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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.AbstractDoubleScalarRel;
5   import org.djunits.value.vdouble.scalar.DoubleScalarInterface;
6   import org.djutils.exceptions.Throw;
7   
8   /**
9    * <p>
10   * Copyright (c) 2013-2019 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved. <br>
11   * BSD-style license. See <a href="http://opentrafficsim.org/node/13">OpenTrafficSim License</a>.
12   * <p>
13   * @version $Revision$, $LastChangedDate$, by $Author$, initial version 17 okt. 2016 <br>
14   * @author <a href="http://www.tbm.tudelft.nl/averbraeck">Alexander Verbraeck</a>
15   * @author <a href="http://www.tudelft.nl/pknoppers">Peter Knoppers</a>
16   * @author <a href="http://www.transport.citg.tudelft.nl">Wouter Schakel</a>
17   * @param <U> unit of the value
18   * @param <T> class of the value
19   * @param <W> weight class
20   */
21  public final class Persistent<U extends Unit<U>, T extends AbstractDoubleScalarRel<U, T>, W extends Number>
22          implements DoubleScalarInterface
23  {
24  
25      /** Unit. */
26      private final U unit;
27  
28      /** Sum of values. */
29      private T sum = null;
30  
31      /** Min value. */
32      private T min = null;
33  
34      /** Max value. */
35      private T max = null;
36  
37      /** Mean value. */
38      private double mean = Double.NaN;
39  
40      /** Variance sum. */
41      private double varianceSum = Double.NaN;
42  
43      /** Sum of weights. */
44      private double weightSum = 0.0;
45  
46      /** Number of measurements. */
47      private long n = 0;
48  
49      /** Semaphore. */
50      private Object semaphore = new Object();
51  
52      /**
53       * Constructor.
54       * @param unit U; unit, does't matter which one, so long it belongs to the right quantity
55       */
56      public Persistent(final U unit)
57      {
58          this.unit = unit;
59      }
60  
61      /**
62       * Creates a copy.
63       * @return a copy
64       */
65      public Persistent<U, T, W> copy()
66      {
67          Persistent<U, T, W> persistent = new Persistent<>(this.unit);
68          persistent.sum = this.sum;
69          persistent.min = this.min;
70          persistent.max = this.max;
71          persistent.mean = this.mean;
72          persistent.varianceSum = this.varianceSum;
73          persistent.weightSum = this.weightSum;
74          persistent.n = this.n;
75          return persistent;
76      }
77  
78      /**
79       * Adds a value with given weight to the persistent.
80       * @param value T; the value
81       * @param weight W; the weight
82       */
83      public void addValue(T value, W weight)
84      {
85          Throw.whenNull(value, "Value may not be null.");
86          Throw.whenNull(weight, "Weight may not be null.");
87          Throw.when(weight.doubleValue() < 0.0, IllegalArgumentException.class, "Weight may not be negative.");
88          synchronized (this.semaphore)
89          {
90              this.n++;
91              this.min = this.min == null || value.lt(this.min) ? value : this.min;
92              this.max = this.max == null || value.gt(this.max) ? value : this.max;
93              this.sum = this.sum != null ? this.sum.plus(value) : value;
94  
95              // see Knuth's The Art Of Computer Programming Volume II: Seminumerical Algorithms
96              double w = weight.doubleValue();
97              if (this.n == 1)
98              {
99                  this.mean = value.si;
100                 this.weightSum = w;
101                 this.varianceSum = 0.0;
102             }
103             else
104             {
105                 if (w > 0.0)
106                 {
107                     double newWeightSum = this.weightSum + w;
108                     double newMean = ((this.mean * this.weightSum) + (value.si * w)) / newWeightSum;
109                     this.varianceSum += (value.si - this.mean) * (value.si - newMean) * w;
110                     this.mean = newMean;
111                     this.weightSum = newWeightSum;
