Source Code Cross Referenced for DescriptiveStatistics.java in  » Science » Apache-commons-math-1.1 » org » apache » commons » math » stat » descriptive » Java Source Code / Java DocumentationJava Source Code and Java Documentation

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Java Source Code / Java Documentation » Science » Apache commons math 1.1 » org.apache.commons.math.stat.descriptive 
Source Cross Referenced  Class Diagram Java Document (Java Doc) 


001:        /*
002:         * Copyright 2003-2004 The Apache Software Foundation.
003:         *
004:         * Licensed under the Apache License, Version 2.0 (the "License");
005:         * you may not use this file except in compliance with the License.
006:         * You may obtain a copy of the License at
007:         *
008:         *      http://www.apache.org/licenses/LICENSE-2.0
009:         *
010:         * Unless required by applicable law or agreed to in writing, software
011:         * distributed under the License is distributed on an "AS IS" BASIS,
012:         * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
013:         * See the License for the specific language governing permissions and
014:         * limitations under the License.
015:         */
016:        package org.apache.commons.math.stat.descriptive;
017:
018:        import java.io.Serializable;
019:        import java.util.Arrays;
020:
021:        import org.apache.commons.discovery.tools.DiscoverClass;
022:        import org.apache.commons.math.stat.descriptive.moment.GeometricMean;
023:        import org.apache.commons.math.stat.descriptive.moment.Kurtosis;
024:        import org.apache.commons.math.stat.descriptive.moment.Mean;
025:        import org.apache.commons.math.stat.descriptive.moment.Skewness;
026:        import org.apache.commons.math.stat.descriptive.moment.Variance;
027:        import org.apache.commons.math.stat.descriptive.rank.Max;
028:        import org.apache.commons.math.stat.descriptive.rank.Min;
029:        import org.apache.commons.math.stat.descriptive.rank.Percentile;
030:        import org.apache.commons.math.stat.descriptive.summary.Sum;
031:        import org.apache.commons.math.stat.descriptive.summary.SumOfSquares;
032:
033:        /**
034:         * Abstract factory class for univariate statistical summaries.
035:         *
036:         * @version $Revision: 348519 $ $Date: 2005-11-23 12:12:18 -0700 (Wed, 23 Nov 2005) $
037:         */
038:        public abstract class DescriptiveStatistics implements 
039:                StatisticalSummary, Serializable {
040:
041:            /** Serialization UID */
042:            private static final long serialVersionUID = 5188298269533339922L;
043:
044:            /**
045:             * Create an instance of a <code>DescriptiveStatistics</code>
046:             * @param cls the type of <code>DescriptiveStatistics</code> object to
047:             *        create. 
048:             * @return a new factory. 
049:             * @throws InstantiationException is thrown if the object can not be
050:             *            created.
051:             * @throws IllegalAccessException is thrown if the type's default
052:             *            constructor is not accessible.
053:             */
054:            public static DescriptiveStatistics newInstance(Class cls)
055:                    throws InstantiationException, IllegalAccessException {
056:                return (DescriptiveStatistics) cls.newInstance();
057:            }
058:
059:            /**
060:             * Create an instance of a <code>DescriptiveStatistics</code>
061:             * @return a new factory. 
062:             */
063:            public static DescriptiveStatistics newInstance() {
064:                DescriptiveStatistics factory = null;
065:                try {
066:                    DiscoverClass dc = new DiscoverClass();
067:                    factory = (DescriptiveStatistics) dc
068:                            .newInstance(DescriptiveStatistics.class,
069:                                    "org.apache.commons.math.stat.descriptive.DescriptiveStatisticsImpl");
070:                } catch (Throwable t) {
071:                    return new DescriptiveStatisticsImpl();
072:                }
073:                return factory;
074:            }
075:
076:            /**
077:             * This constant signals that a Univariate implementation
078:             * takes into account the contributions of an infinite number of
079:             * elements.  In other words, if getWindow returns this
080:             * constant, there is, in effect, no "window".
081:             */
082:            public static final int INFINITE_WINDOW = -1;
083:
084:            /**
085:             * Adds the value to the set of numbers
086:             * @param v the value to be added 
087:             */
088:            public abstract void addValue(double v);
089:
090:            /** 
091:             * Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm">
092:             * arithmetic mean </a> of the available values 
093:             * @return The mean or Double.NaN if no values have been added.
094:             */
095:            public double getMean() {
096:                return apply(new Mean());
097:            }
098:
099:            /** 
100:             * Returns the <a href="http://www.xycoon.com/geometric_mean.htm">
101:             * geometric mean </a> of the available values
102:             * @return The geometricMean, Double.NaN if no values have been added, 
103:             * or if the productof the available values is less than or equal to 0.
104:             */
105:            public double getGeometricMean() {
106:                return apply(new GeometricMean());
107:            }
108:
109:            /** 
110:             * Returns the variance of the available values.
111:             * @return The variance, Double.NaN if no values have been added 
112:             * or 0.0 for a single value set.  
113:             */
114:            public double getVariance() {
115:                return apply(new Variance());
116:            }
117:
118:            /** 
119:             * Returns the standard deviation of the available values.
120:             * @return The standard deviation, Double.NaN if no values have been added 
121:             * or 0.0 for a single value set. 
122:             */
123:            public double getStandardDeviation() {
124:                double stdDev = Double.NaN;
125:                if (getN() > 0) {
126:                    if (getN() > 1) {
127:                        stdDev = Math.sqrt(getVariance());
128:                    } else {
129:                        stdDev = 0.0;
130:                    }
131:                }
132:                return (stdDev);
133:            }
134:
135:            /**
136:             * Returns the skewness of the available values. Skewness is a 
137:             * measure of the assymetry of a given distribution.
