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From sboi...@apache.org
Subject [05/22] ignite git commit: IGNITE-7214: performance measurement for FCM and KNN algorithms
Date Tue, 09 Jan 2018 07:36:41 GMT
IGNITE-7214: performance measurement for FCM and KNN algorithms

this closes #3314


Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/a3b83246
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/a3b83246
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/a3b83246

Branch: refs/heads/ignite-zk
Commit: a3b83246714be990425337522c9fe03fcffbe1a2
Parents: 6efc4d9
Author: Oleg Ignatenko <oignatenko@gridgain.com>
Authored: Fri Dec 29 18:45:20 2017 +0300
Committer: Yury Babak <ybabak@gridgain.com>
Committed: Fri Dec 29 18:45:20 2017 +0300

----------------------------------------------------------------------
 .../ml/clustering/FuzzyCMeansExample.java       | 113 ++---
 .../ml/clustering/FuzzyCMeansLocalExample.java  |  95 ++++
 .../KMeansDistributedClustererExample.java      |   2 +
 .../KNNClassificationExample.java               |  11 +-
 .../ignite/examples/ml/knn/datasets/README.md   |   2 +
 .../ml/knn/datasets/cleared_machines.txt        | 209 +++++++++
 .../ignite/examples/ml/knn/datasets/iris.txt    | 150 ++++++
 .../ml/knn/regression/KNNRegressionExample.java |  18 +-
 .../src/main/resources/datasets/knn/README.md   |   2 -
 .../resources/datasets/knn/cleared_machines.txt | 209 ---------
 .../src/main/resources/datasets/knn/iris.txt    | 150 ------
 .../ignite/ml/structures/LabeledDataset.java    |   4 +-
 .../FuzzyCMeansDistributedClustererTest.java    |  11 +-
 .../FuzzyCMeansLocalClustererTest.java          |  25 +-
 .../yardstick/config/benchmark-ml.properties    |   4 +
 ...uzzyCMeansDistributedClustererBenchmark.java | 130 ++++++
 ...gniteFuzzyCMeansLocalClustererBenchmark.java |  93 ++++
 .../ignite/yardstick/ml/knn/Datasets.java       | 453 +++++++++++++++++++
 .../knn/IgniteKNNClassificationBenchmark.java   |  73 +++
 .../ml/knn/IgniteKNNRegressionBenchmark.java    |  82 ++++
 .../ignite/yardstick/ml/knn/package-info.java   |  22 +
 parent/pom.xml                                  |   5 +
 22 files changed, 1422 insertions(+), 441 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
index 3fce624..23aeed7 100644
--- a/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
@@ -19,6 +19,7 @@ package org.apache.ignite.examples.ml.clustering;
 
 import org.apache.ignite.Ignite;
 import org.apache.ignite.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
 import org.apache.ignite.ml.clustering.BaseFuzzyCMeansClusterer;
 import org.apache.ignite.ml.clustering.FuzzyCMeansDistributedClusterer;
 import org.apache.ignite.ml.clustering.FuzzyCMeansModel;
@@ -30,8 +31,14 @@ import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
 import org.apache.ignite.thread.IgniteThread;
 
 /**
- * This example shows how to use Fuzzy C-Means clusterer
- * ({@link org.apache.ignite.ml.clustering.FuzzyCMeansDistributedClusterer}).
+ * <p>
+ * This example shows how to use {@link FuzzyCMeansDistributedClusterer}.</p>
+ * <p>
+ * Remote nodes should always be started with special configuration file which
+ * enables P2P class loading: {@code 'ignite.{sh|bat} examples/config/example-ignite.xml'}.</p>
+ * <p>
+ * Alternatively you can run {@link ExampleNodeStartup} in another JVM which will start node
+ * with {@code examples/config/example-ignite.xml} configuration.</p>
  */
 public final class FuzzyCMeansExample {
     /**
@@ -40,83 +47,85 @@ public final class FuzzyCMeansExample {
      * @param args Command line arguments, none required.
      */
     public static void main(String[] args) throws InterruptedException {
-        System.out.println();
         System.out.println(">>> Fuzzy C-Means usage example started.");
+
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
             // Start new Ignite thread.
             IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                                                   FuzzyCMeansExample.class.getSimpleName(),
-                                                   () -> {
-
-                // Distance measure that computes distance between two points.
-                DistanceMeasure distanceMeasure = new EuclideanDistance();
+                FuzzyCMeansExample.class.getSimpleName(),
+                () -> {
+                    // Distance measure that computes distance between two points.
+                    DistanceMeasure distanceMeasure = new EuclideanDistance();
 
-                // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
-                double exponentialWeight = 2.0;
+                    // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
+                    double exponentialWeight = 2.0;
 
-                // Condition that indicated when algorithm must stop.
-                // In this example algorithm stops if memberships have changed insignificantly.
-                BaseFuzzyCMeansClusterer.StopCondition stopCond =
+                    // Condition that indicated when algorithm must stop.
+                    // In this example algorithm stops if memberships have changed insignificantly.
+                    BaseFuzzyCMeansClusterer.StopCondition stopCond =
                         BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS;
 
-                // Maximum difference between new and old membership values with which algorithm will continue to work.
-                double maxDelta = 0.01;
+                    // Maximum difference between new and old membership values with which algorithm will continue to work.
+                    double maxDelta = 0.01;
 
-                // The maximum number of FCM iterations.
-                int maxIterations = 50;
+                    // The maximum number of FCM iterations.
+                    int maxIterations = 50;
 
-                // Value that is used to initialize random numbers generator. You can choose it randomly.
-                Long seed = null;
+                    // Value that is used to initialize random numbers generator. You can choose it randomly.
+                    Long seed = null;
 
-                // Number of steps of primary centers selection (more steps more candidates).
-                int initializationSteps = 2;
+                    // Number of steps of primary centers selection (more steps more candidates).
+                    int initializationSteps = 2;
 
-                // Number of K-Means iteration that is used to choose required number of primary centers from candidates.
-                int kMeansMaxIterations = 50;
+                    // Number of K-Means iteration that is used to choose required number of primary centers from candidates.
+                    int kMeansMaxIterations = 50;
 
-                // Create new distributed clusterer with parameters described above.
-                System.out.println(">>> Create new Distributed Fuzzy C-Means clusterer.");
-                FuzzyCMeansDistributedClusterer clusterer = new FuzzyCMeansDistributedClusterer(
+                    // Create new distributed clusterer with parameters described above.
+                    System.out.println(">>> Create new Distributed Fuzzy C-Means clusterer.");
+                    FuzzyCMeansDistributedClusterer clusterer = new FuzzyCMeansDistributedClusterer(
                         distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations,
                         seed, initializationSteps, kMeansMaxIterations);
 
-                // Create sample data.
-                double[][] points = new double[][]{{-10, -10}, {-9, -11}, {-10, -9}, {-11, -9},
-                                                   {10, 10}, {9, 11}, {10, 9}, {11, 9},
-                                                   {-10, 10}, {-9, 11}, {-10, 9}, {-11, 9},
-                                                   {10, -10}, {9, -11}, {10, -9}, {11, -9}};
+                    // Create sample data.
+                    double[][] points = new double[][] {
+                        {-10, -10}, {-9, -11}, {-10, -9}, {-11, -9},
+                        {10, 10}, {9, 11}, {10, 9}, {11, 9},
+                        {-10, 10}, {-9, 11}, {-10, 9}, {-11, 9},
+                        {10, -10}, {9, -11}, {10, -9}, {11, -9}};
 
-                // Initialize matrix of data points. Each row contains one point.
-                int rows = points.length;
-                int cols = points[0].length;
+                    // Initialize matrix of data points. Each row contains one point.
+                    int rows = points.length;
+                    int cols = points[0].length;
 
-                System.out.println(">>> Create the matrix that contains sample points.");
-                SparseDistributedMatrix pntMatrix = new SparseDistributedMatrix(rows, cols,
+                    System.out.println(">>> Create the matrix that contains sample points.");
+                    SparseDistributedMatrix pntMatrix = new SparseDistributedMatrix(rows, cols,
                         StorageConstants.ROW_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
 
-                // Store points into matrix.
-                pntMatrix.assign(points);
+                    // Store points into matrix.
+                    pntMatrix.assign(points);
+
+                    // Call clusterization method with some number of centers.
+                    // It returns model that can predict results for new points.
+                    System.out.println(">>> Perform clusterization.");
+                    int numCenters = 4;
+                    FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
 
