smile visualization histograms / published by https://github.com/dacr/code-examples-manager #25fb6f11-ace3-4e58-bc17-16e1e2287da2/7bac21c74daf6eb136a918e93d7d84e6fc40a346

visualization-swing-hist.sc

// summary : smile visualization histograms
// keywords : smile, chart, visualization
// publish : gist
// authors : David Crosson
// license : Apache NON-AI License Version 2.0 (https://raw.githubusercontent.com/non-ai-licenses/non-ai-licenses/main/NON-AI-APACHE2)
// id : 25fb6f11-ace3-4e58-bc17-16e1e2287da2
// created-on : 2021-03-05T09:23:01Z
// managed-by : https://github.com/dacr/code-examples-manager
// run-with : scala-cli $file

// ---------------------
//> using scala  "3.2.2"
//> using dep "com.github.pathikrit::better-files:3.9.2"
//> using dep "com.github.haifengl:smile-scala_2.13:3.0.1"
//> using dep "org.bytedeco:javacpp-platform:1.5.8"
//> using dep "org.bytedeco:javacpp:1.5.8,classifier=linux-x86_64"
//> using dep "org.bytedeco:arpack-ng:3.8.0-1.5.8,classifier=linux-x86_64"
//> using dep "org.bytedeco:openblas:0.3.21-1.5.8,classifier=linux-x86_64"
//> using dep "org.slf4j:slf4j-nop:2.0.7"
//> using dep "com.lihaoyi::requests:0.8.0"
// ---------------------

import better.files.*
import smile.stat.distribution.*
import smile.math.matrix.*

import smile.plot.swing.*
import smile.plot.show
import smile.plot.Render.*

// examples coming from http://haifengl.github.io/visualization.html

// --------------------------------------------------------------------
val inputFileName = "cow.txt"
val inputFile = inputFileName.toFile
if (inputFile.notExists) {
  val url = "https://gist.githubusercontent.com/dacr/90501dc71a302d1c2b41ffbc17383540/raw/f2531da9031e297943c4b438569f7f60e5348852/cow.txt"
  for {out <- inputFile.newOutputStream.autoClosed} {requests.get(url).writeBytesTo(out)}
}

val cowdf = smile.read.csv(inputFileName, header=false)
println(cowdf.summary())

val cow = cowdf("V1").toDoubleArray

// --------------------------------------------------------------------
implicit val renderer:Canvas=>Unit = JWindow.apply

{
  val canvas = hist(cow, 50)
  canvas.setAxisLabels("Weight", "Probability")
  show(canvas)
}

// --------------------------------------------------------------------

{
  val canvas = hist(cow.filter(_ <= 3500), 50)
  canvas.setAxisLabels("Weight", "Probability")
  show(canvas)
}

// --------------------------------------------------------------------

{
  val gauss = new MultivariateGaussianDistribution(Array(0.0, 0.0), Matrix.of(Array(Array(1.0, 0.6), Array(0.6, 2.0))))
  val data = (0 until 10000) map { (i: Int) => gauss.rand }
  show(hist3(data.toArray, 50, 50))
}

// --------------------------------------------------------------------

println("enter to exit"); scala.io.StdIn.readLine()