Uses of Class
oracle.pgx.api.MatrixFactorizationModel

Packages that use MatrixFactorizationModel

Package	Description
oracle.pgx.api	This package contains the main Java APIs.

Uses of MatrixFactorizationModel in oracle.pgx.api

Methods in oracle.pgx.api that return MatrixFactorizationModel

Modifier and Type	Method	Description
<ID> MatrixFactorizationModel<ID>	Analyst.matrixFactorizationGradientDescent(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> MatrixFactorizationModel<ID>	Analyst.matrixFactorizationGradientDescent(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, double learningRate, double changePerStep, double lambda, int maxStep, int vectorLength)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> MatrixFactorizationModel<ID>	Analyst.matrixFactorizationGradientDescent(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, double learningRate, double changePerStep, double lambda, int maxStep, int vectorLength, VertexProperty<ID,PgxVect<java.lang.Double>> features)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> MatrixFactorizationModel<ID>	Analyst.matrixFactorizationGradientDescent(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, VertexProperty<ID,PgxVect<java.lang.Double>> features)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs

Methods in oracle.pgx.api that return types with arguments of type MatrixFactorizationModel

Modifier and Type	Method	Description
<ID> PgxFuture<MatrixFactorizationModel<ID>>	Analyst.matrixFactorizationGradientDescentAsync(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> PgxFuture<MatrixFactorizationModel<ID>>	Analyst.matrixFactorizationGradientDescentAsync(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, double learningRate, double changePerStep, double lambda, int maxStep, int vectorLength)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> PgxFuture<MatrixFactorizationModel<ID>>	Analyst.matrixFactorizationGradientDescentAsync(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, double learningRate, double changePerStep, double lambda, int maxStep, int vectorLength, VertexProperty<ID,PgxVect<java.lang.Double>> features)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs
<ID> PgxFuture<MatrixFactorizationModel<ID>>	Analyst.matrixFactorizationGradientDescentAsync(BipartiteGraph graph, EdgeProperty<java.lang.Double> weight, VertexProperty<ID,PgxVect<java.lang.Double>> features)	Matrix factorization can be used as a recommendation algorithm for bipartite graphs