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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,
 * 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.commons.math3.fitting;

import java.util.List;
import java.util.ArrayList;
import java.io.Serializable;

/**
 * Simple container for weighted observed points used
 * in {@link AbstractCurveFitter curve fitting} algorithms.
 *
 * @since 3.3
 */
public class WeightedObservedPoints implements Serializable {
    /** Serializable version id. */
    private static final long serialVersionUID = 20130813L;

    /** Observed points. */
    private final List observations
        = new ArrayList();

    /**
     * Adds a point to the sample.
     * Calling this method is equivalent to calling
     * {@code add(1.0, x, y)}.
     *
     * @param x Abscissa of the point.
     * @param y Observed value  at {@code x}. After fitting we should
     * have {@code f(x)} as close as possible to this value.
     *
     * @see #add(double, double, double)
     * @see #add(WeightedObservedPoint)
     * @see #toList()
     */
    public void add(double x, double y) {
        add(1d, x, y);
    }

    /**
     * Adds a point to the sample.
     *
     * @param weight Weight of the observed point.
     * @param x Abscissa of the point.
     * @param y Observed value  at {@code x}. After fitting we should
     * have {@code f(x)} as close as possible to this value.
     *
     * @see #add(double, double)
     * @see #add(WeightedObservedPoint)
     * @see #toList()
     */
    public void add(double weight, double x, double y) {
        observations.add(new WeightedObservedPoint(weight, x, y));
    }

    /**
     * Adds a point to the sample.
     *
     * @param observed Observed point to add.
     *
     * @see #add(double, double)
     * @see #add(double, double, double)
     * @see #toList()
     */
    public void add(WeightedObservedPoint observed) {
        observations.add(observed);
    }

    /**
     * Gets a snapshot of the observed points.
     * The list of stored points is copied in order to ensure that
     * modification of the returned instance does not affect this
     * container.
     * Conversely, further modification of this container (through
     * the {@code add} or {@code clear} methods) will not affect the
     * returned list.
     *
     * @return the observed points, in the order they were added to this
     * container.
     *
     * @see #add(double, double)
     * @see #add(double, double, double)
     * @see #add(WeightedObservedPoint)
     */
    public List toList() {
        // The copy is necessary to ensure thread-safety because of the
        // "clear" method (which otherwise would be able to empty the
        // list of points while it is being used by another thread).
        return new ArrayList(observations);
    }

    /**
     * Removes all observations from this container.
     */
    public void clear() {
        observations.clear();
    }
}