update document with some missing save/load

docs/mllib-clustering.md

@@ -63,6 +63,10 @@ val clusters = KMeans.train(parsedData, numClusters, numIterations)
 val WSSSE = clusters.computeCost(parsedData)
 println("Within Set Sum of Squared Errors = " + WSSSE)
 {% endhighlight %}
+
+// Save and load model
+clusters.save(sc, "myModelPath")
+val sameModel = KMeansModel.load(sc, "myModelPath")
 </div>
 
 <div data-lang="java" markdown="1">
@@ -110,6 +114,10 @@ public class KMeansExample {
     // Evaluate clustering by computing Within Set Sum of Squared Errors
     double WSSSE = clusters.computeCost(parsedData.rdd());
     System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
+
+    // Save and load model
+    clusters.save(sc.sc(), "myModelPath");
+    KMeansModel sameModel = KMeansModel.load(sc.sc(), "myModelPath");
   }
 }
 {% endhighlight %}
@@ -143,6 +151,10 @@ def error(point):
 
 WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y)
 print("Within Set Sum of Squared Error = " + str(WSSSE))
+
+# Save and load model
+clusters.save(sc, "myModelPath")
+sameModel = KMeansModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -325,6 +337,10 @@ val model = pic.run(similarities)
 model.assignments.foreach { a =>
   println(s"${a.id} -> ${a.cluster}")
 }
+
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = PowerIterationClusteringModel.load(sc, "myModelPath")
 {% endhighlight %}
 
 A full example that produces the experiment described in the PIC paper can be found under
@@ -360,6 +376,10 @@ PowerIterationClusteringModel model = pic.run(similarities);
 for (PowerIterationClustering.Assignment a: model.assignments().toJavaRDD().collect()) {
   System.out.println(a.id() + " -> " + a.cluster());
 }
+
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+PowerIterationClusteringModel sameModel = PowerIterationClusteringModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 

docs/mllib-feature-extraction.md

@@ -201,6 +201,11 @@ val synonyms = model.findSynonyms("china", 40)
 for((synonym, cosineSimilarity) <- synonyms) {
   println(s"$synonym $cosineSimilarity")
 }
+
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = Word2VecModel.load(sc, "myModelPath")
+
 {% endhighlight %}
 </div>
 <div data-lang="python">

docs/mllib-isotonic-regression.md

@@ -88,6 +88,10 @@ val predictionAndLabel = test.map { point =>
 // Calculate mean squared error between predicted and real labels.
 val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean()
 println("Mean Squared Error = " + meanSquaredError)
+
+// Save and load model
+model.save(sc, "myModelPath")
+val sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
 {% endhighlight %}
 </div>
 
@@ -150,6 +154,10 @@ Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
 ).rdd()).mean();
 
 System.out.println("Mean Squared Error = " + meanSquaredError);
+
+// Save and load model
+model.save(sc.sc(), "myModelPath");
+IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
 </div>