Mercurial > jdk8-mips64-public > hotspot / file revision

 /*

  * Copyright (c) 2001, 2010, 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

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  */

 #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;

 }

 NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) {

   guarantee(check_nums(total, n, parts), "all seq lengths should match");

   double sum = total->sum();

   for (int i = 0; i < n; ++i) {

     if (parts[i] != NULL)

       sum -= parts[i]->sum();

   }

   _num = total->num();

   _sum = sum;

   // we do not calculate these...

   _sum_of_squares = -1.0;

   _maximum = -1.0;

   _davg = -1.0;

   _dvariance = -1.0;

 }

 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);

   for (int i = 0; i < _length; ++i)

     _sequence[i] = 0.0;

 }

 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");

 }

 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->print_cr("");

 }