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/*
* Copyright (c) 2002, 2011, Oracle and/or its affiliates. All rights reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 only, as
* published by the Free Software Foundation.
*
* This code is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
*
* You should have received a copy of the GNU General Public License version
* 2 along with this work; if not, write to the Free Software Foundation,
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
* or visit www.oracle.com if you need additional information or have any
* questions.
*
*/
#include "precompiled.hpp"
#include "gc_implementation/shared/gcUtil.hpp"
// Catch-all file for utility classes
float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
float average) {
// We smooth the samples by not using weight() directly until we've
// had enough data to make it meaningful. We'd like the first weight
// used to be 1, the second to be 1/2, etc until we have 100/weight
// samples.
unsigned count_weight = 100/count();
unsigned adaptive_weight = (MAX2(weight(), count_weight));
float new_avg = exp_avg(average, new_sample, adaptive_weight);
return new_avg;
}
void AdaptiveWeightedAverage::sample(float new_sample) {
increment_count();
assert(count() != 0,
"Wraparound -- history would be incorrectly discarded");
// Compute the new weighted average
float new_avg = compute_adaptive_average(new_sample, average());
set_average(new_avg);
_last_sample = new_sample;
}
void AdaptiveWeightedAverage::print() const {
print_on(tty);
}
void AdaptiveWeightedAverage::print_on(outputStream* st) const {
guarantee(false, "NYI");
}
void AdaptivePaddedAverage::print() const {
print_on(tty);
}
void AdaptivePaddedAverage::print_on(outputStream* st) const {
guarantee(false, "NYI");
}
void AdaptivePaddedNoZeroDevAverage::print() const {
print_on(tty);
}
void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {
guarantee(false, "NYI");
}
void AdaptivePaddedAverage::sample(float new_sample) {
// Compute new adaptive weighted average based on new sample.
AdaptiveWeightedAverage::sample(new_sample);
// Now update the deviation and the padded average.
float new_avg = average();
float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
deviation());
set_deviation(new_dev);
set_padded_average(new_avg + padding() * new_dev);
_last_sample = new_sample;
}
void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
// Compute our parent classes sample information
AdaptiveWeightedAverage::sample(new_sample);
float new_avg = average();
if (new_sample != 0) {
// We only create a new deviation if the sample is non-zero
float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
deviation());
set_deviation(new_dev);
}
set_padded_average(new_avg + padding() * deviation());
_last_sample = new_sample;
}
LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
_sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0),
_intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {}
void LinearLeastSquareFit::update(double x, double y) {
_sum_x = _sum_x + x;
_sum_x_squared = _sum_x_squared + x * x;
_sum_y = _sum_y + y;
_sum_xy = _sum_xy + x * y;
_mean_x.sample(x);
_mean_y.sample(y);
assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
if ( _mean_x.count() > 1 ) {
double slope_denominator;
slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
// Some tolerance should be injected here. A denominator that is
// nearly 0 should be avoided.
if (slope_denominator != 0.0) {
double slope_numerator;
slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
_slope = slope_numerator / slope_denominator;
// The _mean_y and _mean_x are decaying averages and can
// be used to discount earlier data. If they are used,
// first consider whether all the quantities should be
// kept as decaying averages.
// _intercept = _mean_y.average() - _slope * _mean_x.average();
_intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
}
}
}
double LinearLeastSquareFit::y(double x) {
double new_y;
if ( _mean_x.count() > 1 ) {
new_y = (_intercept + _slope * x);
return new_y;
} else {
return _mean_y.average();
}
}
// Both decrement_will_decrease() and increment_will_decrease() return
// true for a slope of 0. That is because a change is necessary before
// a slope can be calculated and a 0 slope will, in general, indicate
// that no calculation of the slope has yet been done. Returning true
// for a slope equal to 0 reflects the intuitive expectation of the
// dependence on the slope. Don't use the complement of these functions
// since that untuitive expectation is not built into the complement.
bool LinearLeastSquareFit::decrement_will_decrease() {
return (_slope >= 0.00);
}
bool LinearLeastSquareFit::increment_will_decrease() {
return (_slope <= 0.00);
}
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