PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // VariablesTest.cc 00004 // 00005 // Copyright (C) 2008 Olivier Delalleau 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: Olivier Delalleau 00036 00040 #include "VariablesTest.h" 00041 #include <plearn/math/PRandom.h> 00042 #include <plearn/var/ArgminVariable.h> 00043 #include <plearn/var/Func.h> 00044 #include <plearn/var/LogAddVariable.h> 00045 #include <plearn/var/UnfoldedFuncVariable.h> 00046 00047 namespace PLearn { 00048 using namespace std; 00049 00050 PLEARN_IMPLEMENT_OBJECT( 00051 VariablesTest, 00052 "Tests various Variable objects.", 00053 "Feel free to add more Variable tests in there." 00054 ); 00055 00057 // VariablesTest // 00059 VariablesTest::VariablesTest() 00060 { 00061 } 00062 00064 // build // 00066 void VariablesTest::build() 00067 { 00068 inherited::build(); 00069 build_(); 00070 } 00071 00073 // makeDeepCopyFromShallowCopy // 00075 void VariablesTest::makeDeepCopyFromShallowCopy(CopiesMap& copies) 00076 { 00077 inherited::makeDeepCopyFromShallowCopy(copies); 00078 00079 // ### Call deepCopyField on all "pointer-like" fields 00080 // ### that you wish to be deepCopied rather than 00081 // ### shallow-copied. 00082 // ### ex: 00083 // deepCopyField(trainvec, copies); 00084 00085 // ### Remove this line when you have fully implemented this method. 00086 PLERROR("VariablesTest::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); 00087 } 00088 00090 // declareOptions // 00092 void VariablesTest::declareOptions(OptionList& ol) 00093 { 00094 // ### Declare all of this object's options here. 00095 // ### For the "flags" of each option, you should typically specify 00096 // ### one of OptionBase::buildoption, OptionBase::learntoption or 00097 // ### OptionBase::tuningoption. If you don't provide one of these three, 00098 // ### this option will be ignored when loading values from a script. 00099 // ### You can also combine flags, for example with OptionBase::nosave: 00100 // ### (OptionBase::buildoption | OptionBase::nosave) 00101 00102 declareOption(ol, "results_mat", &VariablesTest::results_mat, 00103 OptionBase::buildoption, 00104 "Matrix test results."); 00105 00106 // Now call the parent class' declareOptions 00107 inherited::declareOptions(ol); 00108 } 00109 00111 // build_ // 00113 void VariablesTest::build_() 00114 { 00115 // ### This method should do the real building of the object, 00116 // ### according to set 'options', in *any* situation. 00117 // ### Typical situations include: 00118 // ### - Initial building of an object from a few user-specified options 00119 // ### - Building of a "reloaded" object: i.e. from the complete set of 00120 // ### all serialised options. 00121 // ### - Updating or "re-building" of an object after a few "tuning" 00122 // ### options have been modified. 00123 // ### You should assume that the parent class' build_() has already been 00124 // ### called. 00125 } 00126 00128 // perform // 00130 void VariablesTest::perform() 00131 { 00132 // Declare and initialize variables used in tests. 00133 int is1 = 5; 00134 int n_input2 = 3; 00135 int n_input5 = 4; 00136 Var input1(1, is1, "input1"); 00137 Var input2(n_input2, is1, "input2"); 00138 PRandom::common(false)->fill_random_uniform(input2->matValue, -1, 1); 00139 Var input3(input2->length(), input2->width(), "input3"); 00140 PRandom::common(false)->fill_random_uniform(input3->matValue, -1, 1); 00141 Var input4(1, 1, "input4"); 00142 input4->matValue(0, 0) = 3; 00143 Var input5(n_input5, is1, "input5"); 00144 PRandom::common(false)->fill_random_uniform(input5->matValue, -1, 1); 00145 00146 // Test UnfoldedFuncVariable to compute an argmin over a set of row vectors. 00147 Var argmin1 = argmin(input1); 00148 Func input1_to_argmin1(input1, argmin1); 00149 Var unfolded_argmin1 = new UnfoldedFuncVariable(input2, input1_to_argmin1, 00150 false); 00151 unfolded_argmin1->fprop(); 00152 results_mat["unfolded_argmin"] = unfolded_argmin1->matValue.copy(); 00153 00154 // Test LogAddVariable to compute a logadd over two input matrices. 00155 Var logadd1 = logadd(input2, input3); 00156 logadd1->fprop(); 00157 results_mat["logadd_binary"] = logadd1->matValue.copy(); 00158 00159 // Test LogAddVariable to compute a logadd over sub-columns of its first 00160 // input. 00161 Var logadd2 = new LogAddVariable(input5, input4, "per_row"); 00162 logadd2->fprop(); 00163 results_mat["logadd_per_row"] = logadd2->matValue.copy(); 00164 00165 // Test LogAddVariable to compute a logadd over sub-rows of its first 00166 // input. 00167 Var logadd3 = new LogAddVariable(input5, input4, "per_column"); 00168 logadd3->fprop(); 00169 results_mat["logadd_per_column"] = logadd3->matValue.copy(); 00170 } 00171 00172 } // end of namespace PLearn 00173 00174 00175 /* 00176 Local Variables: 00177 mode:c++ 00178 c-basic-offset:4 00179 c-file-style:"stroustrup" 00180 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00181 indent-tabs-mode:nil 00182 fill-column:79 00183 End: 00184 */ 00185 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :