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The Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
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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.math.random;

import java.io.IOException;
import java.io.File;
import java.net.URL;
import java.util.List;

import org.apache.commons.math.stat.descriptive.StatisticalSummary;
import org.apache.commons.math.stat.descriptive.SummaryStatistics;

/**
 * Represents an 
 * empirical probability distribution -- a probability distribution derived
 * from observed data without making any assumptions about the functional form
 * of the population distribution that the data come from.
 * Implementations of this interface maintain data structures, called
 * distribution digests, that describe empirical distributions and
 * support the following operations: 

 * loading the distribution from a file of observed data values
 * dividing the input data into "bin ranges" and reporting bin frequency
 *     counts (data for histogram)
 * reporting univariate statistics describing the full set of data values
 *     as well as the observations within each bin
 * generating random values from the distribution
 * 
 * Applications can use EmpiricalDistribution implementations to
 * build grouped frequency histograms representing the input data or to
 * generate random values "like" those in the input file -- i.e., the values
 * generated will follow the distribution of the values in the file.
 * 
 * @version $Revision: 670469 $ $Date: 2008-06-23 04:01:38 -0400 (Mon, 23 Jun 2008) $
 */
public interface EmpiricalDistribution {

    /**
     * Computes the empirical distribution from the provided
     * array of numbers.
     * 
     * @param dataArray the data array
     */
    void load(double[] dataArray);

    /**
     * Computes the empirical distribution from the input file.
     * 
     * @param file the input file
     * @throws IOException if an IO error occurs
     */
    void load(File file) throws IOException;

    /**
     * Computes the empirical distribution using data read from a URL.
     * 
     * @param url url of the input file
     * @throws IOException if an IO error occurs
     */
    void load(URL url) throws IOException;

    /**
     * Generates a random value from this distribution.
     * Preconditions:
     * the distribution must be loaded before invoking this method
     * @return the random value.
     * 
     * @throws IllegalStateException if the distribution has not been loaded
     */
    double getNextValue() throws IllegalStateException;


    /**
     * Returns a 
     * {@link org.apache.commons.math.stat.descriptive.StatisticalSummary} 
     * describing this distribution.
     * Preconditions:
     * the distribution must be loaded before invoking this method
     * 
     * 
     * @return the sample statistics
     * @throws IllegalStateException if the distribution has not been loaded
     */
    StatisticalSummary getSampleStats() throws IllegalStateException;

    /**
     * Property indicating whether or not the distribution has been loaded.
     * 
     * @return true if the distribution has been loaded
     */
    boolean isLoaded();

     /**
     * Returns the number of bins.
     * 
     * @return the number of bins
     */
    int getBinCount();

    /**
     * Returns a list of 
     * {@link org.apache.commons.math.stat.descriptive.SummaryStatistics}
     * containing statistics describing the values in each of the bins.  The
     * List is indexed on the bin number.
     * 
     * @return List of bin statistics
     */
    List getBinStats();

    /**
     * Returns the array of upper bounds for the bins.  Bins are: 

     * [min,upperBounds[0]],(upperBounds[0],upperBounds[1]],...,
     *  (upperBounds[binCount-1],max].
     * 
     * @return array of bin upper bounds
     */
    double[] getUpperBounds();

}