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1 /*
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2 * Copyright 2002-2005 Sun Microsystems, Inc. All Rights Reserved.
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3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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4 *
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5 * This code is free software; you can redistribute it and/or modify it
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6 * under the terms of the GNU General Public License version 2 only, as
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7 * published by the Free Software Foundation.
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8 *
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9 * This code is distributed in the hope that it will be useful, but WITHOUT
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10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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12 * version 2 for more details (a copy is included in the LICENSE file that
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13 * accompanied this code).
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14 *
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15 * You should have received a copy of the GNU General Public License version
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16 * 2 along with this work; if not, write to the Free Software Foundation,
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17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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18 *
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19 * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
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20 * CA 95054 USA or visit www.sun.com if you need additional information or
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21 * have any questions.
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22 *
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23 */
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24
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25 # include "incls/_precompiled.incl"
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26 # include "incls/_gcUtil.cpp.incl"
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27
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28 // Catch-all file for utility classes
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29
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30 float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
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31 float average) {
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32 // We smooth the samples by not using weight() directly until we've
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33 // had enough data to make it meaningful. We'd like the first weight
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34 // used to be 1, the second to be 1/2, etc until we have 100/weight
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35 // samples.
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36 unsigned count_weight = 100/count();
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37 unsigned adaptive_weight = (MAX2(weight(), count_weight));
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38
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39 float new_avg = exp_avg(average, new_sample, adaptive_weight);
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40
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41 return new_avg;
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42 }
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43
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44 void AdaptiveWeightedAverage::sample(float new_sample) {
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45 increment_count();
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46 assert(count() != 0,
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47 "Wraparound -- history would be incorrectly discarded");
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48
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49 // Compute the new weighted average
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50 float new_avg = compute_adaptive_average(new_sample, average());
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51 set_average(new_avg);
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52 _last_sample = new_sample;
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53 }
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54
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55 void AdaptivePaddedAverage::sample(float new_sample) {
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56 // Compute our parent classes sample information
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57 AdaptiveWeightedAverage::sample(new_sample);
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58
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59 // Now compute the deviation and the new padded sample
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60 float new_avg = average();
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61 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
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62 deviation());
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63 set_deviation(new_dev);
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64 set_padded_average(new_avg + padding() * new_dev);
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65 _last_sample = new_sample;
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66 }
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67
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68 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
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69 // Compute our parent classes sample information
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70 AdaptiveWeightedAverage::sample(new_sample);
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71
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72 float new_avg = average();
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73 if (new_sample != 0) {
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74 // We only create a new deviation if the sample is non-zero
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75 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
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76 deviation());
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77
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78 set_deviation(new_dev);
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79 }
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80 set_padded_average(new_avg + padding() * deviation());
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81 _last_sample = new_sample;
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82 }
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83
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84 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
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85 _sum_x(0), _sum_y(0), _sum_xy(0),
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86 _mean_x(weight), _mean_y(weight) {}
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87
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88 void LinearLeastSquareFit::update(double x, double y) {
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89 _sum_x = _sum_x + x;
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90 _sum_x_squared = _sum_x_squared + x * x;
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91 _sum_y = _sum_y + y;
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92 _sum_xy = _sum_xy + x * y;
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93 _mean_x.sample(x);
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94 _mean_y.sample(y);
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95 assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
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96 if ( _mean_x.count() > 1 ) {
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97 double slope_denominator;
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98 slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
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99 // Some tolerance should be injected here. A denominator that is
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100 // nearly 0 should be avoided.
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101
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102 if (slope_denominator != 0.0) {
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103 double slope_numerator;
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104 slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
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105 _slope = slope_numerator / slope_denominator;
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106
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107 // The _mean_y and _mean_x are decaying averages and can
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108 // be used to discount earlier data. If they are used,
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109 // first consider whether all the quantities should be
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110 // kept as decaying averages.
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111 // _intercept = _mean_y.average() - _slope * _mean_x.average();
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112 _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
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113 }
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114 }
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115 }
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116
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117 double LinearLeastSquareFit::y(double x) {
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118 double new_y;
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119
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120 if ( _mean_x.count() > 1 ) {
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121 new_y = (_intercept + _slope * x);
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122 return new_y;
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123 } else {
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124 return _mean_y.average();
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125 }
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126 }
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127
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128 // Both decrement_will_decrease() and increment_will_decrease() return
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129 // true for a slope of 0. That is because a change is necessary before
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130 // a slope can be calculated and a 0 slope will, in general, indicate
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131 // that no calculation of the slope has yet been done. Returning true
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132 // for a slope equal to 0 reflects the intuitive expectation of the
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133 // dependence on the slope. Don't use the complement of these functions
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134 // since that untuitive expectation is not built into the complement.
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135 bool LinearLeastSquareFit::decrement_will_decrease() {
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136 return (_slope >= 0.00);
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137 }
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138
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139 bool LinearLeastSquareFit::increment_will_decrease() {
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140 return (_slope <= 0.00);
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141 }
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