PLearn 0.1
ConcatColumnsVariable.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: ConcatColumnsVariable.cc 8956 2008-05-08 19:19:03Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "ConcatColumnsVariable.h"
00044 #include "SubMatVariable.h"
00045 
00046 namespace PLearn {
00047 using namespace std;
00048 
00049 
00052 PLEARN_IMPLEMENT_OBJECT(
00053         ConcatColumnsVariable,
00054         "Concatenation of the columns of several variables",
00055         ""
00056 );
00057 
00059 // ConcatColumnsVariable //
00061 ConcatColumnsVariable::ConcatColumnsVariable(const VarArray& vararray,
00062                                              bool call_build_):
00063     inherited(vararray.nonNull(), vararray.maxLength(), vararray.sumOfWidths(),
00064               call_build_)
00065 {
00066     if (call_build_)
00067         build_();
00068 }
00069 
00071 // build //
00073 void ConcatColumnsVariable::build()
00074 {
00075     inherited::build();
00076     build_();
00077 }
00078 
00080 // build_ //
00082 void ConcatColumnsVariable::build_()
00083 {
00084     if (varray->length()) {
00085         int l = varray[0]->length();
00086         for (int i = 1; i < varray.size(); i++)
00087             if (l != varray[i]->length())
00088                 PLERROR("ConcatColumnsVariable: all non-null variables must have the same length (%d != %d)", l, varray[i]->length());
00089     }
00090 }
00091 
00092 void ConcatColumnsVariable::recomputeSize(int& l, int& w) const
00093 {
00094     if (varray) {
00095         l = varray.maxLength();
00096         w = varray.sumOfWidths();
00097     } else
00098         l = w = 0;
00099 }
00100 
00101 void ConcatColumnsVariable::fprop()
00102 {
00103     int n_rows = matValue.length();
00104     int m_start = 0;
00105     int mod = matValue.mod();
00106     for (int m=0;m<varray.size();m++)
00107     {
00108         real* mp = varray[m]->valuedata;
00109         int n_cols = varray[m]->matValue.width();
00110         real* p = &valuedata[m_start];
00111         for (int i=0;i<n_rows;i++,p+=mod)
00112             for (int j=0;j<n_cols;j++,mp++)
00113                 p[j] = *mp;
00114         m_start+=n_cols;
00115     }
00116 }
00117 
00118 
00119 void ConcatColumnsVariable::bprop()
00120 {
00121     int n_rows = matValue.length();
00122     int m_start = 0;
00123     int mod = matValue.mod();
00124     for (int m=0;m<varray.size();m++)
00125     {
00126         real* mp = varray[m]->gradientdata;
00127         int n_cols = varray[m]->matGradient.width();
00128         real* p = &gradientdata[m_start];
00129         for (int i=0;i<n_rows;i++,p+=mod)
00130             for (int j=0;j<n_cols;j++,mp++)
00131                 *mp += p[j];
00132         m_start+=n_cols;
00133     }
00134 }
00135 
00136 
00137 void ConcatColumnsVariable::symbolicBprop()
00138 {
00139     int k=0;
00140     for (int n=0; n<varray.size(); n++) {
00141         Var vn = varray[n];
00142         vn->accg(new SubMatVariable(g, 0, k, length(), vn->width()));
00143         k += vn->width();
00144     }
00145 }
00146 
00147 
00148 
00149 } // end of namespace PLearn
00150 
00151 
00152 /*
00153   Local Variables:
00154   mode:c++
00155   c-basic-offset:4
00156   c-file-style:"stroustrup"
00157   c-file-offsets:((innamespace . 0)(inline-open . 0))
00158   indent-tabs-mode:nil
00159   fill-column:79
00160   End:
00161 */
00162 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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