1 /*
2 * Copyright (c) 2002, 2005, Oracle and/or its affiliates. All rights reserved.
3 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4 *
5 * This code is free software; you can redistribute it and/or modify it
6 * under the terms of the GNU General Public License version 2 only, as
7 * published by the Free Software Foundation.
8 *
9 * This code is distributed in the hope that it will be useful, but WITHOUT
10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
12 * version 2 for more details (a copy is included in the LICENSE file that
13 * accompanied this code).
14 *
15 * You should have received a copy of the GNU General Public License version
16 * 2 along with this work; if not, write to the Free Software Foundation,
17 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
18 *
19 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
20 * or visit www.oracle.com if you need additional information or have any
21 * questions.
22 *
23 */
24
25 # include "incls/_precompiled.incl"
26 # include "incls/_gcUtil.cpp.incl"
27
28 // Catch-all file for utility classes
29
30 float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,
31 float average) {
32 // We smooth the samples by not using weight() directly until we've
33 // had enough data to make it meaningful. We'd like the first weight
34 // used to be 1, the second to be 1/2, etc until we have 100/weight
35 // samples.
36 unsigned count_weight = 100/count();
37 unsigned adaptive_weight = (MAX2(weight(), count_weight));
38
39 float new_avg = exp_avg(average, new_sample, adaptive_weight);
40
41 return new_avg;
42 }
43
44 void AdaptiveWeightedAverage::sample(float new_sample) {
45 increment_count();
46 assert(count() != 0,
47 "Wraparound -- history would be incorrectly discarded");
48
49 // Compute the new weighted average
50 float new_avg = compute_adaptive_average(new_sample, average());
51 set_average(new_avg);
52 _last_sample = new_sample;
53 }
54
55 void AdaptiveWeightedAverage::print() const {
56 print_on(tty);
57 }
58
59 void AdaptiveWeightedAverage::print_on(outputStream* st) const {
60 guarantee(false, "NYI");
61 }
62
63 void AdaptivePaddedAverage::print() const {
64 print_on(tty);
65 }
66
67 void AdaptivePaddedAverage::print_on(outputStream* st) const {
68 guarantee(false, "NYI");
69 }
70
71 void AdaptivePaddedNoZeroDevAverage::print() const {
72 print_on(tty);
73 }
74
75 void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {
76 guarantee(false, "NYI");
77 }
78
79 void AdaptivePaddedAverage::sample(float new_sample) {
80 // Compute new adaptive weighted average based on new sample.
81 AdaptiveWeightedAverage::sample(new_sample);
82
83 // Now update the deviation and the padded average.
84 float new_avg = average();
85 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
86 deviation());
87 set_deviation(new_dev);
88 set_padded_average(new_avg + padding() * new_dev);
89 _last_sample = new_sample;
90 }
91
92 void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {
93 // Compute our parent classes sample information
94 AdaptiveWeightedAverage::sample(new_sample);
95
96 float new_avg = average();
97 if (new_sample != 0) {
98 // We only create a new deviation if the sample is non-zero
99 float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),
100 deviation());
101
102 set_deviation(new_dev);
103 }
104 set_padded_average(new_avg + padding() * deviation());
105 _last_sample = new_sample;
106 }
107
108 LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :
109 _sum_x(0), _sum_y(0), _sum_xy(0),
110 _mean_x(weight), _mean_y(weight) {}
111
112 void LinearLeastSquareFit::update(double x, double y) {
113 _sum_x = _sum_x + x;
114 _sum_x_squared = _sum_x_squared + x * x;
115 _sum_y = _sum_y + y;
116 _sum_xy = _sum_xy + x * y;
117 _mean_x.sample(x);
118 _mean_y.sample(y);
119 assert(_mean_x.count() == _mean_y.count(), "Incorrect count");
120 if ( _mean_x.count() > 1 ) {
121 double slope_denominator;
122 slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);
123 // Some tolerance should be injected here. A denominator that is
124 // nearly 0 should be avoided.
125
126 if (slope_denominator != 0.0) {
127 double slope_numerator;
128 slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);
129 _slope = slope_numerator / slope_denominator;
130
131 // The _mean_y and _mean_x are decaying averages and can
132 // be used to discount earlier data. If they are used,
133 // first consider whether all the quantities should be
134 // kept as decaying averages.
135 // _intercept = _mean_y.average() - _slope * _mean_x.average();
136 _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());
137 }
138 }
139 }
140
141 double LinearLeastSquareFit::y(double x) {
142 double new_y;
143
144 if ( _mean_x.count() > 1 ) {
145 new_y = (_intercept + _slope * x);
146 return new_y;
147 } else {
148 return _mean_y.average();
149 }
150 }
151
152 // Both decrement_will_decrease() and increment_will_decrease() return
153 // true for a slope of 0. That is because a change is necessary before
154 // a slope can be calculated and a 0 slope will, in general, indicate
155 // that no calculation of the slope has yet been done. Returning true
156 // for a slope equal to 0 reflects the intuitive expectation of the
157 // dependence on the slope. Don't use the complement of these functions
158 // since that untuitive expectation is not built into the complement.
159 bool LinearLeastSquareFit::decrement_will_decrease() {
160 return (_slope >= 0.00);
161 }
162
163 bool LinearLeastSquareFit::increment_will_decrease() {
164 return (_slope <= 0.00);
165 }