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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.

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/*
 * 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,
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package org.apache.commons.math3.linear;

import org.apache.commons.math3.Field;
import org.apache.commons.math3.FieldElement;
import org.apache.commons.math3.util.OpenIntToFieldHashMap;

/**
 * Sparse matrix implementation based on an open addressed map.
 *
 * 

* Caveat: This implementation assumes that, for any {@code x}, * the equality {@code x * 0d == 0d} holds. But it is is not true for * {@code NaN}. Moreover, zero entries will lose their sign. * Some operations (that involve {@code NaN} and/or infinities) may * thus give incorrect results. *

* @param the type of the field elements * @since 2.0 */ public class SparseFieldMatrix> extends AbstractFieldMatrix { /** Storage for (sparse) matrix elements. */ private final OpenIntToFieldHashMap entries; /** Row dimension. */ private final int rows; /** Column dimension. */ private final int columns; /** * Create a matrix with no data. * * @param field Field to which the elements belong. */ public SparseFieldMatrix(final Field field) { super(field); rows = 0; columns= 0; entries = new OpenIntToFieldHashMap(field); } /** * Create a new SparseFieldMatrix with the supplied row and column * dimensions. * * @param field Field to which the elements belong. * @param rowDimension Number of rows in the new matrix. * @param columnDimension Number of columns in the new matrix. * @throws org.apache.commons.math3.exception.NotStrictlyPositiveException * if row or column dimension is not positive. */ public SparseFieldMatrix(final Field field, final int rowDimension, final int columnDimension) { super(field, rowDimension, columnDimension); this.rows = rowDimension; this.columns = columnDimension; entries = new OpenIntToFieldHashMap(field); } /** * Copy constructor. * * @param other Instance to copy. */ public SparseFieldMatrix(SparseFieldMatrix other) { super(other.getField(), other.getRowDimension(), other.getColumnDimension()); rows = other.getRowDimension(); columns = other.getColumnDimension(); entries = new OpenIntToFieldHashMap(other.entries); } /** * Generic copy constructor. * * @param other Instance to copy. */ public SparseFieldMatrix(FieldMatrix other){ super(other.getField(), other.getRowDimension(), other.getColumnDimension()); rows = other.getRowDimension(); columns = other.getColumnDimension(); entries = new OpenIntToFieldHashMap(getField()); for (int i = 0; i < rows; i++) { for (int j = 0; j < columns; j++) { setEntry(i, j, other.getEntry(i, j)); } } } /** {@inheritDoc} */ @Override public void addToEntry(int row, int column, T increment) { checkRowIndex(row); checkColumnIndex(column); final int key = computeKey(row, column); final T value = entries.get(key).add(increment); if (getField().getZero().equals(value)) { entries.remove(key); } else { entries.put(key, value); } } /** {@inheritDoc} */ @Override public FieldMatrix copy() { return new SparseFieldMatrix(this); } /** {@inheritDoc} */ @Override public FieldMatrix createMatrix(int rowDimension, int columnDimension) { return new SparseFieldMatrix(getField(), rowDimension, columnDimension); } /** {@inheritDoc} */ @Override public int getColumnDimension() { return columns; } /** {@inheritDoc} */ @Override public T getEntry(int row, int column) { checkRowIndex(row); checkColumnIndex(column); return entries.get(computeKey(row, column)); } /** {@inheritDoc} */ @Override public int getRowDimension() { return rows; } /** {@inheritDoc} */ @Override public void multiplyEntry(int row, int column, T factor) { checkRowIndex(row); checkColumnIndex(column); final int key = computeKey(row, column); final T value = entries.get(key).multiply(factor); if (getField().getZero().equals(value)) { entries.remove(key); } else { entries.put(key, value); } } /** {@inheritDoc} */ @Override public void setEntry(int row, int column, T value) { checkRowIndex(row); checkColumnIndex(column); if (getField().getZero().equals(value)) { entries.remove(computeKey(row, column)); } else { entries.put(computeKey(row, column), value); } } /** * Compute the key to access a matrix element. * * @param row Row index of the matrix element. * @param column Column index of the matrix element. * @return the key within the map to access the matrix element. */ private int computeKey(int row, int column) { return row * columns + column; } }




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