Fix reorder warning.
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@@ -27,7 +27,7 @@ template<typename eT>
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bool Load(const std::string& filename,
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arma::Mat<eT>& matrix,
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ImageInfo& info,
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const bool fatal,
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const bool /* fatal */,
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const bool transpose)
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{
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Timer::Start("loading_image");
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@@ -19,8 +19,9 @@
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namespace mlpack {
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namespace data {
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ScalingModel::ScalingModel(const int minvalue, const int maxvalue,
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double epsilonvalue) :
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ScalingModel::ScalingModel(const int minvalue,
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const int maxvalue,
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double epsilonvalue) :
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scalerType(0),
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minmaxscale(NULL),
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maxabsscale(NULL),
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@@ -37,22 +38,22 @@ ScalingModel::ScalingModel(const int minvalue, const int maxvalue,
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//! Copy constructor.
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ScalingModel::ScalingModel(const ScalingModel& other) :
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minValue(other.minValue),
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scalerType(other.scalerType),
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maxValue(other.maxValue),
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epsilon(other.epsilon),
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minmaxscale(other.minmaxscale == NULL ? NULL :
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new data::MinMaxScaler(*other.minmaxscale)),
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maxabsscale(other.maxabsscale == NULL ? NULL :
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new data::MaxAbsScaler(*other.maxabsscale)),
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meanscale(other.meanscale == NULL ? NULL :
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new data::MeanNormalization(*other.meanscale)),
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standardscale(other.standardscale == NULL ? NULL :
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new data::StandardScaler(*other.standardscale)),
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pcascale(other.pcascale == NULL ? NULL :
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new data::PCAWhitening(*other.pcascale)),
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zcascale(other.zcascale == NULL ? NULL :
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new data::ZCAWhitening(*other.zcascale)),
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meanscale(other.meanscale == NULL ? NULL :
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new data::MeanNormalization(*other.meanscale)),
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standardscale(other.standardscale == NULL ? NULL :
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new data::StandardScaler(*other.standardscale))
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minValue(other.minValue),
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maxValue(other.maxValue),
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epsilon(other.epsilon)
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{
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// Nothing to do.
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}
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@@ -62,13 +63,13 @@ ScalingModel::ScalingModel(ScalingModel&& other) :
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scalerType(other.scalerType),
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minmaxscale(other.minmaxscale),
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maxabsscale(other.maxabsscale),
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pcascale(other.pcascale),
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zcascale(other.zcascale),
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meanscale(other.meanscale),
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standardscale(other.standardscale),
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epsilon(other.epsilon),
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pcascale(other.pcascale),
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zcascale(other.zcascale),
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minValue(other.minValue),
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maxValue(other.maxValue)
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maxValue(other.maxValue),
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epsilon(other.epsilon)
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{
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other.scalerType = 0;
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other.minmaxscale = NULL;
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@@ -95,7 +95,8 @@ class HardCodedSortModel
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void Train(arma::field<arma::mat>& predictors,
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arma::field<arma::mat>& labels)
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{
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assert(predictors.n_elem == labels.n_elem);
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const bool check = predictors.n_elem == labels.n_elem;
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assert(check == true);
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}
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void Predict(arma::mat& predictors,
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