-                // Call clusterization method with some number of centers.
-                // It returns model that can predict results for new points.
-                System.out.println(">>> Perform clusterization.");
-                int numCenters = 4;
-                FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
+                    // You can also get centers of clusters that is computed by Fuzzy C-Means algorithm.
+                    Vector[] centers = mdl.centers();
 
-                // You can also get centers of clusters that is computed by Fuzzy C-Means algorithm.
-                Vector[] centers = mdl.centers();
+                    String res = ">>> Results:\n"
+                        + ">>> 1st center: " + centers[0].get(0) + " " + centers[0].get(1) + "\n"
+                        + ">>> 2nd center: " + centers[1].get(0) + " " + centers[1].get(1) + "\n"
+                        + ">>> 3rd center: " + centers[2].get(0) + " " + centers[2].get(1) + "\n"
+                        + ">>> 4th center: " + centers[3].get(0) + " " + centers[3].get(1) + "\n";
 
-                StringBuilder results = new StringBuilder(">>> Results:\n");
-                results.append(">>> 1st center: " + centers[0].get(0) + " " + centers[0].get(1) + "\n");
-                results.append(">>> 2nd center: " + centers[1].get(0) + " " + centers[1].get(1) + "\n");
-                results.append(">>> 3rd center: " + centers[2].get(0) + " " + centers[2].get(1) + "\n");
-                results.append(">>> 4th center: " + centers[3].get(0) + " " + centers[3].get(1) + "\n");
+                    System.out.println(res);
 
-                System.out.println(results.toString());
-            });
+                    pntMatrix.destroy();
+                });
 
             igniteThread.start();
             igniteThread.join();

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java
new file mode 100644
index 0000000..5c1753a
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java
@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.examples.ml.clustering;
+
+import org.apache.ignite.ml.clustering.BaseFuzzyCMeansClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansLocalClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansModel;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.distances.DistanceMeasure;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * This example shows how to use {@link FuzzyCMeansLocalClusterer}.
+ */
+public final class FuzzyCMeansLocalExample {
+    /**
+     * Executes example.
+     *
+     * @param args Command line arguments, none required.
+     */
+    public static void main(String[] args) {
+        System.out.println(">>> Local Fuzzy C-Means usage example started.");
+
+        // Distance measure that computes distance between two points.
+        DistanceMeasure distanceMeasure = new EuclideanDistance();
+
+        // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
+        double exponentialWeight = 2.0;
+
+        // Condition that indicated when algorithm must stop.
+        // In this example algorithm stops if memberships have changed insignificantly.
+        BaseFuzzyCMeansClusterer.StopCondition stopCond =
+            BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS;
+
+        // Maximum difference between new and old membership values with which algorithm will continue to work.
+        double maxDelta = 0.01;
+
+        // The maximum number of FCM iterations.
+        int maxIterations = 50;
+
+        // Value that is used to initialize random numbers generator. You can choose it randomly.
+        Long seed = null;
+
+        // Create new distributed clusterer with parameters described above.
+        System.out.println(">>> Create new Local Fuzzy C-Means clusterer.");
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(distanceMeasure,
+            exponentialWeight, stopCond,
+            maxDelta, maxIterations, seed);
+
+        // Create sample data.
+        double[][] points = new double[][] {
+            {-10, -10}, {-9, -11}, {-10, -9}, {-11, -9},
+            {10, 10}, {9, 11}, {10, 9}, {11, 9},
+            {-10, 10}, {-9, 11}, {-10, 9}, {-11, 9},
+            {10, -10}, {9, -11}, {10, -9}, {11, -9}};
+
+        // Initialize matrix of data points. Each row contains one point.
+        System.out.println(">>> Create the matrix that contains sample points.");
+        // Store points into matrix.
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+
+        // Call clusterization method with some number of centers.
+        // It returns model that can predict results for new points.
+        System.out.println(">>> Perform clusterization.");
+        int numCenters = 4;
+        FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
+
+        // You can also get centers of clusters that is computed by Fuzzy C-Means algorithm.
+        Vector[] centers = mdl.centers();
+
+        String res = ">>> Results:\n"
+            + ">>> 1st center: " + centers[0].get(0) + " " + centers[0].get(1) + "\n"
+            + ">>> 2nd center: " + centers[1].get(0) + " " + centers[1].get(1) + "\n"
+            + ">>> 3rd center: " + centers[2].get(0) + " " + centers[2].get(1) + "\n"
+            + ">>> 4th center: " + centers[3].get(0) + " " + centers[3].get(1) + "\n";
+
+        System.out.println(res);
+    }
+}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
index 09f35d2..f8709e6 100644
--- a/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
@@ -53,9 +53,11 @@ public class KMeansDistributedClustererExample {
     public static void main(String[] args) throws InterruptedException {
         // IMPL NOTE based on KMeansDistributedClustererTestSingleNode#testClusterizationOnDatasetWithObviousStructure
         System.out.println(">>> K-means distributed clusterer example started.");
+
         // Start ignite grid.
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
+
             // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
             // because we create ignite cache internally.
             IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
index efdacd7..0e1a52f 100644
--- a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
@@ -19,6 +19,7 @@ package org.apache.ignite.examples.ml.knn.classification;
 
 import java.io.IOException;
 import java.net.URISyntaxException;
+import java.net.URL;
 import java.nio.file.Path;
 import java.nio.file.Paths;
 import java.util.Arrays;
@@ -52,7 +53,7 @@ public class KNNClassificationExample {
     private static final String SEPARATOR = "\t";
 
     /** Path to the Iris dataset. */
-    static final String KNN_IRIS_TXT = "datasets/knn/iris.txt";
+    private static final String KNN_IRIS_TXT = "../datasets/iris.txt";
 
     /**
      * Executes example.
@@ -70,7 +71,11 @@ public class KNNClassificationExample {
 
                 try {
                     // Prepare path to read
-                    Path path = Paths.get(KNNClassificationExample.class.getClassLoader().getResource(KNN_IRIS_TXT).toURI());
+                    URL url = KNNClassificationExample.class.getResource(KNN_IRIS_TXT);
+                    if (url == null)
+                        throw new RuntimeException("Can't get URL for: " + KNN_IRIS_TXT);
+
+                    Path path = Paths.get(url.toURI());
 
                     // Read dataset from file
                     LabeledDataset dataset = LabeledDatasetLoader.loadFromTxtFile(path, SEPARATOR, true, false);
@@ -135,7 +140,7 @@ public class KNNClassificationExample {
                 }
                 catch (URISyntaxException | IOException e) {
                     e.printStackTrace();
-                    System.out.println("\n>>> Check resources");
+                    System.out.println("\n>>> Unexpected exception, check resources: " + e);
                 }
                 finally {
                     System.out.println("\n>>> kNN classification example completed.");

