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1   package org.opentrafficsim.kpi.sampling.indicator;
2   
3   import org.djunits.unit.Unit;
4   import org.djunits.value.vdouble.scalar.base.DoubleScalarRel;
5   import org.djutils.exceptions.Throw;
6   
7   /**
8    * <p>
9    * Copyright (c) 2013-2024 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved. <br>
10   * BSD-style license. See <a href="https://opentrafficsim.org/docs/license.html">OpenTrafficSim License</a>.
11   * </p>
12   * @author <a href="https://github.com/averbraeck">Alexander Verbraeck</a>
13   * @author <a href="https://github.com/peter-knoppers">Peter Knoppers</a>
14   * @author <a href="https://github.com/wjschakel">Wouter Schakel</a>
15   * @param <U> unit of the value
16   * @param <T> class of the value
17   * @param <W> weight class
18   */
19  @Deprecated // non-scalar statistics not yet implemented
20  public class Persistent<U extends Unit<U>, T extends DoubleScalarRel<U, T>, W extends Number>
21  {
22      /** Unit. */
23      private final U displayUnit;
24  
25      /** Sum of values. */
26      private T sum = null;
27  
28      /** Min value. */
29      private T min = null;
30  
31      /** Max value. */
32      private T max = null;
33  
34      /** Mean value. */
35      private double mean = Double.NaN;
36  
37      /** Variance sum. */
38      private double varianceSum = Double.NaN;
39  
40      /** Sum of weights. */
41      private double weightSum = 0.0;
42  
43      /** Number of measurements. */
44      private long n = 0;
45  
46      /** Semaphore. */
47      private Object semaphore = new Object();
48  
49      /**
50       * Constructor.
51       * @param displayUnit unit used for displaying the persistent values, minimum, maximum, mean, etc.
52       */
53      public Persistent(final U displayUnit)
54      {
55          this.displayUnit = displayUnit;
56      }
57  
58      /**
59       * Creates a copy.
60       * @return a copy
61       */
62      public Persistent<U, T, W> copy()
63      {
64          Persistent<U, T, W> persistent = new Persistent<>(this.displayUnit);
65          persistent.sum = this.sum;
66          persistent.min = this.min;
67          persistent.max = this.max;
68          persistent.mean = this.mean;
69          persistent.varianceSum = this.varianceSum;
70          persistent.weightSum = this.weightSum;
71          persistent.n = this.n;
72          return persistent;
73      }
74  
75      /**
76       * Adds a value with given weight to the persistent.
77       * @param value the value
78       * @param weight the weight
79       */
80      public void addValue(final T value, final W weight)
81      {
82          Throw.whenNull(value, "Value may not be null.");
83          Throw.whenNull(weight, "Weight may not be null.");
84          Throw.when(weight.doubleValue() < 0.0, IllegalArgumentException.class, "Weight may not be negative.");
85          synchronized (this.semaphore)
86          {
87              this.n++;
88              this.min = this.min == null || value.lt(this.min) ? value : this.min;
89              this.max = this.max == null || value.gt(this.max) ? value : this.max;
90              this.sum = this.sum != null ? this.sum.plus(value) : value;
91  
92              // see Knuth's The Art Of Computer Programming Volume II: Seminumerical Algorithms
93              double w = weight.doubleValue();
94              if (this.n == 1)
95              {
96                  this.mean = value.si;
97                  this.weightSum = w;
98                  this.varianceSum = 0.0;
99              }
100             else
101             {
102                 if (w > 0.0)
103                 {
104                     double newWeightSum = this.weightSum + w;
105                     double newMean = ((this.mean * this.weightSum) + (value.si * w)) / newWeightSum;
106                     this.varianceSum += (value.si - this.mean) * (value.si - newMean) * w;
107                     this.mean = newMean;
108                     this.weightSum = newWeightSum;
109                 }
110             }
111         }
112     }
113 
114     /**
115      * @param alpha confidence level
