You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mahout.apache.org by ra...@apache.org on 2018/06/29 16:10:54 UTC
[10/18] mahout git commit: MAHOUT-2033 Fixed Map-Reduce Refactor
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/SparseMatrix.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/SparseMatrix.java b/core/src/main/java/org/apache/mahout/math/SparseMatrix.java
deleted file mode 100644
index 31d38a5..0000000
--- a/core/src/main/java/org/apache/mahout/math/SparseMatrix.java
+++ /dev/null
@@ -1,246 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-import it.unimi.dsi.fastutil.ints.Int2ObjectMap.Entry;
-import it.unimi.dsi.fastutil.ints.Int2ObjectOpenHashMap;
-import it.unimi.dsi.fastutil.ints.IntArrayList;
-import it.unimi.dsi.fastutil.objects.ObjectIterator;
-
-import java.util.Iterator;
-import java.util.Map;
-
-import org.apache.mahout.math.flavor.MatrixFlavor;
-import org.apache.mahout.math.function.DoubleDoubleFunction;
-import org.apache.mahout.math.function.Functions;
-//import org.apache.mahout.math.list.IntArrayList;
-
-import com.google.common.collect.AbstractIterator;
-
-/** Doubly sparse matrix. Implemented as a Map of RandomAccessSparseVector rows */
-public class SparseMatrix extends AbstractMatrix {
-
- private Int2ObjectOpenHashMap<Vector> rowVectors;
-
- /**
- * Construct a matrix of the given cardinality with the given row map
- *
- * @param rows no of rows
- * @param columns no of columns
- * @param rowVectors a {@code Map<Integer, RandomAccessSparseVector>} of rows
- */
- public SparseMatrix(int rows, int columns, Map<Integer, Vector> rowVectors) {
- this(rows, columns, rowVectors, false);
- }
-
- public SparseMatrix(int rows, int columns, Map<Integer, Vector> rowVectors, boolean shallow) {
-
- // Why this is passing in a map? iterating it is pretty inefficient as opposed to simple lists...
- super(rows, columns);
- this.rowVectors = new Int2ObjectOpenHashMap<>();
- if (shallow) {
- for (Map.Entry<Integer, Vector> entry : rowVectors.entrySet()) {
- this.rowVectors.put(entry.getKey().intValue(), entry.getValue());
- }
- } else {
- for (Map.Entry<Integer, Vector> entry : rowVectors.entrySet()) {
- this.rowVectors.put(entry.getKey().intValue(), entry.getValue().clone());
- }
- }
- }
-
- /**
- * Construct a matrix with specified number of rows and columns.
- */
- public SparseMatrix(int rows, int columns) {
- super(rows, columns);
- this.rowVectors = new Int2ObjectOpenHashMap<>();
- }
-
- @Override
- public Matrix clone() {
- SparseMatrix clone = new SparseMatrix(numRows(), numCols());
- for (MatrixSlice slice : this) {
- clone.rowVectors.put(slice.index(), slice.clone());
- }
- return clone;
- }
-
- @Override
- public int numSlices() {
- return rowVectors.size();
- }
-
- public Iterator<MatrixSlice> iterateNonEmpty() {
- final int[] keys = rowVectors.keySet().toIntArray();
- return new AbstractIterator<MatrixSlice>() {
- private int slice;
- @Override
- protected MatrixSlice computeNext() {
- if (slice >= rowVectors.size()) {
- return endOfData();
- }
- int i = keys[slice];
- Vector row = rowVectors.get(i);
- slice++;
- return new MatrixSlice(row, i);
- }
- };
- }
-
- @Override
- public double getQuick(int row, int column) {
- Vector r = rowVectors.get(row);
- return r == null ? 0.0 : r.getQuick(column);
- }
-
- @Override
- public Matrix like() {
- return new SparseMatrix(rowSize(), columnSize());
- }
-
- @Override
- public Matrix like(int rows, int columns) {
- return new SparseMatrix(rows, columns);
- }
-
- @Override
- public void setQuick(int row, int column, double value) {
- Vector r = rowVectors.get(row);
- if (r == null) {
- r = new RandomAccessSparseVector(columnSize());
- rowVectors.put(row, r);
- }
- r.setQuick(column, value);
- }
-
- @Override
- public int[] getNumNondefaultElements() {
- int[] result = new int[2];
- result[ROW] = rowVectors.size();
- for (Vector row : rowVectors.values()) {
- result[COL] = Math.max(result[COL], row.getNumNondefaultElements());
- }
- return result;
- }
-
- @Override
- public Matrix viewPart(int[] offset, int[] size) {
- if (offset[ROW] < 0) {
- throw new IndexException(offset[ROW], rowSize());
- }
- if (offset[ROW] + size[ROW] > rowSize()) {
- throw new IndexException(offset[ROW] + size[ROW], rowSize());
- }
- if (offset[COL] < 0) {
- throw new IndexException(offset[COL], columnSize());
- }
- if (offset[COL] + size[COL] > columnSize()) {
- throw new IndexException(offset[COL] + size[COL], columnSize());
- }
- return new MatrixView(this, offset, size);
- }
-
- @Override
- public Matrix assign(Matrix other, DoubleDoubleFunction function) {
- //TODO generalize to other kinds of functions
- if (Functions.PLUS.equals(function) && other instanceof SparseMatrix) {
- int rows = rowSize();
- if (rows != other.rowSize()) {
- throw new CardinalityException(rows, other.rowSize());
- }
- int columns = columnSize();
- if (columns != other.columnSize()) {
- throw new CardinalityException(columns, other.columnSize());
- }
-
- SparseMatrix otherSparse = (SparseMatrix) other;
- for(ObjectIterator<Entry<Vector>> fastIterator = otherSparse.rowVectors.int2ObjectEntrySet().fastIterator();
- fastIterator.hasNext();) {
- final Entry<Vector> entry = fastIterator.next();
- final int rowIndex = entry.getIntKey();
- Vector row = rowVectors.get(rowIndex);
- if (row == null) {
- rowVectors.put(rowIndex, entry.getValue().clone());
- } else {
- row.assign(entry.getValue(), Functions.PLUS);
- }
- }
- return this;
- } else {
- return super.assign(other, function);
- }
- }
-
- @Override
- public Matrix assignColumn(int column, Vector other) {
- if (rowSize() != other.size()) {
- throw new CardinalityException(rowSize(), other.size());
- }
- if (column < 0 || column >= columnSize()) {
- throw new IndexException(column, columnSize());
- }
- for (int row = 0; row < rowSize(); row++) {
- double val = other.getQuick(row);
