| /* |
| * Copyright (c) 2002, 2012, 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 |
| // OLD_THRESHOLD/weight samples. |
| unsigned count_weight = 0; |
| |
| // Avoid division by zero if the counter wraps (7158457) |
| if (!is_old()) { |
| count_weight = OLD_THRESHOLD/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(); |
| |
| // 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); |
| } |