How to use calculateVariance method of io.kotest.matchers.stats.matchers class

Best Kotest code snippet using io.kotest.matchers.stats.matchers.calculateVariance

matchers.kt

Source:matchers.kt

...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)341   return MatcherResult(342      expected.compareTo(actual) == 0,343      { "Collection should have variance $expected but was $actual" },344      {345         "Collection should not have variance $expected but was $actual"346      })347}348private fun <T : Number> testStandardDeviation(349   collection: Collection<T>,350   expectedValue: BigDecimal,351   precision: Int352): MatcherResult {353   val expected = expectedValue.stripTrailingZeros()354   val actual = if (collection.isEmpty()) BigDecimal.ZERO else calculateStandardDeviation(collection).round(precision)...

calculateVariance

Using AI Code Generation

1val list = listOf(1, 2, 3, 4, 5)2list.calculateVariance() shouldBe 2.03val list = listOf(1, 2, 3, 4, 5)4list.calculateStandardDeviation() shouldBe 1.41421356237309515val list = listOf(1, 2, 3, 4, 5)6list.calculateConfidenceInterval(0.95) shouldBe Pair(1.0, 5.0)7val list1 = listOf(1, 2, 3, 4, 5)8val list2 = listOf(1, 2, 3, 4, 5)9list1.calculateCorrelation(list2) shouldBe 1.010val list1 = listOf(1, 2, 3, 4, 5)11val list2 = listOf(1, 2, 3, 4, 5)12list1.calculateCovariance(list2) shouldBe 2.013val list = listOf(1, 2, 3, 4, 5)14list.calculateZScore(3) shouldBe 0.015val list = listOf(1, 2, 3, 4, 5)16list.calculateZScore(3) shouldBe 0.017val list = listOf(1, 2, 3, 4, 5)18list.calculatePValue(3) shouldBe 0.519val list = listOf(1, 2, 3, 4, 5)

calculateVariance

Using AI Code Generation

1    calculateVariance(listOf(1, 2, 3, 4, 5)) shouldBe 2.02    calculateStandardDeviation(listOf(1, 2, 3, 4, 5)) shouldBe 1.41423}4calculateMean(listOf(1, 2, 3, 4, 5)) shouldBe 3.05calculateMedian(listOf(1, 2, 3, 4, 5)) shouldBe 3.06calculateVariance(listOf(1, 2, 3, 4, 5)) shouldBe 2.07calculateStandardDeviation(listOf(1, 2, 3, 4, 5)) shouldBe 1.4142

calculateVariance

Using AI Code Generation

1val variance = calculateVariance(data)2val standardDeviation = calculateStandardDeviation(data)3val meanAbsoluteDeviation = calculateMeanAbsoluteDeviation(data)4val median = calculateMedian(data)5val mode = calculateMode(data)6val skewness = calculateSkewness(data)7val kurtosis = calculateKurtosis(data)8val range = calculateRange(data)9val coefficientOfVariation = calculateCoefficientOfVariation(data)10val interquartileRange = calculateInterquartileRange(data)11val quartiles = calculateQuartiles(data)12val quintiles = calculateQuintiles(data)13val deciles = calculateDeciles(data)14val percentiles = calculatePercentiles(data)15val percentile = calculatePercentile(data, 50.0)16val percentile = calculatePercentile(data, 50.0, 2)

calculateVariance

Using AI Code Generation

1val variance = calculateVariance ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )2val standardDeviation = calculateStandardDeviation ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )3val variancePopulation = calculateVariancePopulation ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )4val standardDeviationPopulation = calculateStandardDeviationPopulation ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )5val coefficientOfVariation = calculateCoefficientOfVariation ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )6val confidenceInterval = calculateConfidenceInterval ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )7val confidenceIntervalPopulation = calculateConfidenceIntervalPopulation ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )8val sampleSize = calculateSampleSize ( 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 )9val sampleSizePopulation = calculateSampleSizePopulation (

calculateVariance

Using AI Code Generation

1val  variance  =  calculateVariance ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 2 variance  should  be ( 2.5 )3val  standardDeviation  =  calculateStandardDeviation ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 4 standardDeviation  should  be ( 1.5811388300841898 )5val  correlation  =  calculateCorrelation ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ,  listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 6 correlation  should  be ( 1.0 )7val  covariance  =  calculateCovariance ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ,  listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 8 covariance  should  be ( 2.5 )9val  mean  =  calculateMean ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 10 mean  should  be ( 3.0 )11val  mode  =  calculateMode ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 12 mode  should  be ( 1 )13val  median  =  calculateMedian ( listOf ( 1 ,  2 ,  3 ,  4 ,  5 ) ) 14 median  should  be ( 3 )

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