Implemented two-dimensional linear regression and according testframe

regression/linregression/LinRegression.cpp

@@ -0,0 +1,78 @@
+/**
+* @file LinRegression.cpp
+* @brief Algorithm for linear regression
+* @author Frank Prüfer
+* @date 08/13/2013
+
+*/  
+
+#include "vislearning/regression/linregression/LinRegression.h"
+
+using namespace OBJREC;
+
+using namespace std;
+
+using namespace NICE;
+
+LinRegression::LinRegression(){
+  dim = 2;
+}
+
+LinRegression::LinRegression(uint dimension){
+  dim = dimension;
+}
+
+LinRegression::~LinRegression()
+{
+}
+
+void LinRegression::teach ( const NICE::VVector & x, const NICE::Vector & y ){
+  
+  if (dim == 0){	//dimension not specified via constructor
+    dim = x[0].size()+1;  //use full dimension of data
+  }
+  
+  cerr<<"dim: "<<dim<<endl;
+  cerr<<"examples: "<<x.size()<<endl;
+  
+  for ( uint i = 0;i < dim;i++ ){  //initialize alpha-vector
+    alpha.push_back(0.0);
+  }
+  
+  if ( dim == 2 ){  //two-dimensional least squares
+    double meanX;
+    double meanY = y.Mean();
+    double sumX = 0.0;
+    
+    for ( uint i = 0;i < x.size();i++ ){
+      sumX += x[i][0];
+    }
+    meanX = sumX / (double)x.size();
+ 
+    
+    for ( uint i = 0; i < x.size(); i++ ){
+	alpha[1] += x[i][0] * y[i];
+    }
+    
+    alpha[1] -= x.size() * meanX * meanY;
+    
+    double tmpAlpha = 0.0;
+    for ( uint i = 0; i < x.size(); i++ ){
+      tmpAlpha += x[i][0] * x[i][0];
+    }
+    tmpAlpha -= x.size() * meanX * meanX;
+    
+    alpha[1] /= tmpAlpha;
+    
+    alpha[0] = meanY - alpha[1] * meanX;    
+  }
+}
+
+double LinRegression::predict ( const NICE::Vector & x ){
+  double y;
+  if ( dim = 2 ){  //two-dimensional least squares
+    y = alpha[0] + alpha[1] * x[0];
+  }
+  
+  return y;
+}

regression/linregression/LinRegression.h

@@ -0,0 +1,47 @@
+/**
+* @file LinRegression.h
+* @brief Algorithm for linear regression
+* @author Frank Prüfer
+* @date 08/13/2013
+
+*/
+#ifndef LINREGRESSIONINCLUDE
+#define LINREGRESSIONINCLUDE
+
+#include "vislearning/regression/regressionbase/RegressionAlgorithm.h"
+
+#include <vector>
+
+#include "core/vector/VectorT.h"
+#include "core/vector/MatrixT.h"
+
+namespace OBJREC
+{
+class LinRegression : public RegressionAlgorithm
+{
+  protected:
+    /** vector containing all model parameters */
+    std::vector<double> alpha;
+    
+    /** dimensionality of the model (i.e. number of model parameters) */
+    uint dim;
+  
+  public:
+    /** simple constructor */
+    LinRegression();
+    
+    /** constructor, specifying the dimensionality of the model*/
+    LinRegression(uint dimension);
+    
+    /** simple destructor */
+    virtual ~LinRegression();
+    
+    /** method to learn model parameters */
+    void teach ( const NICE::VVector & x, const NICE::Vector & y );
+    
+    /** method to predict function value */
+    double predict ( const NICE::Vector & x );
+}; 
+}	//namespace
+
+#endif

regression/linregression/Makefile

@@ -0,0 +1,8 @@
+#TARGETS_FROM:=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM)
+#$(info recursivly going up: $(TARGETS_FROM) ($(shell pwd)))
+
+all:
+
+%:
+	$(MAKE) TARGETS_FROM=$(notdir $(patsubst %/,%,$(shell pwd)))/$(TARGETS_FROM) -C .. $@
+

