SAGA API  v9.5
mat_tools.cpp
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1 
3 // //
4 // SAGA //
5 // //
6 // System for Automated Geoscientific Analyses //
7 // //
8 // Application Programming Interface //
9 // //
10 // Library: SAGA_API //
11 // //
12 //-------------------------------------------------------//
13 // //
14 // mat_tools.cpp //
15 // //
16 // Copyright (C) 2005 by Olaf Conrad //
17 // //
18 //-------------------------------------------------------//
19 // //
20 // This file is part of 'SAGA - System for Automated //
21 // Geoscientific Analyses'. //
22 // //
23 // This library is free software; you can redistribute //
24 // it and/or modify it under the terms of the GNU Lesser //
25 // General Public License as published by the Free //
26 // Software Foundation, either version 2.1 of the //
27 // License, or (at your option) any later version. //
28 // //
29 // This library is distributed in the hope that it will //
30 // be useful, but WITHOUT ANY WARRANTY; without even the //
31 // implied warranty of MERCHANTABILITY or FITNESS FOR A //
32 // PARTICULAR PURPOSE. See the GNU Lesser General Public //
33 // License for more details. //
34 // //
35 // You should have received a copy of the GNU Lesser //
36 // General Public License along with this program; if //
37 // not, see <http://www.gnu.org/licenses/>. //
38 // //
39 //-------------------------------------------------------//
40 // //
41 // contact: Olaf Conrad //
42 // Institute of Geography //
43 // University of Goettingen //
44 // Goldschmidtstr. 5 //
45 // 37077 Goettingen //
46 // Germany //
47 // //
48 // e-mail: oconrad@saga-gis.org //
49 // //
51 
52 //---------------------------------------------------------
53 #include <time.h>
54 #include <cfloat>
55 
56 #include "mat_tools.h"
57 
58 #include "table.h"
59 #include "grid.h"
60 #include "grids.h"
61 
62 
64 // //
65 // //
66 // //
68 
69 //---------------------------------------------------------
70 double SG_Get_Square(double Value)
71 {
72  return( Value * Value );
73 }
74 
75 
77 // //
78 // //
79 // //
81 
82 //---------------------------------------------------------
83 double SG_Get_Rounded(double Value, int Decimals)
84 {
85  if( Decimals < 0 )
86  {
87  return( Value );
88  }
89 
90  if( Decimals == 0 )
91  {
92  return( floor(0.5 + Value) );
93  }
94 
95  double d = pow(10., Decimals);
96  double v = Value * d;
97 
98  if( fabs(v - floor(v)) > 0. )
99  {
100  return( floor(0.5 + v) / d );
101  }
102 
103  return( Value );
104 }
105 
106 //---------------------------------------------------------
107 double SG_Get_Rounded_To_SignificantFigures(double Value, int Decimals)
108 {
109  if( Decimals <= 0 || Value == 0. )
110  {
111  return( (int)(0.5 + Value) );
112  }
113 
114  Decimals = (int)(-(ceil(log10(fabs(Value))) - Decimals));
115 
116  if( Decimals > 0 )
117  {
118  double d = pow(10., Decimals);
119 
120  return( Value < 0.
121  ? -((int)(0.5 - Value * d)) / d
122  : ((int)(0.5 + Value * d)) / d
123  );
124  }
125  else
126  {
127  double d = pow(10., -Decimals);
128 
129  return( Value < 0.
130  ? -((int)(0.5 - Value / d)) * d
131  : ((int)(0.5 + Value / d)) * d
132  );
133  }
134 }
135 
136 
138 // //
139 // //
140 // //
142 
143 //---------------------------------------------------------
144 int SG_Get_Digit_Count(int Number)
145 {
146  Number = abs(Number);
147 
148  return( Number < 10 ? 1 : 1 + (int)log10((double)Number) );
149 }
150 
151 
153 // //
154 // //
155 // //
157 
158 //---------------------------------------------------------
159 CSG_String SG_Get_Double_asString(double Number, int Width, int Precision, bool bScientific)
160 {
161  if( bScientific )
162  {
163  if( Width > 0 && Precision >= 0 ) return( CSG_String::Format("%*.*e", Width, Precision, Number) );
164  if( Width > 0 ) return( CSG_String::Format("%*e" , Width , Number) );
165  if( Precision >= 0 ) return( CSG_String::Format("%.*e" , Precision, Number) );
166 
167  return( CSG_String::Format("%e", Number) );
168  }
169  else
170  {
171  if( Width > 0 && Precision >= 0 ) return( CSG_String::Format("%*.*f", Width, Precision, Number) );
172  if( Width > 0 ) return( CSG_String::Format("%*f" , Width , Number) );
173  if( Precision >= 0 ) return( CSG_String::Format("%.*f" , Precision, Number) );
174 
175  return( CSG_String::Format("%f", Number) );
176  }
177 }
178 
179 
181 // //
182 // //
183 // //
185 
186 //---------------------------------------------------------
187 int SG_Compare_Int(const void *a, const void *b)
188 {
189  if( *((int *)a) < *((int *)b) )
190  return( -1 );
191 
192  if( *((int *)a) > *((int *)b) )
193  return( 1 );
194 
195  return( 0 );
196 }
197 
198 //---------------------------------------------------------
199 int SG_Compare_Double(const void *a, const void *b)
200 {
201  if( *((double *)a) < *((double *)b) )
202  return( -1 );
203 
204  if( *((double *)a) > *((double *)b) )
205  return( 1 );
206 
207  return( 0 );
208 }
209 
210 //---------------------------------------------------------
211 int SG_Compare_Char_Ptr(const void *a, const void *b)
212 {
213  return( strcmp((const char *)a, (const char *)b) );
214 }
215 
216 
218 // //
219 // //
220 // //
222 
223 //---------------------------------------------------------
224 double SG_Degree_To_Decimal(double Deg, double Min, double Sec)
225 {
226  return( Deg > 0.
227  ? (Deg + Min / 60. + Sec / 3600.)
228  : (Deg - Min / 60. - Sec / 3600.)
229  );
230 }
231 
232 //---------------------------------------------------------
233 void SG_Decimal_To_Degree(double Value, double &Deg, double &Min, double &Sec)
234 {
235  Sec = fmod(Value < 0. ? -Value : Value, 360.);
236 
237  Deg = (int)Sec; Sec = 60. * (Sec - Deg);
238  Min = (int)Sec; Sec = 60. * (Sec - Min);
239 
240  if( Value < 0. )
241  {
242  Deg = -Deg;
243  }
244 }
245 
246 
248 // //
249 // //
250 // //
252 
253 //---------------------------------------------------------
254 CSG_Random::CSG_Random(void)
255 {
256  Initialize();
257 }
258 
259 //---------------------------------------------------------
260 void CSG_Random::Initialize(void)
261 {
262  Initialize((unsigned)time(NULL));
263 }
264 
265 //---------------------------------------------------------
266 void CSG_Random::Initialize(unsigned int Value)
267 {
268  srand(Value);
269 }
270 
271 //---------------------------------------------------------
272 // Uniform distributed pseudo-random numbers in the range from 0 to 1.
273 //
274 double CSG_Random::Get_Uniform(void)
275 {
276  return( 1. * rand() / (double)RAND_MAX );
277 }
278 
279 //---------------------------------------------------------
280 // Uniform distributed pseudo-random numbers in the range from min to max.
281 //
282 double CSG_Random::Get_Uniform(double min, double max)
283 {
284  return( min + (max - min) * rand() / (double)RAND_MAX );
285 }
286 
287 //---------------------------------------------------------
288 // Generating Gaussian pseudo-random numbers using
289 // the polar form of the Box-Muller transformation.
290 //
291 // Box, G.E.P, Muller, M.E. (1958):
292 // 'A note on the generation of random normal deviates',
293 // Annals Math. Stat, V. 29, pp. 610-611
294 //
295 // Link: http://www.taygeta.com/random/gaussian.html
296 //
297 //---------------------------------------------------------
298 double CSG_Random::Get_Gaussian(double mean, double stddev)
299 {
300  double x1, x2, w;
301 
302  do
303  {
304  x1 = 2. * Get_Uniform() - 1.;
305  x2 = 2. * Get_Uniform() - 1.;
306 
307  w = x1 * x1 + x2 * x2;
308  }
309  while( w >= 1. );
310 
311  w = sqrt((-2. * log(w)) / w);
312 
313  return( mean + stddev * x1 * w );
314 }
315 
316 
318 // //
319 // //
320 // //
322 
323 //---------------------------------------------------------
324 CSG_Simple_Statistics::CSG_Simple_Statistics(void)
325 {
326  Create(false);
327 }
328 
329 CSG_Simple_Statistics::CSG_Simple_Statistics(bool bHoldValues)
330 {
331  Create(bHoldValues);
332 }
333 
334 CSG_Simple_Statistics::CSG_Simple_Statistics(const CSG_Simple_Statistics &Statistics)
335 {
336  Create(Statistics);
337 }
338 
339 CSG_Simple_Statistics::CSG_Simple_Statistics(double Mean, double StdDev, sLong Count)
340 {
341  Create(Mean, StdDev, Count);
342 }
343 
344 CSG_Simple_Statistics::CSG_Simple_Statistics(const CSG_Vector &Values, bool bHoldValues)
345 {
346  Create(Values, bHoldValues);
347 }
348 
349 //---------------------------------------------------------
350 bool CSG_Simple_Statistics::Create(bool bHoldValues)
351 {
352  Invalidate();
353 
354  m_Values.Create(bHoldValues ? sizeof(double) : 0, 0, TSG_Array_Growth::SG_ARRAY_GROWTH_1);
355 
356  return( true );
357 }
358 
359 bool CSG_Simple_Statistics::Create(const CSG_Simple_Statistics &Statistics)
360 {
361  m_bEvaluated = Statistics.m_bEvaluated;
362 
363  m_nValues = Statistics.m_nValues;
364  m_Weights = Statistics.m_Weights;
365  m_Sum = Statistics.m_Sum;
366  m_Sum2 = Statistics.m_Sum2;
367 
368  m_Minimum = Statistics.m_Minimum;
369  m_Maximum = Statistics.m_Maximum;
370  m_Range = Statistics.m_Range;
371  m_Mean = Statistics.m_Mean;
372  m_Variance = Statistics.m_Variance;
373  m_StdDev = Statistics.m_StdDev;
374 
375  m_Kurtosis = Statistics.m_Kurtosis;
376  m_Skewness = Statistics.m_Skewness;
377 
378  m_Gini = Statistics.m_Gini;
379 
380  m_bSorted = Statistics.m_bSorted;
381  m_Values .Create(Statistics.m_Values);
382 
383  return( true );
384 }
385 
386 bool CSG_Simple_Statistics::Create(double Mean, double StdDev, sLong Count)
387 {
388  Invalidate();
389 
390  m_bEvaluated = 1;
391 
392  m_Mean = Mean;
393  m_StdDev = StdDev;
394  m_Variance = StdDev*StdDev;
395  m_nValues = Count;
396  m_Weights = (double)Count;
397 
398  m_Sum = m_Weights * m_Mean;
399  m_Sum2 = m_Weights * (m_Mean*m_Mean + m_Variance);
400 
401  m_Minimum = m_Mean - 1.5 * m_StdDev;
402  m_Maximum = m_Mean + 1.5 * m_StdDev;
403  m_Range = m_Maximum - m_Minimum;
404 
405  return( true );
406 }
407 
408 bool CSG_Simple_Statistics::Create(const CSG_Vector &Values, bool bHoldValues)
409 {
410  if( Create(bHoldValues) )
411  {
412  for(sLong i=0; i<Values.Get_Size(); i++)
413  {
414  Add_Value(Values[i]);
415  }
416 
417  return( true );
418  }
419 
420  return( false );
421 }
422 
423 //---------------------------------------------------------
424 bool CSG_Simple_Statistics::Set_Count(sLong nValues)
425 {
426  if( m_nValues < 1 || nValues < 1 || m_nValues == nValues )
427  {
428  return( false );
429  }
430 
431  double Scale = nValues / (double)m_nValues;
432 
433  m_Weights *= Scale;
434  m_Sum *= Scale;
435  m_Sum2 *= Scale;
436 
437  m_nValues = nValues;
438 
439  m_bEvaluated = 0;
440 
441  m_Values.Destroy();
442 
443  return( true );
444 }
445 
446 //---------------------------------------------------------
447 void CSG_Simple_Statistics::Invalidate(void)
448 {
449  m_bEvaluated = 0;
450 
451  m_nValues = 0;
452  m_Weights = 0.;
453  m_Sum = 0.;
454  m_Sum2 = 0.;
455 
456  m_Minimum = 0.;
457  m_Maximum = 0.;
458  m_Range = 0.;
459  m_Mean = 0.;
460  m_Variance = 0.;
461  m_StdDev = 0.;
462 
463  m_Kurtosis = 0.;
464  m_Skewness = 0.;
465 
466  m_Gini = -1.;
467 
468  m_bSorted = false;
469  m_Values .Destroy();
470 }
471 
472 //---------------------------------------------------------
473 bool CSG_Simple_Statistics::Evaluate(void)
474 {
475  _Evaluate();
476 
477  return( is_Evaluated() > 0 );
478 }
479 
480 //---------------------------------------------------------
481 void CSG_Simple_Statistics::Add(const CSG_Simple_Statistics &Statistics)
482 {
483  if( Statistics.m_nValues <= 0 )
484  {
485  return;
486  }
487 
488  if( m_nValues == 0 )
489  {
490  Create(Statistics);
491 
492  return;
493  }
494 
495  //--------------------------------------------------------
496  if( (sLong)m_Values.Get_Size() == m_nValues && (sLong)Statistics.m_Values.Get_Size() == Statistics.m_nValues && m_Values.Set_Array((size_t)(m_nValues + Statistics.m_nValues)) )
497  {
498  for(sLong i=0, j=m_nValues; i<Statistics.m_nValues; i++, j++)
499  {
500  ((double *)m_Values.Get_Array())[j] = Statistics.Get_Value(i);
501  }
502  }
503  else
504  {
505  m_Values.Destroy();
506  }
507 
508  m_nValues += Statistics.m_nValues;
509  m_Weights += Statistics.m_Weights;
510  m_Sum += Statistics.m_Sum;
511  m_Sum2 += Statistics.m_Sum2;
512 
513  if( m_Minimum > Statistics.m_Minimum )
514  m_Minimum = Statistics.m_Minimum;
515 
516  if( m_Maximum < Statistics.m_Maximum )
517  m_Maximum = Statistics.m_Maximum;
518 
519  m_Kurtosis = 0.;
520  m_Skewness = 0.;
521 
522  m_bEvaluated = 0;
523  m_bSorted = false;
