Merge pull request #608 from MarkSFrancis/shuf

Added shuf, a way to get a random sample from a large dataset

Author: raklaptudirm

DIRECTORY.md

@@ -75,6 +75,7 @@
  * [MinimumCostPath](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/MinimumCostPath.js)
  * [NumberOfSubsetEqualToGivenSum](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/NumberOfSubsetEqualToGivenSum.js)
  * [SieveOfEratosthenes](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/SieveOfEratosthenes.js)
+ * [Shuf](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/Shuf.js)
  * [SudokuSolver](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/SudokuSolver.js)
  * [TrappingRainWater](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/TrappingRainWater.js)
  * [ZeroOneKnapsack](https://github.com/TheAlgorithms/Javascript/blob/master/Dynamic-Programming/ZeroOneKnapsack.js)

Dynamic-Programming/Shuf.js

@@ -0,0 +1,96 @@
+/*
+Given a data set of an unknown size,
+Get a random sample in a random order
+It's used in data analytics, often as a way to get a small random sample from a data lake or warehouse, or from a large CSV file
+*/
+function shuf (datasetSource, sampleSize) {
+ const output = fillBaseSample(datasetSource, sampleSize)
+
+ return randomizeOutputFromDataset(datasetSource, output)
+}
+
+/**
+ * Fills the output if possible, with the minimum number of values
+ * @param {Iterable.<T>} datasetSource The iterable source of data
+ * @param {number} sampleSize The size of the sample to extract from the dataset
+ * @returns {Array.<T>} The random sample, as an array
+ * @template T
+ */
+function fillBaseSample (datasetSource, sampleSize) {
+ let filledIndexes = []
+ let output = new Array(sampleSize)
+
+ // Spread data out filling the array
+ while (true) {
+ const iterator = datasetSource.next()
+ if (iterator.done) break
+
+ let insertTo = Math.floor(Math.random() * output.length)
+ while (filledIndexes.includes(insertTo)) {
+ insertTo++
+ if (insertTo === output.length) {
+ insertTo = 0
+ }
+ }
+ output[insertTo] = {
+ value: iterator.value
+ }
+
+ filledIndexes = [...filledIndexes, insertTo]
+
+ if (filledIndexes.length === sampleSize) {
+ break
+ }
+ }
+
+ if (filledIndexes.length < output.length) {
+ // Not a large enough dataset to fill the sample - trim empty values
+ output = output.filter((_, i) => filledIndexes.includes(i))
+ }
+
+ return output.map((o) => o.value)
+}
+
+/**
+ * Replaces values in the output randomly with new ones from the dataset
+ * @param {Iterable.<T>} datasetSource The iterable source of data
+ * @param {Array.<T>} output The output so far, filled with data
+ * @returns {Array.<T>} The random sample, as an array
+ * @template T
+ */
+function randomizeOutputFromDataset (datasetSource, output) {
+ const newOutput = [...output]
+ let readSoFar = output.length
+
+ while (true) {
+ const iterator = datasetSource.next()
+ if (iterator.done) break
+ readSoFar++
+
+ const insertTo = Math.floor(Math.random() * readSoFar)
+ if (insertTo < newOutput.length) {
+ newOutput[insertTo] = iterator.value
+ }
+ }
+
+ return newOutput
+}
+
+const main = () => {
+ /**
+ * Generates a random range of data, with values between 0 and 2^31 - 1
+ * @param {number} length The number of data items to generate
+ * @returns {Iterable<number>} Random iterable data
+ */
+ function * generateRandomData (length) {
+ const maxValue = Math.pow(2, 31) - 1
+ for (let i = 0; i < length; i++) {
+ yield Math.floor(Math.random() * maxValue)
+ }
+ }
+
+ const source = generateRandomData(1000)
+ const result = shuf(source, 10)
+ console.log(result)
+}
+main()