Matrices

Value Members

final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

Attributes
protected[java.lang]
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@throws( ... )
def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix

Creates a column-major dense matrix.
Creates a column-major dense matrix.
numRows
number of rows
numCols
number of columns
values
matrix entries in column major

Annotations
@Since( "1.0.0" )
def diag(vector: Vector): Matrix

Generate a diagonal matrix in Matrix format from the supplied values.
Generate a diagonal matrix in Matrix format from the supplied values.
vector
a Vector that will form the values on the diagonal of the matrix
returns
Square Matrix with size values.length x values.length and values on the diagonal

Annotations
@Since( "1.2.0" )
final def eq(arg0: AnyRef): Boolean

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def equals(arg0: Any): Boolean

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def eye(n: Int): Matrix

Generate a dense Identity Matrix in Matrix format.
Generate a dense Identity Matrix in Matrix format.
n
number of rows and columns of the matrix
returns
Matrix with size n x n and values of ones on the diagonal

Annotations
@Since( "1.2.0" )
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
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@throws( classOf[java.lang.Throwable] )
def fromML(m: ml.linalg.Matrix): Matrix

Convert new linalg type to spark.mllib type.
Convert new linalg type to spark.mllib type. Light copy; only copies references

Annotations
@Since( "2.0.0" )
final def getClass(): Class[_]

Definition Classes
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def hashCode(): Int

Definition Classes
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def horzcat(matrices: Array[Matrix]): Matrix

Horizontally concatenate a sequence of matrices.
Horizontally concatenate a sequence of matrices. The returned matrix will be in the format the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in a sparse matrix. If the Array is empty, an empty DenseMatrix will be returned.
matrices
array of matrices
returns
a single Matrix composed of the matrices that were horizontally concatenated

Annotations
@Since( "1.3.0" )
final def isInstanceOf[T0]: Boolean

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final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

Definition Classes
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def ones(numRows: Int, numCols: Int): Matrix

Generate a DenseMatrix consisting of ones.
Generate a DenseMatrix consisting of ones.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
returns
Matrix with size numRows x numCols and values of ones

Annotations
@Since( "1.2.0" )
def rand(numRows: Int, numCols: Int, rng: Random): Matrix

Generate a DenseMatrix consisting of i.i.d. uniform random numbers.
Generate a DenseMatrix consisting of i.i.d. uniform random numbers.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
rng
a random number generator
returns
Matrix with size numRows x numCols and values in U(0, 1)

Annotations
@Since( "1.2.0" )
def randn(numRows: Int, numCols: Int, rng: Random): Matrix

Generate a DenseMatrix consisting of i.i.d. gaussian random numbers.
Generate a DenseMatrix consisting of i.i.d. gaussian random numbers.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
rng
a random number generator
returns
Matrix with size numRows x numCols and values in N(0, 1)

Annotations
@Since( "1.2.0" )
def sparse(numRows: Int, numCols: Int, colPtrs: Array[Int], rowIndices: Array[Int], values: Array[Double]): Matrix

Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.
numRows
number of rows
numCols
number of columns
colPtrs
the index corresponding to the start of a new column
rowIndices
the row index of the entry
values
non-zero matrix entries in column major

Annotations
@Since( "1.2.0" )
def speye(n: Int): Matrix

Generate a sparse Identity Matrix in Matrix format.
Generate a sparse Identity Matrix in Matrix format.
n
number of rows and columns of the matrix
returns
Matrix with size n x n and values of ones on the diagonal

Annotations
@Since( "1.3.0" )
def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix

Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
density
the desired density for the matrix
rng
a random number generator
returns
Matrix with size numRows x numCols and values in U(0, 1)

Annotations
@Since( "1.3.0" )
def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix

Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
density
the desired density for the matrix
rng
a random number generator
returns
Matrix with size numRows x numCols and values in N(0, 1)

Annotations
@Since( "1.3.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
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def toString(): String

Definition Classes
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def vertcat(matrices: Array[Matrix]): Matrix

Vertically concatenate a sequence of matrices.
Vertically concatenate a sequence of matrices. The returned matrix will be in the format the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in a sparse matrix. If the Array is empty, an empty DenseMatrix will be returned.
matrices
array of matrices
returns
a single Matrix composed of the matrices that were vertically concatenated

Annotations
@Since( "1.3.0" )
final def wait(): Unit

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

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

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@throws( ... )
def zeros(numRows: Int, numCols: Int): Matrix

Generate a Matrix consisting of zeros.
Generate a Matrix consisting of zeros.
numRows
number of rows of the matrix
numCols
number of columns of the matrix
returns
Matrix with size numRows x numCols and values of zeros

Annotations
@Since( "1.2.0" )

Related Doc: package linalg

object Matrices

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix

def diag(vector: Vector): Matrix

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def eye(n: Int): Matrix

def finalize(): Unit

def fromML(m: ml.linalg.Matrix): Matrix

final def getClass(): Class[_]

def hashCode(): Int

def horzcat(matrices: Array[Matrix]): Matrix

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def ones(numRows: Int, numCols: Int): Matrix

def rand(numRows: Int, numCols: Int, rng: Random): Matrix

def randn(numRows: Int, numCols: Int, rng: Random): Matrix

def sparse(numRows: Int, numCols: Int, colPtrs: Array[Int], rowIndices: Array[Int], values: Array[Double]): Matrix

def speye(n: Int): Matrix

def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix

def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix

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

def toString(): String

def vertcat(matrices: Array[Matrix]): Matrix

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def zeros(numRows: Int, numCols: Int): Matrix

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