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/README.md
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/README.md b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/README.md
new file mode 100644
index 0000000..2f9c5ec
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/README.md
@@ -0,0 +1,2 @@
+iris.txt and cleared_machines are from Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
+Read more about machine dataset http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.names
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt
new file mode 100644
index 0000000..cf8b6b0
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt
@@ -0,0 +1,209 @@
+199,125,256,6000,256,16,128
+253,29,8000,32000,32,8,32
+253,29,8000,32000,32,8,32
+253,29,8000,32000,32,8,32
+132,29,8000,16000,32,8,16
+290,26,8000,32000,64,8,32
+381,23,16000,32000,64,16,32
+381,23,16000,32000,64,16,32
+749,23,16000,64000,64,16,32
+1238,23,32000,64000,128,32,64
+23,400,1000,3000,0,1,2
+24,400,512,3500,4,1,6
+70,60,2000,8000,65,1,8
+117,50,4000,16000,65,1,8
+15,350,64,64,0,1,4
+64,200,512,16000,0,4,32
+23,167,524,2000,8,4,15
+29,143,512,5000,0,7,32
+22,143,1000,2000,0,5,16
+124,110,5000,5000,142,8,64
+35,143,1500,6300,0,5,32
+39,143,3100,6200,0,5,20
+40,143,2300,6200,0,6,64
+45,110,3100,6200,0,6,64
+28,320,128,6000,0,1,12
+21,320,512,2000,4,1,3
+28,320,256,6000,0,1,6
+22,320,256,3000,4,1,3
+28,320,512,5000,4,1,5
+27,320,256,5000,4,1,6
+102,25,1310,2620,131,12,24
+102,25,1310,2620,131,12,24
+74,50,2620,10480,30,12,24
+74,50,2620,10480,30,12,24
+138,56,5240,20970,30,12,24
+136,64,5240,20970,30,12,24
+23,50,500,2000,8,1,4
+29,50,1000,4000,8,1,5
+44,50,2000,8000,8,1,5
+30,50,1000,4000,8,3,5
+41,50,1000,8000,8,3,5
+74,50,2000,16000,8,3,5
+74,50,2000,16000,8,3,6
+74,50,2000,16000,8,3,6
+54,133,1000,12000,9,3,12
+41,133,1000,8000,9,3,12
+18,810,512,512,8,1,1
+28,810,1000,5000,0,1,1
+36,320,512,8000,4,1,5
+38,200,512,8000,8,1,8
+34,700,384,8000,0,1,1
+19,700,256,2000,0,1,1
+72,140,1000,16000,16,1,3
+36,200,1000,8000,0,1,2
+30,110,1000,4000,16,1,2
+56,110,1000,12000,16,1,2
+42,220,1000,8000,16,1,2
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+34,800,256,8000,0,1,4
+19,125,512,1000,0,8,20
+75,75,2000,8000,64,1,38
+113,75,2000,16000,64,1,38
+157,75,2000,16000,128,1,38
+18,90,256,1000,0,3,10
+20,105,256,2000,0,3,10
+28,105,1000,4000,0,3,24
+33,105,2000,4000,8,3,19
+47,75,2000,8000,8,3,24
+54,75,3000,8000,8,3,48
+20,175,256,2000,0,3,24
+23,300,768,3000,0,6,24
+25,300,768,3000,6,6,24
+52,300,768,12000,6,6,24
+27,300,768,4500,0,1,24
+50,300,384,12000,6,1,24
+18,300,192,768,6,6,24
+53,180,768,12000,6,1,31
+23,330,1000,3000,0,2,4
+30,300,1000,4000,8,3,64
+73,300,1000,16000,8,2,112
+20,330,1000,2000,0,1,2
+25,330,1000,4000,0,3,6
+28,140,2000,4000,0,3,6
+29,140,2000,4000,0,4,8
+32,140,2000,4000,8,1,20
+175,140,2000,32000,32,1,20
+57,140,2000,8000,32,1,54
+181,140,2000,32000,32,1,54
+181,140,2000,32000,32,1,54
+32,140,2000,4000,8,1,20
+82,57,4000,16000,1,6,12
+171,57,4000,24000,64,12,16
+361,26,16000,32000,64,16,24
+350,26,16000,32000,64,8,24
+220,26,8000,32000,0,8,24
+113,26,8000,16000,0,8,16
+15,480,96,512,0,1,1
+21,203,1000,2000,0,1,5
+35,115,512,6000,16,1,6
+18,1100,512,1500,0,1,1
+20,1100,768,2000,0,1,1
+20,600,768,2000,0,1,1
+28,400,2000,4000,0,1,1
+45,400,4000,8000,0,1,1
+18,900,1000,1000,0,1,2
+17,900,512,1000,0,1,2
+26,900,1000,4000,4,1,2
+28,900,1000,4000,8,1,2
+28,900,2000,4000,0,3,6
+31,225,2000,4000,8,3,6
+31,225,2000,4000,8,3,6
+42,180,2000,8000,8,1,6
+76,185,2000,16000,16,1,6
+76,180,2000,16000,16,1,6
+26,225,1000,4000,2,3,6
+59,25,2000,12000,8,1,4
+65,25,2000,12000,16,3,5
+101,17,4000,16000,8,6,12
+116,17,4000,16000,32,6,12
+18,1500,768,1000,0,0,0
+20,1500,768,2000,0,0,0
+20,800,768,2000,0,0,0
+30,50,2000,4000,0,3,6
+44,50,2000,8000,8,3,6
+44,50,2000,8000,8,1,6
+82,50,2000,16000,24,1,6
+82,50,2000,16000,24,1,6
+128,50,8000,16000,48,1,10
+37,100,1000,8000,0,2,6
+46,100,1000,8000,24,2,6
+46,100,1000,8000,24,3,6
+80,50,2000,16000,12,3,16
+88,50,2000,16000,24,6,16
+88,50,2000,16000,24,6,16
+33,150,512,4000,0,8,128
+46,115,2000,8000,16,1,3
+29,115,2000,4000,2,1,5
+53,92,2000,8000,32,1,6
+53,92,2000,8000,32,1,6
+41,92,2000,8000,4,1,6
+86,75,4000,16000,16,1,6
+95,60,4000,16000,32,1,6
+107,60,2000,16000,64,5,8
+117,60,4000,16000,64,5,8
+119,50,4000,16000,64,5,10
+120,72,4000,16000,64,8,16
+48,72,2000,8000,16,6,8
+126,40,8000,16000,32,8,16
+266,40,8000,32000,64,8,24
+270,35,8000,32000,64,8,24
+426,38,16000,32000,128,16,32
+151,48,4000,24000,32,8,24
+267,38,8000,32000,64,8,24
+603,30,16000,32000,256,16,24
+19,112,1000,1000,0,1,4
+21,84,1000,2000,0,1,6
+26,56,1000,4000,0,1,6
+35,56,2000,6000,0,1,8
+41,56,2000,8000,0,1,8
+47,56,4000,8000,0,1,8
+62,56,4000,12000,0,1,8
+78,56,4000,16000,0,1,8
+80,38,4000,8000,32,16,32
+80,38,4000,8000,32,16,32
+142,38,8000,16000,64,4,8
+281,38,8000,24000,160,4,8
+190,38,4000,16000,128,16,32
+21,200,1000,2000,0,1,2
+25,200,1000,4000,0,1,4
+67,200,2000,8000,64,1,5
+24,250,512,4000,0,1,7
+24,250,512,4000,0,4,7
+64,250,1000,16000,1,1,8
+25,160,512,4000,2,1,5
+20,160,512,2000,2,3,8
+29,160,1000,4000,8,1,14
+43,160,1000,8000,16,1,14
+53,160,2000,8000,32,1,13
+19,240,512,1000,8,1,3
+22,240,512,2000,8,1,5
+31,105,2000,4000,8,3,8
+41,105,2000,6000,16,6,16
+47,105,2000,8000,16,4,14
+99,52,4000,16000,32,4,12
+67,70,4000,12000,8,6,8
+81,59,4000,12000,32,6,12
+149,59,8000,16000,64,12,24
+183,26,8000,24000,32,8,16
+275,26,8000,32000,64,12,16
+382,26,8000,32000,128,24,32
+56,116,2000,8000,32,5,28
+182,50,2000,32000,24,6,26
+227,50,2000,32000,48,26,52
+341,50,2000,32000,112,52,104
+360,50,4000,32000,112,52,104
+919,30,8000,64000,96,12,176
+978,30,8000,64000,128,12,176
+24,180,262,4000,0,1,3
+24,180,512,4000,0,1,3
+24,180,262,4000,0,1,3
+24,180,512,4000,0,1,3
+37,124,1000,8000,0,1,8
+50,98,1000,8000,32,2,8
+41,125,2000,8000,0,2,14
+47,480,512,8000,32,0,0
+25,480,1000,4000,0,0,0

