| 1 | /** |
| 2 | * |
| 3 | */ |
| 4 | package de.uka.ipd.sdq.probfunction.math.impl; |
| 5 | |
| 6 | import java.util.ArrayList; |
| 7 | import java.util.Collections; |
| 8 | import java.util.HashMap; |
| 9 | import java.util.HashSet; |
| 10 | import java.util.Iterator; |
| 11 | import java.util.List; |
| 12 | |
| 13 | import de.uka.ipd.sdq.probfunction.math.IBoxedPDF; |
| 14 | import de.uka.ipd.sdq.probfunction.math.IContinuousSample; |
| 15 | import de.uka.ipd.sdq.probfunction.math.IProbabilityDensityFunction; |
| 16 | import de.uka.ipd.sdq.probfunction.math.IRandomGenerator; |
| 17 | import de.uka.ipd.sdq.probfunction.math.ISamplePDF; |
| 18 | import de.uka.ipd.sdq.probfunction.math.IUnit; |
| 19 | import de.uka.ipd.sdq.probfunction.math.exception.DomainNotNumbersException; |
| 20 | import de.uka.ipd.sdq.probfunction.math.exception.DoubleSampleException; |
| 21 | import de.uka.ipd.sdq.probfunction.math.exception.FunctionNotInFrequencyDomainException; |
| 22 | import de.uka.ipd.sdq.probfunction.math.exception.FunctionNotInTimeDomainException; |
| 23 | import de.uka.ipd.sdq.probfunction.math.exception.FunctionsInDifferenDomainsException; |
| 24 | import de.uka.ipd.sdq.probfunction.math.exception.IncompatibleUnitsException; |
| 25 | import de.uka.ipd.sdq.probfunction.math.exception.InvalidSampleValueException; |
| 26 | import de.uka.ipd.sdq.probfunction.math.exception.ProbabilitySumNotOneException; |
| 27 | import de.uka.ipd.sdq.probfunction.math.exception.UnitNameNotSetException; |
| 28 | import de.uka.ipd.sdq.probfunction.math.exception.UnitNotSetException; |
| 29 | import de.uka.ipd.sdq.probfunction.math.exception.UnknownPDFTypeException; |
| 30 | import de.uka.ipd.sdq.probfunction.math.exception.UnorderedDomainException; |
| 31 | import de.uka.ipd.sdq.probfunction.math.util.Line; |
| 32 | import de.uka.ipd.sdq.probfunction.math.util.MathTools; |
| 33 | |
| 34 | /** |
| 35 | * @author Ihssane |
| 36 | * |
| 37 | */ |
| 38 | public class BoxedPDFImpl extends ProbabilityDensityFunctionImpl |
| 39 | implements |
| 40 | IBoxedPDF { |
| 41 | |
| 42 | private List<IContinuousSample> samples; |
| 43 | |
| 44 | protected BoxedPDFImpl(IUnit unit, IRandomGenerator generator) { |
| 45 | super(unit, false); |
| 46 | this.randomGenerator = generator; |
| 47 | samples = new ArrayList<IContinuousSample>(); |
| 48 | } |
| 49 | |
| 50 | public IProbabilityDensityFunction add(IProbabilityDensityFunction pdf) |
| 51 | throws FunctionsInDifferenDomainsException, |
| 52 | UnknownPDFTypeException, IncompatibleUnitsException { |
| 53 | ISamplePDF sPDF = pfFactory.transformToSamplePDF(this); |
| 54 | return sPDF.add(pdf); |
| 55 | } |
| 56 | |
| 57 | public IProbabilityDensityFunction mult(IProbabilityDensityFunction pdf) |
| 58 | throws FunctionsInDifferenDomainsException, |
| 59 | UnknownPDFTypeException, IncompatibleUnitsException { |
| 60 | ISamplePDF sPDF = pfFactory.transformToSamplePDF(this); |
| 61 | return sPDF.mult(pdf); |
| 62 | } |
| 63 | |
| 64 | public IProbabilityDensityFunction scale(double scalar) { |
| 65 | List<IContinuousSample> list = new ArrayList<IContinuousSample>(); |
