vendor/tomotopy/src/TopicModel/SLDA.h in tomoto-0.1.4 vs vendor/tomotopy/src/TopicModel/SLDA.h in tomoto-0.2.0

@@ -7,33 +7,45 @@
 	struct DocumentSLDA : public DocumentLDA<_tw>
 	{
 		using BaseDocument = DocumentLDA<_tw>;
 		using DocumentLDA<_tw>::DocumentLDA;
 		std::vector<Float> y;
+
+		RawDoc::MiscType makeMisc(const ITopicModel* tm) const override
+		{
+			RawDoc::MiscType ret = DocumentLDA<_tw>::makeMisc(tm);
+			ret["y"] = y;
+			return ret;
+		}
 		DEFINE_SERIALIZER_AFTER_BASE_WITH_VERSION(BaseDocument, 0, y);
 		DEFINE_TAGGED_SERIALIZER_AFTER_BASE_WITH_VERSION(BaseDocument, 1, 0x00010001, y);
 	};
 
+	struct SLDAArgs;
+
 	class ISLDAModel : public ILDAModel
 	{
 	public:
 		enum class GLM
 		{
 			linear = 0,
 			binary_logistic = 1,
 		};
 
 		using DefaultDocType = DocumentSLDA<TermWeight::one>;
-		static ISLDAModel* create(TermWeight _weight, size_t _K = 1, 
-			const std::vector<ISLDAModel::GLM>& vars = {},
-			Float alpha = 0.1, Float _eta = 0.01,
-			const std::vector<Float>& _mu = {}, const std::vector<Float>& _nuSq = {},
-			const std::vector<Float>& _glmParam = {},
-			size_t seed = std::random_device{}(),
+		static ISLDAModel* create(TermWeight _weight, const SLDAArgs& args,
 			bool scalarRng = false);
 
 		virtual size_t getF() const = 0;
 		virtual std::vector<Float> getRegressionCoef(size_t f) const = 0;
 		virtual GLM getTypeOfVar(size_t f) const = 0;
 		virtual std::vector<Float> estimateVars(const DocumentBase* doc) const = 0;
+	};
+
+	struct SLDAArgs : public LDAArgs
+	{
+		std::vector<ISLDAModel::GLM> vars;
+		std::vector<Float> mu;
+		std::vector<Float> nuSq;
+		std::vector<Float> glmParam;
 	};
 }
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