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TStatistic.cxx
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1 // @(#)root/base:$Id$
2 // Author: G. Ganis 2012
3 
4 /*************************************************************************
5  * Copyright (C) 1995-2000, Rene Brun and Fons Rademakers. *
6  * All rights reserved. *
7  * *
8  * For the licensing terms see $ROOTSYS/LICENSE. *
9  * For the list of contributors see $ROOTSYS/README/CREDITS. *
10  *************************************************************************/
11 
12 #include "TStatistic.h"
13 
14 #include "TROOT.h"
15 
16 // clang-format off
17 /**
18 * \class TStatistic
19 * \ingroup MathCore
20 * \brief Statistical variable, defined by its mean and variance (RMS). Named, streamable, storable and mergeable.
21 */
22 // clang-format on
23 
25 
26 ////////////////////////////////////////////////////////////////////////////
27 /// \brief Constructor from a vector of values
28 /// \param[in] name The name given to the object
29 /// \param[in] n The total number of entries
30 /// \param[in] val The vector of values
31 /// \param[in] w The vector of weights for the values
32 ///
33 /// Recursively calls the TStatistic::Fill() function to fill the object.
34 TStatistic::TStatistic(const char *name, Int_t n, const Double_t *val, const Double_t *w)
35  : fName(name), fN(0), fW(0.), fW2(0.), fM(0.), fM2(0.), fMin(TMath::Limits<Double_t>::Max()), fMax(TMath::Limits<Double_t>::Min())
36 {
37  if (n > 0) {
38  for (Int_t i = 0; i < n; i++) {
39  if (w) {
40  Fill(val[i], w[i]);
41  } else {
42  Fill(val[i]);
43  }
44  }
45  }
46 }
47 
48 ////////////////////////////////////////////////////////////////////////////////
49 /// TStatistic destructor.
51 {
52  // Required since we overload TObject::Hash.
54 }
55 
56 ////////////////////////////////////////////////////////////////////////////////
57 /// \brief Increment the entries in the object by one value-weight pair.
58 /// \param[in] val Value to fill the Tstatistic with
59 /// \param[in] w The weight of the value
60 ///
61 /// Also updates statistics in the object. For number of entries, sum of weights,
62 /// sum of squared weights and sum of (value*weight), one extra value is added to the
63 /// statistic. For the sum of squared (value*weight) pairs, the function uses formula 1.4
64 /// in Chan-Golub, LeVeque : Algorithms for computing the Sample Variance (1983),
65 /// genralized by LM for the case of weights:
66 /// \f[
67 /// \frac{w_j}{\sum_{i=0}^{j} w_i \cdot \sum_{i=0}^{j-1} w_i} \cdot
68 /// \left(
69 /// \sum_{i=0}^{j} w_i \cdot val_i -
70 /// \sum_{i=0}^{j} \left(w_i \cdot val_i\right)
71 /// \right)
72 /// \f]
73 ///
74 /// The minimum(maximum) is computed by checking that the fill value
75 /// is either less(greater) than the current minimum(maximum)
77 
78 
79  if (w == 0) return;
80  // increase data count
81  fN++;
82 
83  // update sum of weights
84  Double_t tW = fW + w;
85 
86  // update sum of (value * weight) pairs
87  fM += w * val;
88 
89  // update minimum and maximum values
90  fMin = (val < fMin) ? val : fMin;
91  fMax = (val > fMax) ? val : fMax;
92 
93 // Double_t dt = val - fM ;
94  if (tW == 0) {
95  Warning("Fill","Sum of weights is zero - ignore current data point");
96  fN--;
97  return;
98  }
99 
100  if (fW != 0) { // from the second time
101  Double_t rr = ( tW * val - fM);
102  fM2 += w * rr * rr / (tW * fW);
103  }
104  fW = tW;
105  fW2 += w*w;
106 }
107 
108 ////////////////////////////////////////////////////////////////////////////////
109 /// \brief Print the content of the object
110 ///
111 /// Prints the statistics held by the object in one line. These include the mean,
112 /// mean error, RMS, the total number of values, the minimum and the maximum.
115  Printf(" OBJ: TStatistic\t %s \t Mean = %.5g +- %.4g \t RMS = %.5g \t Count = %lld \t Min = %.5g \t Max = %.5g",
116  fName.Data(), GetMean(), GetMeanErr(), GetRMS(), GetN(), GetMin(), GetMax());
117 }
118 
119 ////////////////////////////////////////////////////////////////////////////////
120 /// \brief Merge implementation of TStatistic
121 /// \param[in] in Other TStatistic objects to be added to the current one
122 ///
123 /// The function merges the statistics of all objects together to form a new one.
124 /// Merging quantities is done via simple addition for the following class data members:
125 /// - number of entries fN
126 /// - the sum of weights fW
127 /// - the sum of squared weights fW2
128 /// - the sum of (value*weight) fM
129 ///
130 /// The sum of squared (value*weight) pairs fM2 is updated using the same formula as in
131 /// TStatistic::Fill() function.
