1 | #ifndef _theplu_yat_statistics_histogram_ |
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2 | #define _theplu_yat_statistics_histogram_ |
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3 | |
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4 | // $Id: Histogram.h 2032 2009-08-19 21:41:30Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Jari Häkkinen |
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8 | Copyright (C) 2005 Jari Häkkinen, Peter Johansson |
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9 | Copyright (C) 2006 Jari Häkkinen |
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10 | Copyright (C) 2007, 2008 Jari Häkkinen, Peter Johansson |
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11 | Copyright (C) 2009 Peter Johansson |
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12 | |
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13 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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14 | |
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15 | The yat library is free software; you can redistribute it and/or |
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16 | modify it under the terms of the GNU General Public License as |
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17 | published by the Free Software Foundation; either version 3 of the |
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18 | License, or (at your option) any later version. |
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19 | |
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20 | The yat library is distributed in the hope that it will be useful, |
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21 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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22 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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23 | General Public License for more details. |
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24 | |
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25 | You should have received a copy of the GNU General Public License |
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26 | along with yat. If not, see <http://www.gnu.org/licenses/>. |
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27 | */ |
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28 | |
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29 | #include "AveragerWeighted.h" |
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30 | #include "yat/utility/iterator_traits.h" |
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31 | |
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32 | #include <string> |
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33 | #include <vector> |
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34 | |
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35 | namespace theplu { |
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36 | namespace yat { |
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37 | namespace statistics { |
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38 | |
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39 | /// |
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40 | /// @brief Histograms provide a convenient way of presenting the |
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41 | /// distribution of a set of data. |
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42 | /// |
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43 | /// A histogram consists of a set of |
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44 | /// bins which count the number of events falling into these |
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45 | /// bins. Currently only one dimensional histograms with uniformly |
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46 | /// spaced bins are supported. |
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47 | /// |
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48 | class Histogram |
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49 | { |
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50 | public: |
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51 | |
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52 | /// |
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53 | /// The default constructor. |
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54 | /// |
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55 | Histogram(void); |
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56 | |
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57 | /// |
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58 | /// The copy constructor. |
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59 | /// |
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60 | Histogram(const Histogram&); |
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61 | |
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62 | /// |
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63 | /// Construct a histogram object that covers \f$(xmin,xmax]\f$ |
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64 | /// with the bin spacing \f$(xmax-xmin)/n\f$. |
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65 | /// |
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66 | Histogram(const double xmin, const double xmax, const size_t n); |
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67 | |
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68 | virtual ~Histogram(void); |
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69 | |
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70 | /// |
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71 | /// Update the histogram by adding \a weight to the bin whose |
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72 | /// range contains the observation \a x. No bins are updated when |
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73 | /// \a x lies outside the range of the histogram but the value is |
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74 | /// added to the overall integral of the histogram. |
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75 | /// |
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76 | /// @short Add a data point to the histogram. |
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77 | /// |
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78 | /// @return 0 if \a x lies within the range of the histogram, -1 |
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79 | /// if \a x is smaller than the lower limit of the histogram, and |
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80 | /// similarly, 1 is returned if \a x is greater than or equal to |
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81 | /// the upper limit. |
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82 | /// |
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83 | int add(const double x,const double weight=1.0); |
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84 | |
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85 | /// |
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86 | /// Gives access to the AveragerWeighted object that keeps track of |
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87 | /// average of all events presented to the histogram. |
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88 | /// |
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89 | /// @short Average of all events presented to the histogram. |
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90 | /// |
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91 | /// @return A const reference to an AveragerWeighted object. |
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92 | /// |
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93 | const AveragerWeighted& averager_all(void) const; |
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94 | |
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95 | /// |
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96 | /// Gives access to the AveragerWeighted object that keeps track of |
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97 | /// average of events that fits within the histogram lower and |
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98 | /// upper limits. This function is equivalent to averager(). |
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99 | /// |
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100 | /// @short Average of events fitting within histogram. |
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101 | /// |
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102 | /// @return A const reference to an AveragerWeighted object. |
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103 | /// |
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104 | const AveragerWeighted& averager_histogram(void) const; |
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105 | |
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106 | /// |
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107 | /// @return The number of bins in the histogram |
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108 | /// |
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109 | size_t nof_bins(void) const; |
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110 | |
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111 | /// |
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112 | /// There are two ways to normalize the counts. |
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113 | /// |
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114 | /// If choice is true: The normalized count is the count in a |
