Initial data commit

Task/Statistics-Normal-distribution/Kotlin/statistics-normal-distribution.kotlin

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+// version 1.1.2
+
+val rand = java.util.Random()
+
+fun normalStats(sampleSize: Int) {
+    if (sampleSize < 1) return
+    val r = DoubleArray(sampleSize)
+    val h = IntArray(12) // all zero by default
+    /*
+       Generate 'sampleSize' normally distributed random numbers with mean 0.5 and SD 0.25
+       and calculate in which box they will fall when drawing the histogram
+    */
+    for (i in 0 until sampleSize) {
+        r[i] = 0.5 + rand.nextGaussian() / 4.0
+        when {
+            r[i] <  0.0 -> h[0]++
+            r[i] >= 1.0 -> h[11]++
+            else        -> h[1 + (r[i] * 10).toInt()]++
+        }
+    }
+
+    // adjust one of the h[] values if necessary to ensure they sum to sampleSize
+    val adj = sampleSize - h.sum()
+    if (adj != 0) {
+        for (i in 0..11) {
+            h[i] += adj
+            if (h[i] >= 0) break
+            h[i] -= adj
+        }
+    }
+
+    val mean = r.average()
+    val sd = Math.sqrt(r.map { (it - mean) * (it - mean) }.average())
+
+    // Draw a histogram of the data with interval 0.1
+    var numStars: Int
+    // If sample size > 300 then normalize histogram to 300
+    val scale = if (sampleSize <= 300) 1.0 else 300.0 / sampleSize
+    println("Sample size $sampleSize\n")
+    println("  Mean ${"%1.6f".format(mean)}  SD ${"%1.6f".format(sd)}\n")
+    for (i in 0..11) {
+        when (i) {
+            0    -> print("< 0.00 : ")
+            11   -> print(">=1.00 : ")
+            else -> print("  %1.2f : ".format(i / 10.0))
+        }
+        print("%5d ".format(h[i]))
+        numStars = (h[i] * scale + 0.5).toInt()
+        println("*".repeat(numStars))
+    }
+    println()
+}
+
+fun main(args: Array<String>) {
+    val sampleSizes = intArrayOf(100, 1_000, 10_000, 100_000)
+    for (sampleSize in sampleSizes) normalStats(sampleSize)
+}