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/iris.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/iris.txt b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/iris.txt
new file mode 100644
index 0000000..18f5f7c
--- /dev/null
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/datasets/iris.txt
@@ -0,0 +1,150 @@
+1.0	5.1	3.5	1.4	0.2
+1.0	4.9	3.0	1.4	0.2
+1.0	4.7	3.2	1.3	0.2
+1.0	4.6	3.1	1.5	0.2
+1.0	5.0	3.6	1.4	0.2
+1.0	5.4	3.9	1.7	0.4
+1.0	4.6	3.4	1.4	0.3
+1.0	5.0	3.4	1.5	0.2
+1.0	4.4	2.9	1.4	0.2
+1.0	4.9	3.1	1.5	0.1
+1.0	5.4	3.7	1.5	0.2
+1.0	4.8	3.4	1.6	0.2
+1.0	4.8	3.0	1.4	0.1
+1.0	4.3	3.0	1.1	0.1
+1.0	5.8	4.0	1.2	0.2
+1.0	5.7	4.4	1.5	0.4
+1.0	5.4	3.9	1.3	0.4
+1.0	5.1	3.5	1.4	0.3
+1.0	5.7	3.8	1.7	0.3
+1.0	5.1	3.8	1.5	0.3
+1.0	5.4	3.4	1.7	0.2
+1.0	5.1	3.7	1.5	0.4
+1.0	4.6	3.6	1.0	0.2
+1.0	5.1	3.3	1.7	0.5
+1.0	4.8	3.4	1.9	0.2
+1.0	5.0	3.0	1.6	0.2
+1.0	5.0	3.4	1.6	0.4
+1.0	5.2	3.5	1.5	0.2
+1.0	5.2	3.4	1.4	0.2
+1.0	4.7	3.2	1.6	0.2
+1.0	4.8	3.1	1.6	0.2
+1.0	5.4	3.4	1.5	0.4
+1.0	5.2	4.1	1.5	0.1
+1.0	5.5	4.2	1.4	0.2
+1.0	4.9	3.1	1.5	0.1
+1.0	5.0	3.2	1.2	0.2
+1.0	5.5	3.5	1.3	0.2
+1.0	4.9	3.1	1.5	0.1
+1.0	4.4	3.0	1.3	0.2
+1.0	5.1	3.4	1.5	0.2
+1.0	5.0	3.5	1.3	0.3
+1.0	4.5	2.3	1.3	0.3
+1.0	4.4	3.2	1.3	0.2
+1.0	5.0	3.5	1.6	0.6
+1.0	5.1	3.8	1.9	0.4
+1.0	4.8	3.0	1.4	0.3
+1.0	5.1	3.8	1.6	0.2
+1.0	4.6	3.2	1.4	0.2
+1.0	5.3	3.7	1.5	0.2
+1.0	5.0	3.3	1.4	0.2
+2.0	7.0	3.2	4.7	1.4
+2.0	6.4	3.2	4.5	1.5
+2.0	6.9	3.1	4.9	1.5
+2.0	5.5	2.3	4.0	1.3
+2.0	6.5	2.8	4.6	1.5
+2.0	5.7	2.8	4.5	1.3
+2.0	6.3	3.3	4.7	1.6
+2.0	4.9	2.4	3.3	1.0
+2.0	6.6	2.9	4.6	1.3
+2.0	5.2	2.7	3.9	1.4
+2.0	5.0	2.0	3.5	1.0
+2.0	5.9	3.0	4.2	1.5
+2.0	6.0	2.2	4.0	1.0
+2.0	6.1	2.9	4.7	1.4
+2.0	5.6	2.9	3.6	1.3
+2.0	6.7	3.1	4.4	1.4
+2.0	5.6	3.0	4.5	1.5
+2.0	5.8	2.7	4.1	1.0
+2.0	6.2	2.2	4.5	1.5
+2.0	5.6	2.5	3.9	1.1
+2.0	5.9	3.2	4.8	1.8
+2.0	6.1	2.8	4.0	1.3
+2.0	6.3	2.5	4.9	1.5
+2.0	6.1	2.8	4.7	1.2
+2.0	6.4	2.9	4.3	1.3
+2.0	6.6	3.0	4.4	1.4
+2.0	6.8	2.8	4.8	1.4
+2.0	6.7	3.0	5.0	1.7
+2.0	6.0	2.9	4.5	1.5
+2.0	5.7	2.6	3.5	1.0
+2.0	5.5	2.4	3.8	1.1
+2.0	5.5	2.4	3.7	1.0
+2.0	5.8	2.7	3.9	1.2
+2.0	6.0	2.7	5.1	1.6
+2.0	5.4	3.0	4.5	1.5
+2.0	6.0	3.4	4.5	1.6
+2.0	6.7	3.1	4.7	1.5
+2.0	6.3	2.3	4.4	1.3
+2.0	5.6	3.0	4.1	1.3
+2.0	5.5	2.5	4.0	1.3
+2.0	5.5	2.6	4.4	1.2
+2.0	6.1	3.0	4.6	1.4
+2.0	5.8	2.6	4.0	1.2
+2.0	5.0	2.3	3.3	1.0
+2.0	5.6	2.7	4.2	1.3
+2.0	5.7	3.0	4.2	1.2
+2.0	5.7	2.9	4.2	1.3
+2.0	6.2	2.9	4.3	1.3
+2.0	5.1	2.5	3.0	1.1
+2.0	5.7	2.8	4.1	1.3
+3.0	6.3	3.3	6.0	2.5
+3.0	5.8	2.7	5.1	1.9
+3.0	7.1	3.0	5.9	2.1
+3.0	6.3	2.9	5.6	1.8
+3.0	6.5	3.0	5.8	2.2
+3.0	7.6	3.0	6.6	2.1
+3.0	4.9	2.5	4.5	1.7
+3.0	7.3	2.9	6.3	1.8
+3.0	6.7	2.5	5.8	1.8
+3.0	7.2	3.6	6.1	2.5
+3.0	6.5	3.2	5.1	2.0
+3.0	6.4	2.7	5.3	1.9
+3.0	6.8	3.0	5.5	2.1
+3.0	5.7	2.5	5.0	2.0
+3.0	5.8	2.8	5.1	2.4
+3.0	6.4	3.2	5.3	2.3
+3.0	6.5	3.0	5.5	1.8
+3.0	7.7	3.8	6.7	2.2
+3.0	7.7	2.6	6.9	2.3
+3.0	6.0	2.2	5.0	1.5
+3.0	6.9	3.2	5.7	2.3
+3.0	5.6	2.8	4.9	2.0
+3.0	7.7	2.8	6.7	2.0
+3.0	6.3	2.7	4.9	1.8
+3.0	6.7	3.3	5.7	2.1
+3.0	7.2	3.2	6.0	1.8
+3.0	6.2	2.8	4.8	1.8
+3.0	6.1	3.0	4.9	1.8
+3.0	6.4	2.8	5.6	2.1
+3.0	7.2	3.0	5.8	1.6
+3.0	7.4	2.8	6.1	1.9
+3.0	7.9	3.8	6.4	2.0
+3.0	6.4	2.8	5.6	2.2
+3.0	6.3	2.8	5.1	1.5
+3.0	6.1	2.6	5.6	1.4
+3.0	7.7	3.0	6.1	2.3
+3.0	6.3	3.4	5.6	2.4
+3.0	6.4	3.1	5.5	1.8
+3.0	6.0	3.0	4.8	1.8
+3.0	6.9	3.1	5.4	2.1
+3.0	6.7	3.1	5.6	2.4
+3.0	6.9	3.1	5.1	2.3
+3.0	5.8	2.7	5.1	1.9
+3.0	6.8	3.2	5.9	2.3
+3.0	6.7	3.3	5.7	2.5
+3.0	6.7	3.0	5.2	2.3
+3.0	6.3	2.5	5.0	1.9
+3.0	6.5	3.0	5.2	2.0
+3.0	6.2	3.4	5.4	2.3
+3.0	5.9	3.0	5.1	1.8

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
index 31f7191..b52613a 100644
--- a/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
+++ b/examples/src/main/ml/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
@@ -19,6 +19,7 @@ package org.apache.ignite.examples.ml.knn.regression;
 
 import java.io.IOException;
 import java.net.URISyntaxException;
+import java.net.URL;
 import java.nio.file.Path;
 import java.nio.file.Paths;
 import org.apache.ignite.Ignite;
@@ -53,7 +54,7 @@ public class KNNRegressionExample {
     private static final String SEPARATOR = ",";
 
     /** */
-    public static final String KNN_CLEARED_MACHINES_TXT = "datasets/knn/cleared_machines.txt";
+    private static final String KNN_CLEARED_MACHINES_TXT = "../datasets/cleared_machines.txt";
 
     /**
      * Executes example.
@@ -71,7 +72,11 @@ public class KNNRegressionExample {
 
                 try {
                     // Prepare path to read
-                    Path path = Paths.get(KNNClassificationExample.class.getClassLoader().getResource(KNN_CLEARED_MACHINES_TXT).toURI());
+                    URL url = KNNClassificationExample.class.getResource(KNN_CLEARED_MACHINES_TXT);
+                    if (url == null)
+                        throw new RuntimeException("Can't get URL for: " + KNN_CLEARED_MACHINES_TXT);
+
+                    Path path = Paths.get(url.toURI());
 
                     // Read dataset from file
                     LabeledDataset dataset = LabeledDatasetLoader.loadFromTxtFile(path, SEPARATOR, false, false);
@@ -82,14 +87,15 @@ public class KNNRegressionExample {
                     // Random splitting of iris data as 80% train and 20% test datasets
                     LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.2);
 
-                    System.out.println("\n>>> Amount of observations in train dataset " + split.train().rowSize());
-                    System.out.println("\n>>> Amount of observations in test dataset " + split.test().rowSize());
+                    System.out.println("\n>>> Amount of observations in train dataset: " + split.train().rowSize());
+                    System.out.println("\n>>> Amount of observations in test dataset: " + split.test().rowSize());
 
                     LabeledDataset test = split.test();
                     LabeledDataset train = split.train();
 