- if (val != 0.0) {
- Vector r = rowVectors.get(row);
- if (r == null) {
- r = new RandomAccessSparseVector(columnSize());
- rowVectors.put(row, r);
- }
- r.setQuick(column, val);
- }
- }
- return this;
- }
-
- @Override
- public Matrix assignRow(int row, Vector other) {
- if (columnSize() != other.size()) {
- throw new CardinalityException(columnSize(), other.size());
- }
- if (row < 0 || row >= rowSize()) {
- throw new IndexException(row, rowSize());
- }
- rowVectors.put(row, other);
- return this;
- }
-
- @Override
- public Vector viewRow(int row) {
- if (row < 0 || row >= rowSize()) {
- throw new IndexException(row, rowSize());
- }
- Vector res = rowVectors.get(row);
- if (res == null) {
- res = new RandomAccessSparseVector(columnSize());
- rowVectors.put(row, res);
- }
- return res;
- }
-
- /** special method necessary for efficient serialization */
- public IntArrayList nonZeroRowIndices() {
- return new IntArrayList(rowVectors.keySet().toIntArray());
- }
-
- @Override
- public MatrixFlavor getFlavor() {
- return MatrixFlavor.SPARSEROWLIKE;
- }
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/SparseRowMatrix.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/SparseRowMatrix.java b/core/src/main/java/org/apache/mahout/math/SparseRowMatrix.java
deleted file mode 100644
index ee54ad0..0000000
--- a/core/src/main/java/org/apache/mahout/math/SparseRowMatrix.java
+++ /dev/null
@@ -1,289 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-import org.apache.mahout.math.flavor.MatrixFlavor;
-import org.apache.mahout.math.flavor.TraversingStructureEnum;
-import org.apache.mahout.math.function.DoubleDoubleFunction;
-import org.apache.mahout.math.function.Functions;
-import org.slf4j.Logger;
-import org.slf4j.LoggerFactory;
-
-import java.util.Iterator;
-
-/**
- * sparse matrix with general element values whose rows are accessible quickly. Implemented as a row
- * array of either SequentialAccessSparseVectors or RandomAccessSparseVectors.
- */
-public class SparseRowMatrix extends AbstractMatrix {
- private Vector[] rowVectors;
-
- private final boolean randomAccessRows;
-
- private static final Logger log = LoggerFactory.getLogger(SparseRowMatrix.class);
-
- /**
- * Construct a sparse matrix starting with the provided row vectors.
- *
- * @param rows The number of rows in the result
- * @param columns The number of columns in the result
- * @param rowVectors a Vector[] array of rows
- */
- public SparseRowMatrix(int rows, int columns, Vector[] rowVectors) {
- this(rows, columns, rowVectors, false, rowVectors instanceof RandomAccessSparseVector[]);
- }
-
- public SparseRowMatrix(int rows, int columns, boolean randomAccess) {
- this(rows, columns, randomAccess
- ? new RandomAccessSparseVector[rows]
- : new SequentialAccessSparseVector[rows],
- true,
- randomAccess);
- }
-
- public SparseRowMatrix(int rows, int columns, Vector[] vectors, boolean shallowCopy, boolean randomAccess) {
- super(rows, columns);
- this.randomAccessRows = randomAccess;
- this.rowVectors = vectors.clone();
- for (int row = 0; row < rows; row++) {
- if (vectors[row] == null) {
- // TODO: this can't be right to change the argument
- vectors[row] = randomAccess
- ? new RandomAccessSparseVector(numCols(), 10)
- : new SequentialAccessSparseVector(numCols(), 10);
- }
- this.rowVectors[row] = shallowCopy ? vectors[row] : vectors[row].clone();
- }
- }
-
- /**
- * Construct a matrix of the given cardinality, with rows defaulting to RandomAccessSparseVector
- * implementation
- *
- * @param rows Number of rows in result
- * @param columns Number of columns in result
- */
- public SparseRowMatrix(int rows, int columns) {
- this(rows, columns, true);
- }
-
- @Override
- public Matrix clone() {
- SparseRowMatrix clone = (SparseRowMatrix) super.clone();
- clone.rowVectors = new Vector[rowVectors.length];
- for (int i = 0; i < rowVectors.length; i++) {
- clone.rowVectors[i] = rowVectors[i].clone();
- }
- return clone;
- }
-
- @Override
- public double getQuick(int row, int column) {
- return rowVectors[row] == null ? 0.0 : rowVectors[row].getQuick(column);
- }
-
- @Override
- public Matrix like() {
- return new SparseRowMatrix(rowSize(), columnSize(), randomAccessRows);
- }
-
- @Override
- public Matrix like(int rows, int columns) {
- return new SparseRowMatrix(rows, columns, randomAccessRows);
- }
-
- @Override
- public void setQuick(int row, int column, double value) {
- rowVectors[row].setQuick(column, value);
- }
-
- @Override
- public int[] getNumNondefaultElements() {
- int[] result = new int[2];
- result[ROW] = rowVectors.length;
- for (int row = 0; row < rowSize(); row++) {
- result[COL] = Math.max(result[COL], rowVectors[row].getNumNondefaultElements());
- }
- return result;
- }
-
- @Override
- public Matrix viewPart(int[] offset, int[] size) {
- if (offset[ROW] < 0) {
- throw new IndexException(offset[ROW], rowVectors.length);
- }
- if (offset[ROW] + size[ROW] > rowVectors.length) {
- throw new IndexException(offset[ROW] + size[ROW], rowVectors.length);
- }
- if (offset[COL] < 0) {
- throw new IndexException(offset[COL], rowVectors[ROW].size());
- }
- if (offset[COL] + size[COL] > rowVectors[ROW].size()) {
- throw new IndexException(offset[COL] + size[COL], rowVectors[ROW].size());
- }
- return new MatrixView(this, offset, size);
- }
-
- @Override
- public Matrix assign(Matrix other, DoubleDoubleFunction function) {
- int rows = rowSize();
- if (rows != other.rowSize()) {
- throw new CardinalityException(rows, other.rowSize());
- }
- int columns = columnSize();
- if (columns != other.columnSize()) {
- throw new CardinalityException(columns, other.columnSize());
- }
- for (int row = 0; row < rows; row++) {
- try {
- Iterator<Vector.Element> sparseRowIterator = ((SequentialAccessSparseVector) this.rowVectors[row])
- .iterateNonZero();
- if (function.isLikeMult()) { // TODO: is this a sufficient test?