112                 }
113             }
114         }
115     }
116 
117     /**
118      * @param alpha double; confidence level
119      * @return both-side confidence interval
120      */
121     public ConfidenceInterval<T> getConfidenceInterval(final double alpha)
122     {
123         return getConfidenceInterval(alpha, IntervalSide.BOTH);
124     }
125 
126     /**
127      * @param alpha double; confidence level
128      * @param side IntervalSide; side of confidence interval
129      * @return confidence interval
130      */
131     public ConfidenceInterval<T> getConfidenceInterval(final double alpha, final IntervalSide side)
132     {
133         Throw.whenNull(side, "Interval side may not be null.");
134         if (alpha < 0 || alpha > 1)
135         {
136             throw new IllegalArgumentException("1 >= confidenceLevel >= 0");
137         }
138         synchronized (this.semaphore)
139         {
140             if (Double.isNaN(this.mean) || Double.isNaN(this.getStDev().si))
141             {
142                 return null;
143             }
144             double level = 1 - alpha;
145             if (side == IntervalSide.BOTH)
146             {
147                 level = 1 - alpha / 2.0;
148             }
149             double z = getInverseCumulativeProbability(level);
150             double confidence = z * Math.sqrt(this.getVariance() / this.n);
151             double[] result = {this.mean - confidence, this.mean + confidence};
152             if (side == IntervalSide.LEFT)
153             {
154                 result[1] = this.mean;
155             }
156             if (side == IntervalSide.RIGHT)
157             {
158                 result[0] = this.mean;
159             }
160             result[0] = Math.max(result[0], this.min.si);
161             result[1] = Math.min(result[1], this.max.si);
162             return new ConfidenceInterval<>(instantiate(result[0]), instantiate(result[1]));
163         }
164     }
165 
166     /**
167      * @return min value
168      */
169     public T getMin()
170     {
171         return this.min;
172     }
173 
174     /**
175      * @return max value
176      */
177     public T getMax()
178     {
179         return this.max;
180     }
181 
182     /**
183      * @return number of measurments
184      */
185     public long getN()
186     {
187         return this.n;
188     }
189 
190     /**
191      * @return mean value
192      */
193     public T getMean()
194     {
195         return instantiate(this.mean);
196     }
197 
198     /** {@inheritDoc} */
199     @Override
200     public double getSI()
201     {
202         return this.mean;
203     }
204 
205     /** {@inheritDoc} */
206     @Override
207     public double getInUnit()
208     {
209         return this.mean;
210     }
211 
212     /**
213      * @return variance in si
214      */
215     public double getVariance()
216     {
217         synchronized (this.semaphore)
218         {
219             if (this.n > 1)
220             {
221                 return this.n * this.varianceSum / (this.weightSum * (this.n - 1));
222             }
223             return Double.NaN;
224         }
225     }
226 
227     /**
228      * @return standard deviation
229      */
230     public T getStDev()
231     {
232         synchronized (this.semaphore)
233         {
234             if (this.n > 1)
235             {
236                 return instantiate(Math.sqrt(this.n * this.varianceSum / (this.weightSum * (this.n - 1))));
237             }
238             return instantiate(Double.NaN);
239         }
240     }
241 
242     /**
243      * @param valueSI double; si value
244      * @return instantiate typed value from si value
245      */
246     private final T instantiate(double valueSI)
247     {
248         return this.min.instantiateRel(valueSI, this.unit.getStandardUnit());
249     }
250 
251     /**
252      * @return sum
253      */
254     public T getSum()
255     {
256         return this.sum;
257     }
258 
259     /**
260      * returns the x-value of the given cumulativePropability.
