jdk8-mips64-public/hotspot: src/share/vm/utilities/numberSeq.cpp@dc1769738300 (annotated)

src/share/vm/utilities/numberSeq.cpp@dc1769738300 (annotated)

src/share/vm/utilities/numberSeq.cpp

Tue, 24 Jul 2018 13:22:11 +0800

author: aoqi
date: Tue, 24 Jul 2018 13:22:11 +0800
changeset 9137: dc1769738300
parent 6876: 710a3c8b516e
permissions: -rw-r--r--

#7048 added Loongson release info to hs_err crash files

 /*
  * Copyright (c) 2001, 2014, 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 "memory/allocation.inline.hpp"
 #include "utilities/debug.hpp"
 #include "utilities/globalDefinitions.hpp"
 #include "utilities/numberSeq.hpp"
 AbsSeq::AbsSeq(double alpha) :
   _num(0), _sum(0.0), _sum_of_squares(0.0),
   _davg(0.0), _dvariance(0.0), _alpha(alpha) {
 }
 void AbsSeq::add(double val) {
   if (_num == 0) {
     // if the sequence is empty, the davg is the same as the value
     _davg = val;
     // and the variance is 0
     _dvariance = 0.0;
   } else {
     // otherwise, calculate both
     _davg = (1.0 - _alpha) * val + _alpha * _davg;
     double diff = val - _davg;
     _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance;
   }
 }
 double AbsSeq::avg() const {
   if (_num == 0)
     return 0.0;
   else
     return _sum / total();
 }
 double AbsSeq::variance() const {
   if (_num <= 1)
     return 0.0;
   double x_bar = avg();
   double result = _sum_of_squares / total() - x_bar * x_bar;
   if (result < 0.0) {
     // due to loss-of-precision errors, the variance might be negative
     // by a small bit
     //    guarantee(-0.1 < result && result < 0.0,
     //        "if variance is negative, it should be very small");
     result = 0.0;
   }
   return result;
 }
 double AbsSeq::sd() const {
   double var = variance();
   guarantee( var >= 0.0, "variance should not be negative" );
   return sqrt(var);
 }
 double AbsSeq::davg() const {
   return _davg;
 }
 double AbsSeq::dvariance() const {
   if (_num <= 1)
     return 0.0;
   double result = _dvariance;
   if (result < 0.0) {
     // due to loss-of-precision errors, the variance might be negative
     // by a small bit
     guarantee(-0.1 < result && result < 0.0,
                "if variance is negative, it should be very small");
     result = 0.0;
   }
   return result;
 }
 double AbsSeq::dsd() const {
   double var = dvariance();
   guarantee( var >= 0.0, "variance should not be negative" );
   return sqrt(var);
 }
 NumberSeq::NumberSeq(double alpha) :
   AbsSeq(alpha), _maximum(0.0), _last(0.0) {
 }
 bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
   for (int i = 0; i < n; ++i) {
     if (parts[i] != NULL && total->num() != parts[i]->num())
       return false;
   }
   return true;
 }
 void NumberSeq::add(double val) {
   AbsSeq::add(val);
   _last = val;
   if (_num == 0) {
     _maximum = val;
   } else {
     if (val > _maximum)
       _maximum = val;
   }
   _sum += val;
   _sum_of_squares += val * val;
   ++_num;
 }
 TruncatedSeq::TruncatedSeq(int length, double alpha):
   AbsSeq(alpha), _length(length), _next(0) {
   _sequence = NEW_C_HEAP_ARRAY(double, _length, mtInternal);
   for (int i = 0; i < _length; ++i)
     _sequence[i] = 0.0;
 }
 TruncatedSeq::~TruncatedSeq() {
   FREE_C_HEAP_ARRAY(double, _sequence, mtGC);
 }
 void TruncatedSeq::add(double val) {
   AbsSeq::add(val);
   // get the oldest value in the sequence...
   double old_val = _sequence[_next];
   // ...remove it from the sum and sum of squares
   _sum -= old_val;
   _sum_of_squares -= old_val * old_val;
   // ...and update them with the new value
   _sum += val;
   _sum_of_squares += val * val;
   // now replace the old value with the new one
   _sequence[_next] = val;
   _next = (_next + 1) % _length;
   // only increase it if the buffer is not full
   if (_num < _length)
     ++_num;
   guarantee( variance() > -1.0, "variance should be >= 0" );
 }
 // can't easily keep track of this incrementally...
 double TruncatedSeq::maximum() const {
   if (_num == 0)
     return 0.0;
   double ret = _sequence[0];
   for (int i = 1; i < _num; ++i) {
     double val = _sequence[i];
     if (val > ret)
       ret = val;
   }
   return ret;
 }
 double TruncatedSeq::last() const {
   if (_num == 0)
     return 0.0;
   unsigned last_index = (_next + _length - 1) % _length;
   return _sequence[last_index];
 }
 double TruncatedSeq::oldest() const {
   if (_num == 0)
     return 0.0;
   else if (_num < _length)
     // index 0 always oldest value until the array is full
     return _sequence[0];
   else {
     // since the array is full, _next is over the oldest value
     return _sequence[_next];
   }
 }
 double TruncatedSeq::predict_next() const {
   if (_num == 0)
     return 0.0;
   double num           = (double) _num;
   double x_squared_sum = 0.0;
   double x_sum         = 0.0;
   double y_sum         = 0.0;
   double xy_sum        = 0.0;
   double x_avg         = 0.0;
   double y_avg         = 0.0;
   int first = (_next + _length - _num) % _length;
   for (int i = 0; i < _num; ++i) {
     double x = (double) i;
     double y =  _sequence[(first + i) % _length];
     x_squared_sum += x * x;
     x_sum         += x;
     y_sum         += y;
     xy_sum        += x * y;
   }
   x_avg = x_sum / num;
   y_avg = y_sum / num;
   double Sxx = x_squared_sum - x_sum * x_sum / num;
   double Sxy = xy_sum - x_sum * y_sum / num;
   double b1 = Sxy / Sxx;
   double b0 = y_avg - b1 * x_avg;
   return b0 + b1 * num;
 }
 // Printing/Debugging Support
 void AbsSeq::dump() { dump_on(gclog_or_tty); }
 void AbsSeq::dump_on(outputStream* s) {
   s->print_cr("\t _num = %d, _sum = %7.3f, _sum_of_squares = %7.3f",
                   _num,      _sum,         _sum_of_squares);
   s->print_cr("\t _davg = %7.3f, _dvariance = %7.3f, _alpha = %7.3f",
                   _davg,         _dvariance,         _alpha);
 }
 void NumberSeq::dump_on(outputStream* s) {
   AbsSeq::dump_on(s);
   s->print_cr("\t\t _last = %7.3f, _maximum = %7.3f", _last, _maximum);
 }
 void TruncatedSeq::dump_on(outputStream* s) {
   AbsSeq::dump_on(s);
   s->print_cr("\t\t _length = %d, _next = %d", _length, _next);
   for (int i = 0; i < _length; i++) {
     if (i%5 == 0) {
       s->cr();
       s->print("\t");
     }
     s->print("\t[%d]=%7.3f", i, _sequence[i]);
   }
   s->cr();
 }

Mercurial > jdk8-mips64-public > hotspot / annotate

src/share/vm/utilities/numberSeq.cpp@dc1769738300 (annotated)

src/share/vm/utilities/numberSeq.cpp