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Register MLPPStat into the ClassDB.
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@ -270,3 +270,6 @@ real_t MLPPStat::logMean(const real_t x, const real_t y) {
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}
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return (y - x) / (log(y) - std::log(x));
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}
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void MLPPStat::_bind_methods() {
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}
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@ -10,12 +10,16 @@
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include <vector>
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class MLPPStat {
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class MLPPStat : public Reference {
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GDCLASS(MLPPStat, Reference);
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public:
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// These functions are for univariate lin reg module- not for users.
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real_t b0Estimation(const std::vector<real_t> &x, const std::vector<real_t> &y);
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@ -58,6 +62,9 @@ public:
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real_t stolarskyMean(const real_t x, const real_t y, const real_t p);
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real_t identricMean(const real_t x, const real_t y);
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real_t logMean(const real_t x, const real_t y);
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protected:
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static void _bind_methods();
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};
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#endif /* Stat_hpp */
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@ -32,6 +32,7 @@ SOFTWARE.
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#include "mlpp/regularization/reg.h"
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#include "mlpp/transforms/transforms.h"
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#include "mlpp/utilities/utilities.h"
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#include "mlpp/stat/stat.h"
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#include "mlpp/hidden_layer/hidden_layer.h"
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#include "mlpp/multi_output_layer/multi_output_layer.h"
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@ -63,6 +64,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
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ClassDB::register_class<MLPPActivation>();
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ClassDB::register_class<MLPPCost>();
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ClassDB::register_class<MLPPTransforms>();
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ClassDB::register_class<MLPPStat>();
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ClassDB::register_class<MLPPHiddenLayer>();
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ClassDB::register_class<MLPPOutputLayer>();
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