261      * @param cumulativeProbability double; reflects cum prob
262      * @return double the inverse cumulative probability
263      */
264     private double getInverseCumulativeProbability(final double cumulativeProbability)
265     {
266         if (cumulativeProbability < 0 || cumulativeProbability > 1)
267         {
268             throw new IllegalArgumentException("1<cumulativeProbability<0 ?");
269         }
270         boolean located = false;
271         double prob = cumulativeProbability;
272         if (cumulativeProbability < 0.5)
273         {
274             prob = 1 - cumulativeProbability;
275         }
276         int i = 0;
277         while (!located)
278         {
279             if (CUMULATIVE_NORMAL_PROPABILITIES[i] < prob && CUMULATIVE_NORMAL_PROPABILITIES[i + 1] > prob)
280             {
281                 located = true;
282             }
283             i++;
284         }
285         return (0 + i / 100.0);
286     }
287 
288     /**
289      * CUMULATIVE_NORMAL_PROPABILITIES represents the NORMAL DISTRIBUTION FUNCTION TABLE. In order to keep this table as fast as
290      * possible no x values are stored. The range of the table is {0.00,0.01,0.02,...,10.00}
291      */
292     private static final double[] CUMULATIVE_NORMAL_PROPABILITIES = {0.5000000000000000, 0.5039873616189113, 0.5079763193203305,
293             0.5119644795160448, 0.5159514436524734, 0.5199368135347197, 0.5239201914458871, 0.5279011802661332,
294             0.5318793835914418, 0.5358544058520341, 0.5398258524303582, 0.5437933297786074, 0.5477564455357087,
295             0.5517148086437129, 0.5556680294635363, 0.5596157198900099, 0.5635574934661438, 0.567492965496589,
296             0.5714217531602216, 0.5753434756217956, 0.5792577541426178, 0.5831642121901748, 0.5870624755466856,
297             0.5909521724164968, 0.5948329335322977, 0.5987043922600851, 0.6025661847028365, 0.6064179498028396,
298             0.6102593294426336, 0.6140899685445192, 0.6179095151685767, 0.6217176206091617, 0.6255139394898266,
299             0.6292981298566381, 0.6330698532698229, 0.6368287748937326, 0.6405745635850643, 0.644306891979308,
300             0.6480254365753887, 0.6517298778184553, 0.6554199001807951, 0.6590951922408244, 0.6627554467601383,
301             0.6664003607585898, 0.6700296355873492, 0.6736429769999337, 0.6772400952211824, 0.6808207050141283,
302             0.684384525744776, 0.6879312814447339, 0.6914607008716991, 0.6949725175677556, 0.6984664699154933,
303             0.7019423011919023, 0.7053997596200493, 0.7088385984185037, 0.7122585758485337, 0.7156594552589977,
304             0.719041005128991, 0.722402999108207, 0.7257452160549791, 0.7290674400720636, 0.7323694605400943,
305             0.7356510721487322, 0.73891207492553, 0.7421522742624731, 0.7453714809402076, 0.7485695111499924,
306             0.7517461865133215, 0.754901334099275, 0.7580347864395746, 0.761146381541351, 0.764235962897659, 0.7673033794957203,
307             0.7703484858229172, 0.7733711418705582, 0.7763712131354236, 0.779348570619097, 0.7823030908251122,
308             0.7852346557539338, 0.7881431528957866, 0.79102847522134, 0.7938905211703059, 0.7967291946379159,
309             0.7995444049593787, 0.8023360668922485, 0.8051041005968205, 0.8078484316145099, 0.810568990844285,
310             0.8132657145171739, 0.8159385441688476, 0.8185874266103501, 0.8212123138969823, 0.8238131632953734,
311             0.8263899372487721, 0.8289426033406134, 0.831471134256341, 0.8339755077435994, 0.8364557065707554,
312             0.8389117184838284, 0.8413435361618438, 0.8437511571706939, 0.8461345839154543, 0.8484938235912971,
313             0.8508288881329673, 0.8531397941628848, 0.8554265629379223, 0.8576892202948876, 0.8599277965947512,
314             0.8621423266656558, 0.8643328497447642, 0.866499409418988, 0.8686420535645871, 0.8707608342857796,
315             0.872855807852312, 0.8749270346360735, 0.8769745790467864, 0.8789985094668413, 0.8809988981852741,
316             0.8829758213309686, 0.8849293588050865, 0.886859594212814, 0.8887666147944305, 0.8906505113557653,
317             0.8925113781980463, 0.8943493130472426, 0.8961644169828942, 0.8979567943664809, 0.8997265527693831,
