author | dholmes |
Fri, 04 Dec 2015 04:06:37 -0500 | |
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/* |
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* Copyright (c) 2002, 2015, Oracle and/or its affiliates. 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 Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA |
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* or visit www.oracle.com if you need additional information or have any |
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* questions. |
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* |
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*/ |
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#include "precompiled.hpp" |
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#include "gc/shared/gcUtil.hpp" |
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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 |
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// OLD_THRESHOLD/weight samples. |
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unsigned count_weight = 0; |
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// Avoid division by zero if the counter wraps (7158457) |
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if (!is_old()) { |
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count_weight = OLD_THRESHOLD/count(); |
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} |
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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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// 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 AdaptiveWeightedAverage::print() const { |
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print_on(tty); |
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} |
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void AdaptiveWeightedAverage::print_on(outputStream* st) const { |
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guarantee(false, "NYI"); |
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} |
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void AdaptivePaddedAverage::print() const { |
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print_on(tty); |
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} |
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void AdaptivePaddedAverage::print_on(outputStream* st) const { |
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guarantee(false, "NYI"); |
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} |
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void AdaptivePaddedNoZeroDevAverage::print() const { |
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print_on(tty); |
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} |
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void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const { |
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guarantee(false, "NYI"); |
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} |
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void AdaptivePaddedAverage::sample(float new_sample) { |
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// Compute new adaptive weighted average based on new sample. |
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AdaptiveWeightedAverage::sample(new_sample); |
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// Now update the deviation and the padded average. |
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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_x_squared(0), _sum_y(0), _sum_xy(0), |
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_intercept(0), _slope(0), _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 intuitive 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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} |