116      * @return both-side confidence interval
117      */
118     public ConfidenceInterval<T> getConfidenceInterval(final double alpha)
119     {
120         return getConfidenceInterval(alpha, IntervalSide.BOTH);
121     }
122 
123     /**
124      * @param alpha confidence level
125      * @param side side of confidence interval
126      * @return confidence interval
127      */
128     public ConfidenceInterval<T> getConfidenceInterval(final double alpha, final IntervalSide side)
129     {
130         Throw.whenNull(side, "Interval side may not be null.");
131         if (alpha < 0 || alpha > 1)
132         {
133             throw new IllegalArgumentException("1 >= confidenceLevel >= 0");
134         }
135         synchronized (this.semaphore)
136         {
137             if (Double.isNaN(this.mean) || Double.isNaN(this.getStDev().si))
138             {
139                 return null;
140             }
141             double level = 1 - alpha;
142             if (side == IntervalSide.BOTH)
143             {
144                 level = 1 - alpha / 2.0;
145             }
146             double z = getInverseCumulativeProbability(level);
147             double confidence = z * Math.sqrt(this.getVariance() / this.n);
148             double[] result = {this.mean - confidence, this.mean + confidence};
149             if (side == IntervalSide.LEFT)
150             {
151                 result[1] = this.mean;
152             }
153             if (side == IntervalSide.RIGHT)
154             {
155                 result[0] = this.mean;
156             }
157             result[0] = Math.max(result[0], this.min.si);
158             result[1] = Math.min(result[1], this.max.si);
159             return new ConfidenceInterval<>(instantiate(result[0]), instantiate(result[1]));
160         }
161     }
162 
163     /**
164      * @return min value
165      */
166     public T getMin()
167     {
168         return this.min;
169     }
170 
171     /**
172      * @return max value
173      */
174     public T getMax()
175     {
176         return this.max;
177     }
178 
179     /**
180      * @return number of measurments
181      */
182     public long getN()
183     {
184         return this.n;
185     }
186 
187     /**
188      * @return mean value
189      */
190     public T getMean()
191     {
192         return instantiate(this.mean);
193     }
194 
195     /**
196      * @return variance in si
197      */
198     public double getVariance()
199     {
200         synchronized (this.semaphore)
201         {
202             if (this.n > 1)
203             {
204                 return this.n * this.varianceSum / (this.weightSum * (this.n - 1));
205             }
206             return Double.NaN;
207         }
208     }
209 
210     /**
211      * @return standard deviation
212      */
213     public T getStDev()
214     {
215         synchronized (this.semaphore)
216         {
217             if (this.n > 1)
218             {
219                 return instantiate(Math.sqrt(this.n * this.varianceSum / (this.weightSum * (this.n - 1))));
220             }
221             return instantiate(Double.NaN);
222         }
223     }
224 
225     /**
226      * @param valueSI si value
227      * @return instantiate typed value from si value
228      */
229     private T instantiate(final double valueSI)
230     {
231         return this.min.instantiateRel(valueSI, this.displayUnit.getStandardUnit());
232     }
233 
234     /**
235      * @return sum
236      */
237     public T getSum()
238     {
239         return this.sum;
240     }
241 
242     /**
243      * returns the x-value of the given cumulativePropability.
244      * @param cumulativeProbability reflects cum prob
245      * @return double the inverse cumulative probability
246      */
247     private double getInverseCumulativeProbability(final double cumulativeProbability)
248     {
249         if (cumulativeProbability < 0 || cumulativeProbability > 1)
250         {
251             throw new IllegalArgumentException("1<cumulativeProbability<0 ?");
252         }
253         boolean located = false;
254         double prob = cumulativeProbability;
255         if (cumulativeProbability < 0.5)
256         {
