142 lines
5.0 KiB
C++
142 lines
5.0 KiB
C++
/*
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* Copyright 2002-2005 Sun Microsystems, Inc. All Rights Reserved.
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* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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*
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* This code is free software; you can redistribute it and/or modify it
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* under the terms of the GNU General Public License version 2 only, as
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* published by the Free Software Foundation.
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*
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* This code is distributed in the hope that it will be useful, but WITHOUT
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* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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* version 2 for more details (a copy is included in the LICENSE file that
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* accompanied this code).
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*
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* You should have received a copy of the GNU General Public License version
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* 2 along with this work; if not, write to the Free Software Foundation,
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* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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*
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* Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
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* CA 95054 USA or visit www.sun.com if you need additional information or
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* have any questions.
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*
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*/
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# include "incls/_precompiled.incl"
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# include "incls/_gcUtil.cpp.incl"
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// Catch-all file for utility classes
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float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
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float average) {
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// We smooth the samples by not using weight() directly until we've
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// had enough data to make it meaningful. We'd like the first weight
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// used to be 1, the second to be 1/2, etc until we have 100/weight
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// samples.
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unsigned count_weight = 100/count();
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unsigned adaptive_weight = (MAX2(weight(), count_weight));
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float new_avg = exp_avg(average, new_sample, adaptive_weight);
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return new_avg;
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}
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void AdaptiveWeightedAverage::sample(float new_sample) {
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increment_count();
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assert(count() != 0,
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"Wraparound -- history would be incorrectly discarded");
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// Compute the new weighted average
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float new_avg = compute_adaptive_average(new_sample, average());
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set_average(new_avg);
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_last_sample = new_sample;
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}
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void AdaptivePaddedAverage::sample(float new_sample) {
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// Compute our parent classes sample information
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AdaptiveWeightedAverage::sample(new_sample);
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// Now compute the deviation and the new padded sample
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float new_avg = average();
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float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
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deviation());
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set_deviation(new_dev);
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set_padded_average(new_avg + padding() * new_dev);
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_last_sample = new_sample;
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}
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void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
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// Compute our parent classes sample information
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AdaptiveWeightedAverage::sample(new_sample);
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float new_avg = average();
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if (new_sample != 0) {
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// We only create a new deviation if the sample is non-zero
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float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
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deviation());
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set_deviation(new_dev);
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}
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set_padded_average(new_avg + padding() * deviation());
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_last_sample = new_sample;
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}
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LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
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_sum_x(0), _sum_y(0), _sum_xy(0),
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_mean_x(weight), _mean_y(weight) {}
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void LinearLeastSquareFit::update(double x, double y) {
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_sum_x = _sum_x + x;
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_sum_x_squared = _sum_x_squared + x * x;
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_sum_y = _sum_y + y;
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_sum_xy = _sum_xy + x * y;
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_mean_x.sample(x);
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_mean_y.sample(y);
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assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
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if ( _mean_x.count() > 1 ) {
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double slope_denominator;
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slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
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// Some tolerance should be injected here. A denominator that is
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// nearly 0 should be avoided.
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if (slope_denominator != 0.0) {
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double slope_numerator;
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slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
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_slope = slope_numerator / slope_denominator;
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// The _mean_y and _mean_x are decaying averages and can
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// be used to discount earlier data. If they are used,
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// first consider whether all the quantities should be
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// kept as decaying averages.
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// _intercept = _mean_y.average() - _slope * _mean_x.average();
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_intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
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}
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}
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}
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double LinearLeastSquareFit::y(double x) {
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double new_y;
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if ( _mean_x.count() > 1 ) {
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new_y = (_intercept + _slope * x);
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return new_y;
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} else {
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return _mean_y.average();
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}
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}
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// Both decrement_will_decrease() and increment_will_decrease() return
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// true for a slope of 0. That is because a change is necessary before
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// a slope can be calculated and a 0 slope will, in general, indicate
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// that no calculation of the slope has yet been done. Returning true
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// for a slope equal to 0 reflects the intuitive expectation of the
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// dependence on the slope. Don't use the complement of these functions
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// since that untuitive expectation is not built into the complement.
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bool LinearLeastSquareFit::decrement_will_decrease() {
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return (_slope >= 0.00);
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}
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bool LinearLeastSquareFit::increment_will_decrease() {
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return (_slope <= 0.00);
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}
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