This repository has been archived by the owner on Nov 19, 2020. It is now read-only.
/
WeibullDistribution.cs
548 lines (511 loc) · 22 KB
/
WeibullDistribution.cs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
// Accord Statistics Library
// The Accord.NET Framework
// http://accord-framework.net
//
// Copyright © César Souza, 2009-2017
// cesarsouza at gmail.com
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
//
namespace Accord.Statistics.Distributions.Univariate
{
using System;
using Accord.Math;
using Accord.Statistics.Distributions.Fitting;
using Accord.Compat;
/// <summary>
/// Weibull distribution.
/// </summary>
///
/// <remarks>
/// <para>
/// In probability theory and statistics, the Weibull distribution is a
/// continuous probability distribution. It is named after Waloddi Weibull,
/// who described it in detail in 1951, although it was first identified by
/// Fréchet (1927) and first applied by Rosin and Rammler (1933) to describe a
/// particle size distribution.</para>
///
/// <para>
/// The Weibull distribution is related to a number of other probability distributions;
/// in particular, it interpolates between the <see cref="ExponentialDistribution">
/// exponential distribution</see> (for k = 1) and the <see cref="RayleighDistribution">
/// Rayleigh distribution</see> (when k = 2). </para>
///
/// <para>
/// If the quantity x is a "time-to-failure", the Weibull distribution gives a
/// distribution for which the failure rate is proportional to a power of time.
/// The shape parameter, k, is that power plus one, and so this parameter can be
/// interpreted directly as follows:</para>
///
/// <list type="bullet">
/// <item><description>
/// A value of k < 1 indicates that the failure rate decreases over time. This
/// happens if there is significant "infant mortality", or defective items failing
/// early and the failure rate decreasing over time as the defective items are
/// weeded out of the population.</description></item>
/// <item><description>
/// A value of k = 1 indicates that the failure rate is constant over time. This
/// might suggest random external events are causing mortality, or failure.</description></item>
/// <item><description>
/// A value of k > 1 indicates that the failure rate increases with time. This
/// happens if there is an "aging" process, or parts that are more likely to fail
/// as time goes on.</description></item>
/// </list>
/// <para>
/// In the field of materials science, the shape parameter <c>k</c> of a distribution
/// of strengths is known as the Weibull modulus.</para>
///
/// <para>
/// References:
/// <list type="bullet">
/// <item><description><a href="http://en.wikipedia.org/wiki/Weibull_distribution">
/// Wikipedia, The Free Encyclopedia. Weibull distribution. Available on:
/// http://en.wikipedia.org/wiki/Weibull_distribution </a></description></item>
/// </list></para>
/// </remarks>
///
/// <example>
/// <code>
/// // Create a new Weibull distribution with λ = 0.42 and k = 1.2
/// var weilbull = new WeibullDistribution(scale: 0.42, shape: 1.2);
///
/// // Common measures
/// double mean = weilbull.Mean; // 0.39507546046784414
/// double median = weilbull.Median; // 0.30945951550913292
/// double var = weilbull.Variance; // 0.10932249666369542
/// double mode = weilbull.Mode; // 0.094360430821809421
///
/// // Cumulative distribution functions
/// double cdf = weilbull.DistributionFunction(x: 1.4); // 0.98560487188700052
/// double pdf = weilbull.ProbabilityDensityFunction(x: 1.4); // 0.052326687031379278
/// double lpdf = weilbull.LogProbabilityDensityFunction(x: 1.4); // -2.9502487697674415
///
/// // Probability density functions
/// double ccdf = weilbull.ComplementaryDistributionFunction(x: 1.4); // 0.22369885565908001
/// double icdf = weilbull.InverseDistributionFunction(p: cdf); // 1.400000001051205
///
/// // Hazard (failure rate) functions
/// double hf = weilbull.HazardFunction(x: 1.4); // 1.1093328057258516
/// double chf = weilbull.CumulativeHazardFunction(x: 1.4); // 1.4974545260150962
///
/// // String representation
/// string str = weilbull.ToString(CultureInfo.InvariantCulture); // Weibull(x; λ = 0.42, k = 1.2)
/// </code>
/// </example>
///
[Serializable]
public class WeibullDistribution : UnivariateContinuousDistribution,
ISampleableDistribution<double>, IFormattable
{
// Distribution parameters
private double k; // k > 0
private double lambda; // λ > 0 (lambda)
/// <summary>
/// Initializes a new instance of the <see cref="WeibullDistribution"/> class.
/// </summary>
///
/// <param name="scale">The scale parameter λ (lambda).</param>
/// <param name="shape">The shape parameter k.</param>
///
public WeibullDistribution([Positive] double shape, [Positive] double scale)
{
if (shape <= 0) // k
throw new ArgumentOutOfRangeException("shape", "Shape (k) must be greater than zero.");
if (scale <= 0) // lambda
throw new ArgumentOutOfRangeException("shape", "Scale (lambda) must be greater than zero.");
this.k = shape;
this.lambda = scale;
}
/// <summary>
/// Gets the shape parameter k.
