Ziele Untersuchung
mit Columbo Integrität von
Datenbanken Interaktion und
Portierbarkeit Ergonomie der
Schnittstellen

Angebot Produkte Projekt Beratung

Mittel Analytik Modellierung Sprachen Algebra Logik Hardware Denken Kreativität

Zusammenhänge Gesellschaft Wirtschaft Branche Firma


products/sources/formale Sprachen/Java/Openjdk/src/hotspot/share/utilities/ (Sun/Oracle ^©) Datei vom 13.11.2022 mit Größe 6 kB

Quelle numberSeq.cpp Sprache: C

/*
* 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
    // Formula from "Incremental calculation of weighted mean and variance" by Tony Finch
    // diff := x - mean
    // incr := alpha * diff
    // mean := mean + incr
    // variance := (1 - alpha) * (variance + diff * incr)
    // PDF available at https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf
    double diff = val - _davg;
    double incr = _alpha * diff;
    _davg += incr;
    _dvariance = (1.0 - _alpha) * (_dvariance + diff * incr);
  }
}

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), _last(0.0), _maximum(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);
}

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(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();
}

quality92%

¤ Dauer der Verarbeitung: 0.11 Sekunden (vorverarbeitet) ¤

Wurzel

Suchen

Beweissystem der NASA

Beweissystem Isabelle

NIST Cobol Testsuite

Cephes Mathematical Library

Wiener Entwicklungsmethode

Haftungshinweis

Die Informationen auf dieser Webseite wurden nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit, noch Qualität der bereit gestellten Informationen zugesichert.