Filtering files

include/robust_filtering.hpp

@@ -0,0 +1,92 @@
+/**  \file robust_filtering.hpp \brief Robust filtering for outliers */
+/*
+-------------------------------------------------------------------------
+   This file is part of BayesOpt, an efficient C++ library for 
+   Bayesian optimization.
+
+   Copyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>
+ 
+   BayesOpt is free software: you can redistribute it and/or modify it 
+   under the terms of the GNU Affero General Public License as published by
+   the Free Software Foundation, either version 3 of the License, or
+   (at your option) any later version.
+
+   BayesOpt is distributed in the hope that it will be useful, but 
+   WITHOUT ANY WARRANTY; without even the implied warranty of
+   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+   GNU Affero General Public License for more details.
+
+   You should have received a copy of the GNU Affero General Public License
+   along with BayesOpt.  If not, see <http://www.gnu.org/licenses/>.
+------------------------------------------------------------------------
+*/
+
+
+#ifndef  _ROBUST_FILTERING_HPP_
+#define  _ROBUST_FILTERING_HPP_
+
+#include <boost/scoped_ptr.hpp>
+#include "bayesopt/parameters.hpp"
+#include "specialtypes.hpp"
+#include "dataset.hpp"
+#include "posteriormodel.hpp"
+
+
+/**
+ * Namespace of the library interface
+ */
+namespace bayesopt {
+
+
+  //Forward declaration
+  class PosteriorModel;
+  class Dataset;
+
+  /** \addtogroup BayesOpt
+   *  \brief Filtering for outliers
+   */
+  /*@{*/
+
+  class RobustFiltering
+  {
+  public:
+    /** 
+     * Constructor
+     * @param params set of parameters (see parameters.hpp)
+     */
+    RobustFiltering(size_t dim, Parameters parameters, randEngine& eng);
+
+    /** 
+     * Default destructor
+     */
+    virtual ~RobustFiltering();
+
+    const Dataset* filterPoints();
+
+    void setSamples(const matrixd &x, const vectord &y)
+    {
+      mRobustModel->setSamples(x,y);
+    };
+
+    void addSample(const vectord &x, double y)
+    {
+      mRobustModel->addSample(x,y);
+    };
+
+  private:
+    boost::scoped_ptr<PosteriorModel> mRobustModel;
+    boost::scoped_ptr<Dataset> mFilteredData;
+
+    double up_margin, low_margin;
+
+    RobustFiltering();
+  };
+
+  /**@}*/
+
+
+
+} //namespace bayesopt
+
+
+#endif

src/robust_filtering.cpp

@@ -0,0 +1,60 @@
+/*
+-------------------------------------------------------------------------
+   This file is part of BayesOpt, an efficient C++ library for 
+   Bayesian optimization.
+
+   Copyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>
+ 
+   BayesOpt is free software: you can redistribute it and/or modify it 
+   under the terms of the GNU Affero General Public License as published by
+   the Free Software Foundation, either version 3 of the License, or
+   (at your option) any later version.
+
+   BayesOpt is distributed in the hope that it will be useful, but 
+   WITHOUT ANY WARRANTY; without even the implied warranty of
+   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+   GNU Affero General Public License for more details.
+
+   You should have received a copy of the GNU Affero General Public License
+   along with BayesOpt.  If not, see <http://www.gnu.org/licenses/>.
+------------------------------------------------------------------------
+*/
+
+#include "robust_filtering.hpp"
+
+namespace bayesopt
+{
+  RobustFiltering::RobustFiltering(size_t dim, Parameters parameters, randEngine& eng)
+  {
+    Parameters par2 = parameters;
+    up_margin = (100.0 - parameters.up_margin) / 100.0;
+    low_margin = parameters.low_margin / 100.0;
+    par2.surr_name = "sStudentTProcessNIG";
+    mRobustModel.reset(PosteriorModel::create(dim,par2,eng));
+  }
+
+  RobustFiltering::~RobustFiltering(){}
+
+  const Dataset* RobustFiltering::filterPoints()
+  {
+    mFilteredData.reset();
+    vecOfvec XX = mRobustModel->getData()->mX;
+    vectord YY = mRobustModel->getData()->mY;
+
+    size_t n_points = mRobustModel->getData()->getNSamples();
+
+    for(size_t i = 0; i < n_points; ++i)
+      {
+        ProbabilityDistribution* pd = mRobustModel->getPrediction(XX[i]);
+        double f_up = pd->quantile(up_margin);
+        double f_low = pd->quantile(low_margin);
+        if ((YY[i] < f_up) && (YY[i] > f_low))
+          {
+            mFilteredData->mX.push_back(XX[i]);
+            utils::append(mFilteredData->mY, YY[i]);
+          }
+      }
+    return mFilteredData.get();
+  }
+
+}