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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 "KNN.h"
namespace Torch {
KNN::KNN(int n_outputs_,int K_)
{
// works only for sequences of 1 frame (static data!)
data = NULL;
distances = NULL;
indices = NULL;
setK(K_);
n_outputs = n_outputs_;
outputs = new(allocator) Sequence(1,n_outputs);
n_real_examples = 0;
real_examples = NULL;
}
void KNN::setDataSet(DataSet *dataset_)
{
data = dataset_;
n_real_examples = data->n_examples;
real_examples = (int*)allocator->realloc(real_examples,n_real_examples*sizeof(int));
for (int i=0;i<data->n_examples;i++) {
real_examples[i] = data->selected_examples[i];
}
}
void KNN::setK(int K_)
{
K = K_;
distances = (real *)allocator->realloc(distances,K*sizeof(real));
indices = (int *)allocator->realloc(indices,K*sizeof(int));
}
real KNN::distance(real* v1, real* v2, int n)
{
real dist = 0.;
for(int j=0;j<n;j++) {
real diff = *v1++ - *v2++;
dist += diff*diff;
}
return dist;
}
void KNN::forward(Sequence* inputs)
{
// keep current example in order to restore at the end
data->pushExample();
// verify that n_examples > K
int old_K = K;
if (n_real_examples < K)
K = n_real_examples;
// initialization of distances to big values;
for (int i=0;i<K;i++) {
distances[i] = INF;
indices[i] = -1;
}
// compute the K nearest neighbords
// for each vector in data
int* i_ptr = real_examples;
for (int i=0;i<n_real_examples;i++) {
data->setRealExample(*i_ptr++);
// calculate euclidean distance between example and current vector
real dist = distance(inputs->frames[0],data->inputs->frames[0],data->n_inputs);
/*
real dist = 0;
Sequence* cur = data->inputs;
real *x = inputs->frames[0];
real *datax = cur->frames[0];
for(int j=0;j<data->n_inputs;j++) {
real diff = *x++ - *datax++;
dist += diff*diff;
}
*/
// eventually add current vector to K nearest neighbors
if (dist < distances[K-1]) {
// find insertion point
real* bptr = distances;
real* eptr = distances + K - 1;
real* mptr = bptr + (eptr - bptr) / 2;
do {
if (dist < *mptr)
eptr = mptr;
else
bptr = mptr + 1;
mptr = bptr + (eptr - bptr) / 2;
} while (mptr < eptr);
// insert the point by shifting all subsequent distances
eptr = distances + K - 1;
bptr = eptr - 1;
int* eptr_idx = indices + K - 1;
int* bptr_idx = eptr_idx - 1;
while (eptr > mptr) {
*eptr-- = *bptr--; /* distances */
*eptr_idx-- = *bptr_idx--; /* indices */
}
*mptr = dist;
indices[mptr - distances] = data->real_current_example_index;
}
}
// give an answer as the mean of the answers of the KNNs
// initialize outputs to null
real* out = outputs->frames[0];
for (int j=0;j<n_outputs;j++)
*out++ = 0;
for (int i=0;i<K;i++) {
out = outputs->frames[0];
data->setRealExample(indices[i]);
real *targ = data->targets->frames[0];
for (int j=0;j<n_outputs;j++)
*out++ += *targ++;
}
out = outputs->frames[0];
for (int j=0;j<n_outputs;j++)
*out++ /= (real)K;
// in case K was modified
K = old_K;
// restore current_example
data->popExample();
}
KNN::~KNN()
{
}
}
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