From 2ec43500809b14defa66764c2f8cecf47ec27214 Mon Sep 17 00:00:00 2001
From: Martin Beseda <martin.beseda@vsb.cz>
Date: Tue, 18 Dec 2018 00:13:28 +0100
Subject: [PATCH] FIX: Fixed unallocated vectors during data normalization.
---
src/CSVReader/CSVReader.cpp | 3 +-
src/DataSet/DataSet.cpp | 31 +++++++++++--------
src/DataSet/DataSet.h | 12 +++----
src/LearningMethods/GradientDescent.cpp | 2 +-
src/LearningMethods/ParticleSwarm.cpp | 2 +-
.../NormalizationStrategy.cpp | 9 ++++--
6 files changed, 35 insertions(+), 24 deletions(-)
diff --git a/src/CSVReader/CSVReader.cpp b/src/CSVReader/CSVReader.cpp
index 7a212725..22c739ad 100644
--- a/src/CSVReader/CSVReader.cpp
+++ b/src/CSVReader/CSVReader.cpp
@@ -88,6 +88,7 @@ namespace lib4neuro {
data_set_contents.push_back(std::make_pair(input, output));
}
- return DataSet(&data_set_contents);
+ DoubleUnitStrategy* ds = new DoubleUnitStrategy();
+ return DataSet(&data_set_contents, ds);
}
}
\ No newline at end of file
diff --git a/src/DataSet/DataSet.cpp b/src/DataSet/DataSet.cpp
index fa517d0d..72ce426a 100644
--- a/src/DataSet/DataSet.cpp
+++ b/src/DataSet/DataSet.cpp
@@ -38,8 +38,8 @@ namespace lib4neuro {
if(ns) {
this->normalization_strategy = ns;
- this->max_min_inp_val.at(0) = this->normalization_strategy->get_max_value();
- this->max_min_inp_val.at(1) = this->normalization_strategy->get_min_value();
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_max_value());
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_min_value());
}
//TODO check the complete data set for input/output dimensions
@@ -58,8 +58,8 @@ namespace lib4neuro {
if(ns) {
this->normalization_strategy = ns;
- this->max_min_inp_val.at(0) = this->normalization_strategy->get_max_value();
- this->max_min_inp_val.at(1) = this->normalization_strategy->get_min_value();
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_max_value());
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_min_value());
}
this->add_isotropic_data(lower_bound, upper_bound, size, output);
@@ -78,8 +78,8 @@ namespace lib4neuro {
if(ns) {
this->normalization_strategy = ns;
- this->max_min_inp_val.at(0) = this->normalization_strategy->get_max_value();
- this->max_min_inp_val.at(1) = this->normalization_strategy->get_min_value();
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_max_value());
+// this->max_min_inp_val.emplace_back(this->normalization_strategy->get_min_value());
}
this->add_isotropic_data(bounds, no_elems_in_one_dim, output_func);
@@ -222,12 +222,17 @@ namespace lib4neuro {
}
void DataSet::normalize() {
-// if(this->normalized) {
-// throw std::runtime_error("This data set is already normalized!");
-// }
+ if(!this->normalization_strategy) {
+ throw std::runtime_error("There is no normalization strategy given for this data set, so it can not be "
+ "normalized!");
+ }
/* Find maximum and minimum values */
- this->max_min_inp_val.at(0) = this->max_min_inp_val.at(1) = this->data[0].first.at(0);
+ if(this->max_min_inp_val.empty()) {
+ this->max_min_inp_val.emplace_back(this->data.at(0).first.at(0));
+ this->max_min_inp_val.emplace_back(this->data.at(0).first.at(0));
+ }
+
double tmp, tmp2;
for(auto pair : this->data) {
/* Finding maximum */
@@ -294,9 +299,9 @@ namespace lib4neuro {
return this->normalization_strategy;
}
-// bool DataSet::is_normalized() {
-// return this->normalized;
-// }
+ bool DataSet::is_normalized() {
+ return !this->max_min_inp_val.empty();
+ }
double DataSet::get_max_inp_val() {
return this->max_min_inp_val.at(0);
diff --git a/src/DataSet/DataSet.h b/src/DataSet/DataSet.h
index 0ac92905..e0e04093 100644
--- a/src/DataSet/DataSet.h
+++ b/src/DataSet/DataSet.h
@@ -77,7 +77,7 @@ namespace lib4neuro {
*
*/
//TODO let user choose in the constructor!
- NormalizationStrategy* normalization_strategy = new DoubleUnitStrategy;
+ NormalizationStrategy* normalization_strategy = nullptr; //new DoubleUnitStrategy;
// /**
// *
@@ -248,11 +248,11 @@ namespace lib4neuro {
*/
LIB4NEURO_API NormalizationStrategy* get_normalization_strategy();
-// /**
-// *
-// * @return
-// */
-// LIB4NEURO_API bool is_normalized();
+ /**
+ *
+ * @return
+ */
+ LIB4NEURO_API bool is_normalized();
/**
*
diff --git a/src/LearningMethods/GradientDescent.cpp b/src/LearningMethods/GradientDescent.cpp
index be320f69..a457d0cf 100644
--- a/src/LearningMethods/GradientDescent.cpp
+++ b/src/LearningMethods/GradientDescent.cpp
@@ -44,7 +44,7 @@ namespace lib4neuro {
void GradientDescent::optimize(lib4neuro::ErrorFunction &ef) {
/* Copy data set max and min values, if it's normalized */
- if(!std::isnan(ef.get_dataset()->get_max_inp_val())) {
+ if(ef.get_dataset()->is_normalized()) {
ef.get_network_instance()->set_normalization_strategy_instance(
ef.get_dataset()->get_normalization_strategy());
}
diff --git a/src/LearningMethods/ParticleSwarm.cpp b/src/LearningMethods/ParticleSwarm.cpp
index 7c649f6d..5046f4e1 100644
--- a/src/LearningMethods/ParticleSwarm.cpp
+++ b/src/LearningMethods/ParticleSwarm.cpp
@@ -258,7 +258,7 @@ namespace lib4neuro {
*/
void ParticleSwarm::optimize(lib4neuro::ErrorFunction &ef) {
/* Copy data set max and min values, if it's normalized */
- if(!std::isnan(ef.get_dataset()->get_max_inp_val())) {
+ if(ef.get_dataset()->is_normalized()) {
ef.get_network_instance()->set_normalization_strategy_instance(
ef.get_dataset()->get_normalization_strategy());
}
diff --git a/src/NormalizationStrategy/NormalizationStrategy.cpp b/src/NormalizationStrategy/NormalizationStrategy.cpp
index 9ca3b04e..32f475ca 100644
--- a/src/NormalizationStrategy/NormalizationStrategy.cpp
+++ b/src/NormalizationStrategy/NormalizationStrategy.cpp
@@ -23,8 +23,13 @@ double NormalizationStrategy::get_min_value() {
DoubleUnitStrategy::DoubleUnitStrategy() {}
double DoubleUnitStrategy::normalize(double n, double max, double min) {
- this->max_min_inp_val.at(0) = max;
- this->max_min_inp_val.at(1) = min;
+ if(this->max_min_inp_val.empty()) {
+ this->max_min_inp_val.emplace_back(max);
+ this->max_min_inp_val.emplace_back(min);
+ } else {
+ this->max_min_inp_val.at(0) = max;
+ this->max_min_inp_val.at(1) = min;
+ }
return 2*(n - min)/(max - min) - 1;
}
--
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