class ModelBasedRecommender extends Recommender
The ModelBasedRecommender class is used to perform predictions based on Model based Collaborative Filtering techniques (Pure SVD, Regularized SVD)
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Instance Constructors
- new ModelBasedRecommender(input: MatrixI, m: Int, n: Int)
- input
original matrix
- m
number of rows
- n
number of columns
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- def SVDImp: Unit
Calculate the final predicted matrix based on pure singular value decomposition
- def SVDR: Unit
Calculate the final predicted matrix based on regularized Singular value decompsition
- final def asInstanceOf[T0]: T0
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- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- def colMeans: VectorD
Create a vector of mean values of each column without including 0's
- def crossValidate(tester: MatrixD): Unit
Phase 2: Cross validate the final predictions against the test dataset.
Phase 2: Cross validate the final predictions against the test dataset.
- tester
testing data matrix
- Definition Classes
- Recommender
- def denormalize(i: Int): Double
Create a column denormalized version of 'this' matrix and return the denormalized value used.
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- def error_metrics(input: MatrixI): Unit
Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.
Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.
- input
the test portion of the original 4-column input matrix
- Definition Classes
- Recommender
- def genTrain2(train: MatrixI): Unit
Generates the training matrix for the dataset for Phase 1
Generates the training matrix for the dataset for Phase 1
- train
: training data matrix
- def genTrainTest(exl: VectorI, input: MatrixI): MatrixD
Generates the training matrix for the dataset for Phase 2
Generates the training matrix for the dataset for Phase 2
- exl
vector of index values which will be excluded in the train
- input
original data matrix
- final def getClass(): Class[_ <: AnyRef]
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- @native() @HotSpotIntrinsicCandidate()
- def getStats: Array[Statistic]
Return the variables for the statistics vectors.
Return the variables for the statistics vectors.
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- def hashCode(): Int
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- @native() @HotSpotIntrinsicCandidate()
- def impute: Unit
Column mean Imputation of null values in the input matrix
- final def isInstanceOf[T0]: Boolean
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- def makeRatings(input: MatrixI, m: Int, n: Int): MatrixD
Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns.
Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns. The 'input' matrix has type
MatrixI, while the 'ratings' matrix has typeMatrixD.- input
the original 4-column input data matrix containing ratings, e.g., from a file
- m
the number of rows for the ratings matrix
- n
the number of columns for the ratings matrix
- Definition Classes
- Recommender
- def minNZ: Double
Return the minimum non-zero element of the entire matrix
- final def ne(arg0: AnyRef): Boolean
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- def normalize(): Unit
Create a column normalized version of 'this' matrix.
Create a column normalized version of 'this' matrix. for all values that are not 0 replace with self - row mean (mean calculation doesnot include 0s)
- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- def rate(i: Int, j: Int): Double
Generate a rating based on Singular value Decompostion of matrix
Generate a rating based on Singular value Decompostion of matrix
- i
user
- j
item
- Definition Classes
- ModelBasedRecommender → Recommender
- final def synchronized[T0](arg0: => T0): T0
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- def test(istart: Int, iend: Int, input: MatrixI): Unit
Phase 3: Test the accuracy of the predictions and add it to the statistics vector.
Phase 3: Test the accuracy of the predictions and add it to the statistics vector.
- istart
the start point
- iend
the end point
- input
the original 4-column input matrix
- Definition Classes
- Recommender
- def toString(): String
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- def topk(x: VectorD, k: Int): VectorI
Return the indices of the 'k' largest values in vector 'x'.
Return the indices of the 'k' largest values in vector 'x'. FIX - replace with more efficient top-k algorithm
- x
the input vector
- k
the number of values to be returned
- Definition Classes
- Recommender
- def topk2(x: VectorD, k: Int): Array[Int]
Return the indices of the 'k' largest values in vector 'x'.
Return the indices of the 'k' largest values in vector 'x'.
- x
the input vector
- k
the number of values to be returned
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- Recommender
- final def wait(arg0: Long, arg1: Int): Unit
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- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
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- final def wait(): Unit
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- def zRatings(limit: Int, input: MatrixI): Unit
Returns a matrix with replacing all values with 0 beyond the interval specified corresponds to Phase 3 of the expermiments
Returns a matrix with replacing all values with 0 beyond the interval specified corresponds to Phase 3 of the expermiments
- limit
interval start point
- input
original data matrix
Deprecated Value Members
- def finalize(): Unit
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated