SFA

Sparse Matrix Factorization with L2 loss (similar to ALS).

Parameters

dim(256): Model dimension
uiter(5): Number of iterations on one block of data
miter(5): Number of CG iterations for model updates - not currently used in the SGD implementation.
lambdau(5f): Prior on the user (data) factor
lambdam(5f): Prior on model
regumean(0f): prior on instance mean
regmmean(0f): Prior on feature mean
startup(1): Skip CG for this many iterations
traceConvergence(false): Print out trace info for convergence of the u iterations.
doUser(false): Apply the per-instance mean estimate.
weightByUser(false): Weight loss equally by users, rather than their number of choices.
ueps(1e-10f): A safety floor constant
uconvg(1e-3f): Stop u iteration if error smaller than this.

Other key parameters inherited from the learner, datasource and updater:

batchSize: the number of samples processed in a block
npasses(2): number of complete passes over the dataset
useGPU(true): Use GPU acceleration if available.

Example:

a is a sparse word x document matrix

val (nn, opts) = SFA.learner(a)
opts.what             // prints the available options
opts.uiter=2          // customize options
nn.train              // train the model
nn.modelmat           // get the final model
nn.datamat            // get the other factor (requires opts.putBack=1)

Linear Supertypes

FactorModel, Model, AnyRef, Any

Instance Constructors

new SFA(opts: Opts = new SFA.Options)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
var Minv: Mat
var _modelmats: Array[Mat]

Definition Classes
Model
final def asInstanceOf[T0]: T0

Definition Classes
Any
var avg: Mat
def bind(ds: DataSource): Unit

Definition Classes
Model
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def copyMats(from: Array[Mat], to: Array[Mat]): Unit

Definition Classes
Model
def copyTo(mod: Model): Unit

Definition Classes
Model
var datasource: DataSource

Definition Classes
Model
var diagM: Mat
def doblock(gmats: Array[Mat], ipass: Int, i: Long): Unit

Definition Classes
FactorModel → Model
def doblockg(amats: Array[Mat], ipass: Int, here: Long): Unit

Definition Classes
Model
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def evalblock(mats: Array[Mat], ipass: Int, here: Long): FMat

Definition Classes
FactorModel → Model
def evalblockg(amats: Array[Mat], ipass: Int, here: Long): FMat

Definition Classes
Model
def evalfun(sdata: Mat, user: Mat, ipass: Int): FMat

Definition Classes
SFA → FactorModel
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
var gmats: Array[Mat]

Definition Classes
Model
def hashCode(): Int

Definition Classes
AnyRef → Any
var iavg: Mat
def init(): Unit

Definition Classes
SFA → FactorModel → Model
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
var itemcount: Mat
var itemsum: Mat
var lamu: Mat
var mats: Array[Mat]

Definition Classes
Model
var mlm: Mat
var mm: Mat
def modelmats: Array[Mat]

Definition Classes
Model
def mupdate(sdata0: Mat, user: Mat, ipass: Int): Unit

Definition Classes
SFA → FactorModel
def mupdate0(sdata: Mat, user: Mat, ipass: Int): Unit
def mupdate2(data: Mat, user: Mat, ipass: Int): Unit

Definition Classes
FactorModel
var mzero: Mat
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
var nfeats: Int
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var nratings: Double
val opts: Opts

Definition Classes
SFA → FactorModel → Model
var parent_model: Model

Definition Classes
Model
var pm: Mat
var putBack: Int

Definition Classes
Model
var refresh: Boolean

Definition Classes
Model
def setmodelmats(a: Array[Mat]): Unit

Definition Classes
Model
def setpm(pm0: Mat): Unit
var slm: Mat
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
var totratings: Double
var traceMem: Boolean
def updatePass(ipass: Int): Unit

Definition Classes
SFA → Model
var updatemats: Array[Mat]

Definition Classes
Model
var useDouble: Boolean

Definition Classes
Model
var useGPU: Boolean

Definition Classes
Model
def uupdate(sdata0: Mat, user: Mat, ipass: Int): Unit

Definition Classes
SFA → FactorModel
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

class SFA extends FactorModel

Instance Constructors

new SFA(opts: Opts = new SFA.Options)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var Minv: Mat

var _modelmats: Array[Mat]

final def asInstanceOf[T0]: T0

var avg: Mat

def bind(ds: DataSource): Unit

def clone(): AnyRef

def copyMats(from: Array[Mat], to: Array[Mat]): Unit

def copyTo(mod: Model): Unit

var datasource: DataSource

var diagM: Mat

def doblock(gmats: Array[Mat], ipass: Int, i: Long): Unit

def doblockg(amats: Array[Mat], ipass: Int, here: Long): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evalblock(mats: Array[Mat], ipass: Int, here: Long): FMat

def evalblockg(amats: Array[Mat], ipass: Int, here: Long): FMat

def evalfun(sdata: Mat, user: Mat, ipass: Int): FMat

def finalize(): Unit

final def getClass(): Class[_]

var gmats: Array[Mat]

def hashCode(): Int

var iavg: Mat

def init(): Unit

final def isInstanceOf[T0]: Boolean

var itemcount: Mat

var itemsum: Mat

var lamu: Mat

var mats: Array[Mat]

var mlm: Mat

var mm: Mat

def modelmats: Array[Mat]

def mupdate(sdata0: Mat, user: Mat, ipass: Int): Unit

def mupdate0(sdata: Mat, user: Mat, ipass: Int): Unit

def mupdate2(data: Mat, user: Mat, ipass: Int): Unit

var mzero: Mat

final def ne(arg0: AnyRef): Boolean

var nfeats: Int

final def notify(): Unit

final def notifyAll(): Unit

var nratings: Double

val opts: Opts

var parent_model: Model

var pm: Mat

var putBack: Int

var refresh: Boolean

def setmodelmats(a: Array[Mat]): Unit

def setpm(pm0: Mat): Unit

var slm: Mat

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

var totratings: Double

var traceMem: Boolean

def updatePass(ipass: Int): Unit

var updatemats: Array[Mat]

var useDouble: Boolean

var useGPU: Boolean

def uupdate(sdata0: Mat, user: Mat, ipass: Int): Unit

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from FactorModel

Inherited from Model

Inherited from AnyRef

Inherited from Any

Ungrouped