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9857aaa1de
This commit decouples StdlibUnittest from many details of [Contiguous]Array, so tests can give useful feedback even in the presence of a broken [Contiguous]Array implementation while we refactor. Unfortunately, it wasn't practical to make _UnitTestArray use a storage class other than _ContiguousArrayStorage[Base], so we still have to watch out when making changes there. Swift SVN r22875
73 lines
2.0 KiB
Swift
73 lines
2.0 KiB
Swift
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import Darwin
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public func rand32() -> UInt32 {
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return arc4random()
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}
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public func rand32(#exclusiveUpperBound: UInt32) -> UInt32 {
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return arc4random_uniform(exclusiveUpperBound)
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}
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public func rand64() -> UInt64 {
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return (UInt64(arc4random()) << 32) | UInt64(arc4random())
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}
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public func randInt() -> Int {
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#if arch(i386) || arch(arm)
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return Int(Int32(bitPattern: rand32()))
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#elseif arch(x86_64) || arch(arm64)
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return Int(Int64(bitPattern: rand64()))
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#else
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fatalError("unimplemented")
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#endif
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}
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public func randArray64(count: Int) -> _UnitTestArray<UInt64> {
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var result = _UnitTestArray<UInt64>(count: count, repeatedValue: 0)
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for i in indices(result) {
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result[i] = rand64()
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}
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return result
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}
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/// For a given p-value, returns the critical chi-square value for
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/// a distribution with 1 degree of freedom.
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func _chiSquaredUniform1DFCritical(pValue: Double) -> Double {
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if abs(pValue - 0.05) < 0.00001 { return 0.00393214 }
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if abs(pValue - 0.02) < 0.00001 { return 0.000628450 }
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if abs(pValue - 0.01) < 0.00001 { return 0.000157088 }
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if abs(pValue - 0.007) < 0.00001 { return 0.000076971 }
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if abs(pValue - 0.005) < 0.00001 { return 0.0000392704 }
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if abs(pValue - 0.003) < 0.00001 { return 0.0000141372 }
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if abs(pValue - 0.002) < 0.00001 { return 6.2832e-6 }
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if abs(pValue - 0.001) < 0.00001 { return 1.5708e-6 }
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fatalError("unknown value")
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}
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/// Perform chi-squared test for a discrete uniform distribution with
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/// 2 outcomes.
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public func chiSquaredUniform2(
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trials: Int, observedACount: Int, pValue: Double
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) -> Bool {
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func square(x: Double) -> Double {
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return x * x
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}
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let expectedA = 0.5
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let expectedB = 0.5
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let observedA = Double(observedACount) / Double(trials)
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let observedB = 1.0 - observedA
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let chiSq =
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square(observedA - expectedA) / expectedA +
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square(observedB - expectedB) / expectedB
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if chiSq > _chiSquaredUniform1DFCritical(pValue) {
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println("chi-squared test failed: \(trials) \(observedACount) \(chiSq)")
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return false
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}
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return true
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}
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