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/*
Copyright (C) 1999 CERN - European Organization for Nuclear Research.
Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose
is hereby granted without fee, provided that the above copyright notice appear in all copies and
that both that copyright notice and this permission notice appear in supporting documentation.
CERN makes no representations about the suitability of this software for any purpose.
It is provided "as is" without expressed or implied warranty.
*/
package cern.colt.matrix.tint.impl;
import java.io.IOException;
import cern.colt.map.tlong.AbstractLongIntMap;
import cern.colt.map.tlong.OpenLongIntHashMap;
import cern.colt.matrix.io.MatrixInfo;
import cern.colt.matrix.io.MatrixSize;
import cern.colt.matrix.io.MatrixVectorReader;
import cern.colt.matrix.tint.IntMatrix1D;
import cern.colt.matrix.tint.IntMatrix2D;
/**
* Sparse hashed 2-d matrix holding int elements. First see the package summary and javadoc tree view to get the broad picture.
*
* Implementation:
*
* Note that this implementation is not synchronized. Uses a
* {@link cern.colt.map.tint.OpenIntIntHashMap}, which is a compact and
* performant hashing technique.
*
* Memory requirements:
*
* Cells that
*
* - are never set to non-zero values do not use any memory.
*
- switch from zero to non-zero state do use memory.
*
- switch back from non-zero to zero state also do use memory. However,
* their memory is automatically reclaimed from time to time. It can also
* manually be reclaimed by calling {@link #trimToSize()}.
*
*
* worst case: memory [bytes] = (1/minLoadFactor) * nonZeros * 13.
* best case: memory [bytes] = (1/maxLoadFactor) * nonZeros * 13.
* Where nonZeros = cardinality() is the number of non-zero cells.
* Thus, a 1000 x 1000 matrix with minLoadFactor=0.25 and maxLoadFactor=0.5 and
* 1000000 non-zero cells consumes between 25 MB and 50 MB. The same 1000 x 1000
* matrix with 1000 non-zero cells consumes between 25 and 50 KB.
*
* Time complexity:
*
* This class offers expected time complexity O(1) (i.e.
* constant time) for the basic operations get, getQuick,
* set, setQuick and size assuming the hash function
* disperses the elements properly among the buckets. Otherwise, pathological
* cases, although highly improbable, can occur, degrading performance to
* O(N) in the worst case. As such this sparse class is expected to
* have no worse time complexity than its dense counterpart
* {@link DenseIntMatrix2D}. However, constant factors are considerably larger.
*
* Cells are internally addressed in row-major. Performance sensitive
* applications can exploit this fact. Setting values in a loop row-by-row is
* quicker than column-by-column, because fewer hash collisions occur. Thus
*
*
* for (int row = 0; row < rows; row++) {
* for (int column = 0; column < columns; column++) {
* matrix.setQuick(row, column, someValue);
* }
* }
*
*
* is quicker than
*
*
* for (int column = 0; column < columns; column++) {
* for (int row = 0; row < rows; row++) {
* matrix.setQuick(row, column, someValue);
* }
* }
*
*
* @see cern.colt.map
* @see cern.colt.map.tint.OpenIntIntHashMap
* @author [email protected]
* @version 1.0, 09/24/99
*
* @author Piotr Wendykier ([email protected])
*/
public class SparseIntMatrix2D extends IntMatrix2D {
/**
*
*/
private static final long serialVersionUID = 1L;
/*
* The elements of the matrix.
*/
protected AbstractLongIntMap elements;
/**
* Constructs a matrix with a copy of the given values. values is
* required to have the form values[row][column] and have exactly
* the same number of columns in every row.
*
* The values are copied. So subsequent changes in values are not
* reflected in the matrix, and vice-versa.
*
* @param values
* The values to be filled into the new matrix.
* @throws IllegalArgumentException
* if
* for any 1 <= row < values.length: values[row].length != values[row-1].length
* .
*/
public SparseIntMatrix2D(int[][] values) {
this(values.length, values.length == 0 ? 0 : values[0].length);
assign(values);
}
/**
* Constructs a matrix with a given number of rows and columns and default
* memory usage. All entries are initially 0.
*
* @param rows
* the number of rows the matrix shall have.
* @param columns
* the number of columns the matrix shall have.
* @throws IllegalArgumentException
* if
* rows<0 || columns<0 || (double)columns*rows > Integer.MAX_VALUE
* .
