (varray.size() > 0) {
00066             // l = varH()->length(); // the computed length of this Var
00067             //w = varW()->width(); // the computed width
00068             l = varray[0].length();
00069             w = varray[1].width();
00070         } else
00071             l = w = 0;
00072 }
00073 
00074 // ### computes value from varray values
00075 void TransposedDoubleProductVariable::fprop()
00076 {
00077     Mat w = varW()->matValue,
00078         m = varM()->matValue,
00079         h = varH()->matValue;
00080     
00081     int nw = w.length(),
00082         nm = m.length(),
00083         nx = h.length(),
00084         d = w.width();
00085 
00086     for(int n=0; n<nx; n++)
00087         for(int k=0; k<d; k++)
00088         {
00089             matValue(n,k)=0.;
00090             for(int i=0; i<nw; i++)
00091                 for(int j=0; j<nm; j++)
00092                     matValue(n,k) += w(i,k)*m(j,k)*h(n,i+j*nw);
00093         }
00094 }
00095 
00096 // ### computes varray gradients from gradient
00097 void TransposedDoubleProductVariable::bprop()
00098 {
00099     Mat w = varW()->matValue,
00100         m = varM()->matValue,
00101         h = varH()->matValue,
00102         w_grad = varW()->matGradient,
00103         m_grad = varM()->matGradient,
00104         h_grad = varH()->matGradient,
00105         x_grad = matGradient;
00106     
00107     int nw = w.length(),
00108         nm = m.length(),
00109         nx = h.length(),
00110         d = w.width();
00111 
00112     for(int n=0; n<nx; n++)
00113         for(int k=0; k<d; k++)
00114             for(int i=0; i<nw; i++)
00115                 for(int j=0; j<nm; j++)
00116                 {
00117                     w_grad(i,k) += x_grad(n,k)*m(j,k)*h(n,i+j*nw);
00118                     m_grad(j,k) += x_grad(n,k)*w(i,k)*h(n,i+j*nw);
00119                     h_grad(n,i+j*nw) += x_grad(n,k)*w(i,k)*m(j,k);
00120                 }
00121 }
00122 
00123 // ### You can implement these methods:
00124 // void TransposedDoubleProductVariable::bbprop() {}
00125 // void TransposedDoubleProductVariable::symbolicBprop() {}
00126 // void TransposedDoubleProductVariable::rfprop() {}
00127 
00128 
00129 // ### Nothing to add here, simply calls build_
00130 void TransposedDoubleProductVariable::build()
00131 {
00132     inherited::build();
00133     build_();
00134 }
00135 
00136 void TransposedDoubleProductVariable::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00137 {
00138     inherited::makeDeepCopyFromShallowCopy(copies);
00139 
00140     // ### Call deepCopyField on all "pointer-like" fields
00141     // ### that you wish to be deepCopied rather than
00142     // ### shallow-copied.
00143     // ### ex:
00144     // deepCopyField(trainvec, copies);
00145 
00146     // ### If you want to deepCopy a Var field:
00147     // varDeepCopyField(somevariable, copies);
00148 }
00149 
00150 void TransposedDoubleProductVariable::declareOptions(OptionList& ol)
00151 {
00152     // ### Declare all of this object's options here.
00153     // ### For the "flags" of each option, you should typically specify
00154     // ### one of OptionBase::buildoption, OptionBase::learntoption or
00155     // ### OptionBase::tuningoption. If you don't provide one of these three,
00156     // ### this option will be ignored when loading values from a script.
00157     // ### You can also combine flags, for example with OptionBase::nosave:
00158     // ### (OptionBase::buildoption | OptionBase::nosave)
00159 
00160     // ### ex:
00161     // declareOption(ol, "myoption", &TransposedDoubleProductVariable::myoption,
00162     //               OptionBase::buildoption,
00163     //               "Help text describing this option");
00164     // ...
00165 
00166     // Now call the parent class' declareOptions
00167     inherited::declareOptions(ol);
00168 }
00169 
00170 void TransposedDoubleProductVariable::build_()
00171 {
00172     // ### This method should do the real building of the object,
00173     // ### according to set 'options', in *any* situation.
00174     // ### Typical situations include:
00175     // ###  - Initial building of an object from a few user-specified options
00176     // ###  - Building of a "reloaded" object: i.e. from the complete set of
00177     // ###    all serialised options.
00178     // ###  - Updating or "re-building" of an object after a few "tuning"
00179     // ###    options have been modified.
00180     // ### You should assume that the parent class' build_() has already been
00181     // ### called.
00182 
00183     if (varH().width() != varW().length()*varM().length())
00184         PLERROR("The width of matrix H is incompatible with the lengths of matrix W and M in TranposedDoubleProductVariable");
00185     if (varM().width() != varW().width())
00186         PLERROR("Matrix W and M must have the same width in TranposedDoubleProduct");
00187 
00188 
00189 }
00190 
00191 
00192 } // end of namespace PLearn
00193 
00194 
00195 /*
00196   Local Variables:
00197   mode:c++
00198   c-basic-offset:4
00199   c-file-style:"stroustrup"
00200   c-file-offsets:((innamespace . 0)(inline-open . 0))
00201   indent-tabs-mode:nil
00202   fill-column:79
00203   End:
00204 */
00205 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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