regression/linregression/Makefile.inc

@@ -0,0 +1,103 @@
+# LIBRARY-DIRECTORY-MAKEFILE
+# conventions:
+# - all subdirectories containing a "Makefile.inc" are considered sublibraries
+#   exception: "progs/" and "tests/" subdirectories!
+# - all ".C", ".cpp" and ".c" files in the current directory are linked to a
+#   library
+# - the library depends on all sublibraries 
+# - the library name is created with $(LIBNAME), i.e. it will be somehow
+#   related to the directory name and with the extension .a
+#   (e.g. lib1/sublib -> lib1_sublib.a)
+# - the library will be added to the default build list ALL_LIBRARIES
+
+# --------------------------------
+# - remember the last subdirectory
+#
+# set the variable $(SUBDIR) correctly to the current subdirectory. this
+# variable can be used throughout the current makefile.inc. The many 
+# SUBDIR_before, _add, and everything are only required so that we can recover
+# the previous content of SUBDIR before exitting the makefile.inc
+
+SUBDIR_add:=$(dir $(word $(words $(MAKEFILE_LIST)),$(MAKEFILE_LIST)))
+SUBDIR_before:=$(SUBDIR)
+SUBDIR:=$(strip $(SUBDIR_add))
+SUBDIR_before_$(SUBDIR):=$(SUBDIR_before)
+ifeq "$(SUBDIR)" "./"
+SUBDIR:=
+endif
+
+# ------------------------
+# - include subdirectories
+#
+# note the variables $(SUBDIRS_OF_$(SUBDIR)) are required later on to recover
+# the dependencies automatically. if you handle dependencies on your own, you
+# can also dump the $(SUBDIRS_OF_$(SUBDIR)) variable, and include the
+# makefile.inc of the subdirectories on your own...
+
+SUBDIRS_OF_$(SUBDIR):=$(patsubst %/Makefile.inc,%,$(wildcard $(SUBDIR)*/Makefile.inc))
+include $(SUBDIRS_OF_$(SUBDIR):%=%/Makefile.inc)
+
+# ----------------------------
+# - include local dependencies
+#
+# you can specify libraries needed by the individual objects or by the whole
+# directory. the object specific additional libraries are only considered
+# when compiling the specific object files
+# TODO: update documentation...
+
+-include $(SUBDIR)libdepend.inc
+
+$(foreach d,$(filter-out %progs %tests,$(SUBDIRS_OF_$(SUBDIR))),$(eval $(call PKG_DEPEND_INT,$(d))))
+
+# ---------------------------
+# - objects in this directory
+#
+# the use of the variable $(OBJS) is not mandatory. it is mandatory however
+# to update $(ALL_OBJS) in a way that it contains the path and name of
+# all objects. otherwise we can not include the appropriate .d files.
+
+OBJS:=$(patsubst %.cpp,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.cpp))) \
+      $(patsubst %.C,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.C))) \
+	  $(shell grep -ls Q_OBJECT $(SUBDIR)*.h | sed -e's@^@/@;s@.*/@$(OBJDIR)moc_@;s@\.h$$@.o@') \
+      $(patsubst %.c,$(OBJDIR)%.o,$(notdir $(wildcard $(SUBDIR)*.c)))
+ALL_OBJS += $(OBJS)
+
+# ----------------------------
+# - binaries in this directory
+#
+# output of binaries in this directory. none of the variables has to be used.
+# but everything you add to $(ALL_LIBRARIES) and $(ALL_BINARIES) will be
+# compiled with `make all`. be sure again to add the files with full path.
+
+LIBRARY_BASENAME:=$(call LIBNAME,$(SUBDIR))
+ifneq "$(SUBDIR)" ""
+ALL_LIBRARIES+=$(LIBDIR)$(LIBRARY_BASENAME).$(LINK_FILE_EXTENSION)
+endif
+
+# ---------------------
+# - binary dependencies
+#
+# there is no way of determining the binary dependencies automatically, so we
+# follow conventions. the current library depends on all sublibraries.
+# all other dependencies have to be added manually by specifying, that the
+# current .pc file depends on some other .pc file. binaries depending on
+# libraries should exclusivelly use the .pc files as well.
+
+ifeq "$(SKIP_BUILD_$(OBJDIR))" "1"
+$(LIBDIR)$(LIBRARY_BASENAME).a:
+else
+$(LIBDIR)$(LIBRARY_BASENAME).a:$(OBJS) \
+	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).a,.$(LINK_FILE_EXTENSION))
+endif
+
+$(PKGDIR)$(LIBRARY_BASENAME).pc: \
+	$(call PRINT_INTLIB_DEPS,$(PKGDIR)$(LIBRARY_BASENAME).pc,.pc)
+
+# -------------------
+# - subdir management
+#
+# as the last step, always add this line to correctly recover the subdirectory
+# of the makefile including this one!
+
+SUBDIR:=$(SUBDIR_before_$(SUBDIR))
+