257             prob = 1 - cumulativeProbability;
258         }
259         int i = 0;
260         while (!located)
261         {
262             if (CUMULATIVE_NORMAL_PROPABILITIES[i] < prob && CUMULATIVE_NORMAL_PROPABILITIES[i + 1] > prob)
263             {
264                 located = true;
265             }
266             i++;
267         }
268         return (0 + i / 100.0);
269     }
270 
271     /**
272      * CUMULATIVE_NORMAL_PROPABILITIES represents the NORMAL DISTRIBUTION FUNCTION TABLE. In order to keep this table as fast as
273      * possible no x values are stored. The range of the table is {0.00,0.01,0.02,...,10.00}
274      */
275     private static final double[] CUMULATIVE_NORMAL_PROPABILITIES = {0.5000000000000000, 0.5039873616189113, 0.5079763193203305,
276             0.5119644795160448, 0.5159514436524734, 0.5199368135347197, 0.5239201914458871, 0.5279011802661332,
277             0.5318793835914418, 0.5358544058520341, 0.5398258524303582, 0.5437933297786074, 0.5477564455357087,
278             0.5517148086437129, 0.5556680294635363, 0.5596157198900099, 0.5635574934661438, 0.567492965496589,
279             0.5714217531602216, 0.5753434756217956, 0.5792577541426178, 0.5831642121901748, 0.5870624755466856,
280             0.5909521724164968, 0.5948329335322977, 0.5987043922600851, 0.6025661847028365, 0.6064179498028396,
281             0.6102593294426336, 0.6140899685445192, 0.6179095151685767, 0.6217176206091617, 0.6255139394898266,
282             0.6292981298566381, 0.6330698532698229, 0.6368287748937326, 0.6405745635850643, 0.644306891979308,
283             0.6480254365753887, 0.6517298778184553, 0.6554199001807951, 0.6590951922408244, 0.6627554467601383,
284             0.6664003607585898, 0.6700296355873492, 0.6736429769999337, 0.6772400952211824, 0.6808207050141283,
285             0.684384525744776, 0.6879312814447339, 0.6914607008716991, 0.6949725175677556, 0.6984664699154933,
286             0.7019423011919023, 0.7053997596200493, 0.7088385984185037, 0.7122585758485337, 0.7156594552589977,
287             0.719041005128991, 0.722402999108207, 0.7257452160549791, 0.7290674400720636, 0.7323694605400943,
288             0.7356510721487322, 0.73891207492553, 0.7421522742624731, 0.7453714809402076, 0.7485695111499924,
289             0.7517461865133215, 0.754901334099275, 0.7580347864395746, 0.761146381541351, 0.764235962897659, 0.7673033794957203,
290             0.7703484858229172, 0.7733711418705582, 0.7763712131354236, 0.779348570619097, 0.7823030908251122,
291             0.7852346557539338, 0.7881431528957866, 0.79102847522134, 0.7938905211703059, 0.7967291946379159,
292             0.7995444049593787, 0.8023360668922485, 0.8051041005968205, 0.8078484316145099, 0.810568990844285,
293             0.8132657145171739, 0.8159385441688476, 0.8185874266103501, 0.8212123138969823, 0.8238131632953734,
294             0.8263899372487721, 0.8289426033406134, 0.831471134256341, 0.8339755077435994, 0.8364557065707554,
295             0.8389117184838284, 0.8413435361618438, 0.8437511571706939, 0.8461345839154543, 0.8484938235912971,
296             0.8508288881329673, 0.8531397941628848, 0.8554265629379223, 0.8576892202948876, 0.8599277965947512,
297             0.8621423266656558, 0.8643328497447642, 0.866499409418988, 0.8686420535645871, 0.8707608342857796,
298             0.872855807852312, 0.8749270346360735, 0.8769745790467864, 0.8789985094668413, 0.8809988981852741,
299             0.8829758213309686, 0.8849293588050865, 0.886859594212814, 0.8887666147944305, 0.8906505113557653,
300             0.8925113781980463, 0.8943493130472426, 0.8961644169828942, 0.8979567943664809, 0.8997265527693831,
301             0.9014738029004713, 0.9031986585333613, 0.9049012364333603, 0.9065816562841962, 0.9082400406144879,
302             0.9098765147240866, 0.9114912066102397, 0.9130842468937037, 0.9146557687447538, 0.9162059078092082,
303             0.9177348021344403, 0.9192425920954638, 0.9207294203210978, 0.9221954316202577, 0.9236407729084056,
304             0.9250655931341809, 0.9264700432062887, 0.9278542759206125, 0.9292184458876369, 0.9305627094602053,
305             0.9318872246616042, 0.9331921511140636, 0.9344776499676445, 0.9357438838296055, 0.9369910166942079,
