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src
shogun
preprocessor
SumOne.cpp
Go to the documentation of this file.
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/*
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* This program is free software; you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation; either version 3 of the License, or
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* (at your option) any later version.
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*
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* Written (W) 2012 Sergey Lisitsyn
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* Copyright (C) 2012 Sergey Lisitsyn
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*/
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#include <
shogun/preprocessor/SumOne.h
>
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#include <
shogun/preprocessor/DensePreprocessor.h
>
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#include <
shogun/mathematics/Math.h
>
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#include <
shogun/features/Features.h
>
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using namespace
shogun;
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CSumOne::CSumOne
()
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:
CDensePreprocessor
<
float64_t
>()
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{
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}
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CSumOne::~CSumOne
()
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{
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}
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bool
CSumOne::init(
CFeatures
* features)
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{
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ASSERT
(features->
get_feature_class
()==
C_DENSE
);
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ASSERT
(features->
get_feature_type
()==
F_DREAL
);
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return
true
;
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}
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void
CSumOne::cleanup
()
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{
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}
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bool
CSumOne::load
(FILE* f)
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{
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SG_SET_LOCALE_C
;
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SG_RESET_LOCALE
;
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return
false
;
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}
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bool
CSumOne::save
(FILE* f)
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{
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SG_SET_LOCALE_C
;
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SG_RESET_LOCALE
;
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return
false
;
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}
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SGMatrix<float64_t>
CSumOne::apply_to_feature_matrix
(
CFeatures
* features)
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{
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SGMatrix<float64_t>
feature_matrix=((
CDenseFeatures<float64_t>
*)features)->get_feature_matrix();
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for
(int32_t i=0; i<feature_matrix.
num_cols
; i++)
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{
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float64_t
* vec= &(feature_matrix.
matrix
[i*feature_matrix.
num_rows
]);
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float64_t
sum =
SGVector<float64_t>::sum
(vec,feature_matrix.
num_rows
);
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SGVector<float64_t>::scale_vector
(1.0/sum, vec, feature_matrix.
num_rows
);
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}
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return
feature_matrix;
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}
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SGVector<float64_t>
CSumOne::apply_to_feature_vector
(
SGVector<float64_t>
vector)
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{
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float64_t
* normed_vec =
SG_MALLOC
(
float64_t
, vector.
vlen
);
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float64_t
sum =
SGVector<float64_t>::sum
(vector.
vector
, vector.
vlen
);
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for
(int32_t i=0; i<vector.
vlen
; i++)
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normed_vec[i]=vector.
vector
[i]/sum;
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return
SGVector<float64_t>
(normed_vec,vector.
vlen
);
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}
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Machine Learning Toolbox - Documentation