                     // Builds weighted kNN-regression with Manhattan Distance
-                    KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(7, new ManhattanDistance(), KNNStrategy.WEIGHTED, train);
+                    KNNMultipleLinearRegression knnMdl = new KNNMultipleLinearRegression(7, new ManhattanDistance(),
+                        KNNStrategy.WEIGHTED, train);
 
                     // Clone labels
                     final double[] labels = test.labels();
@@ -137,7 +143,7 @@ public class KNNRegressionExample {
                 }
                 catch (URISyntaxException | IOException e) {
                     e.printStackTrace();
-                    System.out.println("\n>>> Check resources");
+                    System.out.println("\n>>> Unexpected exception, check resources: " + e);
                 }
                 finally {
                     System.out.println("\n>>> kNN regression example completed.");

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/resources/datasets/knn/README.md
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/README.md b/examples/src/main/resources/datasets/knn/README.md
deleted file mode 100644
index 2f9c5ec..0000000
--- a/examples/src/main/resources/datasets/knn/README.md
+++ /dev/null
@@ -1,2 +0,0 @@
-iris.txt and cleared_machines are from Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
-Read more about machine dataset http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.names
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/resources/datasets/knn/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/cleared_machines.txt b/examples/src/main/resources/datasets/knn/cleared_machines.txt
deleted file mode 100644
index cf8b6b0..0000000
--- a/examples/src/main/resources/datasets/knn/cleared_machines.txt
+++ /dev/null
@@ -1,209 +0,0 @@
-199,125,256,6000,256,16,128
-253,29,8000,32000,32,8,32
-253,29,8000,32000,32,8,32
-253,29,8000,32000,32,8,32
-132,29,8000,16000,32,8,16
-290,26,8000,32000,64,8,32
-381,23,16000,32000,64,16,32
-381,23,16000,32000,64,16,32
-749,23,16000,64000,64,16,32
-1238,23,32000,64000,128,32,64
-23,400,1000,3000,0,1,2
-24,400,512,3500,4,1,6
-70,60,2000,8000,65,1,8
-117,50,4000,16000,65,1,8
-15,350,64,64,0,1,4
-64,200,512,16000,0,4,32
-23,167,524,2000,8,4,15
-29,143,512,5000,0,7,32
-22,143,1000,2000,0,5,16
-124,110,5000,5000,142,8,64
-35,143,1500,6300,0,5,32
-39,143,3100,6200,0,5,20
-40,143,2300,6200,0,6,64
-45,110,3100,6200,0,6,64
-28,320,128,6000,0,1,12
-21,320,512,2000,4,1,3
-28,320,256,6000,0,1,6
-22,320,256,3000,4,1,3
-28,320,512,5000,4,1,5
-27,320,256,5000,4,1,6
-102,25,1310,2620,131,12,24
-102,25,1310,2620,131,12,24
-74,50,2620,10480,30,12,24
-74,50,2620,10480,30,12,24
-138,56,5240,20970,30,12,24
-136,64,5240,20970,30,12,24
-23,50,500,2000,8,1,4
-29,50,1000,4000,8,1,5
-44,50,2000,8000,8,1,5
-30,50,1000,4000,8,3,5
-41,50,1000,8000,8,3,5
-74,50,2000,16000,8,3,5
-74,50,2000,16000,8,3,6
-74,50,2000,16000,8,3,6
-54,133,1000,12000,9,3,12
-41,133,1000,8000,9,3,12
-18,810,512,512,8,1,1
-28,810,1000,5000,0,1,1
-36,320,512,8000,4,1,5
-38,200,512,8000,8,1,8
-34,700,384,8000,0,1,1
-19,700,256,2000,0,1,1
-72,140,1000,16000,16,1,3
-36,200,1000,8000,0,1,2
-30,110,1000,4000,16,1,2
-56,110,1000,12000,16,1,2
-42,220,1000,8000,16,1,2
-34,800,256,8000,0,1,4
-34,800,256,8000,0,1,4
-34,800,256,8000,0,1,4
-34,800,256,8000,0,1,4
-34,800,256,8000,0,1,4
-19,125,512,1000,0,8,20
-75,75,2000,8000,64,1,38
-113,75,2000,16000,64,1,38
-157,75,2000,16000,128,1,38
-18,90,256,1000,0,3,10
-20,105,256,2000,0,3,10
-28,105,1000,4000,0,3,24
-33,105,2000,4000,8,3,19
-47,75,2000,8000,8,3,24
-54,75,3000,8000,8,3,48
-20,175,256,2000,0,3,24
-23,300,768,3000,0,6,24
-25,300,768,3000,6,6,24
-52,300,768,12000,6,6,24
-27,300,768,4500,0,1,24
-50,300,384,12000,6,1,24
-18,300,192,768,6,6,24
-53,180,768,12000,6,1,31
-23,330,1000,3000,0,2,4
-30,300,1000,4000,8,3,64
-73,300,1000,16000,8,2,112
-20,330,1000,2000,0,1,2
-25,330,1000,4000,0,3,6
-28,140,2000,4000,0,3,6
-29,140,2000,4000,0,4,8
-32,140,2000,4000,8,1,20
-175,140,2000,32000,32,1,20
-57,140,2000,8000,32,1,54
-181,140,2000,32000,32,1,54
-181,140,2000,32000,32,1,54
-32,140,2000,4000,8,1,20
-82,57,4000,16000,1,6,12
-171,57,4000,24000,64,12,16
-361,26,16000,32000,64,16,24
-350,26,16000,32000,64,8,24
-220,26,8000,32000,0,8,24
-113,26,8000,16000,0,8,16
-15,480,96,512,0,1,1
-21,203,1000,2000,0,1,5
-35,115,512,6000,16,1,6
-18,1100,512,1500,0,1,1
-20,1100,768,2000,0,1,1
-20,600,768,2000,0,1,1
-28,400,2000,4000,0,1,1
-45,400,4000,8000,0,1,1
-18,900,1000,1000,0,1,2
-17,900,512,1000,0,1,2
-26,900,1000,4000,4,1,2
-28,900,1000,4000,8,1,2
-28,900,2000,4000,0,3,6
-31,225,2000,4000,8,3,6
-31,225,2000,4000,8,3,6
-42,180,2000,8000,8,1,6
-76,185,2000,16000,16,1,6
-76,180,2000,16000,16,1,6
-26,225,1000,4000,2,3,6
-59,25,2000,12000,8,1,4
-65,25,2000,12000,16,3,5
-101,17,4000,16000,8,6,12
-116,17,4000,16000,32,6,12
-18,1500,768,1000,0,0,0
-20,1500,768,2000,0,0,0
-20,800,768,2000,0,0,0
-30,50,2000,4000,0,3,6
-44,50,2000,8000,8,3,6
-44,50,2000,8000,8,1,6
-82,50,2000,16000,24,1,6
-82,50,2000,16000,24,1,6
-128,50,8000,16000,48,1,10
-37,100,1000,8000,0,2,6
-46,100,1000,8000,24,2,6
-46,100,1000,8000,24,3,6
-80,50,2000,16000,12,3,16
-88,50,2000,16000,24,6,16
-88,50,2000,16000,24,6,16
-33,150,512,4000,0,8,128
-46,115,2000,8000,16,1,3
-29,115,2000,4000,2,1,5
-53,92,2000,8000,32,1,6
-53,92,2000,8000,32,1,6
-41,92,2000,8000,4,1,6
-86,75,4000,16000,16,1,6
-95,60,4000,16000,32,1,6
-107,60,2000,16000,64,5,8
-117,60,4000,16000,64,5,8
-119,50,4000,16000,64,5,10
-120,72,4000,16000,64,8,16
-48,72,2000,8000,16,6,8
-126,40,8000,16000,32,8,16
-266,40,8000,32000,64,8,24
-270,35,8000,32000,64,8,24
-426,38,16000,32000,128,16,32
-151,48,4000,24000,32,8,24
-267,38,8000,32000,64,8,24
-603,30,16000,32000,256,16,24
-19,112,1000,1000,0,1,4
-21,84,1000,2000,0,1,6
-26,56,1000,4000,0,1,6
-35,56,2000,6000,0,1,8
-41,56,2000,8000,0,1,8
-47,56,4000,8000,0,1,8
-62,56,4000,12000,0,1,8
-78,56,4000,16000,0,1,8
-80,38,4000,8000,32,16,32
-80,38,4000,8000,32,16,32
-142,38,8000,16000,64,4,8
-281,38,8000,24000,160,4,8
-190,38,4000,16000,128,16,32
-21,200,1000,2000,0,1,2
-25,200,1000,4000,0,1,4
-67,200,2000,8000,64,1,5
-24,250,512,4000,0,1,7
-24,250,512,4000,0,4,7
-64,250,1000,16000,1,1,8
-25,160,512,4000,2,1,5
-20,160,512,2000,2,3,8
-29,160,1000,4000,8,1,14
-43,160,1000,8000,16,1,14
-53,160,2000,8000,32,1,13
-19,240,512,1000,8,1,3
-22,240,512,2000,8,1,5
-31,105,2000,4000,8,3,8
-41,105,2000,6000,16,6,16
-47,105,2000,8000,16,4,14
-99,52,4000,16000,32,4,12
-67,70,4000,12000,8,6,8
-81,59,4000,12000,32,6,12
-149,59,8000,16000,64,12,24
-183,26,8000,24000,32,8,16
-275,26,8000,32000,64,12,16
-382,26,8000,32000,128,24,32
-56,116,2000,8000,32,5,28
-182,50,2000,32000,24,6,26
-227,50,2000,32000,48,26,52
-341,50,2000,32000,112,52,104
-360,50,4000,32000,112,52,104
-919,30,8000,64000,96,12,176
-978,30,8000,64000,128,12,176
-24,180,262,4000,0,1,3
-24,180,512,4000,0,1,3
-24,180,262,4000,0,1,3
-24,180,512,4000,0,1,3
-37,124,1000,8000,0,1,8
-50,98,1000,8000,32,2,8
-41,125,2000,8000,0,2,14
-47,480,512,8000,32,0,0
-25,480,1000,4000,0,0,0