524 }
525 
526 //---------------------------------------------------------
527 void CSG_Simple_Statistics::Add_Value(double Value, double Weight)
528 {
529  if( m_nValues == 0 )
530  {
531  m_Minimum = m_Maximum = Value;
532  }
533  else if( m_Minimum > Value )
534  {
535  m_Minimum = Value;
536  }
537  else if( m_Maximum < Value )
538  {
539  m_Maximum = Value;
540  }
541 
542  if( Weight )
543  {
544  m_Weights += fabs(Weight);
545  m_Sum += Weight * Value;
546  m_Sum2 += Weight * Value*Value;
547 
548  m_bEvaluated = 0;
549 
550  if( m_Values.Get_Value_Size() > 0 && m_Values.Inc_Array() )
551  {
552  m_bSorted = false;
553 
554  ((double *)m_Values.Get_Array())[m_nValues] = Value;
555  }
556 
557  m_nValues++;
558  }
559 }
560 
561 //---------------------------------------------------------
562 void CSG_Simple_Statistics::_Evaluate(int Level)
563 {
564  if( m_bEvaluated == 0 && m_Weights > 0. )
565  {
566  m_bEvaluated = 1;
567 
568  m_Range = m_Maximum - m_Minimum;
569  m_Mean = m_Sum / m_Weights;
570  m_Variance = m_Sum2 / m_Weights - m_Mean*m_Mean;
571  m_StdDev = m_Variance > 0. ? sqrt(m_Variance) : 0.;
572  }
573 
574  //-----------------------------------------------------
575  if( m_bEvaluated == 1 && Level > 1 )
576  {
577  m_bEvaluated = 2;
578 
579  m_Kurtosis = 0.;
580  m_Skewness = 0.;
581 
582  if( Get_StdDev() > 0. && m_Values.Get_Size() > 0 )
583  {
584  for(sLong i=0; i<Get_Count(); i++)
585  {
586  double d = (Get_Value(i) - Get_Mean()) / Get_StdDev();
587 
588  m_Kurtosis += d*d*d*d;
589  m_Skewness += d*d*d;
590  }
591 
592  m_Kurtosis /= Get_Count();
593  m_Skewness /= Get_Count();
594  // m_Skewness *= Get_Count() / ((Get_Count() - 1) * (Get_Count() - 2));
595  }
596  }
597 }
598 
599 //---------------------------------------------------------
606 double CSG_Simple_Statistics::Get_SkewnessPearson(void)
607 {
608  return( Get_StdDev() != 0. ? (Get_Mean() - Get_Median()) / Get_StdDev() : 0. );
609 }
610 
611 //---------------------------------------------------------
618 double CSG_Simple_Statistics::Get_Quantile(double Quantile)
619 {
620  if( m_Values.Get_Size() < 1 )
621  {
622  return( m_Mean );
623  }
624 
625  //-----------------------------------------------------
626  if( !m_bSorted )
627  {
628  qsort(m_Values.Get_Array(), m_Values.Get_Size(), sizeof(double), SG_Compare_Double);
629 
630  m_bSorted = true;
631  }
632 
633  if( Quantile <= 0. || m_Values.Get_Size() == 1 )
634  {
635  return( Get_Values()[0] );
636  }
637 
638  if( Quantile >= 1. )
639  {
640  return( Get_Values()[m_Values.Get_Size() - 1] );
641  }
642 
643  //-----------------------------------------------------
644  double r = Quantile * (m_Values.Get_Size() - 1);
645 
646  sLong i = (sLong)r; r -= i;
647 
648  return( r == 0. ? Get_Values()[i] : ((1. - r) * Get_Values()[i] + r * Get_Values()[i + 1]) );
649 }
650 
651 //---------------------------------------------------------
658 double CSG_Simple_Statistics::Get_Percentile(double Percentile)
659 {
660  return( Get_Quantile(Percentile / 100.) );
661 }
662 
663 //---------------------------------------------------------
669 double CSG_Simple_Statistics::Get_Median(void)
670 {
671  return( Get_Quantile(0.5) );
672 }
673 
674 //---------------------------------------------------------
681 double CSG_Simple_Statistics::Get_Gini(void)
682 {
683  if( m_Gini < 0. && m_Values.Get_Size() > 1 )
684  {
685  if( !m_bSorted )
686  {
687  qsort(m_Values.Get_Array(), m_Values.Get_Size(), sizeof(double), SG_Compare_Double);
688 
689  m_bSorted = true;
690  }
691 
692  m_Gini = 0.;
693 
694  for(sLong i=0; i<Get_Count(); i++)
695  {
696  m_Gini += (i + 1.) * Get_Value(i);
697  }
698 
699  m_Gini = 2. * m_Gini / (Get_Count() * Get_Sum()) - (Get_Count() + 1.) / Get_Count();
700  }
701 
702  return( m_Gini );
703 }
704 
705 //---------------------------------------------------------
710 sLong CSG_Simple_Statistics::Get_IndexOfMinimum(void)
711 {
712  if( m_Values.Get_Size() == 0 ) { return( -1 ); }
713 
714  size_t Index = 0;
715  double Value = Get_Values()[Index];
716 
717  for(size_t i=1; i<(size_t)m_Values.Get_Size(); i++)
718  {
719  if( Value > Get_Values()[i] )
720  {
721  Index = i;
722  Value = Get_Values()[i];
723  }
724  }
725 
726  return( (sLong)Index );
727 }
728 
729 //---------------------------------------------------------
734 sLong CSG_Simple_Statistics::Get_IndexOfMaximum(void)
735 {
736  if( m_Values.Get_Size() == 0 ) { return( -1 ); }
737 
738  size_t Index = 0;
739  double Value = Get_Values()[Index];
740 
741  for(size_t i=1; i<(size_t)m_Values.Get_Size(); i++)
742  {
743  if( Value < Get_Values()[i] )
744  {
745  Index = i;
746  Value = Get_Values()[i];
747  }
748  }
749 
750  return( (sLong)Index );
751 }
752 
753 //---------------------------------------------------------
758 sLong CSG_Simple_Statistics::Get_nValues_Above(double Threshold, bool bEquals)
759 {
760  if( m_Values.Get_Size() == 0 ) { return( -1 ); }
761 
762  sLong n = 0;
763 
764  for(sLong i=0; i<Get_Count(); i++)
765  {
766  if( (bEquals && Get_Value(i) >= Threshold) || Get_Value(i) > Threshold )
767  {
768  n++;
769  }
770  }
771 
772  return( n );
773 }
774 
775 //---------------------------------------------------------
780 sLong CSG_Simple_Statistics::Get_nValues_Below(double Threshold, bool bEquals)
781 {
782  if( m_Values.Get_Size() == 0 ) { return( -1 ); }
783 
784  sLong n = 0;
785 
786  for(sLong i=0; i<Get_Count(); i++)
787  {
788  if( (bEquals && Get_Value(i) <= Threshold) || Get_Value(i) < Threshold )
789  {
790  n++;
791  }
792  }
793 
794  return( n );
795 }
796 
797 
799 // //
800 // //
801 // //
803 
804 //---------------------------------------------------------
805 int CSG_Unique_Value_Statistics::Get_Majority(bool bWeighted) const
806 {
807  int Index = 0;
808 
809  bWeighted = bWeighted && m_bWeights;
810 
811  for(int i=1; i<Get_Count(); i++)
812  {
813  if( bWeighted )
814  {
815  if( m_Weight[i] > m_Weight[Index] )
816  {
817  Index = i;
818  }
819  }
820  else
821  {
822  if( m_Count[i] > m_Count[Index] )
823  {
824  Index = i;
825  }
826  }
827  }
828 
829  return( Index );
830 }
831 
832 //---------------------------------------------------------
833 int CSG_Unique_Value_Statistics::Get_Minority(bool bWeighted) const
834 {
835  int Index = 0;
836 
837  bWeighted = bWeighted && m_bWeights;
838 
839  for(int i=1; i<Get_Count(); i++)
840  {
841  if( bWeighted )
842  {
843  if( m_Weight[i] < m_Weight[Index] )
844  {
845  Index = i;
846  }
847  }
848  else
849  {
850  if( m_Count[i] < m_Count[Index] )
851  {
852  Index = i;
853  }
854  }
855  }
856 
857  return( Index );
858 }
859 
860 
862 // //
864 
865 //---------------------------------------------------------
866 void CSG_Unique_Number_Statistics::Create(bool bWeights)
867 {
868  m_bWeights = bWeights;
869 
870  m_Count.Destroy();
871  m_Value.Destroy();
872 }
873 
874 //---------------------------------------------------------
875 void CSG_Unique_Number_Statistics::Add_Value(double Value, double Weight)
876 {
877  for(int i=0; i<Get_Count(); i++)
878  {
879  if( Value == m_Value[i] )
880  {
881  m_Count[i]++;
882 
883  if( m_bWeights && Weight > 0. )
884  {
885  m_Weight[i] += Weight;
886  }
887 
888  return;
889  }
890  }
891 
892  m_Count.Add(1);
893  m_Value.Add_Row(Value);
894 
895  if( m_bWeights && Weight > 0. )
896  {
897  m_Weight.Add_Row(Weight);
898  }
899 }
900 
901 //---------------------------------------------------------
902 int CSG_Unique_Number_Statistics::Get_Class_Index(double Value) const
903 {
904  for(int i=0; i<Get_Count(); i++)
905  {
906  if( Value == m_Value[i] )
907  {
908  return( i );
909  }
910  }
911 
912  return( -1 );
913 }
914 
915 
917 // //
919 
920 //---------------------------------------------------------
921 void CSG_Unique_String_Statistics::Create(bool bWeights)
922 {
923  m_bWeights = bWeights;
924 
925  m_Count.Destroy();
926  m_Value.Clear();
927 }
928 
929 //---------------------------------------------------------
930 void CSG_Unique_String_Statistics::Add_Value(const CSG_String &Value, double Weight)
931 {
932  for(int i=0; i<Get_Count(); i++)
933  {
934  if( Value.Cmp(m_Value[i]) == 0 )
935  {
936  m_Count[i]++;
937 
938  if( m_bWeights && Weight > 0. )
939  {
940  m_Weight[i] += Weight;
941  }
942 
943  return;
944  }
945  }
946 
947  m_Count.Add(1);
948  m_Value.Add(Value);
949 
950  if( m_bWeights && Weight > 0. )
951  {
952  m_Weight.Add_Row(Weight);
953  }
954 }
955 
956 //---------------------------------------------------------
957 int CSG_Unique_String_Statistics::Get_Class_Index(const CSG_String &Value) const
958 {
959  for(int i=0; i<Get_Count(); i++)
960  {
961  if( Value.Cmp(m_Value[i]) == 0 )
962  {
963  return( i );
964  }
965  }
966 
967  return( -1 );
968 }
969 
970 
972 // //
973 // //
974 // //
976 
977 //---------------------------------------------------------
978 CSG_Category_Statistics::CSG_Category_Statistics(TSG_Data_Type Type)
979 {
980  m_pTable = new CSG_Table;
981 
982  Create(Type);
983 }
984 
985 //---------------------------------------------------------
986 CSG_Category_Statistics::~CSG_Category_Statistics(void)
987 {
988  delete(m_pTable);
989 }
990 
991 //---------------------------------------------------------
992 void CSG_Category_Statistics::Create(TSG_Data_Type Type)
993 {
994  m_pTable->Destroy();
995 
996  m_pTable->Add_Field("VALUE", Type);
997  m_pTable->Add_Field("COUNT", SG_DATATYPE_ULong);
998 }
999 
1000 //---------------------------------------------------------
1001 void CSG_Category_Statistics::Destroy(void)
1002 {
1003  m_pTable->Del_Records();
1004 }
1005 
1006 //---------------------------------------------------------
1007 TSG_Data_Type CSG_Category_Statistics::Get_Category_Type(void) const
1008 {
1009  return( m_pTable->Get_Field_Type(0) );
1010 }
1011 
1012 
1014 // //
1016 
1017 //---------------------------------------------------------
1018 int CSG_Category_Statistics::Get_Category(int Value) const
1019 {
1020  CSG_Table_Record *pRecord = m_pTable->Find_Record(0, Value, m_pTable->Get_Count() > 10);
1021 
1022  return( pRecord ? (int)pRecord->Get_Index() : -1);
1023 }
1024 
1025 //---------------------------------------------------------
1026 int CSG_Category_Statistics::Get_Category(double Value) const
1027 {
1028  CSG_Table_Record *pRecord = m_pTable->Find_Record(0, Value, m_pTable->Get_Count() > 10);
1029 
1030  return( pRecord ? (int)pRecord->Get_Index() : -1);
1031 }
1032 
1033 //---------------------------------------------------------
1034 int CSG_Category_Statistics::Get_Category(const CSG_String &Value) const
1035 {
1036  CSG_Table_Record *pRecord = m_pTable->Find_Record(0, Value, m_pTable->Get_Count() > 10);
1037 
1038  return( pRecord ? (int)pRecord->Get_Index() : -1);
1039 }
1040 
1041 
1043 // //
1045 
1046 //---------------------------------------------------------
1047 int CSG_Category_Statistics::Add_Value(int Value)
1048 {
1049  int i = Get_Category(Value);
1050 
1051  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1052 
1053  if( !pRecord )
1054  {
1055  i = (int)m_pTable->Get_Count();
1056 
1057  (pRecord = m_pTable->Add_Record())->Set_Value(0, Value);
1058  }
1059 
1060  pRecord->Add_Value(1, 1);
1061 
1062  return( i );
1063 }
1064 
1065 //---------------------------------------------------------
1066 int CSG_Category_Statistics::Add_Value(double Value)
1067 {
1068  int i = Get_Category(Value);
1069 
1070  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1071 
1072  if( !pRecord )
1073  {
1074  i = (int)m_pTable->Get_Count();
1075 
1076  (pRecord = m_pTable->Add_Record())->Set_Value(0, Value);
1077  }
1078 
1079  pRecord->Add_Value(1, 1);
1080 
1081  return( i );
1082 }
1083 
1084 //---------------------------------------------------------
1085 int CSG_Category_Statistics::Add_Value(const CSG_String &Value)
1086 {
1087  int i = Get_Category(Value);
1088 
1089  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1090 
1091  if( !pRecord )
1092  {
1093  i = (int)m_pTable->Get_Count();
1094 
1095  (pRecord = m_pTable->Add_Record())->Set_Value(0, Value);
1096  }
1097 
1098  pRecord->Add_Value(1, 1);
1099 
1100  return( i );
1101 }
1102 
1103 //---------------------------------------------------------
1104 // sort categories ascending
1105 bool CSG_Category_Statistics::Sort(void)
1106 {
1107  return( m_pTable->Set_Index(0, TABLE_INDEX_Ascending) );
1108 }
1109 
1110 
1112 // //
1114 
1115 //---------------------------------------------------------
1116 // returns the number of categories.