| 66 | for (IContinuousSample s : this.samples) |
| 67 | list.add(pfFactory.createContinuousSample(s.getValue(), s |
| 68 | .getProbability() |
| 69 | * scalar)); |
| 70 | |
| 71 | IBoxedPDF result = null; |
| 72 | try { |
| 73 | result = pfFactory.createBoxedPDF(list, this.getUnit()); |
| 74 | } catch (DoubleSampleException e) { |
| 75 | e.printStackTrace(); |
| 76 | throw new RuntimeException(e); // should never happen |
| 77 | } |
| 78 | return result; |
| 79 | } |
| 80 | |
| 81 | public List<IContinuousSample> getSamples() { |
| 82 | return Collections.unmodifiableList(samples); |
| 83 | } |
| 84 | |
| 85 | public List<Double> getValues() { |
| 86 | List<Double> values = new ArrayList<Double>(); |
| 87 | for (IContinuousSample cs : samples) |
| 88 | values.add(cs.getValue()); |
| 89 | return values; |
| 90 | } |
| 91 | |
| 92 | public List<Double> getProbabilities() { |
| 93 | List<Double> probs = new ArrayList<Double>(); |
| 94 | for (IContinuousSample cs : samples) |
| 95 | probs.add(cs.getProbability()); |
| 96 | return probs; |
| 97 | } |
| 98 | |
| 99 | public void setSamples(List<IContinuousSample> samples) |
| 100 | throws DoubleSampleException { |
| 101 | if (containsDuplicateSamples(samples)) |
| 102 | throw new DoubleSampleException("found duplicate sample values (not probabilities)"); |
| 103 | |
| 104 | Collections.sort(samples, MathTools.getContinuousSampleComparator()); |
| 105 | this.samples = samples; |
| 106 | initDrawSampleDataStructures(); |
| 107 | } |
| 108 | |
| 109 | public IProbabilityDensityFunction div(IProbabilityDensityFunction pdf) |
| 110 | throws FunctionsInDifferenDomainsException, |
| 111 | UnknownPDFTypeException, IncompatibleUnitsException { |
| 112 | ISamplePDF sPDF = pfFactory.transformToSamplePDF(this); |
| 113 | return sPDF.div(pdf); |
| 114 | } |
| 115 | |
| 116 | private void initDrawSampleDataStructures() { |
| 117 | initPartedIntervals(); |
| 118 | initPartedLines(); |
| 119 | } |
| 120 | |
| 121 | private List<Double> partedIntervals = null; |
| 122 | private void initPartedIntervals() { |
| 123 | // StB: getValues() ---> getProbabilities gefixt |
| 124 | partedIntervals = MathTools |
| 125 | .computeCumulativeProbabilities(getProbabilities()); |
| 126 | } |
| 127 | |
| 128 | private HashMap<Double, Line> lines = null; |
| 129 | private void initPartedLines() { |
| 130 | lines = MathTools.computeLines(samples, partedIntervals); |
| 131 | } |
| 132 | |
| 133 | public double drawSample() { |
| 134 | double random = randomGenerator.random(); |
| 135 | for (Double currentInterval : partedIntervals) |
| 136 | if (random < currentInterval) { |
| 137 | return lines.get(currentInterval).getX(random); |
| 138 | } |
| 139 | throw new RuntimeException( |
| 140 | "No interval found for probability. This should never happen!"); |
| 141 | } |
| 142 | |
| 143 | public IProbabilityDensityFunction getFourierTransform() |
| 144 | throws FunctionNotInTimeDomainException { |
| 145 | if (!isInTimeDomain()) |
| 146 | throw new FunctionNotInTimeDomainException(); |
| 147 | |
| 148 | ISamplePDF sPDF = null; |
| 149 | try { |
| 150 | sPDF = pfFactory.transformToSamplePDF(this); |
