jdk

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/*
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 * Copyright (c) 2001, 2023, Oracle and/or its affiliates. All rights reserved.
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 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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 *
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 * This code is free software; you can redistribute it and/or modify it
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 * under the terms of the GNU General Public License version 2 only, as
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 * published by the Free Software Foundation.
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 *
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 * This code is distributed in the hope that it will be useful, but WITHOUT
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 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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 * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License
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 * version 2 for more details (a copy is included in the LICENSE file that
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 * accompanied this code).
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 *
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 * You should have received a copy of the GNU General Public License version
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 * 2 along with this work; if not, write to the Free Software Foundation,
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 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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 *
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 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
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 * or visit www.oracle.com if you need additional information or have any
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 * questions.
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 *
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 */
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#include "precompiled.hpp"
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#include "memory/allocation.inline.hpp"
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#include "utilities/debug.hpp"
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#include "utilities/globalDefinitions.hpp"
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#include "utilities/numberSeq.hpp"
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AbsSeq::AbsSeq(double alpha) :
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  _num(0), _sum(0.0), _sum_of_squares(0.0),
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  _davg(0.0), _dvariance(0.0), _alpha(alpha) {
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}
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void AbsSeq::add(double val) {
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  if (_num == 0) {
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    // if the sequence is empty, the davg is the same as the value
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    _davg = val;
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    // and the variance is 0
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    _dvariance = 0.0;
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  } else {
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    // otherwise, calculate both
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    // Formula from "Incremental calculation of weighted mean and variance" by Tony Finch
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    // diff := x - mean
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    // incr := alpha * diff
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    // mean := mean + incr
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    // variance := (1 - alpha) * (variance + diff * incr)
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    // PDF available at https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf
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    double diff = val - _davg;
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    double incr = _alpha * diff;
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    _davg += incr;
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    _dvariance = (1.0 - _alpha) * (_dvariance + diff * incr);
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  }
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}
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double AbsSeq::avg() const {
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  if (_num == 0)
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    return 0.0;
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  else
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    return _sum / total();
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}
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double AbsSeq::variance() const {
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  if (_num <= 1)
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    return 0.0;
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  double x_bar = avg();
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  double result = _sum_of_squares / total() - x_bar * x_bar;
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  if (result < 0.0) {
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    // due to loss-of-precision errors, the variance might be negative
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    // by a small bit
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    //    guarantee(-0.1 < result && result < 0.0,
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    //        "if variance is negative, it should be very small");
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    result = 0.0;
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  }
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  return result;
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}
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double AbsSeq::sd() const {
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  double var = variance();
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  guarantee( var >= 0.0, "variance should not be negative" );
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  return sqrt(var);
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}
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double AbsSeq::davg() const {
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  return _davg;
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}
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double AbsSeq::dvariance() const {
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  if (_num <= 1)
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    return 0.0;
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  double result = _dvariance;
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  if (result < 0.0) {
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    // due to loss-of-precision errors, the variance might be negative
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    // by a small bit
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    guarantee(-0.1 < result && result < 0.0,
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               "if variance is negative, it should be very small");
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    result = 0.0;
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  }
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  return result;
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}
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double AbsSeq::dsd() const {
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  double var = dvariance();
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  guarantee( var >= 0.0, "variance should not be negative" );
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  return sqrt(var);
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}
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NumberSeq::NumberSeq(double alpha) :
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  AbsSeq(alpha), _last(0.0), _maximum(0.0) {
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}
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bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
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  for (int i = 0; i < n; ++i) {
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    if (parts[i] != nullptr && total->num() != parts[i]->num())
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      return false;
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  }
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  return true;
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}
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void NumberSeq::add(double val) {
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  AbsSeq::add(val);
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  _last = val;
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  if (_num == 0) {
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    _maximum = val;
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  } else {
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    if (val > _maximum)
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      _maximum = val;
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  }
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  _sum += val;
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  _sum_of_squares += val * val;
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  ++_num;
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}
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TruncatedSeq::TruncatedSeq(int length, double alpha):
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  AbsSeq(alpha), _length(length), _next(0) {
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  _sequence = NEW_C_HEAP_ARRAY(double, _length, mtInternal);
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  for (int i = 0; i < _length; ++i)
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    _sequence[i] = 0.0;
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}
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TruncatedSeq::~TruncatedSeq() {
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  FREE_C_HEAP_ARRAY(double, _sequence);
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}
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void TruncatedSeq::add(double val) {
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  AbsSeq::add(val);
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  // get the oldest value in the sequence...