/// </summary>
///
/// <value>The value for this distribution's shape parameter k.</value>
///
public double Shape
{
get { return k; }
}
/// <summary>
/// Gets the scale parameter λ (lambda).
/// </summary>
///
/// <value>The value for this distribution's scale parameter λ (lambda).</value>
///
public double Scale
{
get { return lambda; }
}
/// <summary>
/// Gets the mean for this distribution.
/// </summary>
///
/// <value>The distribution's mean value.</value>
///
public override double Mean
{
get { return lambda * Gamma.Function(1 + 1 / k); }
}
/// <summary>
/// Gets the variance for this distribution.
/// </summary>
///
/// <value>The distribution's variance.</value>
///
public override double Variance
{
get { return lambda * lambda * Gamma.Function(1 + 2 / k) - Mean * Mean; }
}
/// <summary>
/// Gets the median for this distribution.
/// </summary>
///
/// <value>
/// The distribution's median value.
/// </value>
///
public override double Median
{
get { return lambda * Math.Pow(Math.Log(2.0), 1.0 / k); }
}
/// <summary>
/// Gets the mode for this distribution.
/// </summary>
///
/// <value>
/// The distribution's mode value.
/// </value>
///
public override double Mode
{
get { return k > 1 ? lambda * Math.Pow((k - 1) / k, 1 / k) : 0; }
}
/// <summary>
/// Gets the support interval for this distribution.
/// </summary>
///
/// <value>
/// A <see cref="DoubleRange" /> containing
/// the support interval for this distribution.
/// </value>
///
public override DoubleRange Support
{
get { return new DoubleRange(0, Double.PositiveInfinity); }
}
/// <summary>
/// Gets the entropy for this distribution.
/// </summary>
///
/// <value>The distribution's entropy.</value>
///
public override double Entropy
{
get { return Constants.EulerGamma * (1 - 1 / k) + Math.Log(lambda / k) + 1; }
}
/// <summary>
/// Gets the cumulative distribution function (cdf) for
/// this distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <remarks>
/// The Cumulative Distribution Function (CDF) describes the cumulative
/// probability that a given value or any value smaller than it will occur.
/// </remarks>
///
protected internal override double InnerDistributionFunction(double x)
{
double exp = Math.Exp(-Math.Pow(x / lambda, k));
return 1.0 - exp;
}
/// <summary>
/// Gets the probability density function (pdf) for
/// this distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The probability of <c>x</c> occurring
/// in the current distribution.
/// </returns>
///
/// <remarks>
/// The Probability Density Function (PDF) describes the
/// probability that a given value <c>x</c> will occur.
/// </remarks>
///
protected internal override double InnerProbabilityDensityFunction(double x)
{
double a = Math.Pow(x / lambda, k - 1);
double b = Math.Exp(-Math.Pow(x / lambda, k));
return (k / lambda) * a * b;
}
/// <summary>
/// Gets the log-probability density function (pdf) for
/// this distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The logarithm of the probability of <c>x</c>
/// occurring in the current distribution.
/// </returns>
///
/// <remarks>
/// The Probability Density Function (PDF) describes the
/// probability that a given value <c>x</c> will occur.
/// </remarks>
///
protected internal override double InnerLogProbabilityDensityFunction(double x)
{
return Math.Log(k / lambda) + (k - 1) * Math.Log(x / lambda) - Math.Pow(x / lambda, k);
}
/// <summary>
/// Gets the hazard function, also known as the failure rate or
/// the conditional failure density function for this distribution
/// evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The conditional failure density function <c>h(x)</c>
/// evaluated at <c>x</c> in the current distribution.
/// </returns>
///
public override double HazardFunction(double x)
{
return Math.Pow(k * x, k - 1);
}
/// <summary>
/// Gets the cumulative hazard function for this
/// distribution evaluated at point <c>x</c>.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
/// <returns>
/// The cumulative hazard function <c>H(x)</c>
/// evaluated at <c>x</c> in the current distribution.
/// </returns>
///
public override double CumulativeHazardFunction(double x)
{
return Math.Pow(x, k);
}
/// <summary>
/// Gets the complementary cumulative distribution function
/// (ccdf) for this distribution evaluated at point <c>x</c>.
/// This function is also known as the Survival function.
/// </summary>
///
/// <param name="x">A single point in the distribution range.</param>
///
protected internal override double InnerComplementaryDistributionFunction(double x)
{
return Math.Exp(-Math.Pow(x, k));
}
/// <summary>
/// Gets the inverse of the <see cref="UnivariateContinuousDistribution.ComplementaryDistributionFunction(Double)"/>.
/// The inverse complementary distribution function is also known as the
/// inverse survival Function.
/// </summary>
///
public double InverseComplementaryDistributionFunction(double p)
{
return Math.Pow(-Math.Log(p), 1 / k);
}
/// <summary>
/// Fits the underlying distribution to a given set of observations.