- // TODO: this may cause an exception if the row type is not compatible but it is currently guaranteed to be
- // a SequentialAccessSparseVector, should "try" here just in case and Warn
- // TODO: can we use iterateNonZero on both rows until the index is the same to get better speedup?
-
- // TODO: SASVs have an iterateNonZero that returns zeros, this should not hurt but is far from optimal
- // this might perform much better if SparseRowMatrix were backed by RandomAccessSparseVectors, which
- // are backed by fastutil hashmaps and the iterateNonZero actually does only return nonZeros.
- while (sparseRowIterator.hasNext()) {
- Vector.Element element = sparseRowIterator.next();
- int col = element.index();
- setQuick(row, col, function.apply(element.get(), other.getQuick(row, col)));
- }
- } else {
- for (int col = 0; col < columns; col++) {
- setQuick(row, col, function.apply(getQuick(row, col), other.getQuick(row, col)));
- }
- }
-
- } catch (ClassCastException e) {
- // Warn and use default implementation
- log.warn("Error casting the row to SequentialAccessSparseVector, this should never happen because" +
- "SparseRomMatrix is always made of SequentialAccessSparseVectors. Proceeding with non-optimzed" +
- "implementation.");
- for (int col = 0; col < columns; col++) {
- setQuick(row, col, function.apply(getQuick(row, col), other.getQuick(row, col)));
- }
- }
- }
- return this;
- }
-
- @Override
- public Matrix assignColumn(int column, Vector other) {
- if (rowSize() != other.size()) {
- throw new CardinalityException(rowSize(), other.size());
- }
- if (column < 0 || column >= columnSize()) {
- throw new IndexException(column, columnSize());
- }
- for (int row = 0; row < rowSize(); row++) {
- rowVectors[row].setQuick(column, other.getQuick(row));
- }
- return this;
- }
-
- @Override
- public Matrix assignRow(int row, Vector other) {
- if (columnSize() != other.size()) {
- throw new CardinalityException(columnSize(), other.size());
- }
- if (row < 0 || row >= rowSize()) {
- throw new IndexException(row, rowSize());
- }
- rowVectors[row].assign(other);
- return this;
- }
-
- /**
- * @param row an int row index
- * @return a shallow view of the Vector at specified row (ie you may mutate the original matrix
- * using this row)
- */
- @Override
- public Vector viewRow(int row) {
- if (row < 0 || row >= rowSize()) {
- throw new IndexException(row, rowSize());
- }
- return rowVectors[row];
- }
-
- @Override
- public Matrix transpose() {
- SparseColumnMatrix scm = new SparseColumnMatrix(columns, rows);
- for (int i = 0; i < rows; i++) {
- Vector row = rowVectors[i];
- if (row.getNumNonZeroElements() > 0) {
- scm.assignColumn(i, row);
- }
- }
- return scm;
- }
-
- @Override
- public Matrix times(Matrix other) {
- if (columnSize() != other.rowSize()) {
- throw new CardinalityException(columnSize(), other.rowSize());
- }
-
- if (other instanceof SparseRowMatrix) {
- SparseRowMatrix y = (SparseRowMatrix) other;
- SparseRowMatrix result = (SparseRowMatrix) like(rowSize(), other.columnSize());
-
- for (int i = 0; i < rows; i++) {
- Vector row = rowVectors[i];
- for (Vector.Element element : row.nonZeroes()) {
- result.rowVectors[i].assign(y.rowVectors[element.index()], Functions.plusMult(element.get()));
- }
- }
- return result;
- } else {
- if (other.viewRow(0).isDense()) {
- // result is dense, but can be computed relatively cheaply
- Matrix result = other.like(rowSize(), other.columnSize());
-
- for (int i = 0; i < rows; i++) {
- Vector row = rowVectors[i];
- Vector r = new DenseVector(other.columnSize());
- for (Vector.Element element : row.nonZeroes()) {
- r.assign(other.viewRow(element.index()), Functions.plusMult(element.get()));
- }
- result.viewRow(i).assign(r);
- }
- return result;
- } else {
- // other is sparse, but not something we understand intimately
- SparseRowMatrix result = (SparseRowMatrix) like(rowSize(), other.columnSize());
-
- for (int i = 0; i < rows; i++) {
- Vector row = rowVectors[i];
- for (Vector.Element element : row.nonZeroes()) {
- result.rowVectors[i].assign(other.viewRow(element.index()), Functions.plusMult(element.get()));
- }
- }
- return result;
- }
- }
- }
-
- @Override
- public MatrixFlavor getFlavor() {
- return MatrixFlavor.SPARSELIKE;
- }
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/Swapper.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/Swapper.java b/core/src/main/java/org/apache/mahout/math/Swapper.java
deleted file mode 100644
index 1ca3744..0000000
--- a/core/src/main/java/org/apache/mahout/math/Swapper.java
+++ /dev/null
@@ -1,35 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*
-Copyright 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 org.apache.mahout.math;
-
-/**
- * Interface for an object that knows how to swap elements at two positions (a,b).