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  double old_val = _sequence[_next];
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  // ...remove it from the sum and sum of squares
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  _sum -= old_val;
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  _sum_of_squares -= old_val * old_val;
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  // ...and update them with the new value
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  _sum += val;
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  _sum_of_squares += val * val;
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  // now replace the old value with the new one
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  _sequence[_next] = val;
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  _next = (_next + 1) % _length;
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  // only increase it if the buffer is not full
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  if (_num < _length)
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    ++_num;
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  guarantee( variance() > -1.0, "variance should be >= 0" );
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}
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// can't easily keep track of this incrementally...
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double TruncatedSeq::maximum() const {
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  if (_num == 0)
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    return 0.0;
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  double ret = _sequence[0];
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  for (int i = 1; i < _num; ++i) {
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    double val = _sequence[i];
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    if (val > ret)
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      ret = val;
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  }
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  return ret;
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}
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double TruncatedSeq::last() const {
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  if (_num == 0)
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    return 0.0;
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  unsigned last_index = (_next + _length - 1) % _length;
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  return _sequence[last_index];
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}
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double TruncatedSeq::oldest() const {
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  if (_num == 0)
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    return 0.0;
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  else if (_num < _length)
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    // index 0 always oldest value until the array is full
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    return _sequence[0];
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  else {
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    // since the array is full, _next is over the oldest value
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    return _sequence[_next];
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  }
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}
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double TruncatedSeq::predict_next() const {
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  if (_num == 0) {
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    // No data points, pick function: y = 0 + 0*x
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    return 0.0;
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  }
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  if (_num == 1) {
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    // Only one point P, pick function: y = P_y + 0*x
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    return _sequence[0];
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  }
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  double num           = (double) _num;
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  double x_squared_sum = 0.0;
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  double x_sum         = 0.0;
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  double y_sum         = 0.0;
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  double xy_sum        = 0.0;
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  double x_avg         = 0.0;
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  double y_avg         = 0.0;
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  int first = (_next + _length - _num) % _length;
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  for (int i = 0; i < _num; ++i) {
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    double x = (double) i;
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    double y =  _sequence[(first + i) % _length];
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    x_squared_sum += x * x;
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    x_sum         += x;
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    y_sum         += y;
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    xy_sum        += x * y;
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  }
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  x_avg = x_sum / num;
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  y_avg = y_sum / num;
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  double Sxx = x_squared_sum - x_sum * x_sum / num;
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  double Sxy = xy_sum - x_sum * y_sum / num;
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  double b1 = Sxy / Sxx;
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  double b0 = y_avg - b1 * x_avg;
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  return b0 + b1 * num;
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}
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// Printing/Debugging Support
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void AbsSeq::dump() { dump_on(tty); }
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void AbsSeq::dump_on(outputStream* s) {
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  s->print_cr("\t _num = %d, _sum = %7.3f, _sum_of_squares = %7.3f",
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                  _num,      _sum,         _sum_of_squares);
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  s->print_cr("\t _davg = %7.3f, _dvariance = %7.3f, _alpha = %7.3f",
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                  _davg,         _dvariance,         _alpha);
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}
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void NumberSeq::dump_on(outputStream* s) {
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  AbsSeq::dump_on(s);
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  s->print_cr("\t\t _last = %7.3f, _maximum = %7.3f", _last, _maximum);
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}
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void TruncatedSeq::dump_on(outputStream* s) {
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  AbsSeq::dump_on(s);
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  s->print_cr("\t\t _length = %d, _next = %d", _length, _next);
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  for (int i = 0; i < _length; i++) {
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    if (i%5 == 0) {
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      s->cr();
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      s->print("\t");
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    }
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    s->print("\t[%d]=%7.3f", i, _sequence[i]);
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  }
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  s->cr();
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}
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