/*

 * 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

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 */

#include "precompiled.hpp"

#include "gc_implementation/shared/gcUtil.hpp"

// Catch-all file for utility classes

float AdaptiveWeightedAverage::compute_adaptive_average(float new_sample,

                                                        float average) {

  // We smooth the samples by not using weight() directly until we've

  // had enough data to make it meaningful. We'd like the first weight

  // used to be 1, the second to be 1/2, etc until we have 100/weight

  // samples.

  unsigned count_weight = 100/count();

  unsigned adaptive_weight = (MAX2(weight(), count_weight));

  float new_avg = exp_avg(average, new_sample, adaptive_weight);

  return new_avg;

}

void AdaptiveWeightedAverage::sample(float new_sample) {

  increment_count();

  assert(count() != 0,

         "Wraparound -- history would be incorrectly discarded");

  // Compute the new weighted average

  float new_avg = compute_adaptive_average(new_sample, average());

  set_average(new_avg);

  _last_sample = new_sample;

}

void AdaptiveWeightedAverage::print() const {

  print_on(tty);

}

void AdaptiveWeightedAverage::print_on(outputStream* st) const {

  guarantee(false, "NYI");

}

void AdaptivePaddedAverage::print() const {

  print_on(tty);

}

void AdaptivePaddedAverage::print_on(outputStream* st) const {

  guarantee(false, "NYI");

}

void AdaptivePaddedNoZeroDevAverage::print() const {

  print_on(tty);

}

void AdaptivePaddedNoZeroDevAverage::print_on(outputStream* st) const {

  guarantee(false, "NYI");

}

void AdaptivePaddedAverage::sample(float new_sample) {

  // Compute new adaptive weighted average based on new sample.

  AdaptiveWeightedAverage::sample(new_sample);

  // Now update the deviation and the padded average.

  float new_avg = average();

  float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),

                                           deviation());

  set_deviation(new_dev);

  set_padded_average(new_avg + padding() * new_dev);

  _last_sample = new_sample;

}

void AdaptivePaddedNoZeroDevAverage::sample(float new_sample) {

  // Compute our parent classes sample information

  AdaptiveWeightedAverage::sample(new_sample);

  float new_avg = average();

  if (new_sample != 0) {

    // We only create a new deviation if the sample is non-zero

    float new_dev = compute_adaptive_average(fabsd(new_sample - new_avg),

                                             deviation());

    set_deviation(new_dev);

  }

  set_padded_average(new_avg + padding() * deviation());

  _last_sample = new_sample;

}

LinearLeastSquareFit::LinearLeastSquareFit(unsigned weight) :

  _sum_x(0), _sum_x_squared(0), _sum_y(0), _sum_xy(0),

  _intercept(0), _slope(0), _mean_x(weight), _mean_y(weight) {}

void LinearLeastSquareFit::update(double x, double y) {

  _sum_x = _sum_x + x;

  _sum_x_squared = _sum_x_squared + x * x;

  _sum_y = _sum_y + y;

  _sum_xy = _sum_xy + x * y;

  _mean_x.sample(x);

  _mean_y.sample(y);

  assert(_mean_x.count() == _mean_y.count(), "Incorrect count");

  if ( _mean_x.count() > 1 ) {

    double slope_denominator;

    slope_denominator = (_mean_x.count() * _sum_x_squared - _sum_x * _sum_x);

    // Some tolerance should be injected here.  A denominator that is

    // nearly 0 should be avoided.

    if (slope_denominator != 0.0) {

      double slope_numerator;

      slope_numerator = (_mean_x.count() * _sum_xy - _sum_x * _sum_y);

      _slope = slope_numerator / slope_denominator;

      // The _mean_y and _mean_x are decaying averages and can

      // be used to discount earlier data.  If they are used,

      // first consider whether all the quantities should be

      // kept as decaying averages.

      // _intercept = _mean_y.average() - _slope * _mean_x.average();

      _intercept = (_sum_y - _slope * _sum_x) / ((double) _mean_x.count());

    }

  }

}

double LinearLeastSquareFit::y(double x) {

  double new_y;

  if ( _mean_x.count() > 1 ) {

    new_y = (_intercept + _slope * x);

    return new_y;

  } else {

    return _mean_y.average();

  }

}

// Both decrement_will_decrease() and increment_will_decrease() return

// true for a slope of 0.  That is because a change is necessary before

// a slope can be calculated and a 0 slope will, in general, indicate

// that no calculation of the slope has yet been done.  Returning true

// for a slope equal to 0 reflects the intuitive expectation of the

// dependence on the slope.  Don't use the complement of these functions

// since that untuitive expectation is not built into the complement.

bool LinearLeastSquareFit::decrement_will_decrease() {

  return (_slope >= 0.00);

}

bool LinearLeastSquareFit::increment_will_decrease() {

  return (_slope <= 0.00);

}