001 /*
002 // $Id: //open/mondrian/src/main/mondrian/udf/InverseNormalUdf.java#4 $
003 // This software is subject to the terms of the Common Public License
004 // Agreement, available at the following URL:
005 // http://www.opensource.org/licenses/cpl.html.
006 // Copyright (C) 2005-2008 Julian Hyde
007 // All Rights Reserved.
008 // You must accept the terms of that agreement to use this software.
009 */
010 package mondrian.udf;
011
012 import org.apache.commons.math.MathException;
013 import org.apache.commons.math.distribution.DistributionFactory;
014 import org.apache.commons.math.distribution.NormalDistribution;
015 import org.apache.log4j.Logger;
016
017 import mondrian.olap.Evaluator;
018 import mondrian.olap.Syntax;
019 import mondrian.olap.fun.MondrianEvaluationException;
020 import mondrian.olap.type.NumericType;
021 import mondrian.olap.type.Type;
022 import mondrian.spi.UserDefinedFunction;
023
024
025 /**
026 * A user-defined function which returns the inverse normal distribution value
027 * of its argument.
028 *
029 * <p>This particular function is useful in Six Sigma calculations, for
030 * example,
031 *
032 * <blockquote><code><pre>
033 * WITH MEMBER [Measures].[Yield]
034 * AS '([Measures].[Number of Failures] / [Measures].[Population])',
035 * FORMAT_STRING = "0.00%"
036 * MEMBER [Measures].[Sigma]
037 * AS 'IIf([Measures].[Yield] <> 0,
038 * IIf([Measures].[Yield] > 0.5,
039 * 0,
040 * InverseNormal(1 - ([Measures].[Yield])) + 1.5), 6)',
041 * FORMAT_STRING = "0.0000"
042 * </pre></code></blockquote>
043 */
044 public class InverseNormalUdf implements UserDefinedFunction {
045 private static final Logger LOGGER = Logger.getLogger(InverseNormalUdf.class);
046
047 private static DistributionFactory distributionFactory = DistributionFactory.newInstance();
048 private static NormalDistribution nd = distributionFactory.createNormalDistribution();
049
050 public String getName() {
051 return "InverseNormal";
052 }
053
054 public String getDescription() {
055 return "Returns inverse normal distribution of its argument";
056 }
057
058 public Syntax getSyntax() {
059 return Syntax.Function;
060 }
061
062 public Type getReturnType(Type[] types) {
063 return new NumericType();
064 }
065
066 public Type[] getParameterTypes() {
067 return new Type[] {new NumericType()};
068 }
069
070 public Object execute(Evaluator evaluator, Argument[] args) {
071 final Object argValue = args[0].evaluateScalar(evaluator);
072 LOGGER.debug("Inverse Normal argument was : " + argValue);
073 if (!(argValue instanceof Number)) {
074 // Argument might be a RuntimeException indicating that
075 // the cache does not yet have the required cell value. The
076 // function will be called again when the cache is loaded.
077 return null;
078 }
079
080 final Double d = new Double(((Number) argValue).doubleValue());
081 LOGGER.debug("Inverse Normal argument as Double was : " + d);
082
083 if (d.isNaN()) {
084 return null;
085 }
086 /*
087 If probability is nonnumeric or
088 probability < 0 or
089 probability > 1,
090 returns an error.
091 */
092 double dbl = d.doubleValue();
093 if (dbl < 0.0 || dbl > 1.0) {
094 LOGGER.debug("Invalid value for inverse normal distribution: " + dbl);
095 throw new MondrianEvaluationException("Invalid value for inverse normal distribution: " + dbl);
096 }
097 try {
098 Double result = new Double(nd.inverseCumulativeProbability(dbl));
099 LOGGER.debug("Inverse Normal result : " + result.doubleValue());
100 return result;
101 } catch (MathException e) {
102 LOGGER.debug("Exception calculating inverse normal distribution: " + dbl, e);
103 throw new MondrianEvaluationException("Exception calculating inverse normal distribution: " + dbl);
104 }
105 }
106
107 public String[] getReservedWords() {
108 return null;
109 }
110
111 }
112
113 // End InverseNormalUdf.java