001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    package org.apache.commons.math3.stat.regression;
018    
019    import org.apache.commons.math3.exception.MathIllegalArgumentException;
020    import org.apache.commons.math3.exception.NoDataException;
021    
022    /**
023     * An interface for regression models allowing for dynamic updating of the data.
024     * That is, the entire data set need not be loaded into memory. As observations
025     * become available, they can be added to the regression  model and an updated
026     * estimate regression statistics can be calculated.
027     *
028     * @version $Id: UpdatingMultipleLinearRegression.java 1392342 2012-10-01 14:08:52Z psteitz $
029     * @since 3.0
030     */
031    public interface UpdatingMultipleLinearRegression {
032    
033        /**
034         * Returns true if a constant has been included false otherwise.
035         *
036         * @return true if constant exists, false otherwise
037         */
038        boolean hasIntercept();
039    
040        /**
041         * Returns the number of observations added to the regression model.
042         *
043         * @return Number of observations
044         */
045        long getN();
046    
047        /**
048         * Adds one observation to the regression model.
049         *
050         * @param x the independent variables which form the design matrix
051         * @param y the dependent or response variable
052         * @throws ModelSpecificationException if the length of {@code x} does not equal
053         * the number of independent variables in the model
054         */
055        void addObservation(double[] x, double y) throws ModelSpecificationException;
056    
057        /**
058         * Adds a series of observations to the regression model. The lengths of
059         * x and y must be the same and x must be rectangular.
060         *
061         * @param x a series of observations on the independent variables
062         * @param y a series of observations on the dependent variable
063         * The length of x and y must be the same
064         * @throws ModelSpecificationException if {@code x} is not rectangular, does not match
065         * the length of {@code y} or does not contain sufficient data to estimate the model
066         */
067        void addObservations(double[][] x, double[] y) throws ModelSpecificationException;
068    
069        /**
070         * Clears internal buffers and resets the regression model. This means all
071         * data and derived values are initialized
072         */
073        void clear();
074    
075    
076        /**
077         * Performs a regression on data present in buffers and outputs a RegressionResults object
078         * @return RegressionResults acts as a container of regression output
079         * @throws ModelSpecificationException if the model is not correctly specified
080         * @throws NoDataException if there is not sufficient data in the model to
081         * estimate the regression parameters
082         */
083        RegressionResults regress() throws ModelSpecificationException, NoDataException;
084    
085        /**
086         * Performs a regression on data present in buffers including only regressors
087         * indexed in variablesToInclude and outputs a RegressionResults object
088         * @param variablesToInclude an array of indices of regressors to include
089         * @return RegressionResults acts as a container of regression output
090         * @throws ModelSpecificationException if the model is not correctly specified
091         * @throws MathIllegalArgumentException if the variablesToInclude array is null or zero length
092         */
093        RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException, MathIllegalArgumentException;
094    }