306             0.9382192138730403, 0.9394286419258764, 0.940619468592069, 0.941791862722541, 0.9429459942123697, 0.944082033934012,
307             0.9452001536711674, 0.9463005260533299, 0.9473833244910284, 0.9484487231117875, 0.94949689669682,
308             0.9505280206184922, 0.9515422707785594, 0.9525398235471815, 0.9535208557027719, 0.9544855443726583,
309             0.9554340669746081, 0.9563666011591815, 0.9572833247529947, 0.9581844157028426, 0.9590700520207252,
310             0.9599404117298032, 0.9607956728112474, 0.9616360131520566, 0.9624616104937792, 0.9632726423822178,
311             0.9640692861180821, 0.9648517187086078, 0.9656201168201517, 0.9663746567317699, 0.9671155142897785,
312             0.9678428648633103, 0.9685568833008597, 0.9692577438878406, 0.9699456203051116, 0.9706206855885555,
313             0.971283112089614, 0.9719330714368619, 0.9725707344985791, 0.9731962713463076, 0.973809851219447,
314             0.9744116424908279, 0.9750018126333039, 0.9755805281873245, 0.9761479547295168, 0.9767042568422514,
315             0.9772495980842009, 0.9777841409618732, 0.9783080469021415, 0.9788214762257182, 0.979324588121612,
316             0.9798175406225412, 0.9803004905813041, 0.9807735936480722, 0.981237004248657, 0.9816908755636616,
317             0.9821353595085873, 0.9825706067148376, 0.9829967665116119, 0.9834139869087157, 0.9838224145802272,
318             0.9842221948490532, 0.9846134716723274, 0.9849963876276845, 0.9853710839003452, 0.985737700271045,
319             0.9860963751047681, 0.9864472453402844, 0.9867904464804915, 0.9871261125835228, 0.9874543762546124,
320             0.9877753686387415, 0.9880892194139929, 0.988396056785651, 0.9886960074810209, 0.9889891967449299,
321             0.9892757483359246, 0.989555784523144, 0.9898294260838525, 0.9900967923016145, 0.9903580009651043,
322             0.9906131683675308, 0.9908624093066779, 0.9911058370855164, 0.9913435635134026, 0.991575698907852,
323             0.9918023520968361, 0.9920236304216317, 0.9922396397401824, 0.9924504844309792, 0.9926562673974049,
324             0.992857090072596, 0.9930530524247368, 0.9932442529628207, 0.9934307887428471, 0.9936127553744406,
325             0.993790247027868, 0.9939633564414762, 0.9941321749294971, 0.9942967923902228, 0.9944572973145361,
326             0.9946137767947988, 0.9947663165340496, 0.9949150008555357, 0.9950599127125389, 0.9952011336985047,
327             0.9953387440574476, 0.9954728226946147, 0.9956034471874149, 0.9957306937965966, 0.9958546374776436,
328             0.9959753518924065, 0.9960929094209239, 0.9962073811734615, 0.9963188370027265, 0.9964273455162624,
329             0.9965329740889989, 0.9966357888759699, 0.9967358548251734, 0.9968332356905508, 0.9969279940451093,
330             0.9970201912941401, 0.9971098876885598, 0.9971971423383441, 0.9972820132260181, 0.9973645572202651,
331             0.9974448300895796, 0.9975228865159886, 0.9975987801088081, 0.9976725634184758, 0.9977442879503817,
332             0.9978140041787443, 0.9978817615605097, 0.9979476085492539, 0.9980115926090906, 0.9980737602285842,
333             0.9981341569346482, 0.9981928273064313, 0.9982498149891729, 0.998305162708049, 0.9983589122819604,
334             0.9984111046372987, 0.9984617798216666, 0.9985109770175317, 0.9985587345558365, 0.9986050899295523,
335             0.9986500798071501, 0.9986937400460181, 0.9987361057057903, 0.9987772110616016, 0.9988170896172607,
336             0.9988557741183267, 0.9988932965651068, 0.9989296882255506, 0.9989649796480435, 0.9989992006741035,
337             0.9990323804509681, 0.9990645474440748, 0.999095729449436, 0.999125953605889, 0.9991552464072354,
338             0.9991836337142654, 0.9992111407666507, 0.9992377921947238, 0.9992636120311323, 0.9992886237223589,
339             0.9993128501401143, 0.9993363135925993, 0.9993590358356453, 0.999381038083711, 0.9994023410207445,
340             0.9994229648109153, 0.9994429291092068, 0.9994622530718738, 0.9994809553667513, 0.9994990541834521,
341             0.9995165672433891, 0.9995335118096818, 0.9995499046969138, 0.999565762280761, 0.9995811005074615,
