public class SparseMatrix extends Object implements Matrix
1.0 0.0 4.0
0.0 3.0 5.0
2.0 0.0 6.0
values: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
rowIndices=[0, 2, 1, 0, 1, 2], colPointers=[0, 2, 3, 6].
param: numRows number of rows
param: numCols number of columns
param: colPtrs the index corresponding to the start of a new column (if not transposed)
param: rowIndices the row index of the entry (if not transposed). They must be in strictly
increasing order for each column
param: values nonzero matrix entries in column major (if not transposed)
param: isTransposed whether the matrix is transposed. If true, the matrix can be considered
Compressed Sparse Row (CSR) format, where colPtrs behaves as rowPtrs,
and rowIndices behave as colIndices, and values are stored in row major.
| Constructor and Description |
|---|
SparseMatrix(int numRows,
int numCols,
int[] colPtrs,
int[] rowIndices,
double[] values)
Column-major sparse matrix.
|
SparseMatrix(int numRows,
int numCols,
int[] colPtrs,
int[] rowIndices,
double[] values,
boolean isTransposed) |
| Modifier and Type | Method and Description |
|---|---|
double |
apply(int i,
int j)
Gets the (i, j)-th element.
|
SparseMatrix |
asML()
Convert this matrix to the new mllib-local representation.
|
scala.collection.Iterator<Vector> |
colIter()
Returns an iterator of column vectors.
|
int[] |
colPtrs() |
SparseMatrix |
copy()
Get a deep copy of the matrix.
|
boolean |
equals(Object o) |
static SparseMatrix |
fromCOO(int numRows,
int numCols,
scala.collection.Iterable<scala.Tuple3<Object,Object,Object>> entries)
Generate a
SparseMatrix from Coordinate List (COO) format. |
static SparseMatrix |
fromML(SparseMatrix m)
Convert new linalg type to spark.mllib type.
|
int |
hashCode() |
boolean |
isTransposed()
Flag that keeps track whether the matrix is transposed or not.
|
int |
numActives()
Find the number of values stored explicitly.
|
int |
numCols()
Number of columns.
|
int |
numNonzeros()
Find the number of non-zero active values.
|
int |
numRows()
Number of rows.
|
int[] |
rowIndices() |
static SparseMatrix |
spdiag(Vector vector)
Generate a diagonal matrix in
SparseMatrix format from the supplied values. |
static SparseMatrix |
speye(int n)
Generate an Identity Matrix in
SparseMatrix format. |
static SparseMatrix |
sprand(int numRows,
int numCols,
double density,
java.util.Random rng)
Generate a
SparseMatrix consisting of i.i.d. |
static SparseMatrix |
sprandn(int numRows,
int numCols,
double density,
java.util.Random rng)
Generate a
SparseMatrix consisting of i.i.d. |
DenseMatrix |
toDense()
Generate a
DenseMatrix from the given SparseMatrix. |
SparseMatrix |
transpose()
Transpose the Matrix.
|
double[] |
values() |
public SparseMatrix(int numRows,
int numCols,
int[] colPtrs,
int[] rowIndices,
double[] values,
boolean isTransposed)
public SparseMatrix(int numRows,
int numCols,
int[] colPtrs,
int[] rowIndices,
double[] values)
1.0 0.0 4.0
0.0 3.0 5.0
2.0 0.0 6.0
values: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
rowIndices=[0, 2, 1, 0, 1, 2], colPointers=[0, 2, 3, 6].
numRows - number of rowsnumCols - number of columnscolPtrs - the index corresponding to the start of a new columnrowIndices - the row index of the entry. They must be in strictly increasing
order for each columnvalues - non-zero matrix entries in column majorpublic static SparseMatrix fromCOO(int numRows, int numCols, scala.collection.Iterable<scala.Tuple3<Object,Object,Object>> entries)
SparseMatrix from Coordinate List (COO) format. Input must be an array of
(i, j, value) tuples. Entries that have duplicate values of i and j are
added together. Tuples where value is equal to zero will be omitted.numRows - number of rows of the matrixnumCols - number of columns of the matrixentries - Array of (i, j, value) tuplesSparseMatrixpublic static SparseMatrix speye(int n)
SparseMatrix format.n - number of rows and columns of the matrixSparseMatrix with size n x n and values of ones on the diagonalpublic static SparseMatrix sprand(int numRows, int numCols, double density, java.util.Random rng)
SparseMatrix consisting of i.i.d. uniform random numbers. The number of non-zero
elements equal the ceiling of numRows x numCols x density
numRows - number of rows of the matrixnumCols - number of columns of the matrixdensity - the desired density for the matrixrng - a random number generatorSparseMatrix with size numRows x numCols and values in U(0, 1)public static SparseMatrix sprandn(int numRows, int numCols, double density, java.util.Random rng)
SparseMatrix consisting of i.i.d. gaussian random numbers.numRows - number of rows of the matrixnumCols - number of columns of the matrixdensity - the desired density for the matrixrng - a random number generatorSparseMatrix with size numRows x numCols and values in N(0, 1)public static SparseMatrix spdiag(Vector vector)
SparseMatrix format from the supplied values.vector - a Vector that will form the values on the diagonal of the matrixSparseMatrix with size values.length x values.length and non-zero
values on the diagonalpublic static SparseMatrix fromML(SparseMatrix m)
m - (undocumented)public int numRows()
Matrixpublic int numCols()
Matrixpublic int[] colPtrs()
public int[] rowIndices()
public double[] values()
public boolean isTransposed()
MatrixisTransposed in interface Matrixpublic boolean equals(Object o)
equals in class Objectpublic int hashCode()
hashCode in class Objectpublic double apply(int i,
int j)
Matrixpublic SparseMatrix copy()
Matrixpublic SparseMatrix transpose()
MatrixMatrix instance sharing the same underlying data.public DenseMatrix toDense()
DenseMatrix from the given SparseMatrix. The new matrix will have isTransposed
set to false.public int numNonzeros()
MatrixnumNonzeros in interface Matrixpublic int numActives()
MatrixnumActives in interface Matrixpublic scala.collection.Iterator<Vector> colIter()
Matrixpublic SparseMatrix asML()
Matrix