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// Copyright 2018 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#include <stdint.h>
#include <queue>
#include "chromecast/base/statistics/weighted_mean.h"
namespace chromecast {
// Performs linear regression over a set of weighted (x, y) samples.
// Calculates a weighted moving average over a set of weighted (x, y) points.
// The points do not need to be evenly distributed on the X axis, but the
// X coordinate is assumed to be generally increasing.
// Whenever a new sample is added using AddSample(), old samples whose
// x coordinates are farther than |max_x_range_| from the new sample's
// x coordinate will be removed from the regression. Note that |max_x_range_|
// must be non-negative.
class WeightedMovingLinearRegression {
struct Sample {
int64_t x;
int64_t y;
double weight;
explicit WeightedMovingLinearRegression(int64_t max_x_range);
WeightedMovingLinearRegression(const WeightedMovingLinearRegression&) =
WeightedMovingLinearRegression& operator=(
const WeightedMovingLinearRegression&) = delete;
// Returns the current number of samples that are in the regression.
size_t num_samples() const { return samples_.size(); }
// Adds a weighted (x, y) sample to the set. Note that |weight|
// should be positive.
void AddSample(int64_t x, int64_t y, double weight);
// Gets a y value estimate from the linear regression: y = a*x + b, where
// a and b are the slope and intercept estimates from the regression. The
// standard error of the resulting y estimate is also provided.
// Returns false if the y value cannot be estimated, in which case y and
// |error| are not modified. Returns true otherwise.
bool EstimateY(int64_t x, int64_t* y, double* error) const;
// Gets the current estimated slope and slope error from the linear
// regression. Returns false if the slope cannot be estimated, in which
// case |slope| and |error| are not modified. Returns true otherwise.
bool EstimateSlope(double* slope, double* error) const;
// Dumps samples currently in the linear regression.
void DumpSamples() const;
// Returns a const reference to the samples currently captured. Very
// useful for debugging.
const std::deque<Sample>& samples() { return samples_; }
// Reserves space for |count| samples, to reduce memory allocation during use.
void Reserve(int count);
// Resets to initial state.
void Reset();
// Adds (x, y) to the set if |weight| is positive; removes (x, y) from the
// set if |weight| is negative.
void UpdateSet(int64_t x, int64_t y, double weight);
const int64_t max_x_range_;
WeightedMean x_mean_;
WeightedMean y_mean_;
double covariance_ = 0.0;
std::deque<Sample> samples_;
double slope_ = 0.0;
double slope_variance_ = 0.0;
double intercept_variance_ = 0.0;
bool has_estimate_ = false;
} // namespace chromecast