*/
public SparseIntMatrix2D(int rows, int columns) {
this(rows, columns, rows * (columns / 1000), 0.2, 0.5);
}
/**
* Constructs a matrix with a given number of rows and columns using memory
* as specified. All entries are initially 0. For details related
* to memory usage see {@link cern.colt.map.tlong.OpenLongIntHashMap}.
*
* @param rows
* the number of rows the matrix shall have.
* @param columns
* the number of columns the matrix shall have.
* @param initialCapacity
* the initial capacity of the hash map. If not known, set
* initialCapacity=0 or small.
* @param minLoadFactor
* the minimum load factor of the hash map.
* @param maxLoadFactor
* the maximum load factor of the hash map.
* @throws IllegalArgumentException
* if
*
* initialCapacity < 0 || (minLoadFactor < 0.0 || minLoadFactor >= 1.0) || (maxLoadFactor <= 0.0 || maxLoadFactor >= 1.0) || (minLoadFactor >= maxLoadFactor)
* .
* @throws IllegalArgumentException
* if
* rows<0 || columns<0 || (double)columns*rows > Integer.MAX_VALUE
* .
*/
public SparseIntMatrix2D(int rows, int columns, int initialCapacity, double minLoadFactor, double maxLoadFactor) {
try {
setUp(rows, columns);
} catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
if (!"matrix too large".equals(exc.getMessage()))
throw exc;
}
this.elements = new OpenLongIntHashMap(initialCapacity, minLoadFactor, maxLoadFactor);
}
/**
* Constructs a matrix with a copy of the given indexes and a single value.
*
* @param rows
* the number of rows the matrix shall have.
* @param columns
* the number of columns the matrix shall have.
* @param rowIndexes
* row indexes
* @param columnIndexes
* column indexes
* @param value
* numerical value
*/
public SparseIntMatrix2D(int rows, int columns, int[] rowIndexes, int[] columnIndexes, int value) {
try {
setUp(rows, columns);
} catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
if (!"matrix too large".equals(exc.getMessage()))
throw exc;
}
this.elements = new OpenLongIntHashMap(rowIndexes.length);
insert(rowIndexes, columnIndexes, value);
}
/**
* Constructs a matrix with a copy of the given indexes and values.
*
* @param rows
* the number of rows the matrix shall have.
* @param columns
* the number of columns the matrix shall have.
* @param rowIndexes
* row indexes
* @param columnIndexes
* column indexes
* @param values
* numerical values
*/
public SparseIntMatrix2D(int rows, int columns, int[] rowIndexes, int[] columnIndexes, int[] values) {
try {
setUp(rows, columns);
} catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
if (!"matrix too large".equals(exc.getMessage()))
throw exc;
}
this.elements = new OpenLongIntHashMap(rowIndexes.length);
insert(rowIndexes, columnIndexes, values);
}
/**
* Constructs a matrix from MatrixVectorReader.
*
* @param reader
* matrix reader
* @throws IOException
*/
public SparseIntMatrix2D(MatrixVectorReader reader) throws IOException {
MatrixInfo info;
if (reader.hasInfo())
info = reader.readMatrixInfo();
else
info = new MatrixInfo(true, MatrixInfo.MatrixField.Real, MatrixInfo.MatrixSymmetry.General);
if (info.isPattern())
throw new UnsupportedOperationException("Pattern matrices are not supported");
if (info.isDense())
throw new UnsupportedOperationException("Dense matrices are not supported");
if (info.isComplex())
throw new UnsupportedOperationException("Complex matrices are not supported");
MatrixSize size = reader.readMatrixSize(info);
try {
setUp(size.numRows(), size.numColumns());
} catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
if (!"matrix too large".equals(exc.getMessage()))
throw exc;
}
int numEntries = size.numEntries();
int[] columnIndexes = new int[numEntries];
int[] rowIndexes = new int[numEntries];
int[] values = new int[numEntries];
reader.readCoordinate(rowIndexes, columnIndexes, values);
if (info.isSymmetric() || info.isSkewSymmetric()) {
this.elements = new OpenLongIntHashMap(2 * rowIndexes.length);
} else {
this.elements = new OpenLongIntHashMap(rowIndexes.length);
}
insert(rowIndexes, columnIndexes, values);
if (info.isSymmetric()) {
for (int i = 0; i < numEntries; i++) {
if (rowIndexes[i] != columnIndexes[i]) {
set(columnIndexes[i], rowIndexes[i], values[i]);
}
}
} else if (info.isSkewSymmetric()) {
for (int i = 0; i < numEntries; i++) {
if (rowIndexes[i] != columnIndexes[i]) {
set(columnIndexes[i], rowIndexes[i], -values[i]);
}
}
}
}
/**
* Constructs a view with the given parameters.
