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//===--- MonteCarloE.swift ------------------------------------------------===//
//
// This source file is part of the Swift.org open source project
//
// Copyright (c) 2014 - 2021 Apple Inc. and the Swift project authors
// Licensed under Apache License v2.0 with Runtime Library Exception
//
// See https://swift.org/LICENSE.txt for license information
// See https://swift.org/CONTRIBUTORS.txt for the list of Swift project authors
//
//===----------------------------------------------------------------------===//
// This test measures performance of Monte Carlo estimation of the e constant.
//
// We use 'dart' method: we split an interval into N pieces and drop N darts
// to this interval.
// After that we count number of empty intervals. The probability of being
// empty is (1 - 1/N)^N which estimates to e^-1 for large N.
// Thus, e = N / Nempty.
import TestsUtils
public let benchmarks =
BenchmarkInfo(
name: "MonteCarloE",
runFunction: run_MonteCarloE,
tags: [.validation, .algorithm],
legacyFactor: 20)
public func run_MonteCarloE(scale: Int) {
var lfsr = LFSR()
let n = 10_000 * scale
var intervals = [Bool](repeating: false, count: n)
for _ in 1...n {
let pos = Int(UInt(truncatingIfNeeded: lfsr.next()) % UInt(n))
intervals[pos] = true
}
let numEmptyIntervals = intervals.filter{!$0}.count
// If there are no empty intervals, then obviously the random generator is
// not 'random' enough.
check(numEmptyIntervals != n)
let e_estimate = Double(n)/Double(numEmptyIntervals)
let e = 2.71828
check(abs(e_estimate - e) < 0.2)
}
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