318             0.9014738029004713, 0.9031986585333613, 0.9049012364333603, 0.9065816562841962, 0.9082400406144879,
319             0.9098765147240866, 0.9114912066102397, 0.9130842468937037, 0.9146557687447538, 0.9162059078092082,
320             0.9177348021344403, 0.9192425920954638, 0.9207294203210978, 0.9221954316202577, 0.9236407729084056,
321             0.9250655931341809, 0.9264700432062887, 0.9278542759206125, 0.9292184458876369, 0.9305627094602053,
322             0.9318872246616042, 0.9331921511140636, 0.9344776499676445, 0.9357438838296055, 0.9369910166942079,
323             0.9382192138730403, 0.9394286419258764, 0.940619468592069, 0.941791862722541, 0.9429459942123697, 0.944082033934012,
324             0.9452001536711674, 0.9463005260533299, 0.9473833244910284, 0.9484487231117875, 0.94949689669682,
325             0.9505280206184922, 0.9515422707785594, 0.9525398235471815, 0.9535208557027719, 0.9544855443726583,
326             0.9554340669746081, 0.9563666011591815, 0.9572833247529947, 0.9581844157028426, 0.9590700520207252,
327             0.9599404117298032, 0.9607956728112474, 0.9616360131520566, 0.9624616104937792, 0.9632726423822178,
328             0.9640692861180821, 0.9648517187086078, 0.9656201168201517, 0.9663746567317699, 0.9671155142897785,
329             0.9678428648633103, 0.9685568833008597, 0.9692577438878406, 0.9699456203051116, 0.9706206855885555,
330             0.971283112089614, 0.9719330714368619, 0.9725707344985791, 0.9731962713463076, 0.973809851219447,
331             0.9744116424908279, 0.9750018126333039, 0.9755805281873245, 0.9761479547295168, 0.9767042568422514,
332             0.9772495980842009, 0.9777841409618732, 0.9783080469021415, 0.9788214762257182, 0.979324588121612,
333             0.9798175406225412, 0.9803004905813041, 0.9807735936480722, 0.981237004248657, 0.9816908755636616,
334             0.9821353595085873, 0.9825706067148376, 0.9829967665116119, 0.9834139869087157, 0.9838224145802272,
335             0.9842221948490532, 0.9846134716723274, 0.9849963876276845, 0.9853710839003452, 0.985737700271045,
336             0.9860963751047681, 0.9864472453402844, 0.9867904464804915, 0.9871261125835228, 0.9874543762546124,
337             0.9877753686387415, 0.9880892194139929, 0.988396056785651, 0.9886960074810209, 0.9889891967449299,
338             0.9892757483359246, 0.989555784523144, 0.9898294260838525, 0.9900967923016145, 0.9903580009651043,
339             0.9906131683675308, 0.9908624093066779, 0.9911058370855164, 0.9913435635134026, 0.991575698907852,
340             0.9918023520968361, 0.9920236304216317, 0.9922396397401824, 0.9924504844309792, 0.9926562673974049,
341             0.992857090072596, 0.9930530524247368, 0.9932442529628207, 0.9934307887428471, 0.9936127553744406,
342             0.993790247027868, 0.9939633564414762, 0.9941321749294971, 0.9942967923902228, 0.9944572973145361,
343             0.9946137767947988, 0.9947663165340496, 0.9949150008555357, 0.9950599127125389, 0.9952011336985047,
344             0.9953387440574476, 0.9954728226946147, 0.9956034471874149, 0.9957306937965966, 0.9958546374776436,
345             0.9959753518924065, 0.9960929094209239, 0.9962073811734615, 0.9963188370027265, 0.9964273455162624,
346             0.9965329740889989, 0.9966357888759699, 0.9967358548251734, 0.9968332356905508, 0.9969279940451093,
347             0.9970201912941401, 0.9971098876885598, 0.9971971423383441, 0.9972820132260181, 0.9973645572202651,
348             0.9974448300895796, 0.9975228865159886, 0.9975987801088081, 0.9976725634184758, 0.9977442879503817,
349             0.9978140041787443, 0.9978817615605097, 0.9979476085492539, 0.9980115926090906, 0.9980737602285842,
350             0.9981341569346482, 0.9981928273064313, 0.9982498149891729, 0.998305162708049, 0.9983589122819604,
351             0.9984111046372987, 0.9984617798216666, 0.9985109770175317, 0.9985587345558365, 0.9986050899295523,
352             0.9986500798071501, 0.9986937400460181, 0.9987361057057903, 0.9987772110616016, 0.9988170896172607,
353             0.9988557741183267, 0.9988932965651068, 0.9989296882255506, 0.9989649796480435, 0.9989992006741035,
354             0.9990323804509681, 0.9990645474440748, 0.999095729449436, 0.999125953605889, 0.9991552464072354,