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115 | /// bin divided by the total number of observations. In this |
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116 | /// case the relative counts are normalized to sum to unity ( |
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117 | /// minus values outside histogram). This is the intuitive case |
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118 | /// where the height of the histogram bar represents the |
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119 | /// proportion of the data in each class. |
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120 | /// |
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121 | /// If choice is false: The normalized count is the count in the |
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122 | /// class divided by the number of observations times the bin |
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123 | /// width. For this normalization, the area (or integral) under |
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124 | /// the histogram is equal to unity (minus the missing area |
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125 | /// corresponding to counts outside histogram). From a |
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126 | /// probabilistic point of view, this normalization results in a |
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127 | /// relative histogram that is most akin to the probability |
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128 | /// density function If you want to overlay a probability density |
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129 | /// on top of the histogram, use this normalization. Although this |
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130 | /// normalization is less intuitive (relative frequencies greater |
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131 | /// than 1 are quite permissible), it is the appropriate |
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132 | /// normalization if you are using the histogram to model a |
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133 | /// probability density function. |
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134 | /// |
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135 | /// @short Normalizing the histogram |
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136 | /// |
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137 | void normalize(bool choice = true); |
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138 | |
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139 | /// |
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140 | /// @return The value in the middle of bin \a k. |
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141 | /// |
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142 | /// @note No check is done that \a k is within the size of the |
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143 | /// histogram. |
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144 | /// |
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145 | double observation_value(const size_t k) const; |
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146 | |
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147 | /// |
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148 | /// Set everyting to default values, here it means that everything |
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149 | /// is set to zero except the boundary values that are kept. |
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150 | /// |
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151 | void reset(void); |
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152 | |
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153 | /// |
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154 | /// @return The width of the bins in the histogram. |
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155 | /// |
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156 | double spacing(void) const; |
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157 | |
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158 | /// |
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159 | /// @return The histogram upper boundary. |
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160 | /// |
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161 | /// @note The upper boundary value is outside the histogram. |
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162 | /// |
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163 | double xmax(void) const; |
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164 | |
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165 | /// |
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166 | /// @return The histogram lower boundary. |
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167 | /// |
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168 | /// @note The lower boundary value is inside the histogram. |
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169 | /// |
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170 | double xmin(void) const; |
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171 | |
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172 | /// |
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173 | /// @return The count of bin \a k in the histogram. |
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174 | /// |
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175 | double operator[](size_t k) const; |
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176 | |
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177 | /// |
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178 | /// The assignment operator |
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179 | /// |
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180 | const Histogram& operator=(const Histogram&); |
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181 | |
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182 | /** |
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183 | \brief Addition operator |
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184 | |
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185 | \note The bins in lhs and rhs must match, i.e., min, max, and |
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186 | nof_bins() must be equal in lhs and rhs. |
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187 | |
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188 | \since New in yat 0.6 |
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189 | */ |
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190 | const Histogram& operator+=(const Histogram& rhs); |
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191 | |
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192 | private: |
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193 | // Returns zero if outside boundaries |
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194 | size_t bin(double d); |
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195 | |
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196 | std::vector<double> histogram_; |
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197 | double xmax_; |
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198 | double xmin_; |
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199 | statistics::AveragerWeighted sum_all_; // average of all data |
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200 | statistics::AveragerWeighted sum_histogram_;// average of data in histogram |
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201 | }; |
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202 | |
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203 | /** |
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204 | Add a range [first, last) of values to Histogram. |
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205 | |
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206 | \relates Histogram |
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207 | */ |
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208 | template<typename ForwardIterator> |
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209 | void add(Histogram& h, |
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210 | ForwardIterator first, ForwardIterator last) |
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211 | { |
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212 | while (first!=last) { |
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213 | h.add(utility::iterator_traits<ForwardIterator>().data(), |
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214 | utility::iterator_traits<ForwardIterator>().weight()); |
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215 | ++first; |
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216 | } |
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217 | } |
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218 | |
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219 | /** |
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220 | The Histogram output operator |
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221 | |
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222 | \relates Histogram |
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223 | */ |
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224 | std::ostream& operator<<(std::ostream& s,const Histogram&); |
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225 | |
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226 | }}} // of namespace statistics, yat, and theplu |
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227 | |
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228 | #endif |
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