- */
-public interface Swapper {
-
- /** Swaps the generic data g[a] with g[b]. */
- void swap(int a, int b);
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/TransposedMatrixView.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/TransposedMatrixView.java b/core/src/main/java/org/apache/mahout/math/TransposedMatrixView.java
deleted file mode 100644
index ede6f35..0000000
--- a/core/src/main/java/org/apache/mahout/math/TransposedMatrixView.java
+++ /dev/null
@@ -1,147 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-import org.apache.mahout.math.flavor.BackEnum;
-import org.apache.mahout.math.flavor.MatrixFlavor;
-import org.apache.mahout.math.flavor.TraversingStructureEnum;
-import org.apache.mahout.math.function.DoubleDoubleFunction;
-import org.apache.mahout.math.function.DoubleFunction;
-
-/**
- * Matrix View backed by an {@link org.apache.mahout.math.function.IntIntFunction}
- */
-public class TransposedMatrixView extends AbstractMatrix {
-
- private Matrix m;
-
- public TransposedMatrixView(Matrix m) {
- super(m.numCols(), m.numRows());
- this.m = m;
- }
-
- @Override
- public Matrix assignColumn(int column, Vector other) {
- m.assignRow(column,other);
- return this;
- }
-
- @Override
- public Matrix assignRow(int row, Vector other) {
- m.assignColumn(row,other);
- return this;
- }
-
- @Override
- public double getQuick(int row, int column) {
- return m.getQuick(column,row);
- }
-
- @Override
- public Matrix like() {
- return m.like(rows, columns);
- }
-
- @Override
- public Matrix like(int rows, int columns) {
- return m.like(rows,columns);
- }
-
- @Override
- public void setQuick(int row, int column, double value) {
- m.setQuick(column, row, value);
- }
-
- @Override
- public Vector viewRow(int row) {
- return m.viewColumn(row);
- }
-
- @Override
- public Vector viewColumn(int column) {
- return m.viewRow(column);
- }
-
- @Override
- public Matrix assign(double value) {
- return m.assign(value);
- }
-
- @Override
- public Matrix assign(Matrix other, DoubleDoubleFunction function) {
- if (other instanceof TransposedMatrixView) {
- m.assign(((TransposedMatrixView) other).m, function);
- } else {
- m.assign(new TransposedMatrixView(other), function);
- }
- return this;
- }
-
- @Override
- public Matrix assign(Matrix other) {
- if (other instanceof TransposedMatrixView) {
- return m.assign(((TransposedMatrixView) other).m);
- } else {
- return m.assign(new TransposedMatrixView(other));
- }
- }
-
- @Override
- public Matrix assign(DoubleFunction function) {
- return m.assign(function);
- }
-
- @Override
- public MatrixFlavor getFlavor() {
- return flavor;
- }
-
- private MatrixFlavor flavor = new MatrixFlavor() {
- @Override
- public BackEnum getBacking() {
- return m.getFlavor().getBacking();
- }
-
- @Override
- public TraversingStructureEnum getStructure() {
- TraversingStructureEnum flavor = m.getFlavor().getStructure();
- switch (flavor) {
- case COLWISE:
- return TraversingStructureEnum.ROWWISE;
- case SPARSECOLWISE:
- return TraversingStructureEnum.SPARSEROWWISE;
- case ROWWISE:
- return TraversingStructureEnum.COLWISE;
- case SPARSEROWWISE:
- return TraversingStructureEnum.SPARSECOLWISE;
- default:
- return flavor;
- }
- }
-
- @Override
- public boolean isDense() {
- return m.getFlavor().isDense();
- }
- };
-
- Matrix getDelegate() {
- return m;
- }
-
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/UpperTriangular.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/UpperTriangular.java b/core/src/main/java/org/apache/mahout/math/UpperTriangular.java
deleted file mode 100644
index fb99606..0000000
--- a/core/src/main/java/org/apache/mahout/math/UpperTriangular.java
+++ /dev/null
@@ -1,160 +0,0 @@
-/**
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-import org.apache.mahout.math.flavor.BackEnum;
-import org.apache.mahout.math.flavor.MatrixFlavor;
-import org.apache.mahout.math.flavor.TraversingStructureEnum;
-
-/**
- *
- * Quick and dirty implementation of some {@link Matrix} methods
- * over packed upper triangular matrix.
- *
- */
-public class UpperTriangular extends AbstractMatrix {
-
- private static final double EPSILON = 1.0e-12; // assume anything less than
- // that to be 0 during
- // non-upper assignments
-
- private double[] values;
-
- /**
- * represents n x n upper triangular matrix
- *
- * @param n
- */
-
- public UpperTriangular(int n) {
- super(n, n);
- values = new double[n * (n + 1) / 2];
- }
-
- public UpperTriangular(double[] data, boolean shallow) {
- this(elementsToMatrixSize(data != null ? data.length : 0));
- if (data == null) {
- throw new IllegalArgumentException("data");
- }
- values = shallow ? data : data.clone();
- }
-
- public UpperTriangular(Vector data) {
- this(elementsToMatrixSize(data.size()));
-
- for (Vector.Element el:data.nonZeroes()) {
- values[el.index()] = el.get();
- }
- }
-
- private static int elementsToMatrixSize(int dataSize) {
- return (int) Math.round((-1 + Math.sqrt(1 + 8 * dataSize)) / 2);
- }
-
- // copy-constructor
- public UpperTriangular(UpperTriangular mx) {
- this(mx.values, false);
- }
-
- @Override
- public Matrix assignColumn(int column, Vector other) {
- if (columnSize() != other.size()) {
- throw new IndexException(columnSize(), other.size());
- }
- if (other.viewPart(column + 1, other.size() - column - 1).norm(1) > 1.0e-14) {
- throw new IllegalArgumentException("Cannot set lower portion of triangular matrix to non-zero");
- }
- for (Vector.Element element : other.viewPart(0, column).all()) {
- setQuick(element.index(), column, element.get());
- }
- return this;
- }
-
- @Override
- public Matrix assignRow(int row, Vector other) {
- if (columnSize() != other.size()) {
- throw new IndexException(numCols(), other.size());
- }
- for (int i = 0; i < row; i++) {
- if (Math.abs(other.getQuick(i)) > EPSILON) {
- throw new IllegalArgumentException("non-triangular source");
- }
- }
- for (int i = row; i < rows; i++) {
- setQuick(row, i, other.get(i));
- }
- return this;
- }
-
- public Matrix assignNonZeroElementsInRow(int row, double[] other) {
- System.arraycopy(other, row, values, getL(row, row), rows - row);
- return this;
- }
-
- @Override
- public double getQuick(int row, int column) {
- if (row > column) {
- return 0;
- }
- int i = getL(row, column);
- return values[i];
- }
-
- private int getL(int row, int col) {
- /*
- * each row starts with some zero elements that we don't store. this
- * accumulates an offset of (row+1)*row/2
- */
- return col + row * numCols() - (row + 1) * row / 2;
- }
-
- @Override
- public Matrix like() {
- return like(rowSize(), columnSize());
- }
-
- @Override
- public Matrix like(int rows, int columns) {
- return new DenseMatrix(rows, columns);
- }
-
- @Override
- public void setQuick(int row, int column, double value) {
- values[getL(row, column)] = value;
- }
-
- @Override
- public int[] getNumNondefaultElements() {
- throw new UnsupportedOperationException();
- }
-
- @Override
- public Matrix viewPart(int[] offset, int[] size) {
- return new MatrixView(this, offset, size);
- }
-
- public double[] getData() {
- return values;
- }
-
- @Override
- public MatrixFlavor getFlavor() {
- // We kind of consider ourselves a vector-backed but dense matrix for mmul, etc. purposes.