*
* @param rows
* the number of rows the matrix shall have.
* @param columns
* the number of columns the matrix shall have.
* @param elements
* the cells.
* @param rowZero
* the position of the first element.
* @param columnZero
* the position of the first element.
* @param rowStride
* the number of elements between two rows, i.e.
* index(i+1,j)-index(i,j).
* @param columnStride
* the number of elements between two columns, i.e.
* index(i,j+1)-index(i,j).
* @throws IllegalArgumentException
* if
* rows<0 || columns<0 || (double)columns*rows > Integer.MAX_VALUE
* or flip's are illegal.
*/
protected SparseIntMatrix2D(int rows, int columns, AbstractLongIntMap elements, int rowZero, int columnZero,
int rowStride, int columnStride) {
try {
setUp(rows, columns, rowZero, columnZero, rowStride, columnStride);
} catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
if (!"matrix too large".equals(exc.getMessage()))
throw exc;
}
this.elements = elements;
this.isNoView = false;
}
public IntMatrix2D assign(cern.colt.function.tint.IntFunction function) {
if (this.isNoView && function instanceof cern.jet.math.tint.IntMult) { // x[i] = mult*x[i]
this.elements.assign(function);
} else {
super.assign(function);
}
return this;
}
public IntMatrix2D assign(int value) {
// overriden for performance only
if (this.isNoView && value == 0)
this.elements.clear();
else
super.assign(value);
return this;
}
public IntMatrix2D assign(IntMatrix2D source) {
// overriden for performance only
if (!(source instanceof SparseIntMatrix2D)) {
return super.assign(source);
}
SparseIntMatrix2D other = (SparseIntMatrix2D) source;
if (other == this)
return this; // nothing to do
checkShape(other);
if (this.isNoView && other.isNoView) { // quickest
this.elements.assign(other.elements);
return this;
}
return super.assign(source);
}
public IntMatrix2D assign(final IntMatrix2D y, cern.colt.function.tint.IntIntFunction function) {
if (!this.isNoView)
return super.assign(y, function);
checkShape(y);
if (function instanceof cern.jet.math.tint.IntPlusMultSecond) { // x[i] = x[i] + alpha*y[i]
final int alpha = ((cern.jet.math.tint.IntPlusMultSecond) function).multiplicator;
if (alpha == 0)
return this; // nothing to do
y.forEachNonZero(new cern.colt.function.tint.IntIntIntFunction() {
public int apply(int i, int j, int value) {
setQuick(i, j, getQuick(i, j) + alpha * value);
return value;
}
});
} else if (function == cern.jet.math.tint.IntFunctions.mult) { // x[i] = x[i] * y[i]
this.elements.forEachPair(new cern.colt.function.tlong.LongIntProcedure() {
public boolean apply(long key, int value) {
int i = (int) (key / columns);
int j = (int) (key % columns);
int r = value * y.getQuick(i, j);
if (r != value)
elements.put(key, r);
return true;
}
});
} else if (function == cern.jet.math.tint.IntFunctions.div) { // x[i] = x[i] / y[i]
this.elements.forEachPair(new cern.colt.function.tlong.LongIntProcedure() {
public boolean apply(long key, int value) {
int i = (int) (key / columns);
int j = (int) (key % columns);
int r = value / y.getQuick(i, j);
if (r != value)
elements.put(key, r);
return true;
}
});
} else {
super.assign(y, function);
}
return this;
}
/**
* Assigns the result of a function to each cell;
* x[row,col] = function(x[row,col],y[row,col]), where y is given
* in the coordinate form with single numerical value.
*
* @param rowIndexes
* row indexes of y
* @param columnIndexes
* column indexes of y
* @param value
* numerical value of y
* @param function
* a function object taking as first argument the current cell's
* value of this, and as second argument the current
* cell's value of y,
* @return this (for convenience only).
