PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // LocalMedBoost.h 00004 // Copyright (c) 1998-2002 Pascal Vincent 00005 // Copyright (C) 1999-2002 Yoshua Bengio and University of Montreal 00006 // Copyright (c) 2002 Jean-Sebastien Senecal, Xavier Saint-Mleux, Rejean Ducharme 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: LocalMedBoost.h, v 1.0 2004/07/19 10:00:00 Bengio/Kegl/Godbout * 00038 * This file is part of the PLearn library. * 00039 ******************************************************************************** */ 00040 00041 #ifndef LocalMedBoost_INC 00042 #define LocalMedBoost_INC 00043 00044 #include <plearn_learners/generic/PLearner.h> 00045 00046 namespace PLearn { 00047 using namespace std; 00048 class RegressionTree; 00049 class BaseRegressorWrapper; 00050 class RegressionTreeRegisters; 00051 00052 class LocalMedBoost: public PLearner 00053 { 00054 typedef PLearner inherited; 00055 private: 00056 00057 /* 00058 Build options: they have to be set before training 00059 */ 00060 00061 real robustness; // robustness parameter of the boosting algorithm 00062 real adapt_robustness_factor; // if not 0.0, robustness will be adapted at each stage with max(t)min(i) base_award + this constant 00063 real loss_function_weight; // hyper-parameter to balance the error and the confidence factor 00064 string objective_function; // indicates to used l2 or flaten_l2 as base regressor objective function 00065 int regression_tree; // indicator to use the tree_regressor_template if set to 1, and the base_regressor_template otherwise 00066 int max_nstages; // maximum number of nstages in the hyper learner to size the vectors of base learners 00067 PP<PLearner> base_regressor_template; // template for a generic regressor as the base learner to be boosted 00068 PP<RegressionTree> tree_regressor_template; // template for a tree regressor to be boosted as the base regressor 00069 PP<BaseRegressorWrapper> tree_wrapper_template; // template for a tree regressor to be boosted thru a wrapper for a different confidence function 00070 00071 /* 00072 Learnt options: they are sized and initialized if need be, at stage 0 00073 */ 00074 00075 int end_stage; // last train stage after end of training 00076 real bound; // cumulative bound computed after each boosting stage 00077 real maxt_base_award; // max(t)min(i) base_award to adapt robustness at each stage 00078 PP<RegressionTreeRegisters> sorted_train_set; // a sorted train set when using a tree as a base regressor 00079 TVec< PP<PLearner> > base_regressors; // base regressors built at each boosting stage 00080 TVec< PP<RegressionTree> > tree_regressors; // tree regressors built at each boosting stage 00081 TVec< PP<BaseRegressorWrapper> > tree_wrappers; // tree regressors built at each boosting stage 00082 TVec<real> function_weights; // array of function weights built by the boosting algorithm 00083 TVec<real> loss_function; // array of the loss function 00084 TVec<real> sample_weights; // array to represent different distributions on the samples of the training set 00085 00086 /* 00087 Work fields: they are sized and initialized if need be, at buid time 00088 */ 00089 00090 int each_train_sample_index; // index to go thru the train set 00091 int length; // number of samples in train set 00092 int width; // number of columns in train set 00093 int inputsize; // input size of train set 00094 int targetsize; // output size of train set 00095 int weightsize; // weightsize size of train set 00096 bool capacity_too_large; // early stop of the algorithm because capacity is too large 00097 bool capacity_too_small; // early stop of the algorithm because capacity is too small 00098 real edge; // computed sum of the weighted base rewards 00099 real min_margin; // minimum margin for all samples 00100 Vec sample_input; // vector to hold the sample input vector 00101 Vec sample_target; // vector to hold the sample target 00102 real sample_weight; // real to hold the sample weight 00103 Vec sample_output; // vector to compute output from the base regressor and from boosting 00104 Vec sample_costs; // vector to compute cost from boosting for one sample 00105 TVec<real> base_rewards; // vector to compute the base reward of all samples after each boosting stage 00106 TVec<real> base_confidences; // vector to keep the base confidence of all samples after each booosting stage 00107 TVec<real> base_awards; // vector to comput the base awad of all samples after each boosting stage 00108 TVec<real> exp_weighted_edges; // vector to precompute the exp of the weighted edges for all samples after each boosting stages 00109 real sum_exp_weighted_edges; // to sum the exp of the weighted edges for all samples after each boosting stages 00110 00111 /* 00112 Work fields for the line search: they are sized and initialized if need be, at buid time 00113 */ 00114 00115 real bracketing_learning_rate; // various parameter of the line search initialised at the onset 00116 real bracketing_zero; // more of the same 00117 real interpolation_learning_rate; // more of the same 00118 real interpolation_precision; // more of the same 00119 real max_learning_rate; // more of the same 00120 real bracket_a_start; // more of the same 00121 real bracket_b_start; // more of the same 00122 int iter; // iteration counter of the line search 00123 real x_a, x_b, x_c, x_d, x_e; // absissa value of various points thru the interpolation of the search 00124 real x_u, x_v, x_w, x_x; // more of the same 00125 real x_xmed, x_lim; // more of the same 00126 real f_a, f_b, f_c; // corresponding evaluation of the function to minimize 00127 real f_u, f_v, f_w, f_x; // more of the same 00128 real t_p, t_q, t_r, t_sav; // intermediary calculation thru the interpolation 00129 real p_step, p_lim, p_tin; // various parameters set thru the interpolation 00130 real p_to1, p_tol1, p_tol2; // more of the same 00131 00132 public: 00133 LocalMedBoost(); 00134 virtual ~LocalMedBoost(); 00135 00136 PLEARN_DECLARE_OBJECT(LocalMedBoost); 00137 00138 static void declareOptions(OptionList& ol); 00139 virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies); 00140 virtual void build(); 00141 virtual void train(); 00142 virtual void forget(); 00143 virtual int outputsize() const; 00144 virtual TVec<string> getTrainCostNames() const; 00145 virtual TVec<string> getTestCostNames() const; 00146 virtual void computeOutput(const Vec& input, Vec& output) const; 00147 virtual void computeOutputAndCosts(const Vec& input, const Vec& target, Vec& output, Vec& costs) const; 00148 virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 00149 const Vec& target, Vec& costs) const; 00150 private: 00151 void build_(); 00152 void computeBaseAwards(); 00153 void computeLossBound(); 00154 real findArgminFunctionWeight(); 00155 void initializeLineSearch(); 00156 real computeFunctionWeightFormula(real alpha); 00157 void recomputeSampleWeight(); 00158 void initializeSampleWeight(); 00159 void verbose(string the_msg, int the_level); 00160 }; 00161 00162 DECLARE_OBJECT_PTR(LocalMedBoost); 00163 00164 } // end of namespace PLearn 00165 00166 #endif 00167 00168 00169 /* 00170 Local Variables: 00171 mode:c++ 00172 c-basic-offset:4 00173 c-file-style:"stroustrup" 00174 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00175 indent-tabs-mode:nil 00176 fill-column:79 00177 End: 00178 */ 00179 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :