Best Kotest code snippet using io.kotest.matchers.stats.matchers.calculateMean
matchers.kt
Source:matchers.kt
...292 */293fun <T : Number> Collection<T>.shouldNotHaveStandardDeviation(value: Double, precision: Int = 4) = this shouldNot haveStandardDeviation<T>(value, precision)294private val defaultMathContext = MathContext(64, RoundingMode.HALF_UP)295private fun BigDecimal.round(precision: Int): BigDecimal = this.setScale(precision, RoundingMode.HALF_UP).stripTrailingZeros()296private fun <T : Number> calculateMean(collection: Collection<T>): BigDecimal {297 var sum: BigDecimal = BigDecimal.ZERO298 for (elem in collection) {299 sum += BigDecimal(elem.toString())300 }301 return sum.divide(BigDecimal(collection.size), defaultMathContext)302}303private fun <T : Number> calculateVariance(collection: Collection<T>): BigDecimal {304 val mean: BigDecimal = calculateMean(collection)305 var sumOfSquaredDifferences: BigDecimal = BigDecimal.ZERO306 for (elem in collection) {307 sumOfSquaredDifferences += (BigDecimal(elem.toString()) - mean).pow(2)308 }309 return sumOfSquaredDifferences.divide(BigDecimal(collection.size), defaultMathContext)310}311private fun <T : Number> calculateStandardDeviation(collection: Collection<T>): BigDecimal {312 val variance = calculateVariance(collection)313 val two = BigDecimal(2)314 var x0 = BigDecimal.ZERO315 var x1 = BigDecimal(sqrt(variance.toDouble()))316 while (x0 != x1) {317 x0 = x1318 x1 = variance.divide(x0, defaultMathContext)319 x1 = x1.add(x0)320 x1 = x1.divide(two, defaultMathContext)321 }322 return x1323}324private fun <T : Number> testMean(collection: Collection<T>, expectedValue: BigDecimal, precision: Int): MatcherResult {325 val expected = expectedValue.stripTrailingZeros()326 val actual = if (collection.isEmpty()) BigDecimal.ZERO else calculateMean(collection).round(precision)327 return MatcherResult(328 expected.compareTo(actual) == 0,329 { "Collection should have mean $expected but was $actual" },330 {331 "Collection should not have mean $expected but was $actual"332 })333}334private fun <T : Number> testVariance(335 collection: Collection<T>,336 expectedValue: BigDecimal,337 precision: Int338): MatcherResult {339 val expected = expectedValue.stripTrailingZeros()340 val actual = if (collection.isEmpty()) BigDecimal.ZERO else calculateVariance(collection).round(precision)...
calculateMean
Using AI Code Generation
1 val mean = calculateMean(listOf(1,2,3,4,5))2 val mean = calculateMean(listOf(1,2,3,4,5))3 val mean = calculateMean(listOf(1,2,3,4,5))4 val mean = calculateMean(listOf(1,2,3,4,5))5 val mean = calculateMean(listOf(1,2,3,4,5))6 val mean = calculateMean(listOf(1,2,3,4,5))7 val mean = calculateMean(listOf(1,2,3,4,5))8 val mean = calculateMean(listOf(1,2,3,4,5))9 val mean = calculateMean(listOf(1,2,3,4,5))10 val mean = calculateMean(listOf(1,2,3,4,5))11 val mean = calculateMean(listOf(1,2,3,4,5))12 val mean = calculateMean(listOf(1,2,3,4,5))13 val mean = calculateMean(listOf(1,2,3,4,5
calculateMean
Using AI Code Generation
1calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.12calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.13calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.14calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.15calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.16calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.17calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.18calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11.0)) shouldBe 5.8 +- 0.19calculateMean(listOf(2.0, 3.0, 5.0, 7.0, 11
calculateMean
Using AI Code Generation
1val mean = calculateMean(listOf(1.0, 2.0, 3.0, 4.0))2val median = calculateMedian(listOf(1.0, 2.0, 3.0, 4.0))3val mode = calculateMode(listOf(1.0, 2.0, 3.0, 4.0))4val standardDeviation = calculateStandardDeviation(listOf(1.0, 2.0, 3.0, 4.0))5val variance = calculateVariance(listOf(1.0, 2.0, 3.0, 4.0))6val range = calculateRange(listOf(1.0, 2.0, 3.0, 4.0))7val sum = calculateSum(listOf(1.0, 2.0, 3.0, 4.0))8val product = calculateProduct(listOf(1.0, 2.0, 3.0, 4.0))
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