*/
public SparseIntMatrix2D assign(final int[] rowIndexes, final int[] columnIndexes, final int value,
final cern.colt.function.tint.IntIntFunction function) {
int size = rowIndexes.length;
if (function == cern.jet.math.tint.IntFunctions.plus) { // x[i] = x[i] + y[i]
for (int i = 0; i < size; i++) {
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
int sum = elem + value;
if (sum != 0) {
elements.put(index, sum);
} else {
elements.removeKey(index);
}
}
} else {
for (int i = 0; i < size; i++) {
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
int result = function.apply(elem, value);
if (result != 0) {
elements.put(index, result);
} else {
elements.removeKey(index);
}
}
}
return this;
}
/**
* Assigns the result of a function to each cell;
* x[row,col] = function(x[row,col],y[row,col]), where y is given
* in the coordinate form.
*
* @param rowIndexes
* row indexes of y
* @param columnIndexes
* column indexes of y
* @param values
* numerical values of y
* @param function
* a function object taking as first argument the current cell's
* value of this, and as second argument the current
* cell's value of y,
* @return this (for convenience only).
*/
public SparseIntMatrix2D assign(final int[] rowIndexes, final int[] columnIndexes, final int[] values,
final cern.colt.function.tint.IntIntFunction function) {
int size = rowIndexes.length;
if (function == cern.jet.math.tint.IntFunctions.plus) { // x[i] = x[i] + y[i]
for (int i = 0; i < size; i++) {
int value = values[i];
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
value += elem;
if (value != 0) {
elements.put(index, value);
} else {
elements.removeKey(index);
}
}
} else {
for (int i = 0; i < size; i++) {
int value = values[i];
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
value = function.apply(elem, value);
if (value != 0) {
elements.put(index, value);
} else {
elements.removeKey(index);
}
}
}
return this;
}
public int cardinality() {
if (this.isNoView)
return this.elements.size();
else
return super.cardinality();
}
/**
* Returns a new matrix that has the same elements as this matrix, but is in
* a column-compressed form. This method creates a new object (not a view),
* so changes in the returned matrix are NOT reflected in this matrix.
*
* @param sortRowIndexes
* if true, then row indexes in column compressed matrix are
* sorted
*
* @return this matrix in a column-compressed form
*/
public SparseCCIntMatrix2D getColumnCompressed(boolean sortRowIndexes) {
int nnz = cardinality();
long[] keys = elements.keys().elements();
int[] values = elements.values().elements();
int[] rowIndexes = new int[nnz];
int[] columnIndexes = new int[nnz];
for (int k = 0; k < nnz; k++) {
long key = keys[k];
rowIndexes[k] = (int) (key / columns);
columnIndexes[k] = (int) (key % columns);
}
return new SparseCCIntMatrix2D(rows, columns, rowIndexes, columnIndexes, values, false, false, sortRowIndexes);
}
/**
* Returns a new matrix that has the same elements as this matrix, but is in
* a column-compressed modified form. This method creates a new object (not
* a view), so changes in the returned matrix are NOT reflected in this
* matrix.
*
* @return this matrix in a column-compressed modified form
*/
public SparseCCMIntMatrix2D getColumnCompressedModified() {
SparseCCMIntMatrix2D A = new SparseCCMIntMatrix2D(rows, columns);
int nnz = cardinality();
long[] keys = elements.keys().elements();
int[] values = elements.values().elements();
for (int i = 0; i < nnz; i++) {
int row = (int) (keys[i] / columns);
int column = (int) (keys[i] % columns);
A.setQuick(row, column, values[i]);
}
return A;
}
/**
* Returns a new matrix that has the same elements as this matrix, but is in
* a row-compressed form. This method creates a new object (not a view), so
* changes in the returned matrix are NOT reflected in this matrix.
*
* @param sortColumnIndexes
* if true, then column indexes in row compressed matrix are
* sorted
*
* @return this matrix in a row-compressed form
*/
public SparseRCIntMatrix2D getRowCompressed(boolean sortColumnIndexes) {
int nnz = cardinality();
long[] keys = elements.keys().elements();
int[] values = elements.values().elements();
final int[] rowIndexes = new int[nnz];
final int[] columnIndexes = new int[nnz];
for (int k = 0; k < nnz; k++) {
long key = keys[k];
rowIndexes[k] = (int) (key / columns);
columnIndexes[k] = (int) (key % columns);
}
return new SparseRCIntMatrix2D(rows, columns, rowIndexes, columnIndexes, values, false, false,
sortColumnIndexes);
}
/**
* Returns a new matrix that has the same elements as this matrix, but is in
* a row-compressed modified form. This method creates a new object (not a
* view), so changes in the returned matrix are NOT reflected in this
* matrix.