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/examples/src/main/resources/datasets/knn/iris.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/knn/iris.txt b/examples/src/main/resources/datasets/knn/iris.txt
deleted file mode 100644
index 18f5f7c..0000000
--- a/examples/src/main/resources/datasets/knn/iris.txt
+++ /dev/null
@@ -1,150 +0,0 @@
-1.0	5.1	3.5	1.4	0.2
-1.0	4.9	3.0	1.4	0.2
-1.0	4.7	3.2	1.3	0.2
-1.0	4.6	3.1	1.5	0.2
-1.0	5.0	3.6	1.4	0.2
-1.0	5.4	3.9	1.7	0.4
-1.0	4.6	3.4	1.4	0.3
-1.0	5.0	3.4	1.5	0.2
-1.0	4.4	2.9	1.4	0.2
-1.0	4.9	3.1	1.5	0.1
-1.0	5.4	3.7	1.5	0.2
-1.0	4.8	3.4	1.6	0.2
-1.0	4.8	3.0	1.4	0.1
-1.0	4.3	3.0	1.1	0.1
-1.0	5.8	4.0	1.2	0.2
-1.0	5.7	4.4	1.5	0.4
-1.0	5.4	3.9	1.3	0.4
-1.0	5.1	3.5	1.4	0.3
-1.0	5.7	3.8	1.7	0.3
-1.0	5.1	3.8	1.5	0.3
-1.0	5.4	3.4	1.7	0.2
-1.0	5.1	3.7	1.5	0.4
-1.0	4.6	3.6	1.0	0.2
-1.0	5.1	3.3	1.7	0.5
-1.0	4.8	3.4	1.9	0.2
-1.0	5.0	3.0	1.6	0.2
-1.0	5.0	3.4	1.6	0.4
-1.0	5.2	3.5	1.5	0.2
-1.0	5.2	3.4	1.4	0.2
-1.0	4.7	3.2	1.6	0.2
-1.0	4.8	3.1	1.6	0.2
-1.0	5.4	3.4	1.5	0.4
-1.0	5.2	4.1	1.5	0.1
-1.0	5.5	4.2	1.4	0.2
-1.0	4.9	3.1	1.5	0.1
-1.0	5.0	3.2	1.2	0.2
-1.0	5.5	3.5	1.3	0.2
-1.0	4.9	3.1	1.5	0.1
-1.0	4.4	3.0	1.3	0.2
-1.0	5.1	3.4	1.5	0.2
-1.0	5.0	3.5	1.3	0.3
-1.0	4.5	2.3	1.3	0.3
-1.0	4.4	3.2	1.3	0.2
-1.0	5.0	3.5	1.6	0.6
-1.0	5.1	3.8	1.9	0.4
-1.0	4.8	3.0	1.4	0.3
-1.0	5.1	3.8	1.6	0.2
-1.0	4.6	3.2	1.4	0.2
-1.0	5.3	3.7	1.5	0.2
-1.0	5.0	3.3	1.4	0.2
-2.0	7.0	3.2	4.7	1.4
-2.0	6.4	3.2	4.5	1.5
-2.0	6.9	3.1	4.9	1.5
-2.0	5.5	2.3	4.0	1.3
-2.0	6.5	2.8	4.6	1.5
-2.0	5.7	2.8	4.5	1.3
-2.0	6.3	3.3	4.7	1.6
-2.0	4.9	2.4	3.3	1.0
-2.0	6.6	2.9	4.6	1.3
-2.0	5.2	2.7	3.9	1.4
-2.0	5.0	2.0	3.5	1.0
-2.0	5.9	3.0	4.2	1.5
-2.0	6.0	2.2	4.0	1.0
-2.0	6.1	2.9	4.7	1.4
-2.0	5.6	2.9	3.6	1.3
-2.0	6.7	3.1	4.4	1.4
-2.0	5.6	3.0	4.5	1.5
-2.0	5.8	2.7	4.1	1.0
-2.0	6.2	2.2	4.5	1.5
-2.0	5.6	2.5	3.9	1.1
-2.0	5.9	3.2	4.8	1.8
-2.0	6.1	2.8	4.0	1.3
-2.0	6.3	2.5	4.9	1.5
-2.0	6.1	2.8	4.7	1.2
-2.0	6.4	2.9	4.3	1.3
-2.0	6.6	3.0	4.4	1.4
-2.0	6.8	2.8	4.8	1.4
-2.0	6.7	3.0	5.0	1.7
-2.0	6.0	2.9	4.5	1.5
-2.0	5.7	2.6	3.5	1.0
-2.0	5.5	2.4	3.8	1.1
-2.0	5.5	2.4	3.7	1.0
-2.0	5.8	2.7	3.9	1.2
-2.0	6.0	2.7	5.1	1.6
-2.0	5.4	3.0	4.5	1.5
-2.0	6.0	3.4	4.5	1.6
-2.0	6.7	3.1	4.7	1.5
-2.0	6.3	2.3	4.4	1.3
-2.0	5.6	3.0	4.1	1.3
-2.0	5.5	2.5	4.0	1.3
-2.0	5.5	2.6	4.4	1.2
-2.0	6.1	3.0	4.6	1.4
-2.0	5.8	2.6	4.0	1.2
-2.0	5.0	2.3	3.3	1.0
-2.0	5.6	2.7	4.2	1.3
-2.0	5.7	3.0	4.2	1.2
-2.0	5.7	2.9	4.2	1.3
-2.0	6.2	2.9	4.3	1.3
-2.0	5.1	2.5	3.0	1.1
-2.0	5.7	2.8	4.1	1.3
-3.0	6.3	3.3	6.0	2.5
-3.0	5.8	2.7	5.1	1.9
-3.0	7.1	3.0	5.9	2.1
-3.0	6.3	2.9	5.6	1.8
-3.0	6.5	3.0	5.8	2.2
-3.0	7.6	3.0	6.6	2.1
-3.0	4.9	2.5	4.5	1.7
-3.0	7.3	2.9	6.3	1.8
-3.0	6.7	2.5	5.8	1.8
-3.0	7.2	3.6	6.1	2.5
-3.0	6.5	3.2	5.1	2.0
-3.0	6.4	2.7	5.3	1.9
-3.0	6.8	3.0	5.5	2.1
-3.0	5.7	2.5	5.0	2.0
-3.0	5.8	2.8	5.1	2.4
-3.0	6.4	3.2	5.3	2.3
-3.0	6.5	3.0	5.5	1.8
-3.0	7.7	3.8	6.7	2.2
-3.0	7.7	2.6	6.9	2.3
-3.0	6.0	2.2	5.0	1.5
-3.0	6.9	3.2	5.7	2.3
-3.0	5.6	2.8	4.9	2.0
-3.0	7.7	2.8	6.7	2.0
-3.0	6.3	2.7	4.9	1.8
-3.0	6.7	3.3	5.7	2.1
-3.0	7.2	3.2	6.0	1.8
-3.0	6.2	2.8	4.8	1.8
-3.0	6.1	3.0	4.9	1.8
-3.0	6.4	2.8	5.6	2.1
-3.0	7.2	3.0	5.8	1.6
-3.0	7.4	2.8	6.1	1.9
-3.0	7.9	3.8	6.4	2.0
-3.0	6.4	2.8	5.6	2.2
-3.0	6.3	2.8	5.1	1.5
-3.0	6.1	2.6	5.6	1.4
-3.0	7.7	3.0	6.1	2.3
-3.0	6.3	3.4	5.6	2.4
-3.0	6.4	3.1	5.5	1.8
-3.0	6.0	3.0	4.8	1.8
-3.0	6.9	3.1	5.4	2.1
-3.0	6.7	3.1	5.6	2.4
-3.0	6.9	3.1	5.1	2.3
-3.0	5.8	2.7	5.1	1.9
-3.0	6.8	3.2	5.9	2.3
-3.0	6.7	3.3	5.7	2.5
-3.0	6.7	3.0	5.2	2.3
-3.0	6.3	2.5	5.0	1.9
-3.0	6.5	3.0	5.2	2.0
-3.0	6.2	3.4	5.4	2.3
-3.0	5.9	3.0	5.1	1.8