1117 int CSG_Category_Statistics::Get_Count(void) const
1118 {
1119  return( (int)m_pTable->Get_Count() );
1120 }
1121 
1122 //---------------------------------------------------------
1123 // returns the number of observations for the i'th category.
1124 int CSG_Category_Statistics::Get_Count(int i) const
1125 {
1126  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1127 
1128  return( pRecord ? pRecord->asInt(1) : 0 );
1129 }
1130 
1131 //---------------------------------------------------------
1132 int CSG_Category_Statistics::asInt(int i) const
1133 {
1134  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1135 
1136  return( pRecord ? pRecord->asInt(0) : 0 );
1137 }
1138 
1139 //---------------------------------------------------------
1140 double CSG_Category_Statistics::asDouble(int i) const
1141 {
1142  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1143 
1144  return( pRecord ? pRecord->asDouble(0) : 0 );
1145 }
1146 
1147 //---------------------------------------------------------
1148 CSG_String CSG_Category_Statistics::asString(int i) const
1149 {
1150  CSG_Table_Record *pRecord = m_pTable->Get_Record_byIndex(i);
1151 
1152  return( pRecord ? pRecord->asString(0) : SG_T("") );
1153 }
1154 
1155 
1157 // //
1159 
1160 //---------------------------------------------------------
1161 int CSG_Category_Statistics::Get_Majority(void)
1162 {
1163  if( m_pTable->Get_Count() > 0 )
1164  {
1165  int Index = 0, Count = m_pTable->Get_Record_byIndex(0)->asInt(1);
1166 
1167  for(int i=1; i<m_pTable->Get_Count(); i++)
1168  {
1169  if( Count < m_pTable->Get_Record_byIndex(i)->asInt(1) )
1170  {
1171  Index = i;
1172  Count = m_pTable->Get_Record_byIndex(i)->asInt(1);
1173  }
1174  }
1175 
1176  return( Index );
1177  }
1178 
1179  return( -1 );
1180 }
1181 
1182 //---------------------------------------------------------
1183 int CSG_Category_Statistics::Get_Minority(void)
1184 {
1185  if( m_pTable->Get_Count() > 0 )
1186  {
1187  int Index = 0, Count = m_pTable->Get_Record_byIndex(0)->asInt(1);
1188 
1189  for(int i=1; i<m_pTable->Get_Count(); i++)
1190  {
1191  if( Count > m_pTable->Get_Record_byIndex(i)->asInt(1) )
1192  {
1193  Index = i;
1194  Count = m_pTable->Get_Record_byIndex(i)->asInt(1);
1195  }
1196  }
1197 
1198  return( Index );
1199  }
1200 
1201  return( -1 );
1202 }
1203 
1204 
1206 // //
1207 // //
1208 // //
1210 
1211 //---------------------------------------------------------
1212 CSG_Histogram::CSG_Histogram(void)
1213 {
1214  _On_Construction();
1215 }
1216 
1217 //---------------------------------------------------------
1218 CSG_Histogram::CSG_Histogram(const CSG_Histogram &Histogram)
1219 {
1220  _On_Construction();
1221 
1222  Create(Histogram);
1223 }
1224 
1225 //---------------------------------------------------------
1226 CSG_Histogram::CSG_Histogram(size_t nClasses, double Minimum, double Maximum)
1227 {
1228  _On_Construction();
1229 
1230  Create(nClasses, Minimum, Maximum);
1231 }
1232 
1233 //---------------------------------------------------------
1234 CSG_Histogram::CSG_Histogram(size_t nClasses, double Minimum, double Maximum, const CSG_Vector &Values, size_t maxSamples)
1235 {
1236  _On_Construction();
1237 
1238  Create(nClasses, Minimum, Maximum, Values, maxSamples);
1239 }
1240 
1241 //---------------------------------------------------------
1242 CSG_Histogram::CSG_Histogram(size_t nClasses, double Minimum, double Maximum, CSG_Table *pTable, int Field, size_t maxSamples)
1243 {
1244  _On_Construction();
1245 
1246  Create(nClasses, Minimum, Maximum, pTable, Field, maxSamples);
1247 }
1248 
1249 //---------------------------------------------------------
1250 CSG_Histogram::CSG_Histogram(size_t nClasses, double Minimum, double Maximum, class CSG_Grid *pGrid, size_t maxSamples)
1251 {
1252  _On_Construction();
1253 
1254  Create(nClasses, Minimum, Maximum, pGrid, maxSamples);
1255 }
1256 
1257 //---------------------------------------------------------
1258 CSG_Histogram::CSG_Histogram(size_t nClasses, double Minimum, double Maximum, CSG_Grids *pGrids, size_t maxSamples)
1259 {
1260  _On_Construction();
1261 
1262  Create(nClasses, Minimum, Maximum, pGrids, maxSamples);
1263 }
1264 
1265 //---------------------------------------------------------
1266 CSG_Histogram::~CSG_Histogram(void)
1267 {
1268  Destroy();
1269 }
1270 
1271 //---------------------------------------------------------
1272 bool CSG_Histogram::Destroy(void)
1273 {
1274  m_Statistics.Create();
1275 
1276  SG_FREE_SAFE(m_Elements );
1277  SG_FREE_SAFE(m_Cumulative);
1278 
1279  _On_Construction();
1280 
1281  return( true );
1282 }
1283 
1284 
1286 // //
1288 
1289 //---------------------------------------------------------
1290 void CSG_Histogram::_On_Construction(void)
1291 {
1292  m_nClasses = 0;
1293  m_Elements = NULL;
1294  m_Cumulative = NULL;
1295  m_Minimum = 0.;
1296  m_Maximum = 0.;
1297  m_ClassWidth = 1.;
1298 }
1299 
1300 //---------------------------------------------------------
1301 bool CSG_Histogram::_Create(size_t nClasses, double Minimum, double Maximum)
1302 {
1303  if( nClasses > 0 && Minimum < Maximum )
1304  {
1305  Destroy();
1306 
1307  m_Elements = (size_t *)SG_Calloc(nClasses, sizeof(size_t));
1308  m_Cumulative = (size_t *)SG_Calloc(nClasses, sizeof(size_t));
1309 
1310  if( m_Elements && m_Cumulative )
1311  {
1312  m_nClasses = nClasses;
1313  m_Minimum = Minimum;
1314  m_Maximum = Maximum;
1315  m_ClassWidth = (Maximum - Minimum) / (double)m_nClasses;
1316 
1317  return( true );
1318  }
1319  }
1320 
1321  Destroy();
1322 
1323  return( false );
1324 }
1325 
1326 //---------------------------------------------------------
1327 void CSG_Histogram::Add_Value(double Value)
1328 {
1329  m_Statistics += Value;
1330 
1331  if( m_nClasses > 0 && m_Minimum <= Value && Value <= m_Maximum )
1332  {
1333  size_t Class = (size_t)((Value - m_Minimum) / m_ClassWidth);
1334 
1335  if( Class >= m_nClasses )
1336  {
1337  Class = m_nClasses - 1;
1338  }
1339 
1340  m_Elements[Class]++;
1341  }
1342 }
1343 
1344 //---------------------------------------------------------
1345 bool CSG_Histogram::Scale_Element_Count(double Scale)
1346 {
1347  if( m_nClasses > 0 && Scale > 0. )
1348  {
1349  m_Statistics.Set_Count((sLong)(Scale * Get_Element_Count()));
1350 
1351  for(size_t i=0; i<m_nClasses; i++)
1352  {
1353  m_Elements[i] = (size_t)(Scale * m_Elements[i]);
1354  }
1355 
1356  return( Update() );
1357  }
1358 
1359  return( false );
1360 }
1361 
1362 //---------------------------------------------------------
1363 bool CSG_Histogram::Update(void)
1364 {
1365  if( m_nClasses > 0 )
1366  {
1367  m_Statistics.Get_Mean(); // _Evaluate()
1368 
1369  m_nMaximum = m_Cumulative[0] = m_Elements[0];
1370 
1371  for(size_t i=1; i<m_nClasses; i++)
1372  {
1373  m_Cumulative[i] = m_Cumulative[i - 1] + m_Elements[i];
1374 
1375  if( m_nMaximum < m_Elements[i] )
1376  {
1377  m_nMaximum = m_Elements[i];
1378  }
1379  }
1380 
1381  return( Get_Element_Count() > 0 );
1382  }
1383 
1384  return( false );
1385 }
1386 
1387 //---------------------------------------------------------
1388 bool CSG_Histogram::_Update(sLong nElements)
1389 {
1390  if( nElements > 0 && m_Statistics.Get_Count() > 0 )
1391  {
1392  double Scale = (double)nElements / (double)m_Statistics.Get_Count();
1393 
1394  m_Statistics.Create(m_Statistics.Get_Mean(), m_Statistics.Get_StdDev(), nElements);
1395 
1396  for(size_t i=1; i<m_nClasses; i++)
1397  {
1398  m_Elements[i] = (size_t)(0.5 + Scale * m_Elements[i]);
1399  }
1400  }
1401 
1402  return( Update() );
1403 }
1404 
1405 //---------------------------------------------------------
1409 double CSG_Histogram::Get_Quantile(double Quantile) const
1410 {
1411  if( m_nClasses < 2 ) { return( 0. ); }
1412 
1413  if( Quantile <= 0. ) { return( m_Minimum ); }
1414  if( Quantile >= 1. ) { return( m_Maximum ); }
1415 
1416  size_t n = (size_t)(Quantile * Get_Element_Count()); // number of elements
1417 
1418  for(size_t i=0, n0=0; i<m_nClasses; n0=m_Cumulative[i++])
1419  {
1420  if( n < m_Cumulative[i] )
1421  {
1422  if( m_Cumulative[i] >= n0 )
1423  {
1424  return( Get_Center(i) );
1425  }
1426 
1427  double d = (n - n0) / (double)(m_Cumulative[i] - n0);
1428 
1429  return( Get_Break(i) + d * m_ClassWidth );
1430  }
1431  else if( n == m_Cumulative[i] )
1432  {
1433  return( Get_Break(i + 1) );
1434  }
1435  }
1436 
1437  return( m_Maximum );
1438 }
1439 
1440 //---------------------------------------------------------
1444 double CSG_Histogram::Get_Percentile(double Percentile) const
1445 {
1446  return( Get_Quantile(Percentile / 100.) );
1447 }
1448 
1449 //---------------------------------------------------------
1453 double CSG_Histogram::Get_Quantile_Value(double Value) const
1454 {
1455  if( m_nClasses < 2 ) { return( 0. ); }
1456 
1457  if( Value <= m_Minimum ) { return( 0. ); }
1458  if( Value >= m_Maximum ) { return( 1. ); }
1459 
1460  size_t Class = (size_t)(m_nClasses * (Value - m_Minimum) / (m_Maximum - m_Minimum));
1461 
1462  if( Class >= m_nClasses )
1463  {
1464  return( 1. );
1465  }
1466 
1467  if( Class < 1 )
1468  {
1469  double dq = m_Cumulative[Class] / (double)Get_Element_Count();
1470 
1471  return( dq * (Value - m_Minimum) / m_ClassWidth );
1472  }
1473 
1474  double q0 = m_Cumulative[Class - 1] / (double)Get_Element_Count();
1475  double dq = (m_Cumulative[Class ] / (double)Get_Element_Count()) - q0;
1476 
1477  return( q0 + dq * (Value - Get_Break(Class)) / m_ClassWidth );
1478 }
1479 
1480 //---------------------------------------------------------
1484 double CSG_Histogram::Get_Percentile_Value(double Value) const
1485 {
1486  return( Get_Quantile_Value(Value) * 100. );
1487 }
1488 
1489 
1491 // //
1493 
1494 //---------------------------------------------------------
1495 bool CSG_Histogram::Create(const CSG_Histogram &Histogram)
1496 {
1497  if( !_Create(Histogram.m_nClasses, Histogram.m_Minimum, Histogram.m_Maximum) )
1498  {
1499  return( false );
1500  }
1501 
1502  m_Statistics = Histogram.m_Statistics;
1503  m_ClassWidth = Histogram.m_ClassWidth;
1504  m_nMaximum = Histogram.m_nMaximum ;
1505 
1506  for(size_t i=0; i<m_nClasses; i++)
1507  {
1508  m_Cumulative[i] = Histogram.m_Cumulative[i];
1509  m_Elements [i] = Histogram.m_Elements [i];
1510  }
1511 
1512  return( true );
1513 }
1514 
1515 //---------------------------------------------------------
1516 bool CSG_Histogram::Create(size_t nClasses, double Minimum, double Maximum)
1517 {
1518  return( _Create(nClasses, Minimum, Maximum) );
1519 }
1520 
1521 //---------------------------------------------------------
1522 bool CSG_Histogram::Create(size_t nClasses, double Minimum, double Maximum, const CSG_Vector &Values, size_t maxSamples)
1523 {
1524  if( Minimum >= Maximum )
1525  {
1526  CSG_Simple_Statistics s(Values);
1527 
1528  Minimum = s.Get_Minimum();
1529  Maximum = s.Get_Maximum();
1530  }
1531 
1532  if( !_Create(nClasses, Minimum, Maximum) )
1533  {
1534  return( false );
1535  }
1536 
1537  //-----------------------------------------------------
1538  if( maxSamples > 0 && maxSamples < (size_t)Values.Get_N() )
1539  {
1540  double d = (double)Values.Get_N() / (double)maxSamples;
1541 
1542  for(double i=0; i<(double)Values.Get_N(); i+=d)
1543  {
1544  Add_Value(Values[(sLong)i]);
1545  }
1546 
1547  d = (double)m_Statistics.Get_Count() / (double)maxSamples;
1548 
1549  return( _Update(d < 1. ? (int)(d * (double)Values.Get_N()) : Values.Get_N()) );
1550  }
1551 
1552  //-----------------------------------------------------
1553  for(int i=0; i<Values.Get_N(); i++)
1554  {
1555  Add_Value(Values[i]);
1556  }
1557 
1558  return( Update() );
1559 }
1560 
1561 //---------------------------------------------------------
1562 bool CSG_Histogram::Create(size_t nClasses, double Minimum, double Maximum, CSG_Table *pTable, int Field, size_t maxSamples)
1563 {
1564  if( !pTable || Field < 0 || Field >= pTable->Get_Field_Count() || !_Create(nClasses,
1565  Minimum < Maximum ? Minimum : pTable->Get_Minimum(Field),
1566  Minimum < Maximum ? Maximum : pTable->Get_Maximum(Field)) )
1567  {
1568  return( false );
1569  }
1570 
1571  //-----------------------------------------------------
1572  if( maxSamples > 0 && maxSamples < (size_t)pTable->Get_Count() )
1573  {
1574  double d = (double)pTable->Get_Count() / (double)maxSamples;
1575 
1576  for(double i=0; i<(double)pTable->Get_Count(); i+=d)
1577  {
1578  double Value = pTable->Get_Record((int)i)->asDouble(Field);
1579 
1580  if( !pTable->is_NoData_Value(Value) )
1581  {
1582  Add_Value(Value);
1583  }
1584  }
1585 
1586  d = (double)m_Statistics.Get_Count() / (double)maxSamples;
1587 
1588  return( _Update(d < 1. ? (int)(d * pTable->Get_Count()) : pTable->Get_Count()) );
1589  }
1590 
1591  //-----------------------------------------------------
1592  for(sLong i=0; i<pTable->Get_Count(); i++)
1593  {
1594  double Value = pTable->Get_Record(i)->asDouble(Field);
1595 
1596  if( !pTable->is_NoData_Value(Value) )
1597  {
1598  Add_Value(Value);
1599  }
1600  }
1601 
1602  return( Update() );
1603 }
1604 
1605 //---------------------------------------------------------
1606 bool CSG_Histogram::Create(size_t nClasses, double Minimum, double Maximum, CSG_Grid *pGrid, size_t maxSamples)
1607 {
1608  if( !pGrid || !_Create(nClasses,
1609  Minimum < Maximum ? Minimum : pGrid->Get_Min(),
1610  Minimum < Maximum ? Maximum : pGrid->Get_Max()) )
1611  {
1612  return( false );
1613  }
1614 
1615  //-----------------------------------------------------
1616  if( maxSamples > 0 && (sLong)maxSamples < pGrid->Get_NCells() )
1617  {
1618  double d = (double)pGrid->Get_NCells() / (double)maxSamples;
1619 
1620  for(double i=0; i<(double)pGrid->Get_NCells(); i+=d)
1621  {
1622  double Value = pGrid->asDouble((sLong)i);
1623 
1624  if( !pGrid->is_NoData_Value(Value) )
1625  {
1626  Add_Value(Value);
1627  }
1628  }
1629 
1630  d = (double)m_Statistics.Get_Count() / (double)maxSamples;
1631 
1632  return( _Update(d < 1. ? (sLong)(d * (double)pGrid->Get_NCells()) : pGrid->Get_NCells()) );
1633  }
1634 
1635  //-----------------------------------------------------
1636  for(sLong i=0; i<pGrid->Get_NCells(); i++)
1637  {
1638  if( !pGrid->is_NoData(i) )
1639  {
1640  Add_Value(pGrid->asDouble(i));
1641  }
1642  }
1643 
1644  return( Update() );
1645 }
1646 
1647 //---------------------------------------------------------
1648 bool CSG_Histogram::Create(size_t nClasses, double Minimum, double Maximum, CSG_Grids *pGrids, size_t maxSamples)
1649 {
1650  if( !pGrids || !_Create(nClasses,
1651  Minimum < Maximum ? Minimum : pGrids->Get_Min(),
1652  Minimum < Maximum ? Maximum : pGrids->Get_Max()) )
1653  {
1654  return( false );
1655  }
1656 
1657  //-----------------------------------------------------
1658  if( maxSamples > 0 && (sLong)maxSamples < pGrids->Get_NCells() )
1659  {
1660  double d = (double)pGrids->Get_NCells() / (double)maxSamples;
1661 
1662  for(double i=0; i<(double)pGrids->Get_NCells(); i+=d)
1663  {
1664  double Value = pGrids->asDouble((sLong)i);
1665 
1666  if( !pGrids->is_NoData_Value(Value) )
1667  {
1668  Add_Value(Value);
1669  }
1670  }
1671 
1672  d = (double)m_Statistics.Get_Count() / (double)maxSamples;
1673 
1674  return( _Update(d < 1. ? (sLong)(d * (double)pGrids->Get_NCells()) : pGrids->Get_NCells()) );
1675  }
1676 
1677  //-----------------------------------------------------
1678  for(sLong i=0; i<pGrids->Get_NCells(); i++)
1679  {
1680  if( !pGrids->is_NoData(i) )
1681  {
1682  Add_Value(pGrids->asDouble(i));
1683  }
1684  }
1685 
1686  return( Update() );
1687 }
1688 
1689 
1691 // //
1693 
1694 //---------------------------------------------------------
1695 CSG_Histogram & CSG_Histogram::operator = (const CSG_Histogram &Histogram)
1696 {
1697  Create(Histogram);
1698 
1699  return( *this );
1700 }
1701 
1702 
1704 // //
1705 // //
1706 // //
1708 
1709 //---------------------------------------------------------
1710 CSG_Natural_Breaks::CSG_Natural_Breaks(void)
1711 {}
1712 
1713 //---------------------------------------------------------
1714 CSG_Natural_Breaks::~CSG_Natural_Breaks(void)
1715 {}
1716 
1717 //---------------------------------------------------------
1718 CSG_Natural_Breaks::CSG_Natural_Breaks(CSG_Table *pTable, int Field, int nClasses, int Histogram)
1719 {
1720  Create(pTable, Field, nClasses, Histogram);
1721 }
1722 
1723 //---------------------------------------------------------
1724 CSG_Natural_Breaks::CSG_Natural_Breaks(CSG_Grid *pGrid, int nClasses, int Histogram)
1725 {
1726  Create(pGrid, nClasses, Histogram);
1727 }
1728 
1729 //---------------------------------------------------------
1730 CSG_Natural_Breaks::CSG_Natural_Breaks(CSG_Grids *pGrids, int nClasses, int Histogram)
1731 {
1732  Create(pGrids, nClasses, Histogram);
1733 }
1734 
1735 //---------------------------------------------------------
1736 CSG_Natural_Breaks::CSG_Natural_Breaks(const CSG_Vector &Values, int nClasses, int Histogram)
1737 {
1738  Create(Values, nClasses, Histogram);
1739 }
1740 
1741 
1743 // //
1745 
1746 //---------------------------------------------------------
1747 bool CSG_Natural_Breaks::Create(CSG_Table *pTable, int Field, int nClasses, int Histogram)
1748 {
1749  bool bResult = false;
1750 
1751  if( Histogram > 0 )
1752  {
1753  bResult = m_Histogram.Create(Histogram, 0, 0, pTable, Field) && _Histogram(nClasses);
1754  }
1755  else if( Field >= 0 && Field < pTable->Get_Field_Count() )
1756  {
1757  for(sLong i=0; i<pTable->Get_Count(); i++)
1758  {
1759  CSG_Table_Record *pRecord = pTable->Get_Record(i);
1760 
1761  if( !pRecord->is_NoData(Field) )
1762  {
1763  m_Values.Add_Row(pRecord->asDouble(Field));
1764  }
1765  }
1766 
1767  bResult = m_Values.Sort() && _Calculate(nClasses);
1768 
1769  m_Values.Destroy();
1770  }
1771 
1772  return( bResult );
1773 }
1774 
1775 //---------------------------------------------------------
1776 bool CSG_Natural_Breaks::Create(CSG_Grid *pGrid, int nClasses, int Histogram)
1777 {
1778  bool bResult = false;
1779 
1780  if( Histogram > 0 )
1781  {
1782  bResult = m_Histogram.Create(Histogram, 0, 0, pGrid) && _Histogram(nClasses);
1783  }
1784  else
1785  {
1786  for(sLong i=0; i<pGrid->Get_NCells(); i++)
1787  {
1788  if( !pGrid->is_NoData(i) )
1789  {
1790  m_Values.Add_Row(pGrid->asDouble(i));
1791  }
1792  }
1793 
1794  bResult = m_Values.Sort() && _Calculate(nClasses);
1795 
1796  m_Values.Destroy();
1797  }
1798 
1799  return( bResult );
1800 }
1801 
1802 //---------------------------------------------------------
1803 bool CSG_Natural_Breaks::Create(CSG_Grids *pGrids, int nClasses, int Histogram)
1804 {
1805  bool bResult = false;
1806 
1807  if( Histogram > 0 )
1808  {
1809  bResult = m_Histogram.Create(Histogram, 0, 0, pGrids) && _Histogram(nClasses);
1810  }
1811  else
1812  {
1813  for(sLong i=0; i<pGrids->Get_NCells(); i++)
1814  {
1815  if( !pGrids->is_NoData(i) )
1816  {
1817  m_Values.Add_Row(pGrids->asDouble(i));
1818  }
1819  }
1820 
1821  bResult = m_Values.Sort() && _Calculate(nClasses);
1822 
1823  m_Values.Destroy();
1824  }
1825 
1826  return( bResult );
1827 }
1828 
1829 //---------------------------------------------------------
1830 bool CSG_Natural_Breaks::Create(const CSG_Vector &Values, int nClasses, int Histogram)
1831 {
1832  bool bResult = false;
1833 
1834  if( Histogram > 0 )
1835  {
1836  bResult = m_Histogram.Create(Histogram, 0, 0, Values) && _Histogram(nClasses);
1837  }
1838  else
1839  {
1840  bResult = m_Values.Create(Values) && m_Values.Sort() && _Calculate(nClasses);
1841 
1842  m_Values.Destroy();
1843  }
1844 
1845  return( bResult );
1846 }
1847 
1848 
1850 // //
1852 
1853 //---------------------------------------------------------
1854 bool CSG_Natural_Breaks::_Histogram(int nClasses)
1855 {
1856  if( _Calculate(nClasses) )
1857  {
1858  double d = (double)m_Histogram.Get_Class_Count() / m_Histogram.Get_Cumulative((int)(m_Histogram.Get_Class_Count() - 1));
1859 
1860  m_Breaks[0] = m_Histogram.Get_Break(0);
1861 
1862  for(int i=1; i<Get_Count(); i++)
1863  {
1864  m_Breaks[i] = m_Histogram.Get_Value(m_Breaks[i] * d);
1865  }
1866 
1867  m_Breaks[nClasses] = m_Histogram.Get_Break((int)m_Histogram.Get_Class_Count());
1868 
1869  m_Histogram.Destroy();
1870 
1871  return( true );
1872  }
1873 
1874  m_Histogram.Destroy();
1875 
1876  return( false );
1877 }
1878 
1879 //---------------------------------------------------------
1880 inline double CSG_Natural_Breaks::_Get_Value(int i)
1881 {
1882  if( m_Histogram.Get_Class_Count() > 0 )
1883  {
1884  return( (double)m_Histogram.Get_Cumulative(i) );
1885  }
1886 
1887  return( m_Values[i] );
1888 }
1889 
1890 //---------------------------------------------------------
1891 bool CSG_Natural_Breaks::_Calculate(int nClasses)
1892 {
1893  if( m_Histogram.Get_Class_Count() == 0 && m_Values.Get_Size() == 0 )
1894  {
1895  return( false );
1896  }
1897 
1898  int nValues = m_Histogram.Get_Class_Count() > 0 ? (int)m_Histogram.Get_Class_Count() : m_Values.Get_N();
1899 
1900  CSG_Matrix mv(nClasses, nValues); mv.Assign(FLT_MAX);
1901 
1902  int i, **mc = (int **)SG_Malloc(nValues * sizeof(int *));
1903 
1904  mc[0] = (int *)SG_Calloc(nClasses * nValues, sizeof(int));
1905 
1906  for(i=0; i<nValues; i++)
1907  {
1908  mc[i] = mc[0] + i * nClasses;
1909  }
1910 
1911  //-----------------------------------------------------
1912  for(i=1; i<nValues; i++)
1913  {
1914  double v = 0., s1 = 0., s2 = 0., w = 0.;
1915 
1916  for(int m=0, n=i+1; m<=i; m++, n--)
1917  {
1918  v = _Get_Value(n);
1919  s2 += v*v;
1920  s1 += v;
1921  w ++;
1922  v = s2 - (s1 * s1) / w;
1923 
1924  if( n > 0 )
1925  {
1926  for(int j=1; j<nClasses; j++)
1927  {
1928  if( mv[i][j] >= (v + mv[n - 1][j - 1]) )
1929  {
1930  mc[i][j] = n;
1931  mv[i][j] = v + mv[n - 1][j - 1];
1932  }
1933  }
1934  }
1935  }
1936 
1937  mc[i][0] = 0;
1938  mv[i][0] = v;
1939  }
1940 
1941  //-----------------------------------------------------
1942  CSG_Array_Int Class(nClasses);
1943 
1944  for(i=0; i<nClasses; i++)
1945  {
1946  Class[i] = i;
1947  }
1948 
1949  int j = Class[nClasses - 1] = nValues - 1;
1950 
1951  for(i=nClasses-1; i>0; i--)
1952  {
1953  Class[i - 1] = j = mc[j - 1][i];
1954  }
1955 
1956  //-----------------------------------------------------
1957  m_Breaks.Create(nClasses + 1);
1958 
1959  m_Breaks[0] = _Get_Value(0);
1960 
1961  for(i=1; i<nClasses; i++)
1962  {
1963  m_Breaks[i] = _Get_Value(Class[i - 1]);
1964  }
1965 
1966  m_Breaks[nClasses] = _Get_Value(nValues - 1);
1967 
1968  SG_Free(mc[0]); SG_Free(mc);
1969 
1970  return( true );
1971 }
1972 
1973 
1975 // //
1976 // //
1977 // //
1979 
1980 //---------------------------------------------------------
1981 CSG_Cluster_Analysis::CSG_Cluster_Analysis(void)
1982 {
1983  m_nFeatures = 0;
1984  m_Iteration = 0;
1985 }
1986 
1987 //---------------------------------------------------------
1988 CSG_Cluster_Analysis::~CSG_Cluster_Analysis(void)
1989 {
1990  Destroy();
1991 }
1992 
1993 //---------------------------------------------------------
1994 bool CSG_Cluster_Analysis::Destroy(void)
1995 {
1996  m_Centroid.Destroy();
1997  m_Variance.Destroy();
1998  m_nMembers.Destroy();
1999  m_Clusters.Destroy();
2000  m_Features.Destroy();
2001  m_nFeatures = 0;
2002  m_Iteration = 0;
2003 
2004  return( true );
2005 }
2006 
2007 //---------------------------------------------------------
2008 bool CSG_Cluster_Analysis::Create(int nFeatures)
2009 {
2010  Destroy();
2011 
2012  if( nFeatures > 0 )
2013  {
2014  m_nFeatures = nFeatures;
2015 
2016  m_Features.Create(m_nFeatures * sizeof(double), 0, TSG_Array_Growth::SG_ARRAY_GROWTH_3);
2017 
2018  return( true );
2019  }
2020 
2021  return( false );
2022 }
2023 
2024 //---------------------------------------------------------
2025 bool CSG_Cluster_Analysis::Add_Element(void)
2026 {
2027  return( m_nFeatures > 0 && m_Features.Inc_Array() );
2028 }
2029 
2030 //---------------------------------------------------------
2031 bool CSG_Cluster_Analysis::Set_Feature(sLong iElement, int iFeature, double Value)
2032 {
2033  if( iElement >= 0 && iElement < Get_nElements() && iFeature >= 0 && iFeature < m_nFeatures )
2034  {
2035  ((double *)m_Features.Get_Entry(iElement))[iFeature] = Value;
2036 
2037  return( true );
2038  }
2039 
2040  return( false );
2041 }
2042 
2043 //---------------------------------------------------------
2053 //---------------------------------------------------------
2054 bool CSG_Cluster_Analysis::Execute(int Method, int nClusters, int nMaxIterations, int Initialization)
2055 {
2056  if( Get_nElements() < 2 || nClusters < 2 )
2057  {
2058  return( false );
2059  }
2060 
2061  //-----------------------------------------------------
2062  m_nMembers.Create(nClusters);
2063  m_Variance.Create(nClusters);
2064  m_Centroid.Create(m_nFeatures, nClusters);
2065 
2066  //-----------------------------------------------------
2067  m_Clusters.Create(Get_nElements());
2068 
2069  for(int iElement=0; iElement<Get_nElements(); iElement++)
2070  {
2071  switch( Initialization )
2072  {
2073  default: // random
2074  if( (m_Clusters[iElement] = (int)CSG_Random::Get_Uniform(0, nClusters)) >= nClusters )
2075  {
2076  m_Clusters[iElement] = nClusters - 1;
2077  }
2078  break;
2079 
2080  case 1: // periodic
2081  {
2082  m_Clusters[iElement] = iElement % nClusters;
2083  }
2084  break;
2085 
2086  case 2: // keep as is, but check for valid cluster ids
2087  if( 0 > m_Clusters[iElement] || m_Clusters[iElement] >= nClusters )
2088  {
2089  m_Clusters[iElement] = iElement % nClusters;
2090  }
2091  break;
2092  }
2093  }
2094 
2095  //-----------------------------------------------------
2096  bool bResult;
2097 
2098  m_Iteration = 0;
2099 
2100  switch( Method )
2101  {
2102  default: bResult = _Minimum_Distance(true , nMaxIterations); break;
2103  case 1: bResult = _Hill_Climbing (true , nMaxIterations); break;
2104  case 2: bResult = _Minimum_Distance(true , nMaxIterations)
2105  && _Hill_Climbing (false, nMaxIterations); break;
2106  }
2107 
2108  //-----------------------------------------------------
2109  if( bResult )
2110  {
2111  for(int iCluster=0; iCluster<nClusters; iCluster++)
2112  {
2113  m_Variance[iCluster] = m_nMembers[iCluster] <= 0 ? 0. : m_Variance[iCluster] / m_nMembers[iCluster];
2114  }
2115  }
2116 
2117  return( bResult );
2118 }
2119 
2120 //---------------------------------------------------------
2121 bool CSG_Cluster_Analysis::_Minimum_Distance(bool bInitialize, int nMaxIterations)
2122 {
2123  int iElement, iCluster, nClusters = m_Variance.Get_N();
2124 
2125  double SP_Last = -1.;
2126 
2127  //-----------------------------------------------------
2128  for(m_Iteration=1; SG_UI_Process_Get_Okay(); m_Iteration++)
2129  {
2130  m_Variance = 0.;
2131  m_Centroid = 0.;
2132  m_nMembers = 0;
2133 
2134  //-------------------------------------------------
2135  for(iElement=0; iElement<Get_nElements(); iElement++)
2136  {
2137  m_nMembers[iCluster = m_Clusters[iElement]]++;
2138 
2139  double *Feature = (double *)m_Features.Get_Entry(iElement);
2140 
2141  for(int iFeature=0; iFeature<m_nFeatures; iFeature++)
2142  {
2143  m_Centroid[iCluster][iFeature] += Feature[iFeature];
2144  }
2145  }
2146 
2147  //-------------------------------------------------
2148  for(iCluster=0; iCluster<nClusters; iCluster++)
2149  {
2150  double d = m_nMembers[iCluster] > 0 ? 1. / m_nMembers[iCluster] : 0.;
2151 
2152  for(int iFeature=0; iFeature<m_nFeatures; iFeature++)
2153  {
2154  m_Centroid[iCluster][iFeature] *= d;
2155  }
2156  }
2157 
2158  //-------------------------------------------------
2159  int nShifts = 0;
2160 
2161  m_SP = 0.;
2162 
2163  for(iElement=0; iElement<Get_nElements(); iElement++)
2164  {
2165  double *Feature = (double *)m_Features.Get_Entry(iElement);
2166 
2167  double minVariance = -1.;
2168  int minCluster = -1;
2169 
2170  for(iCluster=0; iCluster<nClusters; iCluster++)
2171  {
2172  double Variance = 0.;
2173 
2174  for(int iFeature=0; iFeature<m_nFeatures; iFeature++)
2175  {
2176  Variance += SG_Get_Square(m_Centroid[iCluster][iFeature] - Feature[iFeature]);
2177  }
2178 
2179  if( minVariance < 0. || Variance < minVariance )
2180  {
2181  minVariance = Variance;
2182  minCluster = iCluster;
2183  }
2184  }
2185 
2186  if( m_Clusters[iElement] != minCluster )
2187  {
2188  m_Clusters[iElement] = minCluster;
2189 
2190  nShifts++;
2191  }
2192 
2193  m_SP += minVariance;
2194  m_Variance[minCluster] += minVariance;
2195  }
2196 
2197  //-------------------------------------------------
2198  m_SP /= Get_nElements();
2199 
2200  SG_UI_Process_Set_Text(CSG_String::Format("%s: %d >> %s %f",
2201  _TL("pass" ), m_Iteration,
2202  _TL("change"), m_Iteration < 2 ? m_SP : SP_Last - m_SP
2203  ));
2204 
2205  SP_Last = m_SP;
2206 
2207  if( nShifts == 0 || (nMaxIterations > 0 && nMaxIterations <= m_Iteration) )
2208  {
2209  return( true );
2210  }
2211  }
2212 
2213  return( true );
2214 }
2215 
2216 //---------------------------------------------------------
2217 bool CSG_Cluster_Analysis::_Hill_Climbing(bool bInitialize, int nMaxIterations)
2218 {
2219  int iElement, iCluster, nClusters = m_Variance.Get_N();
2220 
2221  //-----------------------------------------------------
2222  m_Variance = 0.;
2223  m_Centroid = 0.;
2224  m_nMembers = 0;
2225 
2226  for(iElement=0; iElement<Get_nElements(); iElement++)
2227  {
2228  m_nMembers[iCluster = m_Clusters[iElement]]++;
2229 
2230  double *Feature = (double *)m_Features.Get_Entry(iElement);
2231 
2232  for(int iFeature=0; iFeature<m_nFeatures; iFeature++)
2233  {
2234  double d = Feature[iFeature];
2235 
2236  m_Centroid[iCluster][iFeature] += d;
2237  m_Variance[iCluster] += d*d;
2238  }
2239  }
2240 
2241  //-----------------------------------------------------
2242  for(iCluster=0; iCluster<nClusters; iCluster++)
2243  {
2244  double v = 0., d = m_nMembers[iCluster] <= 0 ? 0. : 1. / (double)m_nMembers[iCluster];
2245 
2246  for(int iFeature=0; iFeature<m_nFeatures; iFeature++)
2247  {
2248  m_Centroid[iCluster][iFeature] *= d;
2249  v += SG_Get_Square(m_Centroid[iCluster][iFeature]);
2250  }
2251 
2252  m_Variance[iCluster] -= v * m_nMembers[iCluster];
2253  }
2254 
2255  //-----------------------------------------------------
2256  double SP_Last = -1.; int noShift = 0;
2257 
2258  for(m_Iteration=1; SG_UI_Process_Get_Okay(false); m_Iteration++)
2259  {
2260  for(iElement=0; iElement<Get_nElements(); iElement++)
2261  {
2262  iCluster = m_Clusters[iElement];
2263 
2264  if( noShift++ < Get_nElements() && m_nMembers[iCluster] > 1 )
2265  {
2266  int iFeature; double *Feature = (double *)m_Features.Get_Entry(iElement);
2267 
2268  double V1, V2, Variance = 0.;
2269 
2270  for(iFeature=0; iFeature<m_nFeatures; iFeature++)
2271  {
2272  Variance += SG_Get_Square(m_Centroid[iCluster][iFeature] - Feature[iFeature]);
2273  }
2274 
2275  V1 = Variance * m_nMembers[iCluster] / (m_nMembers[iCluster] - 1.);
2276 
2277  //-----------------------------------------
2278  int kCluster = 0;
2279  double VMin = -1.;
2280 
2281  for(int jCluster=0; jCluster<nClusters; jCluster++)
2282  {
2283  if( jCluster != iCluster )
2284  {
2285  Variance = 0.;
2286 
2287  for(iFeature=0; iFeature<m_nFeatures; iFeature++)
2288  {
2289  Variance += SG_Get_Square(m_Centroid[jCluster][iFeature] - Feature[iFeature]);
2290  }
2291 
2292  V2 = Variance * m_nMembers[jCluster] / (m_nMembers[jCluster] + 1.);
2293 
2294  if( VMin < 0. || V2 < VMin )
2295  {
2296  VMin = V2;
2297  kCluster = jCluster;
2298  }
2299  }
2300  }
2301 
2302  //-----------------------------------------
2303  if( VMin >= 0 && VMin < V1 )
2304  {
2305  noShift = 0;
2306  m_Variance[iCluster] -= V1;
2307  m_Variance[kCluster] += VMin;
2308  V1 = 1. / (m_nMembers[iCluster] - 1.);
2309  V2 = 1. / (m_nMembers[kCluster] + 1.);
2310 
2311  for(iFeature=0; iFeature<m_nFeatures; iFeature++)
2312  {
2313  double d = Feature[iFeature];
2314 
2315  m_Centroid[iCluster][iFeature] = (m_nMembers[iCluster] * m_Centroid[iCluster][iFeature] - d) * V1;
2316  m_Centroid[kCluster][iFeature] = (m_nMembers[kCluster] * m_Centroid[kCluster][iFeature] + d) * V2;
2317  }
2318 
2319  m_Clusters[iElement] = kCluster;
2320 
2321  m_nMembers[iCluster]--;
2322  m_nMembers[kCluster]++;
2323  }
2324  }
2325  }
2326 
2327  //-------------------------------------------------
2328  for(iCluster=0, m_SP=0.; iCluster<nClusters; iCluster++)
2329  {
2330  m_SP += m_Variance[iCluster];
2331  }
2332 
2333  m_SP /= Get_nElements();
2334 
2335  SG_UI_Process_Set_Text(CSG_String::Format("%s: %d >> %s %f",
2336  _TL("pass" ), m_Iteration,
2337  _TL("change"), m_Iteration <= 1 ? m_SP : SP_Last - m_SP
2338  ));
2339 
2340  SP_Last = m_SP;
2341 
2342  if( noShift >= Get_nElements() || (nMaxIterations > 0 && nMaxIterations <= m_Iteration) )
2343  {
2344  return( true );
2345  }
2346  }
2347 
2348  return( true );
2349 }
2350 
2351 
2353 // //
2354 // //
2355 // //
2357 
2358 //---------------------------------------------------------
2359 CSG_Classifier_Supervised::CSG_Classifier_Supervised(void)
2360 {
2361  m_nFeatures = 0;
2362 
2363  m_nClasses = 0;
2364  m_pClasses = NULL;
2365 
2366  m_Threshold_Distance = 0.;
2367  m_Threshold_Angle = 0.;
2368  m_Threshold_Probability = 0.;
2369  m_Probability_Relative = false;
2370 
2371  for(int i=0; i<SG_CLASSIFY_SUPERVISED_WTA; i++)
2372  {
2373  m_bWTA[i] = i == SG_CLASSIFY_SUPERVISED_MinimumDistance
2374  // || i == SG_CLASSIFY_SUPERVISED_Mahalonobis
2375  || i == SG_CLASSIFY_SUPERVISED_MaximumLikelihood
2376  || i == SG_CLASSIFY_SUPERVISED_SAM;
2377  }
2378 }
2379 
2380 //---------------------------------------------------------
2381 CSG_Classifier_Supervised::~CSG_Classifier_Supervised(void)
2382 {
2383  Destroy();
2384 }
2385 
2386 //---------------------------------------------------------
2387 void CSG_Classifier_Supervised::Create(int nFeatures)
2388 {
2389  Destroy();
2390 
2391  if( nFeatures > 0 )
2392  {
2393  m_nFeatures = nFeatures;
2394  }
2395 }
2396 
2397 //---------------------------------------------------------
2398 void CSG_Classifier_Supervised::Destroy(void)
2399 {
2400  if( m_nClasses > 0 )
2401  {
2402  for(int i=0; i<m_nClasses; i++)
2403  {
2404  delete(m_pClasses[i]);
2405  }
2406 
2407  SG_FREE_SAFE(m_pClasses);
2408  }
2409 
2410  m_nFeatures = 0;
2411 
2412  m_Info.Clear();
2413 }
2414 
2415 
2417 // //
2419 
2420 //---------------------------------------------------------
2421 void CSG_Classifier_Supervised::Set_Threshold_Distance (double Value) { m_Threshold_Distance = Value; }
2422 double CSG_Classifier_Supervised::Get_Threshold_Distance (void) { return( m_Threshold_Distance ); }
2423 
2424 //---------------------------------------------------------
2425 void CSG_Classifier_Supervised::Set_Threshold_Angle (double Value) { m_Threshold_Angle = Value; }
2426 double CSG_Classifier_Supervised::Get_Threshold_Angle (void) { return( m_Threshold_Angle ); }
2427 
2428 //---------------------------------------------------------
2429 void CSG_Classifier_Supervised::Set_Threshold_Probability(double Value) { m_Threshold_Probability = Value; }
2430 double CSG_Classifier_Supervised::Get_Threshold_Probability(void) { return( m_Threshold_Probability ); }
2431 
2432 //---------------------------------------------------------
2433 void CSG_Classifier_Supervised::Set_Probability_Relative (bool Value) { m_Probability_Relative = Value; }
2434 bool CSG_Classifier_Supervised::Get_Probability_Relative (void) { return( m_Probability_Relative ); }
2435 
2436 //---------------------------------------------------------
2437 void CSG_Classifier_Supervised::Set_WTA(int Method, bool bOn)
2438 {
2439  if( Method >= 0 && Method < SG_CLASSIFY_SUPERVISED_WTA )
2440  {
2441  m_bWTA[Method] = bOn;
2442  }
2443 }
2444 
2445 bool CSG_Classifier_Supervised::Get_WTA(int Method)
2446 {
2447  return( Method >= 0 && Method < SG_CLASSIFY_SUPERVISED_WTA ? m_bWTA[Method] : false );
2448 }
2449 
2450 
2452 // //
2454 
2455 //---------------------------------------------------------
2456 #include "saga_api.h"
2457 
2458 //---------------------------------------------------------
2459 bool CSG_Classifier_Supervised::Load(const CSG_String &File)
2460 {
2461  int nFeatures = m_nFeatures; Destroy(); m_nFeatures = nFeatures;
2462 
2463  //-----------------------------------------------------
2464  CSG_MetaData Data;
2465 
2466  if( !Data.Load(File) || !Data.Cmp_Name("supervised_classifier") || SG_Compare_Version(Data.Get_Property("saga-version"), "2.1.4") < 0 )
2467  {
2468  return( false );
2469  }
2470 
2471  if( !Data("classes") || !Data("features") || !Data["features"]("count") || Data["features"]["count"].Get_Content().asInt() != m_nFeatures || m_nFeatures == 0 )
2472  {
2473  return( false );
2474  }
2475 
2476  if( Data["features"]("info") )
2477  {
2478  m_Info = Data["features"]["info"].Get_Content();
2479  }
2480 
2481  //-----------------------------------------------------
2482  CSG_MetaData &Classes = *Data.Get_Child("CLASSES");
2483 
2484  for(int i=0; i<Classes.Get_Children_Count(); i++)
2485  {
2486  if( Classes[i].Cmp_Name("class") && Classes[i].Get_Child("id") )
2487  {
2488  bool bAdd = true;
2489 
2490  CClass *pClass = new CClass(Classes[i]["id"].Get_Content());
2491 
2492  if( !pClass->m_Cov .from_String(Classes[i]["cov" ].Get_Content()) || pClass->m_Cov .Get_NX() != m_nFeatures || !pClass->m_Cov.is_Square() ) { bAdd = false; }
2493  if( !pClass->m_Mean.from_String(Classes[i]["mean"].Get_Content()) || pClass->m_Mean.Get_N () != m_nFeatures ) { bAdd = false; }
2494  if( !pClass->m_Min .from_String(Classes[i]["min" ].Get_Content()) || pClass->m_Min .Get_N () != m_nFeatures ) { bAdd = false; }
2495  if( !pClass->m_Max .from_String(Classes[i]["max" ].Get_Content()) || pClass->m_Max .Get_N () != m_nFeatures ) { bAdd = false; }
2496 
2497  //---------------------------------------------
2498  if( !bAdd )
2499  {
2500  delete(pClass);
2501  }
2502  else
2503  {
2504  m_pClasses = (CClass **)SG_Realloc(m_pClasses, (m_nClasses + 1) * sizeof(CClass *));
2505  m_pClasses[m_nClasses++] = pClass;
2506 
2507  pClass->m_Cov_Det = pClass->m_Cov.Get_Determinant();
2508  pClass->m_Cov_Inv = pClass->m_Cov.Get_Inverse();
2509 
2510  pClass->m_Mean_Spectral = CSG_Simple_Statistics(pClass->m_Mean).Get_Mean();
2511  }
2512  }
2513  }
2514 
2515  return( m_nClasses > 0 );
2516 }
2517 
2518 //---------------------------------------------------------
2519 bool CSG_Classifier_Supervised::Save(const CSG_String &File, const SG_Char *Feature_Info)
2520 {
2521  if( m_nFeatures < 1 || m_nClasses < 1 || File.is_Empty() )
2522  {
2523  return( false );
2524  }
2525 
2526  CSG_MetaData Data;
2527 
2528  Data.Set_Name ("supervised_classifier");
2529  Data.Add_Property("saga-version", SAGA_VERSION);
2530 
2531  CSG_MetaData &Features = *Data.Add_Child("features");
2532 
2533  Features.Add_Child("count", m_nFeatures);
2534 
2535  if( Feature_Info && *Feature_Info )
2536  {
2537  Features.Add_Child("info", Feature_Info);
2538  }
2539 
2540  CSG_MetaData &Classes = *Data.Add_Child("classes");
2541 
2542  Classes.Add_Property("count", m_nClasses);
2543 
2544  for(int i=0; i<m_nClasses; i++)
2545  {
2546  CSG_MetaData &Class = *Classes.Add_Child("class");
2547 
2548  CClass *pClass = m_pClasses[i];
2549 
2550  Class.Add_Child("id" , pClass->m_ID );
2551  Class.Add_Child("mean", pClass->m_Mean.to_String());
2552  Class.Add_Child("min" , pClass->m_Min .to_String());
2553  Class.Add_Child("max" , pClass->m_Max .to_String());
2554  Class.Add_Child("cov" , pClass->m_Cov .to_String());
2555  }
2556 
2557  return( Data.Save(File) );
2558 }
2559 
2560 
2562 // //
2564 
2565 //---------------------------------------------------------
2566 CSG_String CSG_Classifier_Supervised::Print(void)
2567 {
2568  CSG_String s;
2569 
2570  if( m_nFeatures > 0 && m_nClasses > 0 )
2571  {
2572  s += "\n";
2573 
2574  for(int iClass=0; iClass<m_nClasses; iClass++)
2575  {
2576  CClass *pClass = m_pClasses[iClass];
2577 
2578  s += "\n____\n" + pClass->m_ID + "\nFeature\tMean\tMin\tMax\tStdDev";
2579 
2580  for(int i=0; i<m_nFeatures; i++)
2581  {
2582  s += CSG_String::Format("\n%3d.", i + 1);
2583  s += "\t" + SG_Get_String(pClass->m_Mean[i]);
2584  s += "\t" + SG_Get_String(pClass->m_Min [i]);
2585  s += "\t" + SG_Get_String(pClass->m_Max [i]);
2586  s += "\t" + SG_Get_String(sqrt(pClass->m_Cov[i][i]));
2587  }
2588 
2589  s += "\n";
2590  }
2591  }
2592 
2593  return( s );
2594 }
2595 
2596 
2598 // //
2600 
2601 //---------------------------------------------------------
2602 bool CSG_Classifier_Supervised::Add_Class(const CSG_String &Class_ID, const CSG_Vector &Mean, const CSG_Vector &Min, const CSG_Vector &Max, const CSG_Matrix &Cov)
2603 {
2604  if( m_nFeatures < 1 || Mean.Get_N() != m_nFeatures || Min.Get_N() != m_nFeatures || Max.Get_N() != m_nFeatures || Cov.Get_NCols() != m_nFeatures || Cov.Get_NRows() != m_nFeatures )
2605  {
2606  return( false );
2607  }
2608 
2609  CClass *pClass, **pClasses = (CClass **)SG_Realloc(m_pClasses, (m_nClasses + 1) * sizeof(CClass *));
2610 
2611  if( pClasses )
2612  {
2613  m_pClasses = pClasses;
2614 
2615  m_pClasses[m_nClasses++] = pClass = new CClass(Class_ID);
2616 
2617  pClass->m_ID = Class_ID;
2618 
2619  pClass->m_Mean = Mean;
2620  pClass->m_Min = Min;
2621  pClass->m_Max = Max;
2622  pClass->m_Cov = Cov;
2623 
2624  pClass->m_Cov_Inv = Cov.Get_Inverse();
2625  pClass->m_Cov_Det = Cov.Get_Determinant();
2626 
2627  pClass->m_Mean_Spectral = CSG_Simple_Statistics(Mean).Get_Mean();
2628 
2629  return( true );
2630  }
2631 
2632  return( false );
2633 }
2634 
2635 
2637 // //
2639 
2640 //---------------------------------------------------------
2641 bool CSG_Classifier_Supervised::Train_Clr_Samples(void)
2642 {
2643  for(int i=0; i<m_nClasses; i++)
2644  {
2645  m_pClasses[i]->m_Samples.Destroy();
2646  }
2647 
2648  return( true );
2649 }
2650 
2651 //---------------------------------------------------------
2652 bool CSG_Classifier_Supervised::Train_Add_Sample(const CSG_String &Class_ID, const CSG_Vector &Features)
2653 {
2654  if( m_nFeatures > 0 && m_nFeatures == Features.Get_N() )
2655  {
2656  int iClass = Get_Class(Class_ID);
2657 
2658  if( iClass < 0 )
2659  {
2660  CClass **pClasses = (CClass **)SG_Realloc(m_pClasses, (m_nClasses + 1) * sizeof(CClass *));
2661 
2662  if( pClasses )
2663  {
2664  m_pClasses = pClasses;
2665 
2666  m_pClasses[iClass = m_nClasses++] = new CClass(Class_ID);
2667  }
2668  }
2669 
2670  if( iClass >= 0 )
2671  {
2672  return( m_pClasses[iClass]->m_Samples.Add_Row(Features) );
2673  }
2674  }
2675 
2676  return( false );
2677 }
2678 
2679 //---------------------------------------------------------
2680 bool CSG_Classifier_Supervised::Train(bool bClear_Samples)
2681 {
2682  if( m_nFeatures < 1 || m_nClasses < 1 )
2683  {
2684  return( false );
2685  }
2686 
2687  for(int iClass=0; iClass<m_nClasses; iClass++)
2688  {
2689  if( !m_pClasses[iClass]->Train() )
2690  {
2691  return( false );
2692  }
2693  }
2694 
2695  if( bClear_Samples )
2696  {
2697  Train_Clr_Samples();
2698  }
2699 
2700  return( true );
2701 }
2702 
2703 
2705 // //
2707 
2708 //---------------------------------------------------------
2709 bool CSG_Classifier_Supervised::CClass::Train(void)
2710 {
2711  if( m_Samples.Get_NCols() < 1 || m_Samples.Get_NRows() < 1 )
2712  {
2713  return( false );
2714  }
2715 
2716  int iFeature;
2717 
2718  //-----------------------------------------------------
2719  m_Mean.Create(m_Samples.Get_NCols());
2720  m_Min .Create(m_Samples.Get_NCols());
2721  m_Max .Create(m_Samples.Get_NCols());
2722 
2723  for(iFeature=0; iFeature<m_Samples.Get_NCols(); iFeature++)
2724  {
2725  CSG_Simple_Statistics s;
2726 
2727  for(int iSample=0; iSample<m_Samples.Get_NRows(); iSample++)
2728  {
2729  s += m_Samples[iSample][iFeature];
2730  }
2731 
2732  m_Mean[iFeature] = s.Get_Mean ();
2733  m_Min [iFeature] = s.Get_Minimum();
2734  m_Max [iFeature] = s.Get_Maximum();
2735  }
2736 
2737  //-----------------------------------------------------
2738  m_Cov.Create(m_Samples.Get_NCols(), m_Samples.Get_NCols());
2739 
2740  for(iFeature=0; iFeature<m_Samples.Get_NCols(); iFeature++)
2741  {
2742  for(int jFeature=iFeature; jFeature<m_Samples.Get_NCols(); jFeature++)
2743  {
2744  double cov = 0.;
2745 
2746  for(int iSample=0; iSample<m_Samples.Get_NRows(); iSample++)