- return new MatrixFlavor.FlavorImpl(BackEnum.JVMMEM, TraversingStructureEnum.VECTORBACKED, true);
- }
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/Vector.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/Vector.java b/core/src/main/java/org/apache/mahout/math/Vector.java
deleted file mode 100644
index c3b1dc9..0000000
--- a/core/src/main/java/org/apache/mahout/math/Vector.java
+++ /dev/null
@@ -1,434 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-
-import org.apache.mahout.math.function.DoubleDoubleFunction;
-import org.apache.mahout.math.function.DoubleFunction;
-
-/**
- * The basic interface including numerous convenience functions <p> NOTE: All implementing classes must have a
- * constructor that takes an int for cardinality and a no-arg constructor that can be used for marshalling the Writable
- * instance <p> NOTE: Implementations may choose to reuse the Vector.Element in the Iterable methods
- */
-public interface Vector extends Cloneable {
-
- /** @return a formatted String suitable for output */
- String asFormatString();
-
- /**
- * Assign the value to all elements of the receiver
- *
- * @param value a double value
- * @return the modified receiver
- */
- Vector assign(double value);
-
- /**
- * Assign the values to the receiver
- *
- * @param values a double[] of values
- * @return the modified receiver
- * @throws CardinalityException if the cardinalities differ
- */
- Vector assign(double[] values);
-
- /**
- * Assign the other vector values to the receiver
- *
- * @param other a Vector
- * @return the modified receiver
- * @throws CardinalityException if the cardinalities differ
- */
- Vector assign(Vector other);
-
- /**
- * Apply the function to each element of the receiver
- *
- * @param function a DoubleFunction to apply
- * @return the modified receiver
- */
- Vector assign(DoubleFunction function);
-
- /**
- * Apply the function to each element of the receiver and the corresponding element of the other argument
- *
- * @param other a Vector containing the second arguments to the function
- * @param function a DoubleDoubleFunction to apply
- * @return the modified receiver
- * @throws CardinalityException if the cardinalities differ
- */
- Vector assign(Vector other, DoubleDoubleFunction function);
-
- /**
- * Apply the function to each element of the receiver, using the y value as the second argument of the
- * DoubleDoubleFunction
- *
- * @param f a DoubleDoubleFunction to be applied
- * @param y a double value to be argument to the function
- * @return the modified receiver
- */
- Vector assign(DoubleDoubleFunction f, double y);
-
- /**
- * Return the cardinality of the recipient (the maximum number of values)
- *
- * @return an int
- */
- int size();
-
- /**
- * true if this implementation should be considered dense -- that it explicitly
- * represents every value
- *
- * @return true or false
- */
- boolean isDense();
-
- /**
- * true if this implementation should be considered to be iterable in index order in an efficient way.
- * In particular this implies that {@link #all()} and {@link #nonZeroes()} ()} return elements
- * in ascending order by index.
- *
- * @return true iff this implementation should be considered to be iterable in index order in an efficient way.
- */
- boolean isSequentialAccess();
-
- /**
- * Return a copy of the recipient
- *
- * @return a new Vector
- */
- @SuppressWarnings("CloneDoesntDeclareCloneNotSupportedException")
- Vector clone();
-
- Iterable<Element> all();
-
- Iterable<Element> nonZeroes();
-
- /**
- * Return an object of Vector.Element representing an element of this Vector. Useful when designing new iterator
- * types.
- *
- * @param index Index of the Vector.Element required
- * @return The Vector.Element Object
- */
- Element getElement(int index);
-
- /**
- * Merge a set of (index, value) pairs into the vector.
- * @param updates an ordered mapping of indices to values to be merged in.
- */
- void mergeUpdates(OrderedIntDoubleMapping updates);
-
- /**
- * A holder for information about a specific item in the Vector. <p>
- * When using with an Iterator, the implementation
- * may choose to reuse this element, so you may need to make a copy if you want to keep it
- */
- interface Element {
-
- /** @return the value of this vector element. */
- double get();
-
- /** @return the index of this vector element. */
- int index();
-
- /** @param value Set the current element to value. */
- void set(double value);
- }
-
- /**
- * Return a new vector containing the values of the recipient divided by the argument
- *
- * @param x a double value
- * @return a new Vector
- */
- Vector divide(double x);
-
- /**
- * Return the dot product of the recipient and the argument
- *
- * @param x a Vector
- * @return a new Vector
- * @throws CardinalityException if the cardinalities differ
- */
- double dot(Vector x);
-
- /**
- * Return the value at the given index
- *
- * @param index an int index
- * @return the double at the index
- * @throws IndexException if the index is out of bounds
- */
- double get(int index);
-
- /**
- * Return the value at the given index, without checking bounds
- *
- * @param index an int index
- * @return the double at the index
- */
- double getQuick(int index);
-
- /**
- * Return an empty vector of the same underlying class as the receiver
- *
- * @return a Vector
- */
- Vector like();
-
- /**
- * Return a new empty vector of the same underlying class as the receiver with given cardinality
- *
- * @param cardinality - size of vector
- * @return {@link Vector}
- */
- Vector like(int cardinality);
-
- /**
- * Return a new vector containing the element by element difference of the recipient and the argument
- *
- * @param x a Vector
- * @return a new Vector
- * @throws CardinalityException if the cardinalities differ
- */
- Vector minus(Vector x);
-
- /**
- * Return a new vector containing the normalized (L_2 norm) values of the recipient
- *
- * @return a new Vector
- */
- Vector normalize();
-
- /**
- * Return a new Vector containing the normalized (L_power norm) values of the recipient. <p>
- * See
- * http://en.wikipedia.org/wiki/Lp_space <p>
- * Technically, when {@code 0 < power < 1}, we don't have a norm, just a metric,
- * but we'll overload this here. <p>
- * Also supports {@code power == 0} (number of non-zero elements) and power = {@link
- * Double#POSITIVE_INFINITY} (max element). Again, see the Wikipedia page for more info
- *
- * @param power The power to use. Must be >= 0. May also be {@link Double#POSITIVE_INFINITY}. See the Wikipedia link
- * for more on this.