132 ///
133 /// The minimum(maximum) is updated by checking that the minimum(maximum) of
134 /// the next TStatistic object in the queue is either less(greater) than the current minimum(maximum).
136 
137  // Let's organise the list of objects to merge excluding the empty ones
138  std::vector<TStatistic*> statPtrs;
139  if (this->fN != 0LL) statPtrs.push_back(this);
140  TStatistic *statPtr;
141  for (auto o : *in) {
142  if ((statPtr = dynamic_cast<TStatistic *>(o)) && statPtr->fN != 0LL) {
143  statPtrs.push_back(statPtr);
144  }
145  }
146 
147  // No object included this has entries
148  const auto nStatsPtrs = statPtrs.size();
149 
150  // Early return possible in case nothing has been filled
151  if (nStatsPtrs == 0) return 0;
152 
153  // Merge the statistic quantities into local variables to then
154  // update the data members of this object
155  auto firstStatPtr = statPtrs[0];
156  auto N = firstStatPtr->fN;
157  auto M = firstStatPtr->fM;
158  auto M2 = firstStatPtr->fM2;
159  auto W = firstStatPtr->fW;
160  auto W2 = firstStatPtr->fW2;
161  auto Min = firstStatPtr->fMin;
162  auto Max = firstStatPtr->fMax;
163  for (auto i = 1U; i < nStatsPtrs; ++i) {
164  auto c = statPtrs[i];
165  double temp = (c->fW) / (W)*M - c->fM;
166  M2 += c->fM2 + W / (c->fW * (c->fW + W)) * temp * temp;
167  M += c->fM;
168  W += c->fW;
169  W2 += c->fW2;
170  N += c->fN;
171  Min = (c->fMin < Min) ? c->fMin : Min;
172  Max = (c->fMax > Max) ? c->fMax : Max;
173  }
174 
175  // Now update the data members of this object
176  fN = N;
177  fW = W;
178  fW2 = W2;
179  fM = M;
180  fM2 = M2;
181  fMin = Min;
182  fMax = Max;
183 
184  return nStatsPtrs;
185 
186 }
Double_t GetMin() const
Definition: TStatistic.h:64
Double_t GetRMS() const
Definition: TStatistic.h:60
Double_t GetMean() const
Definition: TStatistic.h:58
Double_t GetMeanErr() const
Definition: TStatistic.h:59
void CallRecursiveRemoveIfNeeded(TObject &obj)
call RecursiveRemove for obj if gROOT is valid and obj.TestBit(kMustCleanup) is true.
Definition: TROOT.h:404
Double_t fM2
Second order momentum.
Definition: TStatistic.h:41
const char Option_t
Definition: RtypesCore.h:62
Double_t fM
Sum of elements (i.e. sum of (val * weight) pairs.
Definition: TStatistic.h:40
#define N
Short_t Min(Short_t a, Short_t b)
Definition: TMathBase.h:180
void Print(Option_t *="") const
Print the content of the object.
Definition: TStatistic.cxx:113
Double_t fW
Sum of weights.
Definition: TStatistic.h:38
#define templateClassImp(name)
Definition: Rtypes.h:409
TStatistic(const char *name="")
Definition: TStatistic.h:47
TString fName
Name given to the TStatistic object.
Definition: TStatistic.h:36
Statistical variable, defined by its mean and variance (RMS).
Definition: TStatistic.h:33
Long64_t fN
Number of fills.
Definition: TStatistic.h:37
~TStatistic()
TStatistic destructor.
Definition: TStatistic.cxx:50
Collection abstract base class.
Definition: TCollection.h:63
Double_t GetMax() const
Definition: TStatistic.h:65
static void IndentLevel()
Functions used by ls() to indent an object hierarchy.
Definition: TROOT.cxx:2829
void Printf(const char *fmt,...)
Double_t fMin
Minimum value in the TStatistic object.
Definition: TStatistic.h:42
Double_t fW2
Sum of squared weights.
Definition: TStatistic.h:39
void Fill(Double_t val, Double_t w=1.)
Increment the entries in the object by one value-weight pair.
Definition: TStatistic.cxx:76
Int_t Merge(TCollection *in)
Merge implementation of TStatistic.
Definition: TStatistic.cxx:135
Short_t Max(Short_t a, Short_t b)
Definition: TMathBase.h:212
#define c(i)
Definition: RSha256.hxx:101
TMath.
Definition: TMathBase.h:35
Long64_t GetN() const
Definition: TStatistic.h:55
const Int_t n
Definition: legend1.C:16
char name[80]
Definition: TGX11.cxx:109
virtual void Warning(const char *method, const char *msgfmt,...) const
Issue warning message.
Definition: TObject.cxx:873
Double_t fMax
Maximum value in the TStatistic object.
Definition: TStatistic.h:43
const char * Data() const
Definition: TString.h:364