PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // VMat_basic_stats.h 00004 // 00005 // Copyright (C) 2004 Pascal Vincent 00006 // Copyright (C) 2005 University of Montreal 00007 // 00008 // Redistribution and use in source and binary forms, with or without 00009 // modification, are permitted provided that the following conditions are met: 00010 // 00011 // 1. Redistributions of source code must retain the above copyright 00012 // notice, this list of conditions and the following disclaimer. 00013 // 00014 // 2. Redistributions in binary form must reproduce the above copyright 00015 // notice, this list of conditions and the following disclaimer in the 00016 // documentation and/or other materials provided with the distribution. 00017 // 00018 // 3. The name of the authors may not be used to endorse or promote 00019 // products derived from this software without specific prior written 00020 // permission. 00021 // 00022 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR 00023 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00024 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN 00025 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00026 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 00027 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00028 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00029 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00030 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00031 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00032 // 00033 // This file is part of the PLearn library. For more information on the PLearn 00034 // library, go to the PLearn Web site at www.plearn.org 00035 00036 /* ******************************************************* 00037 * $Id: VMat_basic_stats.h 7308 2007-05-24 16:50:41Z dumitruerhan $ 00038 ******************************************************* */ 00039 00040 // Authors: Pascal Vincent 00041 00045 #ifndef VMat_basic_stats_INC 00046 #define VMat_basic_stats_INC 00047 00048 // Put includes here 00049 #include <plearn/math/TVec.h> 00050 #include <plearn/math/TMat.h> 00051 #include <plearn/base/Array.h> 00052 00053 namespace PLearn { 00054 using namespace std; 00055 00056 class VMat; 00057 00058 #define MEAN_ROW 0 00059 #define STDDEV_ROW 1 00060 #define MIN_ROW 2 00061 #define MAX_ROW 3 00062 #define NMISSING_ROW 4 00063 #define NZERO_ROW 5 00064 #define NPOSITIVE_ROW 6 00065 #define NNEGATIVE_ROW 7 00066 #define MEANPOS_ROW 8 00067 #define STDDEVPOS_ROW 9 00068 00081 Mat computeBasicStats(const VMat& m); 00082 00084 void computeRowMean (const VMat& d, Vec& meanvec); 00085 00093 void computeMean (const VMat& d, Vec& meanvec); 00094 void computeMeanAndVariance (const VMat& d, Vec& meanvec, Vec& variancevec, real epsilon=0.0); 00095 void computeMeanAndStddev (const VMat& d, Vec& meanvec, Vec& stddevvec, real epsilon=0.0); 00096 void computeMeanAndCovar (const VMat& d, Vec& meanvec, Mat& covarmat, real epsilon=0.0); 00098 void computeCovar (const VMat& d, const Vec& mu, Mat& covarmat, real epsilon=0.0); 00099 void computeWeightedMean (const Vec& weights, const VMat& d, Vec& meanvec); 00100 void computeWeightedMeanAndCovar(const Vec& weights, const VMat& d, 00101 Vec& meanvec, Mat& covarmat, real epsilon=0.0); 00102 void computeInputMean (const VMat& d, Vec& meanvec); 00104 void computeInputCovar(const VMat& d, const Vec& mu, Mat& covarmat, real epsilon=0.0); 00105 void computeInputMeanAndCovar (const VMat& d, Vec& meanvec, Mat& covarmat, real epsilon=0.0); 00106 void computeInputMeanAndVariance(const VMat& d, Vec& meanvec, Vec& var, real epsilon=0.0); 00107 void computeInputMeanAndStddev (const VMat& d, Vec& meanvec, Vec& stddev, real epsilon=0.0); 00108 00109 void autocorrelation_function(const VMat& data, Mat& acf); 00110 00111 void computeRange(const VMat& d, Vec& minvec, Vec& maxvec); 00112 00127 TVec<Mat> computeConditionalMeans(VMat trainset, int targetsize, Mat& basic_stats); 00128 00134 VMat normalize(const VMat& d, const Vec& meanvec, const Vec& stddevvec); 00135 00137 VMat normalize(const VMat& d, int inputsize, int ntrain); 00139 VMat normalize(const VMat& d, int inputsize); 00140 00149 void correlations(const VMat& x, const VMat& y, Mat& r, Mat& pvalues, bool ignore_missing = false); 00150 00151 } // end of namespace PLearn 00152 #endif 00153 00154 00155 /* 00156 Local Variables: 00157 mode:c++ 00158 c-basic-offset:4 00159 c-file-style:"stroustrup" 00160 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00161 indent-tabs-mode:nil 00162 fill-column:79 00163 End: 00164 */ 00165 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :