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// Copyright (C) 2003--2004 Samy Bengio (bengio@idiap.ch)
//
// This file is part of Torch 3.1.
//
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
// OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
// IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
// NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
// THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#include "Bagging.h"
#include "Random.h"
namespace Torch {
Bagging::Bagging(WeightedSumMachine* w_machine_) : Trainer(w_machine_)
{
w_machine = w_machine_;
n_trainers = w_machine->n_trainers;
n_unselected_examples = (int *)allocator->alloc(sizeof(int)*n_trainers);
unselected_examples = (int **)allocator->alloc(sizeof(int*)*n_trainers);
selected_examples = (int **)allocator->alloc(sizeof(int*)*n_trainers);
is_selected_examples = NULL;
}
void Bagging::bootstrapData(int* selected, int* is_selected, int n_examples)
{
for (int j=0;j<n_examples;j++) {
selected[j] = (int)floor(Random::boundedUniform(0,n_examples));
is_selected[selected[j]] = 1;
}
}
void Bagging::train(DataSet *data, MeasurerList* measurers)
{
// Misc Initializations
int n = data->n_examples;
is_selected_examples = (int *)allocator->realloc(is_selected_examples, sizeof(int)*n);
for (int i = 0; i < n_trainers; i++)
{
unselected_examples[i] = (int *)allocator->realloc(unselected_examples[i], sizeof(int)*n);
selected_examples[i] = (int *)allocator->realloc(selected_examples[i], sizeof(int)*n);
}
for(int i = 0; i < n_trainers; i++)
w_machine->weights[i] = 1./((real)n_trainers);
message("Bagging: training");
w_machine->n_trainers_trained = 0;
for (int i=0;i<n_trainers;i++) {
// initialization
for (int j=0;j<n;j++) {
is_selected_examples[j]=0;
}
// select a bootstrap
bootstrapData(selected_examples[i],is_selected_examples,n);
data->pushSubset(selected_examples[i],n);
// keep in mind examples not used by trainers[i]
int k=0;
for (int j=0;j<n;j++) {
if (!is_selected_examples[j])
unselected_examples[i][k++] = j;
}
n_unselected_examples[i] = k;
// train the trainer
w_machine->trainers[i]->machine->reset();
w_machine->trainers[i]->train(data, w_machine->trainers_measurers ? w_machine->trainers_measurers[i] : NULL);
// put back the selected_examples
data->popSubset();
w_machine->n_trainers_trained = i+1;
// if measurers is given, call the test method by fooling it
// with the number of trainers
if (measurers)
test(measurers);
}
}
Bagging::~Bagging()
{
}
}
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