| 151 | } catch (UnknownPDFTypeException e) { |
| 152 | // should never happen... |
| 153 | e.printStackTrace(); |
| 154 | throw new RuntimeException(e); |
| 155 | } |
| 156 | return sPDF.getFourierTransform(); |
| 157 | } |
| 158 | |
| 159 | public IProbabilityDensityFunction getInverseFourierTransform() |
| 160 | throws FunctionNotInFrequencyDomainException { |
| 161 | if (isInTimeDomain()) |
| 162 | throw new FunctionNotInFrequencyDomainException(); |
| 163 | |
| 164 | ISamplePDF sPDF = null; |
| 165 | try { |
| 166 | sPDF = pfFactory.transformToSamplePDF(this); |
| 167 | } catch (UnknownPDFTypeException e) { |
| 168 | // should never happen... |
| 169 | e.printStackTrace(); |
| 170 | throw new RuntimeException(e); |
| 171 | } |
| 172 | return sPDF.getInverseFourierTransform(); |
| 173 | } |
| 174 | |
| 175 | public double getLowerDomainBorder() { |
| 176 | return 0; |
| 177 | } |
| 178 | |
| 179 | public IProbabilityDensityFunction sub(IProbabilityDensityFunction pdf) |
| 180 | throws FunctionsInDifferenDomainsException, |
| 181 | UnknownPDFTypeException, IncompatibleUnitsException { |
| 182 | ISamplePDF sPDF = pfFactory.transformToSamplePDF(this); |
| 183 | return sPDF.sub(pdf); |
| 184 | } |
| 185 | |
| 186 | /** |
| 187 | * Get the mean value of the BoxedPDF |
| 188 | * @param list |
| 189 | * @return the mean value |
| 190 | */ |
| 191 | public double getArithmeticMeanValue() throws DomainNotNumbersException { |
| 192 | List<IContinuousSample> list = this.getSamples(); |
| 193 | double mean = 0; |
| 194 | double previousValue = 0; |
| 195 | |
| 196 | for (IContinuousSample continuousSample : list) { |
| 197 | double number = (continuousSample.getValue() + previousValue)/2; |
| 198 | mean += number * continuousSample.getProbability(); |
| 199 | previousValue = continuousSample.getValue(); |
| 200 | } |
| 201 | |
| 202 | return mean; |
| 203 | } |
| 204 | |
| 205 | public Object getMedian() throws UnorderedDomainException { |
| 206 | if (!hasOrderedDomain()) |
| 207 | throw new UnorderedDomainException(); |
| 208 | |
| 209 | if (samples.size() % 2 != 0) { |
| 210 | int i = (int) Math.floor(samples.size() / 2.0); |
| 211 | return samples.get(i).getValue(); |
| 212 | } else { |
| 213 | int i1 = (int) Math.round(samples.size() / 2.0); |
| 214 | return (samples.get(i1).getValue() + samples.get(i1 - 1).getValue()) / 2; |
| 215 | } |
| 216 | } |
| 217 | |
| 218 | public Object getPercentile(int p) throws IndexOutOfBoundsException, |
| 219 | UnorderedDomainException { |
| 220 | if (p < 0 || p > 100) |
| 221 | throw new IndexOutOfBoundsException(); |
| 222 | |
| 223 | int rank = (int) Math.round((p * (samples.size() - 1.0)) / 100.0); |
| 224 | return samples.get(rank).getProbability(); |
| 225 | } |
| 226 | |
| 227 | @Override |
| 228 | public boolean isInFrequencyDomain() { |
| 229 | return false; |
| 230 | } |
| 231 | |
| 232 | @Override |
| 233 | public boolean isInTimeDomain() { |
| 234 | return true; |
| 235 | } |
| 236 | |
| 237 | public double getProbabilitySum() { |
| 238 | double sum = 0; |
| 239 | for (IContinuousSample sample : samples) { |