- * @return a new Vector x such that norm(x, power) == 1
- */
- Vector normalize(double power);
-
- /**
- * Return a new vector containing the log(1 + entry)/ L_2 norm values of the recipient
- *
- * @return a new Vector
- */
- Vector logNormalize();
-
- /**
- * Return a new Vector with a normalized value calculated as log_power(1 + entry)/ L_power norm. <p>
- *
- * @param power The power to use. Must be > 1. Cannot be {@link Double#POSITIVE_INFINITY}.
- * @return a new Vector
- */
- Vector logNormalize(double power);
-
- /**
- * Return the k-norm of the vector. <p/> See http://en.wikipedia.org/wiki/Lp_space <p>
- * Technically, when {@code 0 > power < 1}, we don't have a norm, just a metric, but we'll overload this here. Also supports power == 0 (number of
- * non-zero elements) and power = {@link Double#POSITIVE_INFINITY} (max element). Again, see the Wikipedia page for
- * more info.
- *
- * @param power The power to use.
- * @see #normalize(double)
- */
- double norm(double power);
-
- /** @return The minimum value in the Vector */
- double minValue();
-
- /** @return The index of the minimum value */
- int minValueIndex();
-
- /** @return The maximum value in the Vector */
- double maxValue();
-
- /** @return The index of the maximum value */
- int maxValueIndex();
-
- /**
- * Return a new vector containing the sum of each value of the recipient and the argument
- *
- * @param x a double
- * @return a new Vector
- */
- Vector plus(double x);
-
- /**
- * Return a new vector containing the element by element sum of the recipient and the argument
- *
- * @param x a Vector
- * @return a new Vector
- * @throws CardinalityException if the cardinalities differ
- */
- Vector plus(Vector x);
-
- /**
- * Set the value at the given index
- *
- * @param index an int index into the receiver
- * @param value a double value to set
- * @throws IndexException if the index is out of bounds
- */
- void set(int index, double value);
-
- /**
- * Set the value at the given index, without checking bounds
- *
- * @param index an int index into the receiver
- * @param value a double value to set
- */
- void setQuick(int index, double value);
-
- /**
- * Increment the value at the given index by the given value.
- *
- * @param index an int index into the receiver
- * @param increment sets the value at the given index to value + increment;
- */
- void incrementQuick(int index, double increment);
-
- /**
- * Return the number of values in the recipient which are not the default value. For instance, for a
- * sparse vector, this would be the number of non-zero values.
- *
- * @return an int
- */
- int getNumNondefaultElements();
-
- /**
- * Return the number of non zero elements in the vector.
- *
- * @return an int
- */
- int getNumNonZeroElements();
-
- /**
- * Return a new vector containing the product of each value of the recipient and the argument
- *
- * @param x a double argument
- * @return a new Vector
- */
- Vector times(double x);
-
- /**
- * Return a new vector containing the element-wise product of the recipient and the argument
- *
- * @param x a Vector argument
- * @return a new Vector
- * @throws CardinalityException if the cardinalities differ
- */
- Vector times(Vector x);
-
- /**
- * Return a new vector containing the subset of the recipient
- *
- * @param offset an int offset into the receiver
- * @param length the cardinality of the desired result
- * @return a new Vector
- * @throws CardinalityException if the length is greater than the cardinality of the receiver
- * @throws IndexException if the offset is negative or the offset+length is outside of the receiver
- */
- Vector viewPart(int offset, int length);
-
- /**
- * Return the sum of all the elements of the receiver
- *
- * @return a double
- */
- double zSum();
-
- /**
- * Return the cross product of the receiver and the other vector
- *
- * @param other another Vector
- * @return a Matrix
- */
- Matrix cross(Vector other);
-
- /*
- * Need stories for these but keeping them here for now.
- */
- // void getNonZeros(IntArrayList jx, DoubleArrayList values);
- // void foreachNonZero(IntDoubleFunction f);
- // DoubleDoubleFunction map);
- // NewVector assign(Vector y, DoubleDoubleFunction function, IntArrayList
- // nonZeroIndexes);
-
- /**
- * Examples speak louder than words: aggregate(plus, pow(2)) is another way to say
- * getLengthSquared(), aggregate(max, abs) is norm(Double.POSITIVE_INFINITY). To sum all of the positive values,
- * aggregate(plus, max(0)).
- * @param aggregator used to combine the current value of the aggregation with the result of map.apply(nextValue)
- * @param map a function to apply to each element of the vector in turn before passing to the aggregator
- * @return the final aggregation
- */
- double aggregate(DoubleDoubleFunction aggregator, DoubleFunction map);
-
- /**
- * <p>Generalized inner product - take two vectors, iterate over them both, using the combiner to combine together
- * (and possibly map in some way) each pair of values, which are then aggregated with the previous accumulated
- * value in the combiner.</p>
- * <p>
- * Example: dot(other) could be expressed as aggregate(other, Plus, Times), and kernelized inner products (which
- * are symmetric on the indices) work similarly.
- * @param other a vector to aggregate in combination with
- * @param aggregator function we're aggregating with; fa
- * @param combiner function we're combining with; fc
- * @return the final aggregation; {@code if r0 = fc(this[0], other[0]), ri = fa(r_{i-1}, fc(this[i], other[i]))
- * for all i > 0}
- */
- double aggregate(Vector other, DoubleDoubleFunction aggregator, DoubleDoubleFunction combiner);
-
- /**
- * Return the sum of squares of all elements in the vector. Square root of
- * this value is the length of the vector.
- */
- double getLengthSquared();
-
- /**
- * Get the square of the distance between this vector and the other vector.
- */
- double getDistanceSquared(Vector v);
-
- /**
- * Gets an estimate of the cost (in number of operations) it takes to lookup a random element in this vector.
- */
- double getLookupCost();
-
- /**
- * Gets an estimate of the cost (in number of operations) it takes to advance an iterator through the nonzero
- * elements of this vector.
- */
- double getIteratorAdvanceCost();
-
- /**
- * Return true iff adding a new (nonzero) element takes constant time for this vector.
