| /* |
| * 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.math4.legacy.stat.regression; |
| |
| import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException; |
| import org.apache.commons.math4.legacy.exception.NoDataException; |
| |
| /** |
| * An interface for regression models allowing for dynamic updating of the data. |
| * That is, the entire data set need not be loaded into memory. As observations |
| * become available, they can be added to the regression model and an updated |
| * estimate regression statistics can be calculated. |
| * |
| * @since 3.0 |
| */ |
| public interface UpdatingMultipleLinearRegression { |
| |
| /** |
| * Returns true if a constant has been included false otherwise. |
| * |
| * @return true if constant exists, false otherwise |
| */ |
| boolean hasIntercept(); |
| |
| /** |
| * Returns the number of observations added to the regression model. |
| * |
| * @return Number of observations |
| */ |
| long getN(); |
| |
| /** |
| * Adds one observation to the regression model. |
| * |
| * @param x the independent variables which form the design matrix |
| * @param y the dependent or response variable |
| * @throws ModelSpecificationException if the length of {@code x} does not equal |
| * the number of independent variables in the model |
| */ |
| void addObservation(double[] x, double y) throws ModelSpecificationException; |
| |
| /** |
| * Adds a series of observations to the regression model. The lengths of |
| * x and y must be the same and x must be rectangular. |
| * |
| * @param x a series of observations on the independent variables |
| * @param y a series of observations on the dependent variable |
| * The length of x and y must be the same |
| * @throws ModelSpecificationException if {@code x} is not rectangular, does not match |
| * the length of {@code y} or does not contain sufficient data to estimate the model |
| */ |
| void addObservations(double[][] x, double[] y) throws ModelSpecificationException; |
| |
| /** |
| * Clears internal buffers and resets the regression model. This means all |
| * data and derived values are initialized |
| */ |
| void clear(); |
| |
| |
| /** |
| * Performs a regression on data present in buffers and outputs a RegressionResults object. |
| * @return RegressionResults acts as a container of regression output |
| * @throws ModelSpecificationException if the model is not correctly specified |
| * @throws NoDataException if there is not sufficient data in the model to |
| * estimate the regression parameters |
| */ |
| RegressionResults regress() throws ModelSpecificationException, NoDataException; |
| |
| /** |
| * Performs a regression on data present in buffers including only regressors. |
| * indexed in variablesToInclude and outputs a RegressionResults object |
| * @param variablesToInclude an array of indices of regressors to include |
| * @return RegressionResults acts as a container of regression output |
| * @throws ModelSpecificationException if the model is not correctly specified |
| * @throws MathIllegalArgumentException if the variablesToInclude array is null or zero length |
| */ |
| RegressionResults regress(int[] variablesToInclude) throws ModelSpecificationException, MathIllegalArgumentException; |
| } |