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comparison src/share/vm/utilities/numberSeq.cpp @ 342:37f87013dfd8

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6711316: Open source the Garbage-First garbage collector Summary: First mercurial integration of the code for the Garbage-First garbage collector. Reviewed-by: apetrusenko, iveresov, jmasa, sgoldman, tonyp, ysr
author ysr
date Thu, 05 Jun 2008 15:57:56 -0700
parents
children 89f1b9ae8991
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189:0b27f3512f9e 342:37f87013dfd8
1 /*
2 * Copyright 2001-2007 Sun Microsystems, Inc. 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 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
20 * CA 95054 USA or visit www.sun.com if you need additional information or
21 * have any questions.
22 *
23 */
24
25 # include "incls/_precompiled.incl"
26 # include "incls/_numberSeq.cpp.incl"
27
28 AbsSeq::AbsSeq(double alpha) :
29 _num(0), _sum(0.0), _sum_of_squares(0.0),
30 _davg(0.0), _dvariance(0.0), _alpha(alpha) {
31 }
32
33 void AbsSeq::add(double val) {
34 if (_num == 0) {
35 // if the sequence is empty, the davg is the same as the value
36 _davg = val;
37 // and the variance is 0
38 _dvariance = 0.0;
39 } else {
40 // otherwise, calculate both
41 _davg = (1.0 - _alpha) * val + _alpha * _davg;
42 double diff = val - _davg;
43 _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance;
44 }
45 }
46
47 double AbsSeq::avg() const {
48 if (_num == 0)
49 return 0.0;
50 else
51 return _sum / total();
52 }
53
54 double AbsSeq::variance() const {
55 if (_num <= 1)
56 return 0.0;
57
58 double x_bar = avg();
59 double result = _sum_of_squares / total() - x_bar * x_bar;
60 if (result < 0.0) {
61 // due to loss-of-precision errors, the variance might be negative
62 // by a small bit
63
64 // guarantee(-0.1 < result && result < 0.0,
65 // "if variance is negative, it should be very small");
66 result = 0.0;
67 }
68 return result;
69 }
70
71 double AbsSeq::sd() const {
72 double var = variance();
73 guarantee( var >= 0.0, "variance should not be negative" );
74 return sqrt(var);
75 }
76
77 double AbsSeq::davg() const {
78 return _davg;
79 }
80
81 double AbsSeq::dvariance() const {
82 if (_num <= 1)
83 return 0.0;
84
85 double result = _dvariance;
86 if (result < 0.0) {
87 // due to loss-of-precision errors, the variance might be negative
88 // by a small bit
89
90 guarantee(-0.1 < result && result < 0.0,
91 "if variance is negative, it should be very small");
92 result = 0.0;
93 }
94 return result;
95 }
96
97 double AbsSeq::dsd() const {
98 double var = dvariance();
99 guarantee( var >= 0.0, "variance should not be negative" );
100 return sqrt(var);
101 }
102
103 NumberSeq::NumberSeq(double alpha) :
104 AbsSeq(alpha), _maximum(0.0), _last(0.0) {
105 }
106
107 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
108 for (int i = 0; i < n; ++i) {
109 if (parts[i] != NULL && total->num() != parts[i]->num())
110 return false;
111 }
112 return true;
113 }
114
115 NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) {
116 guarantee(check_nums(total, n, parts), "all seq lengths should match");
117 double sum = total->sum();
118 for (int i = 0; i < n; ++i) {
119 if (parts[i] != NULL)
120 sum -= parts[i]->sum();
121 }
122
123 _num = total->num();
124 _sum = sum;
125
126 // we do not calculate these...
127 _sum_of_squares = -1.0;
128 _maximum = -1.0;
129 _davg = -1.0;
130 _dvariance = -1.0;
131 }
132
133 void NumberSeq::add(double val) {
134 AbsSeq::add(val);
135
136 _last = val;
137 if (_num == 0) {
138 _maximum = val;
139 } else {
140 if (val > _maximum)
141 _maximum = val;
142 }
143 _sum += val;
144 _sum_of_squares += val * val;
145 ++_num;
146 }
147
148
149 TruncatedSeq::TruncatedSeq(int length, double alpha):
150 AbsSeq(alpha), _length(length), _next(0) {
151 _sequence = NEW_C_HEAP_ARRAY(double, _length);
152 for (int i = 0; i < _length; ++i)
153 _sequence[i] = 0.0;
154 }
155
156 void TruncatedSeq::add(double val) {
157 AbsSeq::add(val);
158
159 // get the oldest value in the sequence...
160 double old_val = _sequence[_next];
161 // ...remove it from the sum and sum of squares
162 _sum -= old_val;
163 _sum_of_squares -= old_val * old_val;
164
165 // ...and update them with the new value
166 _sum += val;
167 _sum_of_squares += val * val;
168
169 // now replace the old value with the new one
170 _sequence[_next] = val;
171 _next = (_next + 1) % _length;
172
173 // only increase it if the buffer is not full
174 if (_num < _length)
175 ++_num;
176
177 guarantee( variance() > -1.0, "variance should be >= 0" );
178 }
179
180 // can't easily keep track of this incrementally...
181 double TruncatedSeq::maximum() const {
182 if (_num == 0)
183 return 0.0;
184 double ret = _sequence[0];
185 for (int i = 1; i < _num; ++i) {
186 double val = _sequence[i];
187 if (val > ret)
188 ret = val;
189 }
190 return ret;
191 }
192
193 double TruncatedSeq::last() const {
194 if (_num == 0)
195 return 0.0;
196 unsigned last_index = (_next + _length - 1) % _length;
197 return _sequence[last_index];
198 }
199
200 double TruncatedSeq::oldest() const {
201 if (_num == 0)
202 return 0.0;
203 else if (_num < _length)
204 // index 0 always oldest value until the array is full
205 return _sequence[0];
206 else {
207 // since the array is full, _next is over the oldest value
208 return _sequence[_next];
209 }
210 }
211
212 double TruncatedSeq::predict_next() const {
213 if (_num == 0)
214 return 0.0;
215
216 double num = (double) _num;
217 double x_squared_sum = 0.0;
218 double x_sum = 0.0;
219 double y_sum = 0.0;
220 double xy_sum = 0.0;
221 double x_avg = 0.0;
222 double y_avg = 0.0;
223
224 int first = (_next + _length - _num) % _length;
225 for (int i = 0; i < _num; ++i) {
226 double x = (double) i;
227 double y = _sequence[(first + i) % _length];
228
229 x_squared_sum += x * x;
230 x_sum += x;
231 y_sum += y;
232 xy_sum += x * y;
233 }
234 x_avg = x_sum / num;
235 y_avg = y_sum / num;
236
237 double Sxx = x_squared_sum - x_sum * x_sum / num;
238 double Sxy = xy_sum - x_sum * y_sum / num;
239 double b1 = Sxy / Sxx;
240 double b0 = y_avg - b1 * x_avg;
241
242 return b0 + b1 * num;
243 }