- */
- boolean isAddConstantTime();
-}
http://git-wip-us.apache.org/repos/asf/mahout/blob/49ad8cb4/core/src/main/java/org/apache/mahout/math/VectorBinaryAggregate.java
----------------------------------------------------------------------
diff --git a/core/src/main/java/org/apache/mahout/math/VectorBinaryAggregate.java b/core/src/main/java/org/apache/mahout/math/VectorBinaryAggregate.java
deleted file mode 100644
index b86cab4..0000000
--- a/core/src/main/java/org/apache/mahout/math/VectorBinaryAggregate.java
+++ /dev/null
@@ -1,483 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements. See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License. You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.mahout.math;
-
-import org.apache.mahout.math.function.DoubleDoubleFunction;
-//import org.apache.mahout.math.set.OpenIntHashSet;
-
-import java.util.HashSet;
-import java.util.Iterator;
-import java.util.Set;
-
-/**
- * Abstract class encapsulating different algorithms that perform the Vector operations aggregate().
- * x.aggregte(y, fa, fc), for x and y Vectors and fa, fc DoubleDouble functions:
- * - applies the function fc to every element in x and y, fc(xi, yi)
- * - constructs a result iteratively, r0 = fc(x0, y0), ri = fc(r_{i-1}, fc(xi, yi)).
- * This works essentially like a map/reduce functional combo.
- *
- * The names of variables, methods and classes used here follow the following conventions:
- * The vector being assigned to (the left hand side) is called this or x.
- * The right hand side is called that or y.
- * The aggregating (reducing) function to be applied is called fa.
- * The combining (mapping) function to be applied is called fc.
- *
- * The different algorithms take into account the different characteristics of vector classes:
- * - whether the vectors support sequential iteration (isSequential())
- * - what the lookup cost is (getLookupCost())
- * - what the iterator advancement cost is (getIteratorAdvanceCost())
- *
- * The names of the actual classes (they're nested in VectorBinaryAssign) describe the used for assignment.
- * The most important optimization is iterating just through the nonzeros (only possible if f(0, 0) = 0).
- * There are 4 main possibilities:
- * - iterating through the nonzeros of just one vector and looking up the corresponding elements in the other
- * - iterating through the intersection of nonzeros (those indices where both vectors have nonzero values)
- * - iterating through the union of nonzeros (those indices where at least one of the vectors has a nonzero value)
- * - iterating through all the elements in some way (either through both at the same time, both one after the other,
- * looking up both, looking up just one).
- *
- * The internal details are not important and a particular algorithm should generally not be called explicitly.
- * The best one will be selected through assignBest(), which is itself called through Vector.assign().
- *
- * See https://docs.google.com/document/d/1g1PjUuvjyh2LBdq2_rKLIcUiDbeOORA1sCJiSsz-JVU/edit# for a more detailed
- * explanation.
- */
-public abstract class VectorBinaryAggregate {
- public static final VectorBinaryAggregate[] OPERATIONS = {
- new AggregateNonzerosIterateThisLookupThat(),
- new AggregateNonzerosIterateThatLookupThis(),
-
- new AggregateIterateIntersection(),
-
- new AggregateIterateUnionSequential(),
- new AggregateIterateUnionRandom(),
-
- new AggregateAllIterateSequential(),
- new AggregateAllIterateThisLookupThat(),
- new AggregateAllIterateThatLookupThis(),
- new AggregateAllLoop(),
- };
-
- /**
- * Returns true iff we can use this algorithm to apply fc to x and y component-wise and aggregate the result using fa.
- */
- public abstract boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc);
-
- /**
- * Estimates the cost of using this algorithm to compute the aggregation. The algorithm is assumed to be valid.
- */
- public abstract double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc);
-
- /**
- * Main method that applies fc to x and y component-wise aggregating the results with fa. It returns the result of
- * the aggregation.
- */
- public abstract double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc);
-
- /**
- * The best operation is the least expensive valid one.
- */
- public static VectorBinaryAggregate getBestOperation(Vector x, Vector y, DoubleDoubleFunction fa,
- DoubleDoubleFunction fc) {
- int bestOperationIndex = -1;
- double bestCost = Double.POSITIVE_INFINITY;
- for (int i = 0; i < OPERATIONS.length; ++i) {
- if (OPERATIONS[i].isValid(x, y, fa, fc)) {
- double cost = OPERATIONS[i].estimateCost(x, y, fa, fc);
- if (cost < bestCost) {
- bestCost = cost;
- bestOperationIndex = i;
- }
- }
- }
- return OPERATIONS[bestOperationIndex];
- }
-
- /**
- * This is the method that should be used when aggregating. It selects the best algorithm and applies it.
- */
- public static double aggregateBest(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return getBestOperation(x, y, fa, fc).aggregate(x, y, fa, fc);
- }
-
- public static class AggregateNonzerosIterateThisLookupThat extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return fa.isLikeRightPlus() && (fa.isAssociativeAndCommutative() || x.isSequentialAccess())
- && fc.isLikeLeftMult();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return x.getNumNondefaultElements() * x.getIteratorAdvanceCost() * y.getLookupCost();
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> xi = x.nonZeroes().iterator();
- if (!xi.hasNext()) {
- return 0;
- }
- Vector.Element xe = xi.next();
- double result = fc.apply(xe.get(), y.getQuick(xe.index()));
- while (xi.hasNext()) {
- xe = xi.next();
- result = fa.apply(result, fc.apply(xe.get(), y.getQuick(xe.index())));
- }
- return result;
- }
- }
-
- public static class AggregateNonzerosIterateThatLookupThis extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return fa.isLikeRightPlus() && (fa.isAssociativeAndCommutative() || y.isSequentialAccess())
- && fc.isLikeRightMult();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return y.getNumNondefaultElements() * y.getIteratorAdvanceCost() * x.getLookupCost() * x.getLookupCost();
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> yi = y.nonZeroes().iterator();
- if (!yi.hasNext()) {
- return 0;
- }
- Vector.Element ye = yi.next();
- double result = fc.apply(x.getQuick(ye.index()), ye.get());
- while (yi.hasNext()) {
- ye = yi.next();
- result = fa.apply(result, fc.apply(x.getQuick(ye.index()), ye.get()));
- }
- return result;
- }
- }
-
- public static class AggregateIterateIntersection extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return fa.isLikeRightPlus() && fc.isLikeMult() && x.isSequentialAccess() && y.isSequentialAccess();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return Math.min(x.getNumNondefaultElements() * x.getIteratorAdvanceCost(),