342             0.9995959349031589, 0.9996102805830935, 0.9996241522606701, 0.9996375642563711, 0.9996505305065254,
343             0.9996630645719558, 0.9996751796464766, 0.9996868885652497, 0.9996982038129988, 0.9997091375321072,
344             0.999719701530551, 0.9997299072897112, 0.9997397659720517, 0.9997492884286557, 0.9997584852066235,
345             0.9997673665563579, 0.9997759424386968, 0.9997842225319191, 0.9997922162386252, 0.9997999326924907,
346             0.999807380764881, 0.999814569071358, 0.9998215059780381, 0.9998281996078514, 0.9998346578466425,
347             0.9998408883492018, 0.9998468985451213, 0.9998526956445724, 0.999858286643944, 0.9998636783313704,
348             0.9998688772921471, 0.9998738899140359, 0.9998787223924446, 0.9998833807355126, 0.9998878707690767,
349             0.9998921981415287, 0.9998963683285831, 0.9999003866379228, 0.9999042582137387, 0.99990798804119, 0.999911580950741,
350             0.9999150416224173, 0.9999183745899578, 0.9999215842448586, 0.9999246748403525, 0.9999276504952729,
351             0.9999305151978185, 0.9999332728092672, 0.9999359270675735, 0.9999384815908634, 0.9999409398808894,
352             0.9999433053263691, 0.9999455812062524, 0.9999477706929066, 0.999949876855225, 0.9999519026616563,
353             0.9999538509831598, 0.9999557245960793, 0.9999575261849584, 0.9999592583452697, 0.9999609235860767,
354             0.9999625243326383, 0.9999640629289291, 0.9999655416401065, 0.9999669626549059, 0.9999683280879766,
355             0.9999696399821539, 0.9999709003106689, 0.9999721109793053, 0.9999732738284871, 0.9999743906353185,
356             0.9999754631155693, 0.9999764929255975, 0.9999774816642223, 0.9999784308745513, 0.9999793420457469,
357             0.9999802166147403, 0.9999810559679307, 0.9999818614427762, 0.9999826343293992, 0.9999833758721115,
358             0.9999840872709006, 0.999984769682885, 0.9999854242237042, 0.9999860519689024, 0.9999866539552403,
359             0.9999872311819792, 0.9999877846121323, 0.9999883151736708, 0.9999888237607027, 0.9999893112346047,
360             0.999989778425137, 0.9999902261314975, 0.9999906551233723, 0.9999910661419341, 0.9999914599008299,
361             0.9999918370871163, 0.999992198362175, 0.9999925443626037, 0.9999928757010765, 0.999993192967172,
362             0.9999934967281847, 0.9999937875299059, 0.9999940658973759, 0.9999943323356203, 0.9999945873303567,
363             0.9999948313486832, 0.9999950648397335, 0.9999952882353275, 0.9999955019505884, 0.9999957063845496,
364             0.9999959019207282, 0.9999960889276895, 0.9999962677595902, 0.9999964387567071, 0.9999966022459394,
365             0.9999967585412978, 0.9999969079443901, 0.9999970507448684, 0.9999971872208745, 0.9999973176394685,
366             0.9999974422570479, 0.9999975613197305, 0.9999976750637555, 0.9999977837158475, 0.9999978874935767,
367             0.9999979866057059, 0.9999980812525245, 0.9999981716261707, 0.9999982579109443, 0.9999983402836066,
368             0.9999984189136624, 0.999998493963655, 0.9999985655894209, 0.9999986339403611, 0.9999986991596813,
369             0.9999987613846415, 0.9999988207467845, 0.9999988773721528, 0.9999989313815221, 0.999998982890599,
370             0.9999990320102132, 0.9999990788465284, 0.9999991235012167, 0.9999991660716405, 0.9999992066510266,
371             0.99999924532863, 0.9999992821898891, 0.9999993173165908, 0.9999993507870083, 0.999999382676051, 0.9999994130553951,
372             0.9999994419936197, 0.9999994695563318, 0.999999495806289, 0.9999995208035128, 0.9999995446054075,
373             0.9999995672668669, 0.9999995888403767, 0.9999996093761104, 0.9999996289220386, 0.9999996475240089,
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475 
476     @Override
477     public String toString()
478     {
479         return "Persistent [unit=" + this.displayUnit + ", sum=" + this.sum + ", min=" + this.min + ", max=" + this.max
480                 + ", mean=" + this.mean + ", varianceSum=" + this.varianceSum + ", weightSum=" + this.weightSum + ", n="
481                 + this.n + ", semaphore=" + this.semaphore + "]";
482     }
483 
484 }