| 240 | sum += sample.getProbability(); |
| 241 | } |
| 242 | return sum; |
| 243 | } |
| 244 | |
| 245 | private boolean containsDuplicateSamples(List<IContinuousSample> samples) { |
| 246 | HashSet<Double> set = new HashSet<Double>(); |
| 247 | for (IContinuousSample s : samples) |
| 248 | set.add(s.getValue()); |
| 249 | |
| 250 | return set.size() != samples.size(); |
| 251 | } |
| 252 | |
| 253 | public void checkConstrains() throws InvalidSampleValueException, |
| 254 | UnitNameNotSetException, UnitNotSetException, |
| 255 | ProbabilitySumNotOneException { |
| 256 | if (!MathTools.equalsDouble(getProbabilitySum(), 1.0)) |
| 257 | throw new ProbabilitySumNotOneException(); |
| 258 | |
| 259 | // TODO: Refactor to new UNIT framework |
| 260 | // if (getUnit() == null) |
| 261 | // throw new UnitNotSetException(); |
| 262 | // if (getUnit().getUnitName() == null) |
| 263 | // throw new UnitNameNotSetException(); |
| 264 | |
| 265 | double value = 0; |
| 266 | for (IContinuousSample s : samples) { |
| 267 | if (s == null || s.getValue() < 0.0 || s.getProbability() < 0.0 |
| 268 | || s.getProbability() > 1.0) |
| 269 | throw new InvalidSampleValueException(); |
| 270 | //Samples must be ordered by their value. |
| 271 | if (s.getValue() < value){ |
| 272 | throw new InvalidSampleValueException(); |
| 273 | } |
| 274 | value = s.getValue(); |
| 275 | } |
| 276 | } |
| 277 | public IProbabilityDensityFunction getCumulativeFunction() { |
| 278 | List<Double> cumulativeProbabilities = MathTools |
| 279 | .computeCumulativeProbabilities(getProbabilities()); |
| 280 | List<IContinuousSample> cdfSamples = new ArrayList<IContinuousSample>(); |
| 281 | |
| 282 | for (int i = 0; i < cumulativeProbabilities.size(); i++) { |
| 283 | double value = samples.get(i).getValue(); |
| 284 | double cumulativeProb = cumulativeProbabilities.get(i); |
| 285 | IContinuousSample sample = pfFactory.createContinuousSample(value, |
| 286 | cumulativeProb); |
| 287 | cdfSamples.add(sample); |
| 288 | } |
| 289 | |
| 290 | IBoxedPDF bpdf = null; |
| 291 | try { |
| 292 | bpdf = pfFactory.createBoxedPDF(cdfSamples, this.getUnit()); |
| 293 | } catch (DoubleSampleException e) { |
| 294 | // should never happen |
| 295 | e.printStackTrace(); |
| 296 | throw new RuntimeException(e); |
| 297 | } |
| 298 | return bpdf; |
| 299 | } |
| 300 | |
| 301 | /** |
| 302 | * |
| 303 | */ |
| 304 | @Override |
| 305 | public boolean equals(Object obj) { |
| 306 | if (obj instanceof IBoxedPDF) { |
| 307 | IBoxedPDF pdf = (IBoxedPDF) obj; |
| 308 | |
| 309 | if (pdf.getSamples().size() != samples.size()) |
| 310 | return false; |
| 311 | |
| 312 | Iterator<IContinuousSample> iter = pdf.getSamples().iterator(); |
| 313 | for (IContinuousSample s : samples) |
| 314 | if (!s.equals(iter.next())) |
| 315 | return false; |
| 316 | return true; |
| 317 | } |
| 318 | return false; |
| 319 | } |
| 320 | |
| 321 | public double probabilisticEquals(IProbabilityDensityFunction pdf) { |
| 322 | // TODO Auto-generated method stub |
| 323 | return 0; |
| 324 | } |
| 325 | |
| 326 | public double greaterThan(IProbabilityDensityFunction pdf) { |