- y.getNumNondefaultElements() * y.getIteratorAdvanceCost());
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> xi = x.nonZeroes().iterator();
- Iterator<Vector.Element> yi = y.nonZeroes().iterator();
- Vector.Element xe = null;
- Vector.Element ye = null;
- boolean advanceThis = true;
- boolean advanceThat = true;
- boolean validResult = false;
- double result = 0;
- while (true) {
- if (advanceThis) {
- if (xi.hasNext()) {
- xe = xi.next();
- } else {
- break;
- }
- }
- if (advanceThat) {
- if (yi.hasNext()) {
- ye = yi.next();
- } else {
- break;
- }
- }
- if (xe.index() == ye.index()) {
- double thisResult = fc.apply(xe.get(), ye.get());
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- advanceThis = true;
- advanceThat = true;
- } else {
- if (xe.index() < ye.index()) { // f(x, 0) = 0
- advanceThis = true;
- advanceThat = false;
- } else { // f(0, y) = 0
- advanceThis = false;
- advanceThat = true;
- }
- }
- }
- return result;
- }
- }
-
- public static class AggregateIterateUnionSequential extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return fa.isLikeRightPlus() && !fc.isDensifying()
- && x.isSequentialAccess() && y.isSequentialAccess();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return Math.max(x.getNumNondefaultElements() * x.getIteratorAdvanceCost(),
- y.getNumNondefaultElements() * y.getIteratorAdvanceCost());
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> xi = x.nonZeroes().iterator();
- Iterator<Vector.Element> yi = y.nonZeroes().iterator();
- Vector.Element xe = null;
- Vector.Element ye = null;
- boolean advanceThis = true;
- boolean advanceThat = true;
- boolean validResult = false;
- double result = 0;
- while (true) {
- if (advanceThis) {
- if (xi.hasNext()) {
- xe = xi.next();
- } else {
- xe = null;
- }
- }
- if (advanceThat) {
- if (yi.hasNext()) {
- ye = yi.next();
- } else {
- ye = null;
- }
- }
- double thisResult;
- if (xe != null && ye != null) { // both vectors have nonzero elements
- if (xe.index() == ye.index()) {
- thisResult = fc.apply(xe.get(), ye.get());
- advanceThis = true;
- advanceThat = true;
- } else {
- if (xe.index() < ye.index()) { // f(x, 0)
- thisResult = fc.apply(xe.get(), 0);
- advanceThis = true;
- advanceThat = false;
- } else {
- thisResult = fc.apply(0, ye.get());
- advanceThis = false;
- advanceThat = true;
- }
- }
- } else if (xe != null) { // just the first one still has nonzeros
- thisResult = fc.apply(xe.get(), 0);
- advanceThis = true;
- advanceThat = false;
- } else if (ye != null) { // just the second one has nonzeros
- thisResult = fc.apply(0, ye.get());
- advanceThis = false;
- advanceThat = true;
- } else { // we're done, both are empty
- break;
- }
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- }
- return result;
- }
- }
-
- public static class AggregateIterateUnionRandom extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return fa.isLikeRightPlus() && !fc.isDensifying()
- && (fa.isAssociativeAndCommutative() || (x.isSequentialAccess() && y.isSequentialAccess()));
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return Math.max(x.getNumNondefaultElements() * x.getIteratorAdvanceCost() * y.getLookupCost(),
- y.getNumNondefaultElements() * y.getIteratorAdvanceCost() * x.getLookupCost());
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- HashSet<Integer> visited = new HashSet(); //new OpenIntHashSet();
- Iterator<Vector.Element> xi = x.nonZeroes().iterator();
- boolean validResult = false;
- double result = 0;
- double thisResult;
- while (xi.hasNext()) {
- Vector.Element xe = xi.next();
- thisResult = fc.apply(xe.get(), y.getQuick(xe.index()));
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- visited.add(xe.index());
- }
- Iterator<Vector.Element> yi = y.nonZeroes().iterator();
- while (yi.hasNext()) {
- Vector.Element ye = yi.next();
- if (!visited.contains(ye.index())) {
- thisResult = fc.apply(x.getQuick(ye.index()), ye.get());
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- }
- }
- return result;
- }
- }
-
- public static class AggregateAllIterateSequential extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return x.isSequentialAccess() && y.isSequentialAccess() && !x.isDense() && !y.isDense();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return Math.max(x.size() * x.getIteratorAdvanceCost(), y.size() * y.getIteratorAdvanceCost());
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> xi = x.all().iterator();
- Iterator<Vector.Element> yi = y.all().iterator();
- boolean validResult = false;
- double result = 0;
- while (xi.hasNext() && yi.hasNext()) {
- Vector.Element xe = xi.next();
- double thisResult = fc.apply(xe.get(), yi.next().get());
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- }
- return result;
- }
- }
-
- public static class AggregateAllIterateThisLookupThat extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return (fa.isAssociativeAndCommutative() || x.isSequentialAccess())
- && !x.isDense();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return x.size() * x.getIteratorAdvanceCost() * y.getLookupCost();
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> xi = x.all().iterator();
- boolean validResult = false;
- double result = 0;
- while (xi.hasNext()) {
- Vector.Element xe = xi.next();
- double thisResult = fc.apply(xe.get(), y.getQuick(xe.index()));
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- }
- return result;
- }
- }
-
- public static class AggregateAllIterateThatLookupThis extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return (fa.isAssociativeAndCommutative() || y.isSequentialAccess())
- && !y.isDense();
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return y.size() * y.getIteratorAdvanceCost() * x.getLookupCost();
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- Iterator<Vector.Element> yi = y.all().iterator();
- boolean validResult = false;
- double result = 0;
- while (yi.hasNext()) {
- Vector.Element ye = yi.next();
- double thisResult = fc.apply(x.getQuick(ye.index()), ye.get());
- if (validResult) {
- result = fa.apply(result, thisResult);
- } else {
- result = thisResult;
- validResult = true;
- }
- }
- return result;
- }
- }
-
- public static class AggregateAllLoop extends VectorBinaryAggregate {
-
- @Override
- public boolean isValid(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return true;
- }
-
- @Override
- public double estimateCost(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- return x.size() * x.getLookupCost() * y.getLookupCost();
- }
-
- @Override
- public double aggregate(Vector x, Vector y, DoubleDoubleFunction fa, DoubleDoubleFunction fc) {
- double result = fc.apply(x.getQuick(0), y.getQuick(0));
- int s = x.size();
- for (int i = 1; i < s; ++i) {
- result = fa.apply(result, fc.apply(x.getQuick(i), y.getQuick(i)));
- }
- return result;
- }
- }
-}