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/*
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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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// Catch-all file for utility classes
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// A weighted average maintains a running, weighted average
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// of some float value (templates would be handy here if we
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// need different types).
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//
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// The average is adaptive in that we smooth it for the
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// initial samples; we don't use the weight until we have
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// enough samples for it to be meaningful.
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//
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// This serves as our best estimate of a future unknown.
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//
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class AdaptiveWeightedAverage : public CHeapObj {
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private:
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float _average; // The last computed average
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unsigned _sample_count; // How often we've sampled this average
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unsigned _weight; // The weight used to smooth the averages
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// A higher weight favors the most
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// recent data.
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protected:
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float _last_sample; // The last value sampled.
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void increment_count() { _sample_count++; }
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void set_average(float avg) { _average = avg; }
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// Helper function, computes an adaptive weighted average
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// given a sample and the last average
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float compute_adaptive_average(float new_sample, float average);
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public:
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// Input weight must be between 0 and 100
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AdaptiveWeightedAverage(unsigned weight) :
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_average(0.0), _sample_count(0), _weight(weight), _last_sample(0.0) {
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}
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// Accessors
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float average() const { return _average; }
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unsigned weight() const { return _weight; }
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unsigned count() const { return _sample_count; }
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float last_sample() const { return _last_sample; }
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// Update data with a new sample.
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void sample(float new_sample);
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static inline float exp_avg(float avg, float sample,
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unsigned int weight) {
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assert(0 <= weight && weight <= 100, "weight must be a percent");
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return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;
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}
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static inline size_t exp_avg(size_t avg, size_t sample,
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unsigned int weight) {
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// Convert to float and back to avoid integer overflow.
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return (size_t)exp_avg((float)avg, (float)sample, weight);
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}
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};
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// A weighted average that includes a deviation from the average,
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// some multiple of which is added to the average.
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//
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// This serves as our best estimate of an upper bound on a future
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// unknown.
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class AdaptivePaddedAverage : public AdaptiveWeightedAverage {
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private:
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float _padded_avg; // The last computed padded average
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float _deviation; // Running deviation from the average
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unsigned _padding; // A multiple which, added to the average,
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// gives us an upper bound guess.
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protected:
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void set_padded_average(float avg) { _padded_avg = avg; }
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void set_deviation(float dev) { _deviation = dev; }
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public:
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AdaptivePaddedAverage() :
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AdaptiveWeightedAverage(0),
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_padded_avg(0.0), _deviation(0.0), _padding(0) {}
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AdaptivePaddedAverage(unsigned weight, unsigned padding) :
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AdaptiveWeightedAverage(weight),
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_padded_avg(0.0), _deviation(0.0), _padding(padding) {}
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// Placement support
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void* operator new(size_t ignored, void* p) { return p; }
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// Allocator
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void* operator new(size_t size) { return CHeapObj::operator new(size); }
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// Accessor
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float padded_average() const { return _padded_avg; }
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float deviation() const { return _deviation; }
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unsigned padding() const { return _padding; }
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// Override
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void sample(float new_sample);
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};
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// A weighted average that includes a deviation from the average,
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// some multiple of which is added to the average.
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//
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// This serves as our best estimate of an upper bound on a future
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// unknown.
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// A special sort of padded average: it doesn't update deviations
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// if the sample is zero. The average is allowed to change. We're
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// preventing the zero samples from drastically changing our padded
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// average.
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class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {
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public:
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AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :
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AdaptivePaddedAverage(weight, padding) {}
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// Override
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void sample(float new_sample);
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};
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// Use a least squares fit to a set of data to generate a linear
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// equation.
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// y = intercept + slope * x
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class LinearLeastSquareFit : public CHeapObj {
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double _sum_x; // sum of all independent data points x
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double _sum_x_squared; // sum of all independent data points x**2
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double _sum_y; // sum of all dependent data points y
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double _sum_xy; // sum of all x * y.
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double _intercept; // constant term
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double _slope; // slope
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// The weighted averages are not currently used but perhaps should
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// be used to get decaying averages.
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AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable
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AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable
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public:
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LinearLeastSquareFit(unsigned weight);
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void update(double x, double y);
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double y(double x);
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double slope() { return _slope; }
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// Methods to decide if a change in the dependent variable will
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// achive a desired goal. Note that these methods are not
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// complementary and both are needed.
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bool decrement_will_decrease();
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bool increment_will_decrease();
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};
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class GCPauseTimer : StackObj {
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elapsedTimer* _timer;
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public:
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GCPauseTimer(elapsedTimer* timer) {
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_timer = timer;
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_timer->stop();
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}
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~GCPauseTimer() {
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_timer->start();
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}
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};
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