355             0.9991836337142654, 0.9992111407666507, 0.9992377921947238, 0.9992636120311323, 0.9992886237223589,
356             0.9993128501401143, 0.9993363135925993, 0.9993590358356453, 0.999381038083711, 0.9994023410207445,
357             0.9994229648109153, 0.9994429291092068, 0.9994622530718738, 0.9994809553667513, 0.9994990541834521,
358             0.9995165672433891, 0.9995335118096818, 0.9995499046969138, 0.999565762280761, 0.9995811005074615,
359             0.9995959349031589, 0.9996102805830935, 0.9996241522606701, 0.9996375642563711, 0.9996505305065254,
360             0.9996630645719558, 0.9996751796464766, 0.9996868885652497, 0.9996982038129988, 0.9997091375321072,
361             0.999719701530551, 0.9997299072897112, 0.9997397659720517, 0.9997492884286557, 0.9997584852066235,
362             0.9997673665563579, 0.9997759424386968, 0.9997842225319191, 0.9997922162386252, 0.9997999326924907,
363             0.999807380764881, 0.999814569071358, 0.9998215059780381, 0.9998281996078514, 0.9998346578466425,
364             0.9998408883492018, 0.9998468985451213, 0.9998526956445724, 0.999858286643944, 0.9998636783313704,
365             0.9998688772921471, 0.9998738899140359, 0.9998787223924446, 0.9998833807355126, 0.9998878707690767,
366             0.9998921981415287, 0.9998963683285831, 0.9999003866379228, 0.9999042582137387, 0.99990798804119, 0.999911580950741,
367             0.9999150416224173, 0.9999183745899578, 0.9999215842448586, 0.9999246748403525, 0.9999276504952729,
368             0.9999305151978185, 0.9999332728092672, 0.9999359270675735, 0.9999384815908634, 0.9999409398808894,
369             0.9999433053263691, 0.9999455812062524, 0.9999477706929066, 0.999949876855225, 0.9999519026616563,
370             0.9999538509831598, 0.9999557245960793, 0.9999575261849584, 0.9999592583452697, 0.9999609235860767,
371             0.9999625243326383, 0.9999640629289291, 0.9999655416401065, 0.9999669626549059, 0.9999683280879766,
372             0.9999696399821539, 0.9999709003106689, 0.9999721109793053, 0.9999732738284871, 0.9999743906353185,
373             0.9999754631155693, 0.9999764929255975, 0.9999774816642223, 0.9999784308745513, 0.9999793420457469,
374             0.9999802166147403, 0.9999810559679307, 0.9999818614427762, 0.9999826343293992, 0.9999833758721115,
375             0.9999840872709006, 0.999984769682885, 0.9999854242237042, 0.9999860519689024, 0.9999866539552403,
376             0.9999872311819792, 0.9999877846121323, 0.9999883151736708, 0.9999888237607027, 0.9999893112346047,
377             0.999989778425137, 0.9999902261314975, 0.9999906551233723, 0.9999910661419341, 0.9999914599008299,
378             0.9999918370871163, 0.999992198362175, 0.9999925443626037, 0.9999928757010765, 0.999993192967172,
379             0.9999934967281847, 0.9999937875299059, 0.9999940658973759, 0.9999943323356203, 0.9999945873303567,
380             0.9999948313486832, 0.9999950648397335, 0.9999952882353275, 0.9999955019505884, 0.9999957063845496,
381             0.9999959019207282, 0.9999960889276895, 0.9999962677595902, 0.9999964387567071, 0.9999966022459394,
382             0.9999967585412978, 0.9999969079443901, 0.9999970507448684, 0.9999971872208745, 0.9999973176394685,
383             0.9999974422570479, 0.9999975613197305, 0.9999976750637555, 0.9999977837158475, 0.9999978874935767,
384             0.9999979866057059, 0.9999980812525245, 0.9999981716261707, 0.9999982579109443, 0.9999983402836066,
385             0.9999984189136624, 0.999998493963655, 0.9999985655894209, 0.9999986339403611, 0.9999986991596813,
386             0.9999987613846415, 0.9999988207467845, 0.9999988773721528, 0.9999989313815221, 0.999998982890599,
387             0.9999990320102132, 0.9999990788465284, 0.9999991235012167, 0.9999991660716405, 0.9999992066510266,
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492 
493     /** {@inheritDoc} */
494     @Override
495     public final String toString()
496     {
497         return "Persistent [unit=" + this.unit + ", sum=" + this.sum + ", min=" + this.min + ", max=" + this.max + ", mean="
498                 + this.mean + ", varianceSum=" + this.varianceSum + ", weightSum=" + this.weightSum + ", n=" + this.n
499                 + ", semaphore=" + this.semaphore + "]";
500     }
501 
502 }