2747  {
2748  cov += (m_Samples[iSample][iFeature] - m_Mean[iFeature]) * (m_Samples[iSample][jFeature] - m_Mean[jFeature]);
2749  }
2750 
2751  if( m_Samples.Get_NRows() > 1 )
2752  {
2753  cov /= m_Samples.Get_NRows() - 1;
2754  }
2755 
2756  m_Cov[iFeature][jFeature] = m_Cov[jFeature][iFeature] = cov;
2757  }
2758  }
2759 
2760  m_Cov_Inv = m_Cov.Get_Inverse ();
2761  m_Cov_Det = m_Cov.Get_Determinant();
2762 
2763  m_Mean_Spectral = CSG_Simple_Statistics(m_Mean).Get_Mean();
2764 
2765  //-----------------------------------------------------
2766  return( true );
2767 }
2768 
2769 
2771 // //
2773 
2774 //---------------------------------------------------------
2775 int CSG_Classifier_Supervised::Get_Class(const CSG_String &Class_ID)
2776 {
2777  if( m_nFeatures > 0 )
2778  {
2779  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2780  {
2781  if( !Get_Class_ID(iClass).Cmp(Class_ID) )
2782  {
2783  return( iClass );
2784  }
2785  }
2786  }
2787 
2788  return( -1 );
2789 }
2790 
2791 //---------------------------------------------------------
2792 bool CSG_Classifier_Supervised::Get_Class(const CSG_Vector &Features, int &Class, double &Quality, int Method)
2793 {
2794  Class = -1;
2795  Quality = 0.;
2796 
2797  if( Get_Feature_Count() == Features.Get_N() )
2798  {
2799  switch( Method )
2800  {
2801  case SG_CLASSIFY_SUPERVISED_BinaryEncoding : _Get_Binary_Encoding (Features, Class, Quality); break;
2802  case SG_CLASSIFY_SUPERVISED_ParallelEpiped : _Get_Parallel_Epiped (Features, Class, Quality); break;
2803  case SG_CLASSIFY_SUPERVISED_MinimumDistance : _Get_Minimum_Distance (Features, Class, Quality); break;
2804  case SG_CLASSIFY_SUPERVISED_Mahalonobis : _Get_Mahalanobis_Distance (Features, Class, Quality); break;
2805  case SG_CLASSIFY_SUPERVISED_MaximumLikelihood: _Get_Maximum_Likelihood (Features, Class, Quality); break;
2806  case SG_CLASSIFY_SUPERVISED_SAM : _Get_Spectral_Angle_Mapping(Features, Class, Quality); break;
2807  case SG_CLASSIFY_SUPERVISED_SID : _Get_Spectral_Divergence (Features, Class, Quality); break;
2808  case SG_CLASSIFY_SUPERVISED_WTA : _Get_Winner_Takes_All (Features, Class, Quality); break;
2809  }
2810 
2811  return( Class >= 0 );
2812  }
2813 
2814  return( false );
2815 }
2816 
2817 
2819 // //
2821 
2822 //---------------------------------------------------------
2823 CSG_String CSG_Classifier_Supervised::Get_Name_of_Method(int Method)
2824 {
2825  switch( Method )
2826  {
2827  case SG_CLASSIFY_SUPERVISED_BinaryEncoding : return( _TL("Binary Encoding") );
2828  case SG_CLASSIFY_SUPERVISED_ParallelEpiped : return( _TL("Parallelepiped") );
2829  case SG_CLASSIFY_SUPERVISED_MinimumDistance : return( _TL("Minimum Distance") );
2830  case SG_CLASSIFY_SUPERVISED_Mahalonobis : return( _TL("Mahalanobis Distance") );
2831  case SG_CLASSIFY_SUPERVISED_MaximumLikelihood: return( _TL("Maximum Likelihood") );
2832  case SG_CLASSIFY_SUPERVISED_SAM : return( _TL("Spectral Angle Mapping") );
2833  case SG_CLASSIFY_SUPERVISED_SID : return( _TL("Spectral Information Divergence") );
2834  case SG_CLASSIFY_SUPERVISED_SVM : return( _TL("Support Vector Machine") );
2835  case SG_CLASSIFY_SUPERVISED_WTA : return( _TL("Winner Takes All") );
2836  }
2837 
2838  return( SG_T("") );
2839 }
2840 
2841 //---------------------------------------------------------
2842 CSG_String CSG_Classifier_Supervised::Get_Name_of_Quality(int Method)
2843 {
2844  switch( Method )
2845  {
2846  case SG_CLASSIFY_SUPERVISED_BinaryEncoding : return( _TL("Difference") );
2847  case SG_CLASSIFY_SUPERVISED_ParallelEpiped : return( _TL("Memberships") );
2848  case SG_CLASSIFY_SUPERVISED_MinimumDistance : return( _TL("Distance") );
2849  case SG_CLASSIFY_SUPERVISED_Mahalonobis : return( _TL("Distance") );
2850  case SG_CLASSIFY_SUPERVISED_MaximumLikelihood: return( _TL("Proximity") );
2851  case SG_CLASSIFY_SUPERVISED_SAM : return( _TL("Angle") );
2852  case SG_CLASSIFY_SUPERVISED_SID : return( _TL("Divergence") );
2853  case SG_CLASSIFY_SUPERVISED_SVM : return( _TL("") );
2854  case SG_CLASSIFY_SUPERVISED_WTA : return( _TL("Votes") );
2855  }
2856 
2857  return( SG_T("") );
2858 }
2859 
2860 
2862 // //
2864 
2865 //---------------------------------------------------------
2866 // Mazer, A. S., Martin, M., Lee, M., and Solomon, J. E. (1988):
2867 // Image Processing Software for Imaging Spectrometry Analysis.
2868 // Remote Sensing of Environment, v. 24, no. 1, p. 201-210.
2869 //
2870 void CSG_Classifier_Supervised::_Get_Binary_Encoding(const CSG_Vector &Features, int &Class, double &Quality)
2871 {
2872  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2873  {
2874  CClass *pClass = m_pClasses[iClass];
2875 
2876  double Mean_Spectral = CSG_Simple_Statistics(Features).Get_Mean();
2877 
2878  int d = 0;
2879 
2880  for(int iFeature=0; iFeature<Get_Feature_Count(); iFeature++)
2881  {
2882  d += (Features(iFeature) < Mean_Spectral) == (pClass->m_Mean[iFeature] < pClass->m_Mean_Spectral) ? 0 : 1;
2883 
2884  if( iFeature == 0 ) // spectral slopes
2885  {
2886  d += (Features[iFeature ] < Features[iFeature + 1]) == (pClass->m_Mean[iFeature ] < pClass->m_Mean[iFeature + 1]) ? 0 : 1;
2887  }
2888  else if( iFeature == Get_Feature_Count() - 1 )
2889  {
2890  d += (Features[iFeature - 1] < Features[iFeature ]) == (pClass->m_Mean[iFeature - 1] < pClass->m_Mean[iFeature ]) ? 0 : 1;
2891  }
2892  else
2893  {
2894  d += (Features[iFeature - 1] < Features[iFeature + 1]) == (pClass->m_Mean[iFeature - 1] < pClass->m_Mean[iFeature + 1]) ? 0 : 1;
2895  }
2896  }
2897 
2898  if( Class < 0 || Quality > d ) // find the minimum 'Hamming' distance
2899  {
2900  Quality = d;
2901  Class = iClass;
2902  }
2903  }
2904 }
2905 
2906 //---------------------------------------------------------
2907 void CSG_Classifier_Supervised::_Get_Parallel_Epiped(const CSG_Vector &Features, int &Class, double &Quality)
2908 {
2909  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2910  {
2911  CClass *pClass = m_pClasses[iClass];
2912 
2913  bool bMember = true;
2914 
2915  for(int iFeature=0; bMember && iFeature<Get_Feature_Count(); iFeature++)
2916  {
2917  bMember = pClass->m_Min[iFeature] <= Features[iFeature] && Features[iFeature] <= pClass->m_Max[iFeature];
2918  }
2919 
2920  if( bMember )
2921  {
2922  Quality ++;
2923  Class = iClass;
2924  }
2925  }
2926 }
2927 
2928 //---------------------------------------------------------
2929 void CSG_Classifier_Supervised::_Get_Minimum_Distance(const CSG_Vector &Features, int &Class, double &Quality)
2930 {
2931  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2932  {
2933  CClass *pClass = m_pClasses[iClass];
2934 
2935  double Distance = (Features - pClass->m_Mean).Get_Length();
2936 
2937  if( Class < 0 || Quality > Distance )
2938  {
2939  Quality = Distance;
2940  Class = iClass;
2941  }
2942  }
2943 
2944  if( m_Threshold_Distance > 0. && Quality > m_Threshold_Distance )
2945  {
2946  Class = -1;
2947  }
2948 }
2949 
2950 //---------------------------------------------------------
2951 void CSG_Classifier_Supervised::_Get_Mahalanobis_Distance(const CSG_Vector &Features, int &Class, double &Quality)
2952 {
2953  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2954  {
2955  CClass *pClass = m_pClasses[iClass];
2956 
2957  CSG_Vector D = Features - pClass->m_Mean;
2958 
2959  double Distance = D * (pClass->m_Cov_Inv * D);
2960 
2961  if( Class < 0 || Quality > Distance )
2962  {
2963  Quality = Distance;
2964  Class = iClass;
2965  }
2966  }
2967 
2968  if( m_Threshold_Distance > 0. && Quality > m_Threshold_Distance )
2969  {
2970  Class = -1;
2971  }
2972 }
2973 
2974 //---------------------------------------------------------
2975 void CSG_Classifier_Supervised::_Get_Maximum_Likelihood(const CSG_Vector &Features, int &Class, double &Quality)
2976 {
2977  double dSum = 0.;
2978 
2979  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
2980  {
2981  CClass *pClass = m_pClasses[iClass];
2982 
2983  CSG_Vector D = Features - pClass->m_Mean;
2984 
2985  double Distance = D * (pClass->m_Cov_Inv * D);
2986 
2987  double Probability = pow(2. * M_PI, -0.5 * m_nFeatures) * pow(pClass->m_Cov_Det, -0.5) * exp(-0.5 * Distance);
2988  // double Probability = -log(pClass->m_Cov_Det) - Distance;
2989 
2990  dSum += Probability;
2991 
2992  if( Class < 0 || Quality < Probability )
2993  {
2994  Quality = Probability;
2995  Class = iClass;
2996  }
2997  }
2998 
2999  if( Class >= 0 )
3000  {
3001  if( m_Probability_Relative )
3002  {
3003  Quality = 100. * Quality / dSum;
3004  }
3005 
3006  if( m_Threshold_Probability > 0. && Quality < m_Threshold_Probability )
3007  {
3008  Class = -1;
3009  }
3010  }
3011 }
3012 
3013 //---------------------------------------------------------
3014 void CSG_Classifier_Supervised::_Get_Spectral_Angle_Mapping(const CSG_Vector &Features, int &Class, double &Quality)
3015 {
3016  for(int iClass=0; iClass<Get_Class_Count(); iClass++)
3017  {
3018  CClass *pClass = m_pClasses[iClass];
3019 
3020  double Angle = Features.Get_Angle(pClass->m_Mean);
3021 
3022  if( Class < 0 || Quality > Angle )
3023  {
3024  Quality = Angle;
3025  Class = iClass;
3026  }
3027  }
3028 
3029  Quality *= M_RAD_TO_DEG;
3030 
3031  if( m_Threshold_Angle > 0. && Quality > m_Threshold_Angle )
3032  {
3033  Class = -1;
3034  }
3035 }
3036 
3037 //---------------------------------------------------------
3038 void CSG_Classifier_Supervised::_Get_Spectral_Divergence(const CSG_Vector &Features, int &Class, double &Quality)
3039 {
3040 }
3041 
3042 //---------------------------------------------------------
3043 void CSG_Classifier_Supervised::_Get_Winner_Takes_All(const CSG_Vector &Features, int &Class, double &Quality)
3044 {
3045  int *Votes = (int *)SG_Calloc(Get_Class_Count(), sizeof(int));
3046 
3047  for(int iMethod=0; iMethod<SG_CLASSIFY_SUPERVISED_WTA; iMethod++)
3048  {
3049  int iClass;
3050  double iQuality;
3051 
3052  if( m_bWTA[iMethod] && Get_Class(Features, iClass, iQuality, iMethod) && ++Votes[iClass] > Quality )
3053  {
3054  Quality = Votes[iClass];
3055  Class = iClass;
3056  }
3057  }
3058 
3059  SG_Free(Votes);
3060 }
3061 
3062 
3064 // //
3065 // //
3066 // //
3068 
3069 //---------------------------------------------------------
3070 // source: http://psydok.sulb.uni-saarland.de/volltexte/2004/268/html/
3071 
3072 //---------------------------------------------------------
3073 double CSG_Test_Distribution::Get_T_Tail(double T, int df, TSG_Test_Distribution_Type Type)
3074 { // Hill's approx. to cumulative t-dist, Commun.A.C.M. 13,617-619.
3075  // See: J.H.Maindonald, Computational Statistics, p.295.
3076  // Calculates p given t and tail type.
3077 
3078  if( !T || !df || df < 1. )
3079  {
3080  return( -1. );
3081  }
3082 
3083  return( _Change_Tail_Type(Get_T_P(T, df), TESTDIST_TYPE_TwoTail, Type, T < 0.) );
3084 }
3085 
3086 //---------------------------------------------------------
3087 double CSG_Test_Distribution::Get_T_Inverse(double p, int df, TSG_Test_Distribution_Type Type)
3088 { // Keith Dear & Robert Brennan.
3089  // Returns an accurate t to tol sig. fig.'s given p & df.
