1 | #ifndef _theplu_yat_normalizer_qquantile_normalizer_ |
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2 | #define _theplu_yat_normalizer_qquantile_normalizer_ |
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3 | |
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4 | /* |
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5 | Copyright (C) 2009 Jari Häkkinen |
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6 | |
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7 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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8 | |
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9 | The yat library is free software; you can redistribute it and/or |
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10 | modify it under the terms of the GNU General Public License as |
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11 | published by the Free Software Foundation; either version 3 of the |
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12 | License, or (at your option) any later version. |
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13 | |
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14 | The yat library is distributed in the hope that it will be useful, |
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15 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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16 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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17 | General Public License for more details. |
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18 | |
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19 | You should have received a copy of the GNU General Public License |
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20 | along with yat. If not, see <http://www.gnu.org/licenses/>. |
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21 | */ |
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22 | |
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23 | #include "yat/utility/Vector.h" |
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24 | |
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25 | namespace theplu { |
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26 | namespace yat { |
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27 | namespace utility { |
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28 | class Matrix; |
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29 | class VectorBase; |
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30 | } |
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31 | namespace normalizer { |
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32 | |
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33 | /** |
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34 | \brief Partition a vector of data into equal sizes. |
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35 | |
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36 | The class also calculates the average of each part and assigns |
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37 | the average to the mid point of each part. The midpoint is a |
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38 | double, i.e., it is not forced to be an integer index. |
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39 | */ |
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40 | class Partitioner |
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41 | { |
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42 | public: |
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43 | /** |
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44 | \brief Create the partition and perform required calculations. |
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45 | */ |
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46 | Partitioner(const utility::VectorBase& vec, unsigned int N); |
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47 | |
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48 | /** |
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49 | \brief Return the averages for each part. |
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50 | |
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51 | \return The average vector. |
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52 | */ |
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53 | const utility::Vector& averages(void) const; |
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54 | |
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55 | /** |
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56 | \brief Return the mid point for each partition. |
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57 | |
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58 | \return The index vector. |
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59 | */ |
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60 | const utility::Vector& index(void) const; |
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61 | |
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62 | /** |
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63 | \return The number of parts. |
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64 | */ |
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65 | size_t size(void) const; |
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66 | |
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67 | private: |
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68 | utility::Vector average_; |
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69 | utility::Vector index_; |
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70 | }; |
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71 | |
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72 | |
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73 | /** |
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74 | \brief Perform Q-quantile normalization |
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75 | |
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76 | After a Q-quantile normalization each column has approximately |
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77 | the same distribution of data (the Q-quantiles are the |
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78 | same). Also, within each column the rank of an element is not |
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79 | changed. |
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80 | |
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81 | There is currently no weighted version of qQuantileNormalizer |
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82 | |
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83 | The normalization goes like this |
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84 | - Data is not assumed to be sorted. |
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85 | - Partition the target data in N parts. |
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86 | - Calculate the arithmetic mean for each part, the mean is |
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87 | assigned to the mid point of each part. |
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88 | - Do the same for the data to be tranformed (called source |
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89 | here). |
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90 | - For each part, calculate the difference between the target and |
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91 | the source. Now we have N differences d_i with associated rank |
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92 | (midpoint of each part). |
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93 | - Create a cubic spline fit to this difference vector d. The |
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94 | resulting curve is used to recalculate all column values. |
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95 | - Use the cubic spline fit for values within the cubic spline |
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96 | fit range [midpoint 1st part, midpoint last part]. |
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97 | - For data outside the cubic spline fit use linear |
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98 | extrapolation, i.e., a constant shift. d_first for points |
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99 | below fit range, and d_last for points above fit range. |
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100 | |
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101 | \since New in yat 0.5 |
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102 | */ |
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103 | class qQuantileNormalizer |
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104 | { |
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105 | public: |
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106 | /** |
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107 | \brief Documentation please. |
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108 | |
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109 | \a Q is the number of parts and must be within \f$ [2,N] \f$ |
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110 | where \f$ N \f$ is the total number of data points in the |
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111 | target. However, if \f$ N \f$ is larger than the number of points |
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112 | in the data to be normalized the behaviour of the code is |
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113 | undefined. Keep \f$ N \f$ equal to or less than the smallest |
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114 | number of data points in the target or each data set to be |
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115 | normalized against a ginven target. |
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116 | */ |
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117 | qQuantileNormalizer(const utility::VectorBase& target, unsigned int Q); |
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118 | |
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119 | /** |
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120 | \brief perform the Q-quantile normalization. |
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121 | |
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122 | It is possible to normalize "in place"; it is permissible for |
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123 | \a matrix and \a result to reference the same Matrix. |
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124 | |
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125 | \note dimensions of \a matrix and \a result must match. |
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126 | */ |
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127 | void operator()(const utility::Matrix& matrix, |
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128 | utility::Matrix& result) const; |
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129 | |
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130 | private: |
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131 | Partitioner target_; |
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132 | }; |
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133 | |
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134 | }}} // end of namespace normalizer, yat and thep |
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135 | |
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136 | #endif |
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