/**
* 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 com.google.common.base.Preconditions;
import org.apache.hadoop.io.Writable;
import org.apache.mahout.math.list.IntArrayList;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
public class MatrixWritable implements Writable {
private static final int FLAG_DENSE = 0x01;
private static final int FLAG_SEQUENTIAL = 0x02;
private static final int FLAG_LABELS = 0x04;
private static final int FLAG_SPARSE_ROW = 0x08;
private static final int NUM_FLAGS = 4;
private Matrix matrix;
public MatrixWritable() {}
public MatrixWritable(Matrix m) {
this.matrix = m;
}
public Matrix get() {
return matrix;
}
public void set(Matrix matrix) {
this.matrix = matrix;
}
@Override
public void write(DataOutput out) throws IOException {
writeMatrix(out, matrix);
}
@Override
public void readFields(DataInput in) throws IOException {
matrix = readMatrix(in);
}
public static void readLabels(DataInput in,
Map<String, Integer> columnLabelBindings,
Map<String, Integer> rowLabelBindings) throws IOException {
int colSize = in.readInt();
if (colSize > 0) {
for (int i = 0; i < colSize; i++) {
columnLabelBindings.put(in.readUTF(), in.readInt());
}
}
int rowSize = in.readInt();
if (rowSize > 0) {
for (int i = 0; i < rowSize; i++) {
rowLabelBindings.put(in.readUTF(), in.readInt());
}
}
}
public static void writeLabelBindings(DataOutput out,
Map<String, Integer> columnLabelBindings,
Map<String, Integer> rowLabelBindings) throws IOException {
if (columnLabelBindings == null) {
out.writeInt(0);
} else {
out.writeInt(columnLabelBindings.size());
for (Map.Entry<String, Integer> stringIntegerEntry : columnLabelBindings.entrySet()) {
out.writeUTF(stringIntegerEntry.getKey());
out.writeInt(stringIntegerEntry.getValue());
}
}
if (rowLabelBindings == null) {
out.writeInt(0);
} else {
out.writeInt(rowLabelBindings.size());
for (Map.Entry<String, Integer> stringIntegerEntry : rowLabelBindings.entrySet()) {
out.writeUTF(stringIntegerEntry.getKey());
out.writeInt(stringIntegerEntry.getValue());
}
}
}
/** Reads a typed Matrix instance from the input stream */
public static Matrix readMatrix(DataInput in) throws IOException {
int flags = in.readInt();
Preconditions.checkArgument(flags >> NUM_FLAGS == 0, "Unknown flags set: %d", Integer.toString(flags, 2));
boolean dense = (flags & FLAG_DENSE) != 0;
boolean sequential = (flags & FLAG_SEQUENTIAL) != 0;
boolean hasLabels = (flags & FLAG_LABELS) != 0;
boolean isSparseRowMatrix = (flags & FLAG_SPARSE_ROW) != 0;
int rows = in.readInt();
int columns = in.readInt();
byte vectorFlags = in.readByte();
Matrix matrix;
if (dense) {
matrix = new DenseMatrix(rows, columns);
for (int row = 0; row < rows; row++) {
matrix.assignRow(row, VectorWritable.readVector(in, vectorFlags, columns));
}
} else if (isSparseRowMatrix) {
Vector[] rowVectors = new Vector[rows];
for (int row = 0; row < rows; row++) {
rowVectors[row] = VectorWritable.readVector(in, vectorFlags, columns);
}
matrix = new SparseRowMatrix(rows, columns, rowVectors, true, !sequential);
} else {
matrix = new SparseMatrix(rows, columns);
int numNonZeroRows = in.readInt();
int rowsRead = 0;
while (rowsRead++ < numNonZeroRows) {
int rowIndex = in.readInt();
matrix.assignRow(rowIndex, VectorWritable.readVector(in, vectorFlags, columns));
}
}
if (hasLabels) {
Map<String,Integer> columnLabelBindings = new HashMap<>();
Map<String,Integer> rowLabelBindings = new HashMap<>();
readLabels(in, columnLabelBindings, rowLabelBindings);
if (!columnLabelBindings.isEmpty()) {
matrix.setColumnLabelBindings(columnLabelBindings);
}
if (!rowLabelBindings.isEmpty()) {
matrix.setRowLabelBindings(rowLabelBindings);
}
}
return matrix;
}
/** Writes a typed Matrix instance to the output stream */
public static void writeMatrix(final DataOutput out, Matrix matrix) throws IOException {
int flags = 0;
Vector row = matrix.viewRow(0);
boolean isDense = row.isDense();
if (isDense) {
flags |= FLAG_DENSE;
}
if (row.isSequentialAccess()) {
flags |= FLAG_SEQUENTIAL;
}
if (matrix.getRowLabelBindings() != null || matrix.getColumnLabelBindings() != null) {
flags |= FLAG_LABELS;
}
boolean isSparseRowMatrix = matrix instanceof SparseRowMatrix;
if (isSparseRowMatrix) {
flags |= FLAG_SPARSE_ROW;
}
out.writeInt(flags);
out.writeInt(matrix.rowSize());
out.writeInt(matrix.columnSize());
// We only use vectors of the same type, so we write out the type information only once!
byte vectorFlags = VectorWritable.flags(matrix.viewRow(0), false);
out.writeByte(vectorFlags);
if (isDense || isSparseRowMatrix) {
for (int i = 0; i < matrix.rowSize(); i++) {
VectorWritable.writeVectorContents(out, matrix.viewRow(i), vectorFlags);
}
} else {
IntArrayList rowIndices = ((SparseMatrix) matrix).nonZeroRowIndices();
int numNonZeroRows = rowIndices.size();
out.writeInt(numNonZeroRows);
for (int i = 0; i < numNonZeroRows; i++) {
int rowIndex = rowIndices.getQuick(i);
out.writeInt(rowIndex);
VectorWritable.writeVectorContents(out, matrix.viewRow(rowIndex), vectorFlags);
}
}
if ((flags & FLAG_LABELS) != 0) {
writeLabelBindings(out, matrix.getColumnLabelBindings(), matrix.getRowLabelBindings());
}
}
}