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## Uses of Class
prea.data.structure.DenseVector

Packages that use DenseVector
prea.data.structure |
prea.recommender.etc |

Uses of DenseVector in prea.data.structure

Methods in prea.data.structure that return DenseVector
`DenseVector` | `DenseVector.add(double alpha)` Scalar addition operator.
`DenseVector` | `DenseVector.commonMinus(DenseVector b)` Vector subtraction (a - b), for only existing values.
`DenseVector` | `DenseVector.copy()` Copy the whole dense vector and make a clone.
`DenseVector` | `DenseMatrix.diagonal()` Return items in the diagonal in vector form.
`DenseVector` | `DenseVector.exp(double alpha)` Exponential of a given constant.
`DenseVector` | `DenseMatrix.getCol(int index)` Return a copy of a given column.
`DenseVector` | `DenseMatrix.getColRef(int index)` Return a reference of a given column.
`DenseVector` | `DenseMatrix.getRow(int index)` Return a copy of a given row.
`DenseVector` | `DenseMatrix.getRowRef(int index)` Return a reference of a given row.
`DenseVector` | `DenseVector.minus(DenseVector b)` Vector subtraction (a - b)
`DenseVector` | `DenseVector.partMinus(DenseVector b, int[] indexList)` Vector subtraction (a - b) for indices only in the given indices.
`DenseVector` | `DenseVector.partPlus(DenseVector b, int[] indexList)` Vector sum (a + b) for indices only in the given indices.
`DenseVector` | `DenseMatrix.partTimes(DenseVector x, int[] indexList)` Matrix-vector product (b = Ax) only with indices in indexList.
`DenseVector` | `DenseVector.plus(DenseVector b)` Vector sum (a + b)
`DenseVector` | `DenseVector.power(double alpha)` Scalar power operator.
`DenseVector` | `DenseVector.scale(double alpha)` Scalar multiplication operator.
`DenseVector` | `DenseVector.sub(double alpha)` Scalar subtraction operator.
`DenseVector` | `DenseMatrix.times(DenseVector x)` Matrix-vector product (b = Ax)
`DenseVector` | `SparseVector.toDenseSubset(int[] indexList)` Convert the vector into the array-based dense representation, but only with the selected indices.
`DenseVector` | `DenseVector.toDenseSubset(int[] indexList)` Convert the vector into a sparse vector, but only with the selected indices.
`DenseVector` | `SparseVector.toDenseVector()` Convert the vector into the array-based dense representation.

Methods in prea.data.structure with parameters of type DenseVector
`DenseVector` | `DenseVector.commonMinus(DenseVector b)` Vector subtraction (a - b), for only existing values.
`double` | `DenseVector.innerProduct(DenseVector b)` Inner product of two vectors.
`DenseVector` | `DenseVector.minus(DenseVector b)` Vector subtraction (a - b)
`DenseMatrix` | `DenseVector.outerProduct(DenseVector b)` Outer product of two vectors.
`double` | `DenseVector.partInnerProduct(DenseVector b, int[] indexList)` Inner-product for indices only in the given indices.
`DenseVector` | `DenseVector.partMinus(DenseVector b, int[] indexList)` Vector subtraction (a - b) for indices only in the given indices.
`DenseMatrix` | `DenseVector.partOuterProduct(DenseVector b, int[] indexList)` Outer-product for indices only in the given indices.
`DenseVector` | `DenseVector.partPlus(DenseVector b, int[] indexList)` Vector sum (a + b) for indices only in the given indices.
`DenseVector` | `DenseMatrix.partTimes(DenseVector x, int[] indexList)` Matrix-vector product (b = Ax) only with indices in indexList.
`DenseVector` | `DenseVector.plus(DenseVector b)` Vector sum (a + b)
`DenseVector` | `DenseMatrix.times(DenseVector x)` Matrix-vector product (b = Ax)

Uses of DenseVector in prea.recommender.etc

Fields in prea.recommender.etc declared as DenseVector
`DenseVector` | `NonlinearPMF.kernParam` kernel parameter value

Methods in prea.recommender.etc that return DenseVector
`private DenseVector` | `NonlinearPMF.collabLogLikeGradients(int user, DenseVector kernParam, double momentum, double learningRate)` Compute the gradients of the model (latent factors and kernel parameters) given one users ratings.
`private DenseVector` | `NonlinearPMF.distancePairWise(DenseMatrix X1, DenseVector x)` Compute the squared euclidean distance between the row vectors of one matrix and a vector.
`private DenseVector` | `NonlinearPMF.expTransform(DenseVector x, int transformType)` Transform a vector by log function, exponential function or linear function.
`private DenseVector` | `NonlinearPMF.getEstimation(int u, int[] testItemsRaw)` Estimate of ratings for a given user and a set of test items.

Methods in prea.recommender.etc with parameters of type DenseVector
`private DenseVector` | `NonlinearPMF.collabLogLikeGradients(int user, DenseVector kernParam, double momentum, double learningRate)` Compute the gradients of the model (latent factors and kernel parameters) given one users ratings.
`private DenseVector` | `NonlinearPMF.distancePairWise(DenseMatrix X1, DenseVector x)` Compute the squared euclidean distance between the row vectors of one matrix and a vector.
`private DenseVector` | `NonlinearPMF.expTransform(DenseVector x, int transformType)` Transform a vector by log function, exponential function or linear function.
`private DenseMatrix` | `NonlinearPMF.rbfKernGradXpoint(DenseVector x, DenseMatrix X2, double kernInverseWidth, double kernVarianceRbf)` Compute the gradient of RBF kernel with respect to input locations.

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