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
index 53f74f3..c5581cb 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java
@@ -149,7 +149,7 @@ public class LabeledDataset<L, Row extends LabeledVector> extends Dataset<Row> {
      * @return Label.
      */
     public double label(int idx) {
-        LabeledVector labeledVector = (LabeledVector)data[idx];
+        LabeledVector labeledVector = data[idx];
 
         if(labeledVector!=null)
             return (double)labeledVector.label();
@@ -182,7 +182,7 @@ public class LabeledDataset<L, Row extends LabeledVector> extends Dataset<Row> {
      * @param lb The given label.
      */
     public void setLabel(int idx, double lb) {
-        LabeledVector labeledVector = data[idx];
+        LabeledVector<Vector, Double> labeledVector = data[idx];
 
         if(labeledVector != null)
             labeledVector.setLabel(lb);

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
index 0aa8f83..4b415bb 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansDistributedClustererTest.java
@@ -88,6 +88,8 @@ public class FuzzyCMeansDistributedClustererTest extends GridCommonAbstractTest
         assertEquals(0, measure.compute(centers[1], new DenseLocalOnHeapVector(new double[]{10, -10})), 1);
         assertEquals(0, measure.compute(centers[2], new DenseLocalOnHeapVector(new double[]{10, 10})), 1);
         assertEquals(0, measure.compute(centers[3], new DenseLocalOnHeapVector(new double[]{-10, 10})), 1);
+
+        pntMatrix.destroy();
     }
 
     /** Perform N tests each of which contains M random points placed around K centers on the plane. */
@@ -116,7 +118,7 @@ public class FuzzyCMeansDistributedClustererTest extends GridCommonAbstractTest
      * @param distributedClusterer Tested clusterer.
      * @param seed Seed for the random numbers generator.
      */
-    public void performRandomTest(FuzzyCMeansDistributedClusterer distributedClusterer, long seed) {
+    private void performRandomTest(FuzzyCMeansDistributedClusterer distributedClusterer, long seed) {
         final int minNumCenters = 2;
         final int maxNumCenters = 5;
         final double maxRadius = 1000;
@@ -130,11 +132,10 @@ public class FuzzyCMeansDistributedClustererTest extends GridCommonAbstractTest
         double[][] centers = new double[numCenters][2];
 
         for (int i = 0; i < numCenters; i++) {
-            double radius = maxRadius;
             double angle = Math.PI * 2.0 * i / numCenters;
 
-            centers[i][0] = Math.cos(angle) * radius;
-            centers[i][1] = Math.sin(angle) * radius;
+            centers[i][0] = Math.cos(angle) * maxRadius;
+            centers[i][1] = Math.sin(angle) * maxRadius;
         }
 
         int numPoints = minPoints + random.nextInt(maxPoints - minPoints);
@@ -173,5 +174,7 @@ public class FuzzyCMeansDistributedClustererTest extends GridCommonAbstractTest
         }
 
         assertEquals(0, cntr);
+
+        pntMatrix.destroy();
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
index 2af94aa..4fe1eee 100644
--- a/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/clustering/FuzzyCMeansLocalClustererTest.java
@@ -21,7 +21,6 @@ import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.Comparator;
-import org.apache.ignite.ml.math.Matrix;
 import org.apache.ignite.ml.math.Vector;
 import org.apache.ignite.ml.math.distances.DistanceMeasure;
 import org.apache.ignite.ml.math.distances.EuclideanDistance;
@@ -38,7 +37,7 @@ public class FuzzyCMeansLocalClustererTest {
     /** Test FCM on points that forms three clusters on the line. */
     @Test
     public void equalWeightsOneDimension() {
-        BaseFuzzyCMeansClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_CENTERS,
                 0.01, 10, null);
 
@@ -46,7 +45,7 @@ public class FuzzyCMeansLocalClustererTest {
                                            {7},   {8},  {9},  {10},
                                            {-1},  {0},  {1}};
 
-        Matrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
 
         FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, 3);
 
@@ -60,7 +59,7 @@ public class FuzzyCMeansLocalClustererTest {
     /** Test FCM on points that forms four clusters on the plane. */
     @Test
     public void equalWeightsTwoDimensions() {
-        BaseFuzzyCMeansClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_CENTERS,
                 0.01, 20, null);
 
@@ -69,7 +68,7 @@ public class FuzzyCMeansLocalClustererTest {
                                            {-10, 10},  {-9, 11},  {-10, 9},  {-11, 9},
                                            {10, -10},  {9, -11},  {10, -9},  {11, -9}};
 
-        Matrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
 
         FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, 4);
         Vector[] centers = mdl.centers();
@@ -86,12 +85,12 @@ public class FuzzyCMeansLocalClustererTest {
     /** Test FCM on points which have the equal coordinates. */
     @Test
     public void checkCentersOfTheSamePointsTwoDimensions() {
-        BaseFuzzyCMeansClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS, 0.01, 10, null);
 
         double[][] points = new double[][] {{3.3, 10}, {3.3, 10}, {3.3, 10}, {3.3, 10}, {3.3, 10}};
 
-        Matrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
 
         int k = 2;
         FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, k);
@@ -107,7 +106,7 @@ public class FuzzyCMeansLocalClustererTest {
     /** Test FCM on points located on the circle. */
     @Test
     public void checkCentersLocationOnSphere() {
-        BaseFuzzyCMeansClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_CENTERS, 0.01, 100, null);
 
         int numOfPoints = 650;
@@ -119,7 +118,7 @@ public class FuzzyCMeansLocalClustererTest {
             points[i][1] = Math.sin(Math.PI * 2 * i / numOfPoints) * radius;
         }
 
-        Matrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
 
         int k = 10;
         FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, k);
@@ -134,12 +133,12 @@ public class FuzzyCMeansLocalClustererTest {
     /** Test FCM on points that forms the line located on the plane. */
     @Test
     public void test2DLineClustering() {
-        BaseFuzzyCMeansClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(new EuclideanDistance(),
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_CENTERS, 0.01, 50, null);
 
         double[][] points = new double[][]{{1, 2}, {3, 6}, {5, 10}};
 
-        Matrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
 
         int k = 2;
         FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, k);
@@ -185,7 +184,7 @@ public class FuzzyCMeansLocalClustererTest {
                 2, BaseFuzzyCMeansClusterer.StopCondition.STABLE_CENTERS, 0.01, 10, null);
         double[][] points = new double[][]{{1}, {2}, {3}, {4}};
 
-        FuzzyCMeansModel cluster = clusterer.cluster(new DenseLocalOnHeapMatrix(points), 1);
+        clusterer.cluster(new DenseLocalOnHeapMatrix(points), 1);
     }
 
     /** Test FCM on different numbers of points and weights. */
@@ -198,6 +197,6 @@ public class FuzzyCMeansLocalClustererTest {
         ArrayList<Double> weights = new ArrayList<>();
         Collections.addAll(weights, 1.0, 34.0, 2.5, 5.0, 0.5);
 