3090 
3091  if( p <= 0. || p >= 1. || df < 1 )
3092  {
3093  return( -1. );
3094  }
3095 
3096  bool bNegative = (Type == TESTDIST_TYPE_Left && p < 0.5) || (Type == TESTDIST_TYPE_Right && p > 0.5);
3097  double t, p0, p1, diff;
3098 
3099  p0 = p1 = _Change_Tail_Type(p, Type, TESTDIST_TYPE_TwoTail, bNegative);
3100  diff = 1.;
3101 
3102  while( fabs(diff) > 0.0001 )
3103  {
3104  t = Get_T_Inv(p1, df); // initial rough value
3105  diff = Get_T_P(t, df) - p0; // compare result with forward fn
3106  p1 = p1 - diff; // small adjustment to p1
3107  }
3108 
3109  return( bNegative ? -t : t );
3110 }
3111 
3112 //---------------------------------------------------------
3113 double CSG_Test_Distribution::_Change_Tail_Type(double p, TSG_Test_Distribution_Type from, TSG_Test_Distribution_Type to, bool bNegative)
3114 {
3115  if( from != to )
3116  {
3117  switch( from ) // convert any tail type to 'left'
3118  {
3119  case TESTDIST_TYPE_Left : break;
3120  case TESTDIST_TYPE_Right : p = 1. - p; break;
3121  case TESTDIST_TYPE_Middle : p = p / 2. + 0.5; if( bNegative ) p = 1. - p; break;
3122  case TESTDIST_TYPE_TwoTail: p = 1. - p / 2. ; if( bNegative ) p = 1. - p; break;
3123  // case TESTDIST_TYPE_Half : p = p + 0.5 ; if( bNegative ) p = 1. - p; break;
3124  }
3125 
3126  switch( to ) // convert p from tail type 'left' to any other
3127  {
3128  case TESTDIST_TYPE_Left : break;
3129  case TESTDIST_TYPE_Right : p = 1. - p; break;
3130  case TESTDIST_TYPE_Middle : if( bNegative ) p = 1. - p; p = 2. * (1. - p); break;
3131  case TESTDIST_TYPE_TwoTail: if( bNegative ) p = 1. - p; p = 2. * p - 1. ; break;
3132  // case TESTDIST_TYPE_Half : if( bNegative ) p = 1. - p; p = p - 0.5 ; break;
3133  }
3134  }
3135 
3136  return( p );
3137 }
3138 
3139 //---------------------------------------------------------
3140 double CSG_Test_Distribution::Get_Norm_P(double z)
3141 { // Returns the two-tailed standard normal probability of z
3142  const double a1 = 0.0000053830, a2 = 0.0000488906, a3 = 0.0000380036,
3143  a4 = 0.0032776263, a5 = 0.0211410061, a6 = 0.0498673470;
3144 
3145  double p;
3146 
3147  z = fabs(z);
3148 
3149  p = (((((a1 * z + a2) * z + a3) * z + a4) * z + a5) * z + a6) * z + 1.;
3150 
3151  return( pow(p, -16) );
3152 }
3153 
3154 //---------------------------------------------------------
3155 double CSG_Test_Distribution::Get_Norm_Z(double p)
3156 { // Returns z given a half-middle tail type p.
3157  const double a0 = 2.5066282, a1 = -18.6150006, a2 = 41.3911977, a3 = -25.4410605,
3158  b1 = -8.4735109, b2 = 23.0833674, b3 = -21.0622410, b4 = 3.1308291,
3159  c0 = -2.7871893, c1 = -2.2979648, c2 = 4.8501413, c3 = 2.3212128,
3160  d1 = 3.5438892, d2 = 1.6370678;
3161 
3162  double r, z;
3163 
3164  if( p > 0.42 )
3165  {
3166  r = sqrt(-log(0.5 - p));
3167  z = (((c3 * r + c2) * r + c1) * r + c0) / ((d2 * r + d1) * r + 1.);
3168  }
3169  else
3170  {
3171  r = p * p;
3172  z = p * (((a3 * r + a2) * r + a1) * r + a0) / ((((b4 * r + b3) * r + b2) * r + b1) * r + 1.);
3173  }
3174 
3175  return( z );
3176 }
3177 
3178 //---------------------------------------------------------
3179 double CSG_Test_Distribution::Get_T_P(double T, int df)
3180 { // Returns two-tail probability level given t and df.
3181  return( df == 1 ? 1. - 2. * atan(fabs(T)) / M_PI
3182  : df == 2 ? 1. - fabs(T) / sqrt(T*T + 2.)
3183  : df == 3 ? 1. - 2. * (atan(fabs(T) / sqrt(3.)) + fabs(T) * sqrt(3.) / (T*T + 3.)) / M_PI
3184  : df == 4 ? 1. - fabs(T) * (1. + 2. / (T*T + 4.)) / sqrt(T*T + 4.)
3185  : Get_Norm_P(Get_T_Z(fabs(T), df))
3186  );
3187 }
3188 
3189 //---------------------------------------------------------
3190 double CSG_Test_Distribution::Get_T_Z(double T, int df)
3191 { // Converts a t value to an approximate z value w.r.t the given df
3192  // s.t. std.norm.(z) = t(z, df) at the two-tail probability level.
3193 
3194  double A9, B9, T9, Z8, P7, B7, z;
3195 
3196  A9 = df - 0.5;
3197  B9 = 48. * A9*A9,
3198  T9 = T*T / df;
3199  Z8 = T9 >= 0.04
3200  ? A9 * log(1. + T9)
3201  : A9 * (((1. - T9 * 0.75) * T9 / 3. - 0.5) * T9 + 1.) * T9;
3202  P7 = ((0.4 * Z8 + 3.3) * Z8 + 24.) * Z8 + 85.5;
3203  B7 = 0.8 * pow(Z8, 2.) + 100. + B9;
3204  z = (1. + (-P7 / B7 + Z8 + 3.) / B9) * sqrt(Z8);
3205 
3206  return( z );
3207 }
3208 
3209 //---------------------------------------------------------
3210 double CSG_Test_Distribution::Get_T_Inv(double p, int df)
3211 { // Hill's approx. inverse t-dist.: Comm. of A.C.M Vol.13 No.10 1970 pg 620.
3212  // Calculates t given df and two-tail probability.
3213 
3214  if( df == 1 )
3215  {
3216  return( cos(p * M_PI / 2.) / sin(p * M_PI / 2.) );
3217  }
3218 
3219  if( df == 2 )
3220  {
3221  return( sqrt(2. / (p * (2. - p)) - 2.) );
3222  }
3223 
3224  double a, b, c, d, x, y;
3225 
3226  a = 1. / (df - 0.5);
3227  b = 48. / (a*a);
3228  c = ((20700. * a / b - 98.) * a - 16.) * a + 96.36;
3229  d = ((94.5 / (b + c) - 3.) / b + 1.) * sqrt(a * M_PI / 2.) * df;
3230  x = d * p;
3231  y = pow(x, 2. / df);
3232 
3233  if( y > 0.05 + a )
3234  {
3235  x = Get_Norm_Z(0.5 * (1. - p));
3236  y = x*x;
3237 
3238  if( df < 5 )
3239  {
3240  c = c + 0.3 * (df - 4.5) * (x + 0.6);
3241  }
3242 
3243  c = (((0.05 * d * x - 5) * x - 7.) * x - 2.) * x + b + c;
3244  y = (((((0.4 * y + 6.3) * y + 36.) * y + 94.5) / c - y - 3.) / b + 1.) * x;
3245  y = a * y*y;
3246 
3247  if( y > 0.002 )
3248  {
3249  y = exp(y) - 1.;
3250  }
3251  else
3252  {
3253  y = 0.5 * y*y + y;
3254  }
3255  }
3256  else
3257  {
3258  y = ((1. / (((df + 6.) / (df * y) - 0.089 * d - 0.822) * (df + 2.) * 3.)
3259  + 0.5 / (df + 4.)) * y - 1.) * (df + 1.) / (df + 2.) + 1. / y;
3260  }
3261 
3262  return( sqrt(df * y) );
3263 }
3264 
3265 
3267 // //
3269 
3270 //---------------------------------------------------------
3271 double CSG_Test_Distribution::Get_F_Tail_from_R2(double R2, int nPredictors, int nSamples, TSG_Test_Distribution_Type Type)
3272 {
3273  double F = (nSamples - nPredictors - 1) * (R2 / nPredictors) / (1. - R2);
3274 
3275  return( CSG_Test_Distribution::Get_F_Tail(F, nPredictors, nSamples - nPredictors - 1, Type) );
3276 }
3277 
3278 //---------------------------------------------------------
3279 double CSG_Test_Distribution::Get_F_Tail(double F, int dfn, int dfd, TSG_Test_Distribution_Type Type)
3280 {
3281  // calculates for F, dfn(ominator) and dfd(enominator) the "tail" of the F-distribution
3282 
3283  double p = 1.;
3284 
3285  if( F >= 0.00001 && dfn > 0 && dfd > 0 )
3286  {
3287  if( F * dfn >= dfd || F > 1. + 20. / dfn + 10. / sqrt((double)dfn) )
3288  {
3289  p = Get_Gamma(F, dfn, dfd);
3290  }
3291  else
3292  {
3293  p = 1. - Get_Gamma(1. / F, dfd, dfn);
3294  }
3295  }
3296 
3297  if( p <= 0. || p >= 1. )
3298  {
3299  p = F > 1. ? 0. : F < 1. ? 1. : 0.5;
3300  }
3301 
3302  return( Type == TESTDIST_TYPE_Right ? p : 1. - p );
3303 }
3304 
3305 //---------------------------------------------------------
3306 double CSG_Test_Distribution::Get_F_Inverse(double alpha, int dfn, int dfd, TSG_Test_Distribution_Type Type)
3307 {
3308  if( alpha < 0. || alpha > 1. || dfd < 0 || dfn < 0 )
3309  {
3310  return( -1 );
3311  }
3312 
3313  if( Type != TESTDIST_TYPE_Right )
3314  {
3315  alpha = 1. - alpha;
3316  }
3317 
3318  const int ITERMAX = 100;
3319  const double EPSILON = 0.0001;
3320 
3321  int i;
3322  double lo, hi, mid, p;
3323 
3324  if( alpha <= 0.5 )
3325  {
3326  lo = 0.5;
3327  hi = lo;
3328 
3329  for(i=0; i<ITERMAX; i++)
3330  {
3331  hi *= 2.;
3332  p = Get_F_Tail(hi, dfn, dfd);
3333 
3334  if( p > alpha )
3335  {
3336  lo = hi;
3337  }
3338  else
3339  {
3340  break;
3341  }
3342  }
3343 
3344  if( p > alpha )
3345  {
3346  return( hi );
3347  }
3348  }
3349  else
3350  {
3351  hi = 2;
3352  lo = hi;
3353 
3354  for(i=0; i<ITERMAX; i++)
3355  {
3356  lo /= 2.;
3357  p = Get_F_Tail(lo, dfn, dfd);
3358 
3359  if( p < alpha )
3360  {
3361  hi = lo;
3362  }
3363  else
3364  {
3365  break;
3366  }
3367  }
3368 
3369  if( p < alpha )
3370  {
3371  return( lo );
3372  }
3373  }
3374 
3375  mid = (hi + lo) / 2.;
3376 
3377  for(i=0; i<ITERMAX && (hi-lo)>EPSILON*mid; i++)
3378  {
3379  mid = (hi + lo) / 2.;
3380  p = Get_F_Tail(mid, dfn, dfd);
3381 
3382  if( p < alpha )
3383  hi = mid;
3384  else if( p > alpha )
3385  lo = mid;
3386  else
3387  break;
3388  }
3389 
3390  return( mid );
3391 }
3392 
3393 //---------------------------------------------------------
3394 double CSG_Test_Distribution::Get_Gamma(double F, double dfn, double dfd)
3395 {
3396  // calculates for F, dfn(ominator) and dfd(enominator) the incomplete Gamma-function
3397 
3398  const double EXPMIN = -30.;
3399  const double SMALL = 0.00000000001;
3400 
3401  double x, c, er, s, n, t1, t;
3402 
3403  dfn /= 2.;
3404  dfd /= 2.;
3405 
3406  x = dfd / (dfd + dfn * F);
3407  c = Get_Log_Gamma(dfn + dfd) - Get_Log_Gamma(dfn) - Get_Log_Gamma(dfd + 1.) + dfd * log(x) + dfn * log(1. - x);
3408 
3409  if( c < EXPMIN )
3410  {
3411  return( -1. );
3412  }
3413 
3414  dfn += dfd;
3415  dfd += 1.;
3416  c = exp(c);
3417  er = SMALL / c;
3418  t = dfn * x / dfd;
3419  t1 = 0.;
3420  s = t + 1.;
3421  n = 0;
3422 
3423  while( t > er || t > t1 )
3424  {
3425  n += 1;
3426  t1 = t;
3427  t *= ((dfn + n) * x / (dfd + n));
3428  s += t;
3429  }
3430 
3431  return( s * c );
3432 }
3433 
3434 //---------------------------------------------------------
3435 double CSG_Test_Distribution::Get_Log_Gamma(double a)
3436 {
3437  // calculates the logarithm of the Gamma-function
3438 
3439  const int ARGMIN = 6;
3440 
3441  const double HL2PI = 0.91893853320467275; // = log(2. * M_PI) / 2.
3442 
3443  int n = (int)floor(ARGMIN - a + 0.0001);
3444 
3445  if( n > 0 )
3446  {
3447  a += n;
3448  }
3449 
3450  double g;
3451 
3452  g = 1. / (a*a);
3453  g = (1. - g * (1. / 30. - g * (1. / 105. - g * (1. / 140. - g / 99.)))) / (12. * a);
3454  g = g + ((a - 0.5) * log(a) - a + HL2PI);
3455 
3456  for(int i=0; i<n; i++)
3457  {
3458  a = a - 1.;
3459  g = g - log(a);
3460  }
3461 
3462  return( g );
3463 }
3464 
3465 
3467 // //
3468 // //
3469 // //
3471 
3472 //---------------------------------------------------------
3473 CSG_Matrix SG_Get_Correlation_Matrix (const CSG_Matrix &Values, bool bCovariances)
3474 {
3475  int nVariables = Values.Get_NX();
3476  int nSamples = Values.Get_NY();
3477 
3478  //-----------------------------------------------------
3479  int i, j, k;
3480  CSG_Simple_Statistics *S;
3481  CSG_Matrix C;
3482 
3483  C.Create(nVariables, nVariables);
3484 
3485  //-----------------------------------------------------
3486  S = new CSG_Simple_Statistics[nVariables];
3487 
3488  for(j=0; j<nVariables; j++)
3489  {
3490  for(i=0; i<nSamples; i++)
3491  {
3492  S[j] += Values[i][j];
3493  }
3494  }
3495 
3496  //-----------------------------------------------------
3497  for(k=0; k<nVariables; k++)
3498  {
3499  for(j=k; j<nVariables; j++)
3500  {
3501  double cov = 0.;
3502 
3503  for(i=0; i<nSamples; i++)
3504  {
3505  cov += (Values[i][j] - S[j].Get_Mean()) * (Values[i][k] - S[k].Get_Mean());
3506  }
3507 
3508  cov /= nSamples;
3509 
3510  if( !bCovariances )
3511  {
3512  cov /= (S[j].Get_StdDev() * S[k].Get_StdDev());
3513  }
3514 
3515  C[j][k] = C[k][j] = cov;
3516  }
3517  }
3518 
3519  //-----------------------------------------------------
3520  delete[](S);
3521 
3522  return( C );
3523 }
3524 
3525 
3527 // //
3528 // //
3529 // //
3531 
3532 //---------------------------------------------------------
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TESTDIST_TYPE_Left
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Returns a pointer to the memory address of the requested variable. You have to type cast and derefere...
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grids.h