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/*
**
** Copyright 2014, The Android Open Source Project
**
** Licensed under the Apache License, Version 2.0 (the "License");
** you may not use this file except in compliance with the License.
** You may obtain a copy of the License at
**
** http://www.apache.org/licenses/LICENSE-2.0
**
** Unless required by applicable law or agreed to in writing, software
** distributed under the License is distributed on an "AS IS" BASIS,
** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
** See the License for the specific language governing permissions and
** limitations under the License.
*/
#ifndef CLOCK_ESTIMATOR_H_
#define CLOCK_ESTIMATOR_H_
#include <utils/RefBase.h>
#include <utils/Vector.h>
namespace android {
// ---------------------------------------------------------------------------
struct ClockEstimator : RefBase {
virtual double estimate(double x, double y) = 0;
virtual void reset() = 0;
};
struct WindowedLinearFitEstimator : ClockEstimator {
struct LinearFit {
/**
* Fit y = a * x + b, where each input has a weight
*/
double mX; // sum(w_i * x_i)
double mXX; // sum(w_i * x_i^2)
double mY; // sum(w_i * y_i)
double mYY; // sum(w_i * y_i^2)
double mXY; // sum(w_i * x_i * y_i)
double mW; // sum(w_i)
LinearFit();
void reset();
void combine(const LinearFit &lf);
void add(double x, double y, double w);
void scale(double w);
double interpolate(double x);
double size() const;
DISALLOW_EVIL_CONSTRUCTORS(LinearFit);
};
/**
* Estimator for f(x) = y' where input y' is noisy, but
* theoretically linear:
*
* y' =~ y = a * x + b
*
* It uses linear fit regression over a tapering rolling window
* to get an estimate for y (from the current and past inputs
* (x, y')).
*
* ____________
* /| |\
* / | | \
* / | | \ <--- new data (x, y')
* / | main | \
* <--><----------><-->
* tail head
*
* weight is 1 under the main window, tapers exponentially by
* the factors given in the head and the tail.
*
* Assuming that x and y' are monotonic, that x is somewhat
* evenly sampled, and that a =~ 1, the estimated y is also
* going to be monotonic.
*/
WindowedLinearFitEstimator(
size_t headLength = 5, double headFactor = 0.5,
size_t mainLength = 0, double tailFactor = 0.99);
virtual void reset();
// add a new sample (x -> y') and return an estimated value for the true y
virtual double estimate(double x, double y);
private:
Vector<double> mXHistory; // circular buffer
Vector<double> mYHistory; // circular buffer
LinearFit mHead;
LinearFit mMain;
LinearFit mTail;
double mHeadFactorInv;
double mTailFactor;
double mFirstWeight;
size_t mHistoryLength;
size_t mHeadLength;
size_t mNumSamples;
size_t mSampleIx;
DISALLOW_EVIL_CONSTRUCTORS(WindowedLinearFitEstimator);
};
}; // namespace android
#endif
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