regression/linregression/libdepend.inc

@@ -0,0 +1 @@
+$(call PKG_DEPEND_INT,vislearning/regression/regressionbase)

regression/progs/testLinRegression.cpp

@@ -0,0 +1,285 @@
+/**
+* @file testLinRegression.cpp
+* @brief test of linear regression
+* @author Frank Prüfer
+* @date 08/13/2013
+
+*/
+
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <string>
+#include <vector>
+#include <stdlib.h>
+#include <assert.h>
+
+#include "core/basics/Config.h"
+#include "core/vector/VectorT.h"
+#include "core/vector/VVector.h"
+
+#include "vislearning/baselib/ICETools.h"
+
+#include "vislearning/regression/linregression/LinRegression.h"
+
+using namespace OBJREC;
+using namespace NICE;
+using namespace std;
+
+void csvline_populate ( vector<string> &record,
+                       const string& line,
+                       char delimiter )
+{
+  int linepos=0;
+  int inquotes=false;
+  char c;
+  int linemax=line.length();
+  string curstring;
+  record.clear();
+
+  while(line[linepos]!=0 && linepos < linemax)
+  {
+    c = line[linepos];
+
+    if (!inquotes && curstring.length()==0 && c=='"')
+    {
+      //beginquotechar
+      inquotes=true;
+    }
+    else if (inquotes && c=='"')
+    {
+      //quotechar
+      if ( (linepos+1 <linemax) && (line[linepos+1]=='"') )
+      {
+        //encountered 2 double quotes in a row (resolves to 1 double quote)
+        curstring.push_back(c);
+        linepos++;
+      }
+      else
+      {
+        //endquotechar
+        inquotes=false;
+      }
+    }
+    else if (!inquotes && c==delimiter)
+    {
+      //end of field
+      record.push_back( curstring );
+      curstring="";
+    }
+    else if (!inquotes && (c=='\r' || c=='\n') )
+    {
+     record.push_back( curstring );
+     return;
+    }
+    else
+    {
+      curstring.push_back(c);
+    }
+    linepos++;
+  }
+  
+  record.push_back( curstring );
+}
+
+void loadData( NICE::VVector &Data,
+               NICE::Vector &y,
+               const string &path,
+               const string &xdat,
+               const string &ydat )
+{
+
+  vector<string> row;
+  string line;
+
+  cerr<<"Preloading Data...";
+  ifstream in( (path+xdat).c_str() );
+  if ( in.fail() )
+  {
+    cout << "File not found" <<endl;
+    exit(EXIT_FAILURE);
+  }
+
+  int numData = 0;
+
+  while ( getline(in, line)  && in.good() )
+  {
+    csvline_populate(row, line, ',');
+    vector<double> vec;
+    for (int i = 0; i < (int)row.size(); i++)
+    {
+      double dval = 0.0;
+      dval = atof(row[i].data() );
+      vec.push_back(dval);
+    }
+    NICE::Vector nvec(vec);
+    Data.push_back(nvec);
+    numData++;
+  }
+  in.close();
+
+  cerr<<"Finished."<<endl<<"Starting to get preloaded Labels...";
+
+  in.open( (path+ydat).c_str() );
+  if ( in.fail() )
+  {
+    cout << "File not found! Setting default value 0.0..." <<endl;
+    y.resize(numData);
+    y.set(0.0);
+  }
+  else
+  {
+    y.resize(numData);
+    int count = 0;
+    while(getline(in, line)  && in.good() )
+    {
+      csvline_populate(row, line, ',');
+      for ( int i = 0; i < (int)row.size(); i++ )
+      {
+        double dval = 0.0;
+        dval = atof(row[i].data() );
+        y.set(count,dval);
+        count++;
+      }
+    }
+    in.close();
+  }
+
+  cerr<<"Finished."<<endl;
+}
+
+void testFrame (  Config conf,
+		  NICE::VVector &xdata,
+		  NICE::Vector &y )
+{
+  cerr<<"\nStarting test framework..."<<endl;
+  
+  /*------------Initialize Variables-----------*/
+  ofstream storeEvalData;
+  double trainRatio = conf.gD( "debug", "training_ratio", .9 );
+  
+  int trainingSize = (int)(trainRatio*xdata.size());
+  int testingSize = xdata.size() - trainingSize;
