/*

 * Copyright 2001-2007 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.

 *

 */

# include "incls/_precompiled.incl"

# include "incls/_numberSeq.cpp.incl"

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;

}