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2c13d9eb57
- Separate `Scheduler` from `BasicNetwork`. - Add an event/collector framework for monitoring invariants and calculating statistics. - Allow distinct network and trust connections between Peers. - Add a simple routing strategy to support broadcasting arbitrary messages. - Add a common directed graph (`Digraph`) class for representing network and trust topologies. - Add a `PeerGroup` class for simpler specification of the trust and network topologies. - Add a `LedgerOracle` class to ensure distinct ledger histories and simplify branch checking. - Add a `Submitter` to send transactions in at fixed or random intervals to fixed or random peers. Co-authored-by: Joseph McGee
132 lines
3.5 KiB
C++
132 lines
3.5 KiB
C++
//------------------------------------------------------------------------------
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/*
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This file is part of rippled: https://github.com/ripple/rippled
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Copyright (c) 2012-2017 Ripple Labs Inc
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Permission to use, copy, modify, and/or distribute this software for any
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purpose with or without fee is hereby granted, provided that the above
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copyright notice and this permission notice appear in all copies.
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THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
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WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
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MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
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ANY SPECIAL , DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
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WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
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ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
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OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
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*/
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//==============================================================================
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#ifndef RIPPLE_TEST_CSF_HISTOGRAM_H_INCLUDED
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#define RIPPLE_TEST_CSF_HISTOGRAM_H_INCLUDED
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#include <map>
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#include <chrono>
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#include <algorithm>
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namespace ripple {
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namespace test {
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namespace csf {
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/** Basic histogram.
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Histogram for a type `T` that satisfies
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- Default construction: T{}
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- Comparison : T a, b; bool res = a < b
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- Addition: T a, b; T c = a + b;
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- Multiplication : T a, std::size_t b; T c = a * b;
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- Divison: T a; std::size_t b; T c = a/b;
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*/
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template <class T, class Compare = std::less<T>>
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class Histogram
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{
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// TODO: Consider logarithimic bins around expected median if this becomes
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// unscaleable
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std::map<T, std::size_t, Compare> counts_;
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std::size_t samples = 0;
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public:
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/** Insert an sample */
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void
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insert(T const & s)
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{
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++counts_[s];
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++samples;
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}
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/** The number of samples */
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std::size_t
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size() const
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{
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return samples;
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}
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/** The number of distinct samples (bins) */
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std::size_t
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numBins() const
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{
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return counts_.size();
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}
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/** Minimum observed value */
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T
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minValue() const
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{
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return counts_.empty() ? T{} : counts_.begin()->first;
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}
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/** Maximum observed value */
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T
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maxValue() const
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{
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return counts_.empty() ? T{} : counts_.rbegin()->first;
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}
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/** Histogram average */
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T
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avg() const
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{
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T tmp{};
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if(samples == 0)
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return tmp;
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// Since counts are sorted, shouldn't need to worry much about numerical
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// error
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for (auto const& it : counts_)
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{
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tmp += it.first * it.second;
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}
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return tmp/samples;
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}
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/** Calculate the given percentile of the distribution.
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@param p Percentile between 0 and 1, e.g. 0.50 is 50-th percentile
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If the percentile falls between two bins, uses the nearest bin.
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@return The given percentile of the distribution
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*/
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T
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percentile(float p) const
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{
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assert(p >= 0 && p <=1);
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std::size_t pos = std::round(p * samples);
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if(counts_.empty())
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return T{};
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auto it = counts_.begin();
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std::size_t cumsum = it->second;
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while (it != counts_.end() && cumsum < pos)
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{
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++it;
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cumsum += it->second;
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}
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return it->first;
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}
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};
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} // namespace csf
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} // namespace test
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} // namespace ripple
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#endif
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