/*

 * Copyright 2002-2008 Sun Microsystems, Inc.  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 Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,

 * CA 95054 USA or visit www.sun.com if you need additional information or

 * have any questions.

 *

 */

// Catch-all file for utility classes

// A weighted average maintains a running, weighted average

// of some float value (templates would be handy here if we

// need different types).

//

// The average is adaptive in that we smooth it for the

// initial samples; we don't use the weight until we have

// enough samples for it to be meaningful.

//

// This serves as our best estimate of a future unknown.

//

class AdaptiveWeightedAverage : public CHeapObj {

 private:

  float            _average;        // The last computed average

  unsigned         _sample_count;   // How often we've sampled this average

  unsigned         _weight;         // The weight used to smooth the averages

                                    //   A higher weight favors the most

                                    //   recent data.

 protected:

  float            _last_sample;    // The last value sampled.

  void  increment_count()       { _sample_count++;       }

  void  set_average(float avg)  { _average = avg;        }

  // Helper function, computes an adaptive weighted average

  // given a sample and the last average

  float compute_adaptive_average(float new_sample, float average);

 public:

  // Input weight must be between 0 and 100

  }

  void clear() {

    _average = 0;

    _sample_count = 0;

    _last_sample = 0;

  }

  // Accessors

  float    average() const       { return _average;       }

  unsigned weight()  const       { return _weight;        }

  unsigned count()   const       { return _sample_count;  }

  float    last_sample() const   { return _last_sample; }

  // Update data with a new sample.

  void sample(float new_sample);

  static inline float exp_avg(float avg, float sample,

                               unsigned int weight) {

    assert(0 <= weight && weight <= 100, "weight must be a percent");

    return (100.0F - weight) * avg / 100.0F + weight * sample / 100.0F;

  }

  static inline size_t exp_avg(size_t avg, size_t sample,

                               unsigned int weight) {

    // Convert to float and back to avoid integer overflow.

    return (size_t)exp_avg((float)avg, (float)sample, weight);

  }

};

// A weighted average that includes a deviation from the average,

// some multiple of which is added to the average.

//

// This serves as our best estimate of an upper bound on a future

// unknown.

class AdaptivePaddedAverage : public AdaptiveWeightedAverage {

 private:

  float          _padded_avg;     // The last computed padded average

  float          _deviation;      // Running deviation from the average

  unsigned       _padding;        // A multiple which, added to the average,

                                  // gives us an upper bound guess.

 protected:

  void set_padded_average(float avg)  { _padded_avg = avg;  }

  void set_deviation(float dev)       { _deviation  = dev;  }

 public:

  AdaptivePaddedAverage() :

    AdaptiveWeightedAverage(0),

    _padded_avg(0.0), _deviation(0.0), _padding(0) {}

  AdaptivePaddedAverage(unsigned weight, unsigned padding) :

    AdaptiveWeightedAverage(weight),

    _padded_avg(0.0), _deviation(0.0), _padding(padding) {}

  // Placement support

  void* operator new(size_t ignored, void* p) { return p; }

  // Allocator

  void* operator new(size_t size) { return CHeapObj::operator new(size); }

  // Accessor

  float padded_average() const         { return _padded_avg; }

  float deviation()      const         { return _deviation;  }

  unsigned padding()     const         { return _padding;    }

  void clear() {

    AdaptiveWeightedAverage::clear();

    _padded_avg = 0;

    _deviation = 0;

  }

  // Override

  void  sample(float new_sample);

};

// A weighted average that includes a deviation from the average,

// some multiple of which is added to the average.

//

// This serves as our best estimate of an upper bound on a future

// unknown.

// A special sort of padded average:  it doesn't update deviations

// if the sample is zero. The average is allowed to change. We're

// preventing the zero samples from drastically changing our padded

// average.

class AdaptivePaddedNoZeroDevAverage : public AdaptivePaddedAverage {

public:

  AdaptivePaddedNoZeroDevAverage(unsigned weight, unsigned padding) :

    AdaptivePaddedAverage(weight, padding)  {}

  // Override

  void  sample(float new_sample);

};

// Use a least squares fit to a set of data to generate a linear

// equation.

//              y = intercept + slope * x

class LinearLeastSquareFit : public CHeapObj {

  double _sum_x;        // sum of all independent data points x

  double _sum_x_squared; // sum of all independent data points x**2

  double _sum_y;        // sum of all dependent data points y

  double _sum_xy;       // sum of all x * y.

  double _intercept;     // constant term

  double _slope;        // slope

  // The weighted averages are not currently used but perhaps should

  // be used to get decaying averages.

  AdaptiveWeightedAverage _mean_x; // weighted mean of independent variable

  AdaptiveWeightedAverage _mean_y; // weighted mean of dependent variable

 public:

  LinearLeastSquareFit(unsigned weight);

  void update(double x, double y);

  double y(double x);

  double slope() { return _slope; }

  // Methods to decide if a change in the dependent variable will

  // achive a desired goal.  Note that these methods are not

  // complementary and both are needed.

  bool decrement_will_decrease();

  bool increment_will_decrease();

};

class GCPauseTimer : StackObj {

  elapsedTimer* _timer;

 public:

  GCPauseTimer(elapsedTimer* timer) {

    _timer = timer;

    _timer->stop();

  }

  ~GCPauseTimer() {

    _timer->start();

  }

};