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- /*
- * Copyright (c) 2018 The WebRTC project authors. All Rights Reserved.
- *
- * Use of this source code is governed by a BSD-style license
- * that can be found in the LICENSE file in the root of the source
- * tree. An additional intellectual property rights grant can be found
- * in the file PATENTS. All contributing project authors may
- * be found in the AUTHORS file in the root of the source tree.
- */
- #ifndef RTC_TOOLS_FRAME_ANALYZER_LINEAR_LEAST_SQUARES_H_
- #define RTC_TOOLS_FRAME_ANALYZER_LINEAR_LEAST_SQUARES_H_
- #include <stdint.h>
- #include <valarray>
- #include <vector>
- #include "absl/types/optional.h"
- namespace webrtc {
- namespace test {
- // This class is used for finding a matrix b that roughly solves the equation:
- // y = x * b. This is generally impossible to do exactly, so the problem is
- // rephrased as finding the matrix b that minimizes the difference:
- // |y - x * b|^2. Calling multiple AddObservations() is equivalent to
- // concatenating the observation vectors and calling AddObservations() once. The
- // reason for doing it incrementally is that we can't store the raw YUV values
- // for a whole video file in memory at once. This class has a constant memory
- // footprint, regardless how may times AddObservations() is called.
- class IncrementalLinearLeastSquares {
- public:
- IncrementalLinearLeastSquares();
- ~IncrementalLinearLeastSquares();
- // Add a number of observations. The subvectors of x and y must have the same
- // length.
- void AddObservations(const std::vector<std::vector<uint8_t>>& x,
- const std::vector<std::vector<uint8_t>>& y);
- // Calculate and return the best linear solution, given the observations so
- // far.
- std::vector<std::vector<double>> GetBestSolution() const;
- private:
- // Running sum of x^T * x.
- absl::optional<std::valarray<std::valarray<uint64_t>>> sum_xx;
- // Running sum of x^T * y.
- absl::optional<std::valarray<std::valarray<uint64_t>>> sum_xy;
- };
- } // namespace test
- } // namespace webrtc
- #endif // RTC_TOOLS_FRAME_ANALYZER_LINEAR_LEAST_SQUARES_H_
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