-        FuzzyCMeansModel cluster = clusterer.cluster(new DenseLocalOnHeapMatrix(points), 2, weights);
+        clusterer.cluster(new DenseLocalOnHeapMatrix(points), 2, weights);
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/yardstick/config/benchmark-ml.properties
----------------------------------------------------------------------
diff --git a/modules/yardstick/config/benchmark-ml.properties b/modules/yardstick/config/benchmark-ml.properties
index 5992665..acc9c5a 100644
--- a/modules/yardstick/config/benchmark-ml.properties
+++ b/modules/yardstick/config/benchmark-ml.properties
@@ -89,4 +89,8 @@ CONFIGS="\
 -cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteSparseDistributedMatrixMul2Benchmark -sn IgniteNode -ds ${ver}sparse-distributed-matrix-mul2-${b}-backup,\
 -cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteColumnDecisionTreeVarianceBenchmark -sn IgniteNode -ds ${ver}column-decision-tree-variance-${b}-backup,\
 -cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteColumnDecisionTreeGiniBenchmark -sn IgniteNode -ds ${ver}column-decision-tree-gini-${b}-backup,\
+-cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteKNNClassificationBenchmark -sn IgniteNode -ds ${ver}knn-classification-${b}-backup,\
+-cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteKNNRegressionBenchmark -sn IgniteNode -ds ${ver}knn-regression-${b}-backup,\
+-cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteFuzzyCMeansLocalClustererBenchmark -sn IgniteNode -ds ${ver}fuzzy-cmeans-local-${b}-backup,\
+-cfg ${SCRIPT_DIR}/../config/ignite-localhost-config.xml -nn ${nodesNum} -b ${b} -w ${w} -d ${d} -t ${t} -sm ${sm} -dn IgniteFuzzyCMeansDistributedClustererBenchmark -sn IgniteNode -ds ${ver}fuzzy-cmeans-distributed-${b}-backup,\
 "

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansDistributedClustererBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansDistributedClustererBenchmark.java b/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansDistributedClustererBenchmark.java
new file mode 100644
index 0000000..e356746
--- /dev/null
+++ b/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansDistributedClustererBenchmark.java
@@ -0,0 +1,130 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.yardstick.ml.clustering;
+
+import java.util.Map;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.ml.clustering.BaseFuzzyCMeansClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansDistributedClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansModel;
+import org.apache.ignite.ml.math.StorageConstants;
+import org.apache.ignite.ml.math.distances.DistanceMeasure;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
+import org.apache.ignite.resources.IgniteInstanceResource;
+import org.apache.ignite.thread.IgniteThread;
+import org.apache.ignite.yardstick.IgniteAbstractBenchmark;
+import org.apache.ignite.yardstick.ml.DataChanger;
+
+/**
+ * Ignite benchmark that performs ML Grid operations.
+ */
+@SuppressWarnings("unused")
+public class IgniteFuzzyCMeansDistributedClustererBenchmark extends IgniteAbstractBenchmark {
+    /** */
+    @IgniteInstanceResource
+    private Ignite ignite;
+
+    /** {@inheritDoc} */
+    @Override public boolean test(Map<Object, Object> ctx) throws Exception {
+        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
+        // because we create ignite cache internally.
+        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+            this.getClass().getSimpleName(), new Runnable() {
+            /** {@inheritDoc} */
+            @Override public void run() {
+                // IMPL NOTE originally taken from FuzzyCMeansExample.
+                // Distance measure that computes distance between two points.
+                DistanceMeasure distanceMeasure = new EuclideanDistance();
+
+                // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
+                double exponentialWeight = 2.0;
+
+                // Condition that indicated when algorithm must stop.
+                // In this example algorithm stops if memberships have changed insignificantly.
+                BaseFuzzyCMeansClusterer.StopCondition stopCond =
+                    BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS;
+
+                // Maximum difference between new and old membership values with which algorithm will continue to work.
+                double maxDelta = 0.01;
+
+                // The maximum number of FCM iterations.
+                int maxIterations = 50;
+
+                // Number of steps of primary centers selection (more steps more candidates).
+                int initializationSteps = 2;
+
+                // Number of K-Means iteration that is used to choose required number of primary centers from candidates.
+                int kMeansMaxIterations = 50;
+
+                // Create new distributed clusterer with parameters described above.
+                FuzzyCMeansDistributedClusterer clusterer = new FuzzyCMeansDistributedClusterer(
+                    distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations,
+                    null, initializationSteps, kMeansMaxIterations);
+
+                // Create sample data.
+                double[][] points = shuffle((int)(DataChanger.next()));
+
+                // Initialize matrix of data points. Each row contains one point.
+                int rows = points.length;
+                int cols = points[0].length;
+
+                // Create the matrix that contains sample points.
+                SparseDistributedMatrix pntMatrix = new SparseDistributedMatrix(rows, cols,
+                    StorageConstants.ROW_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
+
+                // Store points into matrix.
+                pntMatrix.assign(points);
+
+                // Call clusterization method with some number of centers.
+                // It returns model that can predict results for new points.
+                int numCenters = 4;
+                FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
+
+                // Get centers of clusters that is computed by Fuzzy C-Means algorithm.
+                mdl.centers();
+
+                pntMatrix.destroy();
+            }
+        });
+
+        igniteThread.start();
+
+        igniteThread.join();
+
+        return true;
+    }
+
+    /** */
+    private double[][] shuffle(int off) {
+        final double[][] points = new double[][] {
+            {-10, -10}, {-9, -11}, {-10, -9}, {-11, -9},
+            {10, 10}, {9, 11}, {10, 9}, {11, 9},
+            {-10, 10}, {-9, 11}, {-10, 9}, {-11, 9},
+            {10, -10}, {9, -11}, {10, -9}, {11, -9}};
+
+        final int size = points.length;
+
+        final double[][] res = new double[size][];
+
+        for (int i = 0; i < size; i++)
+            res[i] = points[(i + off) % size];
+
+        return res;
+    }
+}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a3b83246/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansLocalClustererBenchmark.java
----------------------------------------------------------------------
diff --git a/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansLocalClustererBenchmark.java b/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansLocalClustererBenchmark.java
new file mode 100644
index 0000000..8c4c9ce
--- /dev/null
+++ b/modules/yardstick/src/main/ml/org/apache/ignite/yardstick/ml/clustering/IgniteFuzzyCMeansLocalClustererBenchmark.java
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.yardstick.ml.clustering;
+
+import java.util.Map;
+import org.apache.ignite.ml.clustering.BaseFuzzyCMeansClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansLocalClusterer;
+import org.apache.ignite.ml.clustering.FuzzyCMeansModel;
+import org.apache.ignite.ml.math.distances.DistanceMeasure;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+import org.apache.ignite.yardstick.IgniteAbstractBenchmark;
+import org.apache.ignite.yardstick.ml.DataChanger;
+
+/**
+ * Ignite benchmark that performs ML Grid operations.
+ */
+@SuppressWarnings("unused")
+public class IgniteFuzzyCMeansLocalClustererBenchmark extends IgniteAbstractBenchmark {
+    /** {@inheritDoc} */
+    @Override public boolean test(Map<Object, Object> ctx) throws Exception {
+        // IMPL NOTE originally taken from FuzzyLocalCMeansExample.
+        // Distance measure that computes distance between two points.
+        DistanceMeasure distanceMeasure = new EuclideanDistance();
+
+        // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
+        double exponentialWeight = 2.0;
+
+        // Condition that indicated when algorithm must stop.
+        // In this example algorithm stops if memberships have changed insignificantly.
+        BaseFuzzyCMeansClusterer.StopCondition stopCond =
+            BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS;
+
+        // Maximum difference between new and old membership values with which algorithm will continue to work.
+        double maxDelta = 0.01;
+
+        // The maximum number of FCM iterations.
+        int maxIterations = 50;
+
+        // Create new local clusterer with parameters described above.
+        FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(distanceMeasure,
+            exponentialWeight, stopCond, maxDelta, maxIterations, null);
+
+        // Create sample data.
+        double[][] points = shuffle((int)(DataChanger.next()));
+
+        // Create the matrix that contains sample points.
+        DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
+
+        // Call clusterization method with some number of centers.
+        // It returns model that can predict results for new points.
+        int numCenters = 4;
+        FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
+
+        // Get centers of clusters that is computed by Fuzzy C-Means algorithm.
+        mdl.centers();
+
+        return true;
+    }
+
+    /** */
+    private double[][] shuffle(int off) {
+        final double[][] points = new double[][] {
+            {-10, -10}, {-9, -11}, {-10, -9}, {-11, -9},
+            {10, 10}, {9, 11}, {10, 9}, {11, 9},
+            {-10, 10}, {-9, 11}, {-10, 9}, {-11, 9},
+            {10, -10}, {9, -11}, {10, -9}, {11, -9}};
+
+        final int size = points.length;
+
+        final double[][] res = new double[size][];
+
+        for (int i = 0; i < size; i++)
+            res[i] = points[(i + off) % size];
+
+        return res;
+    }
+}


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