PLearn 0.1
DisplayUtils.h
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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 and University of Montreal
00006 // Copyright (C) 2004 ApSTAT Technologies Inc.
00007 //
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 
00040 /* *******************************************************      
00041    * $Id: DisplayUtils.h 8230 2007-11-07 20:48:13Z nouiz $
00042    * AUTHORS: Pascal Vincent & Yoshua Bengio
00043    * This file is part of the PLearn library.
00044    ******************************************************* */
00045 
00046 
00049 #ifndef DISPLAYUTILS_INC
00050 #define DISPLAYUTILS_INC
00051 
00052 #include <plearn_learners/generic/PLearner.h>
00053 #include "GhostScript.h"
00054 #include <plearn/math/Mat.h>
00055 #include "Gnuplot.h"
00056 #include <plearn/var/Func.h>
00057 
00058 namespace PLearn {
00059 using namespace std;
00060 
00061 
00062 
00069   void scores_to_winners(Mat scores, Mat& winners);
00070 
00071   void color_luminance_to_rgb(int colornum, real luminance, real& r, real& g, real& b);
00072 
00073   real color_luminance_to_rgbreal(int colornum, real luminance);
00074 
00075   void color_luminance_to_rgbreal(Vec colornum, Vec luminance, Vec& rgbreal);
00076     
00077   void transform_perclass_values_into_luminance(Vec classnums, const Vec& values, int ndiscretevals);
00078 
00079   void regulargrid_x_y_rgbreal_to_bitmap(Mat& regulargrid_x_y_rgbreal, 
00080                                          Mat& bm, real& xlow, real& xhigh, real& ylow, real& yhigh);
00081 
00082   void regulargrid_x_y_outputs_to_bitmap(Mat regulargrid_x_y_outputs, bool output_margin, int ndiscretevals,
00083                                          Mat& bm, real& xlow, real& xhigh, real& ylow, real& yhigh);
00084 
00085 
00086 
00098 void displayHistogram(Gnuplot& gs, Mat dataColumn,
00099                       int n_bins=0, Vec* bins=0, 
00100                       bool regular_bins=false,
00101                       bool normalized=false, string extra_args="");
00102 
00103 
00106   void displayVarGraph(const VarArray& outputs, bool display_values=false, real boxwidth=100, const char* the_filename=0, bool must_wait=true, VarArray display0_only_these=VarArray());
00107   void displayFunction(Func f, bool display_values=false, bool display_differentiation=false, real boxwidth=100, const char* the_filename=0, bool must_wait=true);
00108 
00109 
00110 
00111 /* This will return a length*width matrix containing the computed outputs
00112     for a learner which has 2 dimensional input, where the inputs are
00113     taken on a regular grid ranging [min_x,max_x]x[min_y,max_y].  The
00114     mapping to the matrix m is m(i,j) =
00115     f(min_x+i*(max_x-min_x)/(length-1), min_y+j*(max_y-min_y)/(width-1))
00116     If the output is of length 1: (class depends on which side of the threshold we are)
00117       the result put in m is output[0] - singleoutput_threshold
00118     If the output is of length 2: (score for each class)
00119       the result put in m is output[0]-output[1]
00120 */
00121 Mat compute2dGridOutputs(PP<PLearner> learner, real min_x=-1, real max_x=+1, real min_y=-1, real max_y=+1, 
00122                          int length=200, int width=200, real singleoutput_threshold=0.);
00123 
00124 
00125 
00127 void displayPoints(GhostScript& gs, Mat data, real radius, bool color=false);
00128 
00134   void displayDecisionSurface(GhostScript& gs, real destx, real desty, real destwidth, real destheight, 
00135                               PP<PLearner> learner, Mat trainset,
00136                               Vec svindexes=Vec(), Vec outlierindexes=Vec(), int nextsvindex=-1,
00137                               real min_x=-1, real max_x=+1, real min_y=-1, real max_y=+1,
00138                               real radius=0.05, 
00139                               int nx=200, int ny=200);
00140 
00141 } // end of namespace PLearn
00142 
00143 #endif
00144 
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