*
* @return this matrix in a row-compressed modified form
*/
public SparseRCMIntMatrix2D getRowCompressedModified() {
SparseRCMIntMatrix2D A = new SparseRCMIntMatrix2D(rows, columns);
int nnz = cardinality();
long[] keys = elements.keys().elements();
int[] values = elements.values().elements();
for (int i = 0; i < nnz; i++) {
int row = (int) (keys[i] / columns);
int column = (int) (keys[i] % columns);
A.setQuick(row, column, values[i]);
}
return A;
}
public AbstractLongIntMap elements() {
return elements;
}
public void ensureCapacity(int minCapacity) {
this.elements.ensureCapacity(minCapacity);
}
public IntMatrix2D forEachNonZero(final cern.colt.function.tint.IntIntIntFunction function) {
if (this.isNoView) {
this.elements.forEachPair(new cern.colt.function.tlong.LongIntProcedure() {
public boolean apply(long key, int value) {
int i = (int) (key / columns);
int j = (int) (key % columns);
int r = function.apply(i, j, value);
if (r != value)
elements.put(key, r);
return true;
}
});
} else {
super.forEachNonZero(function);
}
return this;
}
public synchronized int getQuick(int row, int column) {
return this.elements.get((long) rowZero + (long) row * (long) rowStride + (long) columnZero + (long) column
* (long) columnStride);
}
public long index(int row, int column) {
return (long) rowZero + (long) row * (long) rowStride + (long) columnZero + (long) column * (long) columnStride;
}
public IntMatrix2D like(int rows, int columns) {
return new SparseIntMatrix2D(rows, columns);
}
public IntMatrix1D like1D(int size) {
return new SparseIntMatrix1D(size);
}
public synchronized void setQuick(int row, int column, int value) {
long index = (long) rowZero + (long) row * (long) rowStride + (long) columnZero + (long) column
* (long) columnStride;
if (value == 0)
this.elements.removeKey(index);
else
this.elements.put(index, value);
}
public String toString() {
StringBuilder builder = new StringBuilder();
builder.append(rows).append(" x ").append(columns).append(" sparse matrix, nnz = ").append(cardinality())
.append('\n');
for (int r = 0; r < rows; r++) {
for (int c = 0; c < columns; c++) {
int elem = getQuick(r, c);
if (elem != 0) {
builder.append('(').append(r).append(',').append(c).append(')').append('\t').append(elem).append(
'\n');
}
}
}
return builder.toString();
}
public void trimToSize() {
this.elements.trimToSize();
}
public IntMatrix1D vectorize() {
SparseIntMatrix1D v = new SparseIntMatrix1D((int) size());
int idx = 0;
for (int c = 0; c < columns; c++) {
for (int r = 0; r < rows; r++) {
int elem = getQuick(r, c);
v.setQuick(idx++, elem);
}
}
return v;
}
public IntMatrix1D zMult(IntMatrix1D y, IntMatrix1D z, final int alpha, int beta, final boolean transposeA) {
int rowsA = rows;
int columnsA = columns;
if (transposeA) {
rowsA = columns;
columnsA = rows;
}
boolean ignore = (z == null);
if (z == null)
z = new DenseIntMatrix1D(rowsA);
if (!(this.isNoView && y instanceof DenseIntMatrix1D && z instanceof DenseIntMatrix1D)) {
return super.zMult(y, z, alpha, beta, transposeA);
}
if (columnsA != y.size() || rowsA > z.size())
throw new IllegalArgumentException("Incompatible args: "
+ ((transposeA ? viewDice() : this).toStringShort()) + ", " + y.toStringShort() + ", "
+ z.toStringShort());
if (!ignore)
z.assign(cern.jet.math.tint.IntFunctions.mult(beta));
DenseIntMatrix1D zz = (DenseIntMatrix1D) z;
final int[] elementsZ = zz.elements;
final int strideZ = zz.stride();
final int zeroZ = (int) z.index(0);
DenseIntMatrix1D yy = (DenseIntMatrix1D) y;
final int[] elementsY = yy.elements;
final int strideY = yy.stride();
final int zeroY = (int) y.index(0);
if (elementsY == null || elementsZ == null)
throw new InternalError();
this.elements.forEachPair(new cern.colt.function.tlong.LongIntProcedure() {
