jdk8-mips64-public/hotspot: src/share/vm/utilities/numberSeq.cpp@0fbdb4381b99

6814575: Update copyright year
Summary: Update copyright for files that have been modified in 2009, up to 03/09
Reviewed-by: katleman, tbell, ohair

     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  */

    25 # include "incls/_precompiled.incl"

    26 # include "incls/_numberSeq.cpp.incl"

    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 }

    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 }

    47 double AbsSeq::avg() const {

    48   if (_num == 0)

    49     return 0.0;

    50   else

    51     return _sum / total();

    52 }

    54 double AbsSeq::variance() const {

    55   if (_num <= 1)

    56     return 0.0;

    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

    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 }

    71 double AbsSeq::sd() const {

    72   double var = variance();

    73   guarantee( var >= 0.0, "variance should not be negative" );

    74   return sqrt(var);

    75 }

    77 double AbsSeq::davg() const {

    78   return _davg;

    79 }

    81 double AbsSeq::dvariance() const {

    82   if (_num <= 1)

    83     return 0.0;

    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

    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 }

    97 double AbsSeq::dsd() const {

    98   double var = dvariance();

    99   guarantee( var >= 0.0, "variance should not be negative" );

   100   return sqrt(var);

   101 }

   103 NumberSeq::NumberSeq(double alpha) :

   104   AbsSeq(alpha), _maximum(0.0), _last(0.0) {

   105 }

   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 }

   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   }

   123   _num = total->num();

   124   _sum = sum;

   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 }

   133 void NumberSeq::add(double val) {

   134   AbsSeq::add(val);

   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 }

   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 }

   156 void TruncatedSeq::add(double val) {

   157   AbsSeq::add(val);

   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;

   165   // ...and update them with the new value

   166   _sum += val;

   167   _sum_of_squares += val * val;

   169   // now replace the old value with the new one

   170   _sequence[_next] = val;

   171   _next = (_next + 1) % _length;

   173   // only increase it if the buffer is not full

   174   if (_num < _length)

   175     ++_num;

   177   guarantee( variance() > -1.0, "variance should be >= 0" );

   178 }

   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 }

   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 }

   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 }

   212 double TruncatedSeq::predict_next() const {

   213   if (_num == 0)

   214     return 0.0;

   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;

   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];

   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;

   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;

   242   return b0 + b1 * num;

   243 }

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