Documentation has been fixed.

README.md

@@ -165,11 +165,17 @@ Dump the entries into the CSV file:
 Read the CSV file and extract the features and the labels for the particular sensor:
 
 ```scala
+import smile.data._
+import smile.data.formula._
+import smile.data.`type`._
+
+import scala.jdk.CollectionConverters._
+
 // Declare the class to get better visibility on the data
 case class Row(sensor: String, ts: String, value: Double, anomaly: Int)
 
 // Read the values from the CSV file
-val iter = scala.io.Source.fromFile("list.csv").getLines
+val iter = scala.io.Source.fromFile("list.csv").getLines()
 
 // Get the data
 val l = iter.map(_.split(",") match {
@@ -179,11 +185,20 @@ val l = iter.map(_.split(",") match {
 // Get the sensor name for further analysis
 val name = l.head.sensor
 
-// Features are multi-dimensional, labels are integers
-val mapping = (x: Row) => (Array(x.value), x.anomaly)
-
-// Extract the features and the labels for the given sensor
-val (features, labels) = l.filter(_.sensor == name).map(mapping).unzip
+// Prepare data frame for the given sensor
+val data = DataFrame.of(
+  l.filter(_.sensor == name)
+    .map(row => Tuple.of(
+      Array(
+        row.value.asInstanceOf[AnyRef],
+        row.anomaly.asInstanceOf[AnyRef]),
+      DataTypes.struct(
+        new StructField("value", DataTypes.DoubleType),
+        new StructField("anomaly", DataTypes.IntegerType))))
+    .asJava)
+
+// Declare formula for the features and the labels
+val formula = "anomaly" ~ "value"
 
 ```
 
@@ -193,7 +208,8 @@ Fast analysis (labels are ignored because we don't use any training here):
 
 ```scala
 // Get the first 200 values
-val values = features.flatten.take(200)
+val values = data.stream()
+  .limit(200).map(_.getDouble(0)).iterator().asScala.toArray
 
 // Use the fast analyzer for the sample values
 val samples = Seq(10, 200, -100)
@@ -214,10 +230,10 @@ import scala.sys.process._
 import smile.classification.randomForest
 
 // Fit the model
-val rf = randomForest(features.toArray, labels.toArray)
+val rf = randomForest(formula, data)
 
 // Get the dot diagram for a sample tree
-val desc = rf.getTrees()(0).dot
+val desc = rf.trees()(0).dot
 
 // View the diagram (macOS example)
 s"echo $desc" #| "dot -Tpng" #| "open -a Preview -f" !
@@ -251,11 +267,16 @@ val futureRf = using(new ObjectInputStream(new FileInputStream("target/rf.bin"))
 val rf = futureRf.get
 
 // Use the loaded model for the sample values
-val samples = Seq(10, 200, -100)
+val samples = Seq(10.0, 200.0, -100.0)
 samples.map { sample =>
-  val probability = new Array[Double](2)
-  val prediction = rf.predict(Array(sample), probability)
-  (prediction, probability)
+  val posteriori = new Array[Double](2)
+  val prediction = rf.predict(
+    Tuple.of(
+      Array(sample),
+      DataTypes.struct(
+        new StructField("value", DataTypes.DoubleType))),
+    posteriori)
+  (prediction, posteriori)
 }
 
 ```
@@ -278,7 +299,7 @@ Verify the history of detecting anomalies using CQL:
     $ cqlsh -e "select * from sandbox.analysis limit 10;"
 
 In most cases it's better to use specialized solutions for clustering,
-for example, [Kubernetes](https://developer.lightbend.com/guides/akka-cluster-kubernetes-k8s-deploy/).
+for example, [Kubernetes](https://doc.akka.io/docs/akka-management/current/kubernetes-deployment/forming-a-cluster.html).
 However, in the sample project the server and clients are configured manually
 for the demonstration purposes.
 

resources/akka/cluster.conf

@@ -1,14 +1,14 @@
 akka {
-  remote {
-    log-remote-lifecycle-events = off
-    netty.tcp {
+  remote.artery {
+    canonical {
       hostname = "<client-host>"
       port = <client-port>
     }
   }
 
   cluster {
-    seed-nodes = ["akka.tcp://cluster@<server-host>:2551"]
+    seed-nodes = ["akka://cluster@<server-host>:2551"]
     roles = [<role>]
+    downing-provider-class = "akka.cluster.sbr.SplitBrainResolverProvider"
   }
 }

src/main/scala/mqtt/Producer.scala

@@ -5,21 +5,25 @@ import akka.event.LoggingAdapter
 import akka.http.scaladsl.Http.ServerBinding
 import akka.http.scaladsl.model.{ContentTypes, HttpEntity, StatusCodes}
 import akka.http.scaladsl.server.Directives._
-import org.stringtemplate.v4._
-import org.eclipse.paho.client.mqttv3._
 import lib._
+import org.eclipse.paho.client.mqttv3._
+import org.stringtemplate.v4._
 
-import scala.concurrent.duration._
+import scala.beans.BeanProperty
 import scala.concurrent.ExecutionContext
+import scala.concurrent.duration._
 import scala.io.Source
+import scala.jdk.CollectionConverters._
 import scala.util.{Failure, Success}
 
 object Producer {
   def props(mqttClient: MqttClient) =
     Props(classOf[Producer], mqttClient)
 
   final case class MqttEntry(sensor: String, value: Double, anomaly: Int)
-  final case class SensorModel(name: String, isNormal: Boolean)
+  final case class SensorModel(
+    @BeanProperty name: String,
+    @BeanProperty isNormal: Boolean)
 
   private final case object Tick
 }
@@ -90,7 +94,7 @@ class Producer(mqttClient: MqttClient)
           get {
             val src = Source.fromFile("resources/producer/index.html").mkString
             val model = sensors.map(name => SensorModel(name, state(name) == "normal"))
-            val template = new ST(src, '$', '$').add("sensors", model)
+            val template = new ST(src, '$', '$').add("sensors", model.asJava)
             val dst = template.render()
             complete(HttpEntity(ContentTypes.`text/html(UTF-8)`, dst))
           }

src/test/scala/lib/EntriesFixture.scala

@@ -28,6 +28,7 @@ class EntriesFixture {
     // Get the sensor name for further analysis
     val name = l.head.sensor
 
+    // Prepare data frame for the given sensor
     val data = DataFrame.of(
       l.filter(_.sensor == name)
         .map(row => Tuple.of(
@@ -37,7 +38,9 @@ class EntriesFixture {
           DataTypes.struct(
             new StructField("value", DataTypes.DoubleType),
             new StructField("anomaly", DataTypes.IntegerType))))
-          .asJava)
+        .asJava)
+
+    // Declare formula for the features and the labels
     val formula = "anomaly" ~ "value"
     (data, formula)
   }