1 /*
2 * Copyright (c) 2001, 2023, 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 "precompiled.hpp"
26 #include "memory/allocation.inline.hpp"
27 #include "utilities/debug.hpp"
28 #include "utilities/globalDefinitions.hpp"
29 #include "utilities/numberSeq.hpp"
30
31 AbsSeq::AbsSeq(double alpha) :
32 _num(0), _sum(0.0), _sum_of_squares(0.0),
33 _davg(0.0), _dvariance(0.0), _alpha(alpha) {
34 }
35
36 void AbsSeq::add(double val) {
37 if (_num == 0) {
38 // if the sequence is empty, the davg is the same as the value
39 _davg = val;
40 // and the variance is 0
41 _dvariance = 0.0;
42 } else {
43 // otherwise, calculate both
44 // Formula from "Incremental calculation of weighted mean and variance" by Tony Finch
45 // diff := x - mean
46 // incr := alpha * diff
47 // mean := mean + incr
48 // variance := (1 - alpha) * (variance + diff * incr)
49 // PDF available at https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf
50 double diff = val - _davg;
51 double incr = _alpha * diff;
52 _davg += incr;
53 _dvariance = (1.0 - _alpha) * (_dvariance + diff * incr);
54 }
55 }
56
57 double AbsSeq::avg() const {
58 if (_num == 0)
59 return 0.0;
60 else
61 return _sum / total();
62 }
63
64 double AbsSeq::variance() const {
65 if (_num <= 1)
66 return 0.0;
67
68 double x_bar = avg();
69 double result = _sum_of_squares / total() - x_bar * x_bar;
70 if (result < 0.0) {
71 // due to loss-of-precision errors, the variance might be negative
72 // by a small bit
73
74 // guarantee(-0.1 < result && result < 0.0,
75 // "if variance is negative, it should be very small");
76 result = 0.0;
77 }
78 return result;
79 }
80
81 double AbsSeq::sd() const {
82 double var = variance();
83 guarantee( var >= 0.0, "variance should not be negative" );
84 return sqrt(var);
85 }
86
87 double AbsSeq::davg() const {
88 return _davg;
89 }
90
91 double AbsSeq::dvariance() const {
92 if (_num <= 1)
93 return 0.0;
94
95 double result = _dvariance;
96 if (result < 0.0) {
97 // due to loss-of-precision errors, the variance might be negative
98 // by a small bit
99
100 guarantee(-0.1 < result && result < 0.0,
101 "if variance is negative, it should be very small");
102 result = 0.0;
103 }
104 return result;
105 }
106
107 double AbsSeq::dsd() const {
108 double var = dvariance();
109 guarantee( var >= 0.0, "variance should not be negative" );
110 return sqrt(var);
111 }
112
113 NumberSeq::NumberSeq(double alpha) :
114 AbsSeq(alpha), _last(0.0), _maximum(0.0) {
115 }
116
117 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
118 for (int i = 0; i < n; ++i) {
119 if (parts[i] != nullptr && total->num() != parts[i]->num())
120 return false;
121 }
122 return true;
123 }
124
125 void NumberSeq::add(double val) {
126 AbsSeq::add(val);
127
128 _last = val;
129 if (_num == 0) {
130 _maximum = val;
131 } else {
132 if (val > _maximum)
133 _maximum = val;
134 }
135 _sum += val;
136 _sum_of_squares += val * val;
137 ++_num;
138 }
139
140
141 TruncatedSeq::TruncatedSeq(int length, double alpha):
142 AbsSeq(alpha), _length(length), _next(0) {
143 _sequence = NEW_C_HEAP_ARRAY(double, _length, mtInternal);
144 for (int i = 0; i < _length; ++i)
145 _sequence[i] = 0.0;
146 }
147
148 TruncatedSeq::~TruncatedSeq() {
149 FREE_C_HEAP_ARRAY(double, _sequence);
150 }
151
152 void TruncatedSeq::add(double val) {
153 AbsSeq::add(val);
154
155 // get the oldest value in the sequence...
156 double old_val = _sequence[_next];
157 // ...remove it from the sum and sum of squares
158 _sum -= old_val;
159 _sum_of_squares -= old_val * old_val;
160
161 // ...and update them with the new value
162 _sum += val;
163 _sum_of_squares += val * val;
164
165 // now replace the old value with the new one
166 _sequence[_next] = val;
167 _next = (_next + 1) % _length;
168
169 // only increase it if the buffer is not full
170 if (_num < _length)
171 ++_num;
172
173 guarantee( variance() > -1.0, "variance should be >= 0" );
174 }
175
176 // can't easily keep track of this incrementally...
177 double TruncatedSeq::maximum() const {
178 if (_num == 0)
179 return 0.0;
180 double ret = _sequence[0];
181 for (int i = 1; i < _num; ++i) {
182 double val = _sequence[i];
183 if (val > ret)
184 ret = val;
185 }
186 return ret;
187 }
188
189 double TruncatedSeq::last() const {
190 if (_num == 0)
191 return 0.0;
192 unsigned last_index = (_next + _length - 1) % _length;
193 return _sequence[last_index];
194 }
195
196 double TruncatedSeq::oldest() const {
197 if (_num == 0)
198 return 0.0;
199 else if (_num < _length)
200 // index 0 always oldest value until the array is full
201 return _sequence[0];
202 else {
203 // since the array is full, _next is over the oldest value
204 return _sequence[_next];
205 }
206 }
207
208 double TruncatedSeq::predict_next() const {
209 if (_num == 0)
210 return 0.0;
211
212 double num = (double) _num;
213 double x_squared_sum = 0.0;
214 double x_sum = 0.0;
215 double y_sum = 0.0;
216 double xy_sum = 0.0;
217 double x_avg = 0.0;
218 double y_avg = 0.0;
219
220 int first = (_next + _length - _num) % _length;
221 for (int i = 0; i < _num; ++i) {
222 double x = (double) i;
223 double y = _sequence[(first + i) % _length];
224
225 x_squared_sum += x * x;
226 x_sum += x;
227 y_sum += y;
228 xy_sum += x * y;
229 }
230 x_avg = x_sum / num;
231 y_avg = y_sum / num;
232
233 double Sxx = x_squared_sum - x_sum * x_sum / num;
234 double Sxy = xy_sum - x_sum * y_sum / num;
235 double b1 = Sxy / Sxx;
236 double b0 = y_avg - b1 * x_avg;
237
238 return b0 + b1 * num;
239 }
240
241
242 // Printing/Debugging Support
243
244 void AbsSeq::dump() { dump_on(tty); }
245
246 void AbsSeq::dump_on(outputStream* s) {
247 s->print_cr("\t _num = %d, _sum = %7.3f, _sum_of_squares = %7.3f",
248 _num, _sum, _sum_of_squares);
249 s->print_cr("\t _davg = %7.3f, _dvariance = %7.3f, _alpha = %7.3f",
250 _davg, _dvariance, _alpha);
251 }
252
253 void NumberSeq::dump_on(outputStream* s) {
254 AbsSeq::dump_on(s);
255 s->print_cr("\t\t _last = %7.3f, _maximum = %7.3f", _last, _maximum);
256 }
257
258 void TruncatedSeq::dump_on(outputStream* s) {
259 AbsSeq::dump_on(s);
260 s->print_cr("\t\t _length = %d, _next = %d", _length, _next);
261 for (int i = 0; i < _length; i++) {
262 if (i%5 == 0) {
263 s->cr();
264 s->print("\t");
265 }
266 s->print("\t[%d]=%7.3f", i, _sequence[i]);
267 }
268 s->cr();
269 }