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// Copyright 2017 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
#ifndef COMPONENTS_ZUCCHINI_BINARY_DATA_HISTOGRAM_H_
#define COMPONENTS_ZUCCHINI_BINARY_DATA_HISTOGRAM_H_
#include <stddef.h>
#include <stdint.h>
#include <memory>
#include <string>
#include "components/zucchini/buffer_view.h"
namespace zucchini {
// A class to detect outliers in a list of doubles using Chauvenet's criterion:
// Compute mean and standard deviation of observations, then determine whether
// a query value lies beyond a fixed number of standard deviations (sigmas) from
// the mean. The purpose of this test is to reduce the chance of false-positive
// ensemble matches.
class OutlierDetector {
public:
OutlierDetector();
OutlierDetector(const OutlierDetector&) = delete;
const OutlierDetector& operator=(const OutlierDetector&) = delete;
~OutlierDetector();
// Incorporates |sample| into mean and standard deviation.
void Add(double sample);
// Prepares basic statistics for DecideOutlier() calls. Should be called after
// all samples have been added.
void Prepare();
// Renders current statistics as strings for logging.
std::string RenderStats();
// Heuristically decides whether |sample| is an outlier. Returns 1 if |sample|
// is "too high", 0 if |sample| is "normal", and -1 if |sample| is "too low".
// Must be called after Prepare().
int DecideOutlier(double sample);
private:
size_t n_ = 0;
double sum_ = 0;
double sum_of_squares_ = 0;
double mean_ = 0;
double standard_deviation_ = 0;
};
// A class to compute similarity score between binary data. The heuristic here
// preprocesses input data to a size-65536 histogram, counting the frequency of
// consecutive 2-byte sequences. Therefore data with lengths < 2 are considered
// invalid -- but this is okay for Zucchini's use case.
class BinaryDataHistogram {
public:
BinaryDataHistogram();
BinaryDataHistogram(const BinaryDataHistogram&) = delete;
const BinaryDataHistogram& operator=(const BinaryDataHistogram&) = delete;
~BinaryDataHistogram();
// Attempts to compute the histogram, returns true iff successful.
bool Compute(ConstBufferView region);
bool IsValid() const { return static_cast<bool>(histogram_); }
// Returns distance to another histogram (heuristics). If two binaries are
// identical then their histogram distance is 0. However, the converse is not
// true in general. For example, "aba" and "bab" are different, but their
// histogram distance is 0 (both histograms are {"ab": 1, "ba": 1}).
double Distance(const BinaryDataHistogram& other) const;
private:
enum { kNumBins = 1 << (sizeof(uint16_t) * 8) };
static_assert(kNumBins == 65536, "Incorrect constant computation.");
// Size, in bytes, of the data over which the histogram was computed.
size_t size_ = 0;
// 2^16 buckets holding counts of all 2-byte sequences in the data. The counts
// are stored as signed values to simplify computing the distance between two
// histograms.
std::unique_ptr<int32_t[]> histogram_;
};
} // namespace zucchini
#endif // COMPONENTS_ZUCCHINI_BINARY_DATA_HISTOGRAM_H_
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