138:             * @return The skewness, Double.NaN if no values have been added 
139:             * or 0.0 for a value set &lt;=2. 
140:             */
141:            public double getSkewness() {
142:                return apply(new Skewness());
143:            }
144:
145:            /**
146:             * Returns the Kurtosis of the available values. Kurtosis is a 
147:             * measure of the "peakedness" of a distribution
148:             * @return The kurtosis, Double.NaN if no values have been added, or 0.0 
149:             * for a value set &lt;=3. 
150:             */
151:            public double getKurtosis() {
152:                return apply(new Kurtosis());
153:            }
154:
155:            /** 
156:             * Returns the maximum of the available values
157:             * @return The max or Double.NaN if no values have been added.
158:             */
159:            public double getMax() {
160:                return apply(new Max());
161:            }
162:
163:            /** 
164:             * Returns the minimum of the available values
165:             * @return The min or Double.NaN if no values have been added.
166:             */
167:            public double getMin() {
168:                return apply(new Min());
169:            }
170:
171:            /** 
172:             * Returns the number of available values
173:             * @return The number of available values
174:             */
175:            public abstract long getN();
176:
177:            /**
178:             * Returns the sum of the values that have been added to Univariate.
179:             * @return The sum or Double.NaN if no values have been added
180:             */
181:            public double getSum() {
182:                return apply(new Sum());
183:            }
184:
185:            /**
186:             * Returns the sum of the squares of the available values.
187:             * @return The sum of the squares or Double.NaN if no 
188:             * values have been added.
189:             */
190:            public double getSumsq() {
191:                return apply(new SumOfSquares());
192:            }
193:
194:            /** 
195:             * Resets all statistics and storage
196:             */
197:            public abstract void clear();
198:
199:            /**
200:             * Univariate has the ability to return only measures for the
201:             * last N elements added to the set of values.
202:             * @return The current window size or -1 if its Infinite.
203:             */
204:
205:            public abstract int getWindowSize();
206:
207:            /**
208:             * WindowSize controls the number of values which contribute 
209:             * to the values returned by Univariate.  For example, if 
210:             * windowSize is set to 3 and the values {1,2,3,4,5} 
211:             * have been added <strong> in that order</strong> 
212:             * then the <i>available values</i> are {3,4,5} and all
213:             * reported statistics will be based on these values
214:             * @param windowSize sets the size of the window.
215:             */
216:            public abstract void setWindowSize(int windowSize);
217:
218:            /**
219:             * Returns the current set of values in an array of double primitives.  
220:             * The order of addition is preserved.  The returned array is a fresh
221:             * copy of the underlying data -- i.e., it is not a reference to the
222:             * stored data.
223:             * 
224:             * @return returns the current set of numbers in the order in which they 
225:             *         were added to this set
226:             */
227:            public abstract double[] getValues();
228:
229:            /**
230:             * Returns the current set of values in an array of double primitives,  
231:             * sorted in ascending order.  The returned array is a fresh
232:             * copy of the underlying data -- i.e., it is not a reference to the
233:             * stored data.
234:             * @return returns the current set of 
235:             * numbers sorted in ascending order        
236:             */
237:            public double[] getSortedValues() {
238:                double[] sort = getValues();
239:                Arrays.sort(sort);
240:                return sort;
241:            }
242:
243:            /**
244:             * Returns the element at the specified index
245:             * @param index The Index of the element
246:             * @return return the element at the specified index
247:             */
248:            public abstract double getElement(int index);
249:
250:            /**
251:             * Returns an estimate for the pth percentile of the stored values. 
252:             * <p>
253:             * The implementation provided here follows the first estimation procedure presented
254:             * <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm">here.</a>
255:             * <p>
256:             * <strong>Preconditions</strong>:<ul>
257:             * <li><code>0 &lt; p &lt; 100</code> (otherwise an 
258:             * <code>IllegalArgumentException</code> is thrown)</li>
259:             * <li>at least one value must be stored (returns <code>Double.NaN
260:             *     </code> otherwise)</li>
261:             * </ul>
262:             * 
263:             * @param p the requested percentile (scaled from 0 - 100)
264:             * @return An estimate for the pth percentile of the stored data 
265:             * values
266:             */
267:            public double getPercentile(double p) {
268:                return apply(new Percentile(p));
269:            }
270:
271:            /**
272:             * Generates a text report displaying univariate statistics from values
273:             * that have been added.  Each statistic is displayed on a separate
274:             * line.
275:             * 
276:             * @return String with line feeds displaying statistics
277:             */
278:            public String toString() {
279:                StringBuffer outBuffer = new StringBuffer();
280:                outBuffer.append("DescriptiveStatistics:\n");
281:                outBuffer.append("n: " + getN() + "\n");
282:                outBuffer.append("min: " + getMin() + "\n");
283:                outBuffer.append("max: " + getMax() + "\n");
284:                outBuffer.append("mean: " + getMean() + "\n");
285:                outBuffer.append("std dev: " + getStandardDeviation() + "\n");
286:                outBuffer.append("median: " + getPercentile(50) + "\n");
287:                outBuffer.append("skewness: " + getSkewness() + "\n");
288:                outBuffer.append("kurtosis: " + getKurtosis() + "\n");
289:                return outBuffer.toString();
290:            }
291:
292:            /**
293:             * Apply the given statistic to the data associated with this set of statistics.
294:             * @param stat the statistic to apply
295:             * @return the computed value of the statistic.
296:             */
297:            public abstract double apply(UnivariateStatistic stat);
298:
299:        }
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