public boolean apply(long key, int value) {
int i = (int) (key / columns);
int j = (int) (key % columns);
if (transposeA) {
int tmp = i;
i = j;
j = tmp;
}
elementsZ[zeroZ + strideZ * i] += alpha * value * elementsY[zeroY + strideY * j];
return true;
}
});
return z;
}
public IntMatrix2D zMult(IntMatrix2D B, IntMatrix2D C, final int alpha, int beta, final boolean transposeA,
boolean transposeB) {
if (!(this.isNoView)) {
return super.zMult(B, C, alpha, beta, transposeA, transposeB);
}
if (transposeB)
B = B.viewDice();
int rowsA = rows;
int columnsA = columns;
if (transposeA) {
rowsA = columns;
columnsA = rows;
}
int p = B.columns();
boolean ignore = (C == null);
if (C == null)
C = new DenseIntMatrix2D(rowsA, p);
if (B.rows() != columnsA)
throw new IllegalArgumentException("Matrix2D inner dimensions must agree:" + toStringShort() + ", "
+ (transposeB ? B.viewDice() : B).toStringShort());
if (C.rows() != rowsA || C.columns() != p)
throw new IllegalArgumentException("Incompatibel result matrix: " + toStringShort() + ", "
+ (transposeB ? B.viewDice() : B).toStringShort() + ", " + C.toStringShort());
if (this == C || B == C)
throw new IllegalArgumentException("Matrices must not be identical");
if (!ignore)
C.assign(cern.jet.math.tint.IntFunctions.mult(beta));
// cache views
final IntMatrix1D[] Brows = new IntMatrix1D[columnsA];
for (int i = columnsA; --i >= 0;)
Brows[i] = B.viewRow(i);
final IntMatrix1D[] Crows = new IntMatrix1D[rowsA];
for (int i = rowsA; --i >= 0;)
Crows[i] = C.viewRow(i);
final cern.jet.math.tint.IntPlusMultSecond fun = cern.jet.math.tint.IntPlusMultSecond.plusMult(0);
this.elements.forEachPair(new cern.colt.function.tlong.LongIntProcedure() {
public boolean apply(long key, int value) {
int i = (int) (key / columns);
int j = (int) (key % columns);
fun.multiplicator = value * alpha;
if (!transposeA)
Crows[i].assign(Brows[j], fun);
else
Crows[j].assign(Brows[i], fun);
return true;
}
});
return C;
}
private void insert(int[] rowIndexes, int[] columnIndexes, int value) {
int size = rowIndexes.length;
for (int i = 0; i < size; i++) {
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
if (value != 0) {
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
if (elem == 0) {
elements.put(index, value);
} else {
int sum = elem + value;
if (sum == 0) {
elements.removeKey(index);
} else {
elements.put(index, sum);
}
}
}
}
}
private void insert(int[] rowIndexes, int[] columnIndexes, int[] values) {
int size = rowIndexes.length;
for (int i = 0; i < size; i++) {
int value = values[i];
int row = rowIndexes[i];
int column = columnIndexes[i];
if (row >= rows || column >= columns) {
throw new IndexOutOfBoundsException("row: " + row + ", column: " + column);
}
if (value != 0) {
int index = rowZero + row * rowStride + columnZero + column * columnStride;
int elem = elements.get(index);
if (elem == 0) {
elements.put(index, value);
} else {
int sum = elem + value;
if (sum == 0) {
elements.removeKey(index);
} else {
elements.put(index, sum);
}
}
}
}
}
protected boolean haveSharedCellsRaw(IntMatrix2D other) {
if (other instanceof SelectedSparseIntMatrix2D) {
SelectedSparseIntMatrix2D otherMatrix = (SelectedSparseIntMatrix2D) other;
return this.elements == otherMatrix.elements;
} else if (other instanceof SparseIntMatrix2D) {
SparseIntMatrix2D otherMatrix = (SparseIntMatrix2D) other;
return this.elements == otherMatrix.elements;
}
return false;
}
protected IntMatrix1D like1D(int size, int offset, int stride) {
return new SparseIntMatrix1D(size, this.elements, offset, stride);
}
protected IntMatrix2D viewSelectionLike(int[] rowOffsets, int[] columnOffsets) {
return new SelectedSparseIntMatrix2D(this.elements, rowOffsets, columnOffsets, 0);
}
}