+  
+  vector<int> indices;
+  for ( int i = 0; i < (int)xdata.size(); i++ )
+    indices.push_back(i);
+  
+  int nfolds = conf.gI( "debug", "nfolds", 10 );
+  Vector mef_v ( nfolds );
+  Vector corr_v ( nfolds );
+  Vector resub_v ( nfolds );
+  Vector diff_v ( nfolds );
+
+  bool saveConfig = conf.gB( "debug", "save_config", false );
+  
+  /*------------Store Configuration------------*/
+  string filename = conf.gS( "debug", "filename" );
+  
+  if ( saveConfig )
+  {
+    cout << "Configuration will be stored in: " << filename << "_config" << endl;
+    
+    storeEvalData.open ( (filename+"_config").c_str() );
+
+    storeEvalData.close();
+  } else
+  {
+    cout << "Configuration will not be stored." << endl;
+  }
+  
+  /*------------Setting up PreRDF--------------*/
+  for ( int k = 0; k < nfolds; k++)
+  {
+    string fold;
+    ostringstream convert;
+    convert << k;
+    fold = convert.str();
+    
+    cout << "\nFOLD " << k << ":\n======" << endl;
+    
+
+    cerr << "Initializing LinRegression...";
+    LinRegression *linReg = new LinRegression ();
+    cerr << "Finished." << endl;
+    
+    cerr << "Teaching the LinRegression algorithm...";
+    NICE::VVector trainData, testData;
+    NICE::Vector trainVals ( trainingSize );
+    NICE::Vector testVals ( testingSize );
+    random_shuffle( indices.begin(), indices.end() );
+    for ( int i = 0; i < trainingSize; i++ )
+    {
+      trainData.push_back ( xdata[ indices[i] ] );
+      trainVals.set( i, y[ indices[i] ] );
+    }
+    for ( int j = 0; j < testingSize; j++ )
+    {
+      testData.push_back ( xdata[ indices[j+trainingSize] ] );
+      testVals.set( j, y[ indices[j+trainingSize] ] );
+    }
+    
+    linReg->teach ( trainData, trainVals );
+    cerr << "Finished." << endl;
+    
+    /*-------------Testing RDF-GP--------------*/
+
+    cerr << "\nGetting prediction values for all data points...";
+    NICE::Vector predictionValues( testingSize );
+    predictionValues.set ( 0.0 );
+    for ( int j = 0; j < testingSize; j++ )
+    {
+      predictionValues[j] = linReg->predict( testData[j] );
+    }
+    cerr << "Finished." << endl;
+    
+    /*---------------Evaluation----------------*/
+    NICE::Vector diff = testVals - predictionValues;
+    
+    double mod_var = diff.StdDev()*diff.StdDev();
+    double tar_var = testVals.StdDev()*testVals.StdDev();
+    mef_v.set( k, (1-mod_var/tar_var) );
+    
+    NICE::Vector meanv( predictionValues.size() );
+    meanv.set( diff.Mean() );
+    NICE::Vector lhs = diff - meanv;
+    meanv.set( testVals.Mean() );
+    NICE::Vector rhs = testVals - meanv;
+    lhs *= rhs;
+    double corr = lhs.Mean() / sqrt( diff.StdDev()*diff.StdDev()*testVals.StdDev()*testVals.StdDev() );
+    corr_v.set( k, corr );
+    
+    diff *= diff;
+    diff_v.set( k, diff.Mean());
+    resub_v.set( k, (diff.Mean() / tar_var) );
+  }
+  
+  /*------------------Output-------------------*/
+  cout << "\nSimple Cross Validation Stats:\n==============================" << endl;
+  cout << "  Modelling Efficiency: " << mef_v.Mean() << endl;
+  cout << "  Correlation: " << corr_v.Mean() << endl;
+  cout << "  Mean Square Error: " << diff_v.Mean() << endl;
+  cout << "  Standardized MSE: " << resub_v.Mean() << endl;
+}
+
+
+int main (int argc, char **argv) {
+
+  Config conf ( argc, argv );   //get config from user input
+  
+  string path = conf.gS( "debug", "path", "." );
+  string dataset = conf.gS( "debug", "dataset", "flux" );
+
+  NICE::VVector xdata;
+  NICE::Vector y;
+
+  loadData(xdata, y, path, (dataset+"_x.csv"), (dataset+"_y.csv") ); //load all data
+  
+  testFrame( conf, xdata, y );
+
+  return 0;
+}
+
+
+