Modifier and Type | Field and Description |
---|---|
DenseVector |
ArffDataConvertor.oneHotRatingVector |
Modifier and Type | Method and Description |
---|---|
DenseVector |
DenseVector.add(DenseVector vec)
Do vector operation:
a + b |
DenseVector |
DenseVector.add(double val)
Return a new dense vector by adding a value to all entries of current vector
a[i] = b[i] + c |
DenseVector |
DenseVector.addEqual(DenseVector vec)
Do vector operation:
a + b |
DenseVector |
DenseVector.addEqual(double val)
Return this dense vector by adding a value to all entries of current vector
b[i] = b[i] + c |
DenseVector |
DenseVector.clone()
Make a deep copy of current vector
|
DenseVector |
DenseMatrix.column(int column)
Return a copy of column data as a dense vector.
|
static DenseVector |
DenseVector.kroneckerProduct(DenseVector M,
DenseVector N)
Return the Kronecker product of two vectors
|
DenseVector |
DenseVector.minus(DenseVector vec)
Do vector operation:
a - b |
DenseVector |
DenseVector.minus(double val)
Return a new dense vector by substructing a value from all entries of current vector
a[i] = b[i] - c |
DenseVector |
DenseVector.minusEqual(DenseVector vec)
Do vector operation:
a - b |
DenseVector |
DenseVector.minusEqual(double val)
Return this dense vector by substructing a value from all entries of current vector
b[i] = b[i] - c |
DenseVector |
DenseMatrix.mult(DenseVector vec)
Do
matrix x vector between current matrix and a given vector |
DenseVector |
DenseMatrix.mult(SparseVector vec) |
DenseVector |
DenseMatrix.row(int rowId)
Return a copy of row data as a dense vector.
|
DenseVector |
DenseMatrix.row(int rowId,
boolean deep)
Return a vector of a specific row.
|
DenseVector |
DenseVector.scale(double val)
Return a new dense vector by scaling a value to all entries of current vector
a = b.scale(c) |
DenseVector |
DenseVector.scaleEqual(double val)
Return this dense vector by scaling a value to all entries of current vector
b = b.scale(c) . |
Modifier and Type | Method and Description |
---|---|
DenseVector |
DenseVector.add(DenseVector vec)
Do vector operation:
a + b |
DenseVector |
DenseVector.addEqual(DenseVector vec)
Do vector operation:
a + b |
double |
SparseVector.inner(DenseVector vec)
Return inner product with a given dense vector.
|
double |
DenseVector.inner(DenseVector vec)
Do vector operation:
a^t * b |
static DenseVector |
DenseVector.kroneckerProduct(DenseVector M,
DenseVector N)
Return the Kronecker product of two vectors
|
DenseVector |
DenseVector.minus(DenseVector vec)
Do vector operation:
a - b |
DenseVector |
DenseVector.minusEqual(DenseVector vec)
Do vector operation:
a - b |
SparseTensor |
SparseTensor.modeProduct(DenseVector vec,
int dim)
n-mode product of a tensor A (I1 x I2 x ...
|
DenseVector |
DenseMatrix.mult(DenseVector vec)
Do
matrix x vector between current matrix and a given vector |
DenseMatrix |
DenseVector.outer(DenseVector vec)
Do vector operation:
a * b^t |
void |
DenseMatrix.setRow(int row,
DenseVector vals)
Set values of one dense vector to a specific row.
|
Constructor and Description |
---|
DenseVector(DenseVector vec)
Construct a dense vector by deeply copying data from a given vector
|
Modifier and Type | Field and Description |
---|---|
protected DenseVector |
FactorizationMachineRecommender.W
weight vector
|
Modifier and Type | Field and Description |
---|---|
protected DenseVector |
LDARecommender.alpha
vector of hyperparameters for alpha and beta
|
protected DenseVector |
ItemBigramRecommender.alpha
vector of hyperparameters for alpha
|
protected DenseVector |
LDARecommender.beta
vector of hyperparameters for alpha and beta
|
protected DenseVector |
PLSARecommender.numItemsRateByUser
entry[u]: number of tokens rated by user u.
|
protected DenseVector |
AspectModelRecommender.topicProbs
topic distribution: P(z)
|
protected DenseVector |
AspectModelRecommender.topicProbsSum
topic distribution: P(z)
|
protected DenseVector |
LDARecommender.topicTokenNumbers
entry[k]: number of tokens assigned to topic t.
|
DenseVector |
ListRankMFRecommender.userExp |
protected DenseVector |
LDARecommender.userTokenNumbers
entry[u]: number of tokens rated by user u.
|
protected DenseVector |
ItemBigramRecommender.userTokenNumbers
entry[u]: number of tokens rated by user u.
|
Modifier and Type | Method and Description |
---|---|
java.util.List<java.util.Map.Entry<java.lang.Integer,java.lang.Double>> |
AoBPRRecommender.sortByDenseVectorValue(DenseVector vector) |
Modifier and Type | Field and Description |
---|---|
protected DenseVector |
BiasedMFRecommender.itemBiases
user biases
|
protected DenseVector |
BPoissMFRecommender.GammaDenseVector.logValue |
DenseVector |
BPMFRecommender.HyperParameters.mu |
protected DenseVector |
BPoissMFRecommender.GammaDenseVector.rate |
protected DenseVector |
BPoissMFRecommender.GammaDenseMatrixGR.rate |
protected DenseVector |
BPoissMFRecommender.GammaDenseVector.shape |
protected DenseVector |
AspectModelRecommender.topicProbs |
protected DenseVector |
AspectModelRecommender.topicProbsMean |
protected DenseVector |
AspectModelRecommender.topicProbsMeanSum |
protected DenseVector |
AspectModelRecommender.topicProbsSum |
protected DenseVector |
AspectModelRecommender.topicProbsVariance |
protected DenseVector |
AspectModelRecommender.topicProbsVarianceSum |
protected DenseVector |
BiasedMFRecommender.userBiases
user biases
|
protected DenseVector |
GPLSARecommender.userMu |
protected DenseVector |
GPLSARecommender.userSigma |
protected DenseVector |
BPoissMFRecommender.GammaDenseVector.value |
Modifier and Type | Method and Description |
---|---|
protected DenseVector |
BPoissMFRecommender.getPhi(DenseMatrix Theta,
int indexTheta,
DenseMatrix Beta,
int indexBeta,
int number) |
protected DenseVector |
BPMFRecommender.updateParameters(DenseMatrix factors,
SparseVector ratings,
BPMFRecommender.HyperParameters hyperParameters) |
Modifier and Type | Method and Description |
---|---|
protected BPMFRecommender.HyperParameters |
BPMFRecommender.samplingHyperParameters(BPMFRecommender.HyperParameters hyperParameters,
DenseMatrix factors,
DenseVector normalMu0,
double normalBeta0,
DenseMatrix WishartScale0,
double WishartNu0) |
Constructor and Description |
---|
LLORMAUpdater(int threadIDParam,
int numFactorsParam,
int numUsersParam,
int numItemsParam,
int anchorUserParam,
int anchorItemParam,
double learnRateParam,
double localRegUserParam,
double localRegItemParam,
int localIterationParam,
DenseVector userWeightsParam,
DenseVector itemWeightsParam,
SparseMatrix trainMatrixParam)
Construct a local model for singleton LLORMA.
|
Modifier and Type | Field and Description |
---|---|
protected DenseVector |
HFTRecommender.itemBiases
user biases
|
protected DenseVector |
HFTRecommender.userBiases
user biases
|
Copyright © 2017. All Rights Reserved.