 Matrices

Related Doc: package linalg

object Matrices

Factory methods for org.apache.spark.mllib.linalg.Matrix.

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@Since( "1.0.0" )
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Matrices.scala
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1. final def !=(arg0: Any): Boolean

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2. final def ##(): Int

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6. 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

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@Since( "1.0.0" )
7. 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

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

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9. def equals(arg0: Any): Boolean

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10. 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

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@Since( "1.2.0" )
11. def finalize(): Unit

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12. 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

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

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14. def hashCode(): Int

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15. 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

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

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17. final def ne(arg0: AnyRef): Boolean

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18. final def notify(): Unit

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19. final def notifyAll(): Unit

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20. 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

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@Since( "1.2.0" )
21. 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)

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@Since( "1.2.0" )
22. 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)

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@Since( "1.2.0" )
23. 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

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@Since( "1.2.0" )
24. 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

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

Generate a SparseMatrix consisting of i.i.d. uniform random numbers.

Generate a SparseMatrix consisting of i.i.d. uniform 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)

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@Since( "1.3.0" )
26. 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)

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

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28. def toString(): String

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29. 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

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@Since( "1.3.0" )
30. final def wait(): Unit

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

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32. final def wait(arg0: Long): Unit

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33. 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" )