| 327 | // TODO Auto-generated method stub |
| 328 | return 0; |
| 329 | } |
| 330 | |
| 331 | public double lessThan(IProbabilityDensityFunction pdf) { |
| 332 | // TODO Auto-generated method stub |
| 333 | return 0; |
| 334 | } |
| 335 | |
| 336 | /** |
| 337 | * {@inheritDoc} |
| 338 | * |
| 339 | * Scalar must not be 0. If it is 0, a RuntimeException is thrown. |
| 340 | * @param scalar must not be 0 |
| 341 | */ |
| 342 | public IProbabilityDensityFunction stretchDomain(double scalar) { |
| 343 | |
| 344 | List<IContinuousSample> newSamples = new ArrayList<IContinuousSample>(); |
| 345 | if (scalar != 0){ |
| 346 | for (IContinuousSample oldSample : samples) { |
| 347 | newSamples.add(pfFactory.createContinuousSample(oldSample |
| 348 | .getValue() |
| 349 | * scalar, oldSample.getProbability())); |
| 350 | } |
| 351 | } else { |
| 352 | //TODO: Is there a better way to handle a factor 0 for stretching the domain? Maybe creating a static 0-PDF? |
| 353 | //TODO: Introduce proper error handling in whole probfunction package. |
| 354 | throw new RuntimeException("Error: Stretching the domain of PDF "+this.toString()+" with factor 0 is undefined. Please change your models so that no PDf is multiplied by 0."); |
| 355 | } |
| 356 | |
| 357 | IBoxedPDF result = null; |
| 358 | try { |
| 359 | result = pfFactory.createBoxedPDF(newSamples, this.getUnit()); |
| 360 | } catch (DoubleSampleException e) { |
| 361 | e.printStackTrace(); |
| 362 | throw new RuntimeException(e); |
| 363 | } |
| 364 | return result; |
| 365 | } |
| 366 | |
| 367 | /** |
| 368 | * {@inheritDoc} |
| 369 | * |
| 370 | * Returns itself if scalar == 0. |
| 371 | */ |
| 372 | public IProbabilityDensityFunction shiftDomain(double scalar) |
| 373 | throws DomainNotNumbersException { |
| 374 | // Achtung: does not work with negative scalars! |
| 375 | |
| 376 | if (scalar == 0){ |
| 377 | return this; |
| 378 | } |
| 379 | |
| 380 | List<IContinuousSample> newSamples = new ArrayList<IContinuousSample>(); |
| 381 | if (samples.get(0).getProbability() != 0.0){ |
| 382 | newSamples.add(pfFactory.createContinuousSample(scalar, 0.0)); |
| 383 | } |
| 384 | |
| 385 | for (IContinuousSample oldSample: samples){ |
| 386 | newSamples.add(pfFactory.createContinuousSample(oldSample.getValue()+scalar, oldSample.getProbability())); |
| 387 | } |
| 388 | |
| 389 | IBoxedPDF result = null; |
| 390 | try { |
| 391 | result = pfFactory.createBoxedPDF(newSamples, this.getUnit()); |
| 392 | } catch (DoubleSampleException e) { |
| 393 | e.printStackTrace(); |
| 394 | throw new RuntimeException(e); // should never happen |
| 395 | } |
| 396 | return result; |
| 397 | } |
| 398 | |
| 399 | @Override |
| 400 | public String toString() { |
| 401 | String result = ""; |
| 402 | result += "samples: "; |
| 403 | boolean isFirst = true; |
| 404 | for (IContinuousSample ics : samples){ |
| 405 | if (isFirst) { |
| 406 | isFirst = false; |
| 407 | } else { |
| 408 | result += ", "; |
| 409 | } |
| 410 | result += "(" + ics.getValue() + ", " + ics.getProbability() + ")"; |
| 411 | } |
| 412 | |
| 413 | return result; |
| 414 | } |
| 415 | |
| 416 | } |