/// </summary>
///
/// <param name="observations">The array of observations to fit the model against. The array
/// elements can be either of type double (for univariate data) or
/// type double[] (for multivariate data).</param>
/// <param name="weights">The weight vector containing the weight for each of the samples.</param>
/// <param name="options">Optional arguments which may be used during fitting, such
/// as regularization constants and additional parameters.</param>
///
/// <remarks>
/// Although both double[] and double[][] arrays are supported,
/// providing a double[] for a multivariate distribution or a
/// double[][] for a univariate distribution may have a negative
/// impact in performance.
/// </remarks>
///
public override void Fit(double[] observations, double[] weights, IFittingOptions options)
{
throw new NotImplementedException();
}
/// <summary>
/// Creates a new object that is a copy of the current instance.
/// </summary>
///
/// <returns>
/// A new object that is a copy of this instance.
/// </returns>
///
public override object Clone()
{
return new WeibullDistribution(lambda, k);
}
#region ISampleableDistribution<double> Members
/// <summary>
/// Generates a random vector of observations from the current distribution.
/// </summary>
///
/// <param name="samples">The number of samples to generate.</param>
/// <param name="result">The location where to store the samples.</param>
/// <param name="source">The random number generator to use as a source of randomness.
/// Default is to use <see cref="Accord.Math.Random.Generator.Random"/>.</param>
///
/// <returns>A random vector of observations drawn from this distribution.</returns>
///
public override double[] Generate(int samples, double[] result, Random source)
{
return Random(k, lambda, samples, result, source);
}
/// <summary>
/// Generates a random observation from the current distribution.
/// </summary>
///
/// <param name="source">The random number generator to use as a source of randomness.
/// Default is to use <see cref="Accord.Math.Random.Generator.Random"/>.</param>
///
/// <returns>A random observations drawn from this distribution.</returns>
///
public override double Generate(Random source)
{
return Random(k, lambda, source);
}
/// <summary>
/// Generates a random vector of observations from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
/// <param name="samples">The number of samples to generate.</param>
///
/// <returns>An array of double values sampled from the specified Weibull distribution.</returns>
///
public static double[] Random(double shape, double scale, int samples)
{
return Random(shape, scale, samples, new double[samples]);
}
/// <summary>
/// Generates a random vector of observations from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
/// <param name="samples">The number of samples to generate.</param>
/// <param name="source">The random number generator to use as a source of randomness.
/// Default is to use <see cref="Accord.Math.Random.Generator.Random"/>.</param>
///
/// <returns>An array of double values sampled from the specified Weibull distribution.</returns>
///
public static double[] Random(double shape, double scale, int samples, Random source)
{
return Random(shape, scale, samples, new double[samples], source);
}
/// <summary>
/// Generates a random vector of observations from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
/// <param name="samples">The number of samples to generate.</param>
/// <param name="result">The location where to store the samples.</param>
///
/// <returns>An array of double values sampled from the specified Weibull distribution.</returns>
///
public static double[] Random(double shape, double scale, int samples, double[] result)
{
return Random(shape, scale, samples, result, Accord.Math.Random.Generator.Random);
}
/// <summary>
/// Generates a random vector of observations from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
/// <param name="samples">The number of samples to generate.</param>
/// <param name="result">The location where to store the samples.</param>
/// <param name="source">The random number generator to use as a source of randomness.
/// Default is to use <see cref="Accord.Math.Random.Generator.Random"/>.</param>
///
/// <returns>An array of double values sampled from the specified Weibull distribution.</returns>
///
public static double[] Random(double shape, double scale, int samples, double[] result, Random source)
{
for (int i = 0; i < samples; i++)
result[i] = scale * Math.Pow(-Math.Log(source.NextDouble()), 1 / shape);
return result;
}
/// <summary>
/// Generates a random observation from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
///
/// <returns>A random double value sampled from the specified Weibull distribution.</returns>
///
public static double Random(double shape, double scale)
{
return Random(shape, scale, Accord.Math.Random.Generator.Random);
}
/// <summary>
/// Generates a random observation from the
/// Weibull distribution with the given parameters.
/// </summary>
///
/// <param name="scale">The scale parameter lambda.</param>
/// <param name="shape">The shape parameter k.</param>
/// <param name="source">The random number generator to use as a source of randomness.
/// Default is to use <see cref="Accord.Math.Random.Generator.Random"/>.</param>
///
/// <returns>A random double value sampled from the specified Weibull distribution.</returns>
///
public static double Random(double shape, double scale, Random source)
{
return scale * Math.Pow(-Math.Log(source.NextDouble()), 1 / shape);
}
#endregion
/// <summary>
/// Returns a <see cref="System.String"/> that represents this instance.
/// </summary>
///
/// <returns>
/// A <see cref="System.String"/> that represents this instance.
/// </returns>
///
public override string ToString(string format, IFormatProvider formatProvider)
{
return String.Format(formatProvider, "Weibull(x; λ = {0}, k = {1})",
lambda.ToString(format, formatProvider),
k.ToString(format, formatProvider));
}
}
}