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Posted to commits@ignite.apache.org by av...@apache.org on 2018/01/17 10:16:41 UTC
[10/11] ignite git commit: IGNITE-7203 Java 8 by default
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/DatasetWithObviousStructure.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/DatasetWithObviousStructure.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/DatasetWithObviousStructure.java
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+/*
+ * 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 java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+import java.util.stream.Collectors;
+import java.util.stream.IntStream;
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.VectorUtils;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+
+/**
+ * See KMeansDistributedClustererTestSingleNode#testClusterizationOnDatasetWithObviousStructure.
+ */
+class DatasetWithObviousStructure {
+ /** */
+ private final Random rnd = new Random(123456L);
+
+ /** Let centers be in the vertices of square. */
+ private final Map<Integer, Vector> centers = new HashMap<>();
+
+ /** Square side length. */
+ private final int squareSideLen;
+
+ /** */
+ DatasetWithObviousStructure(int squareSideLen) {
+ this.squareSideLen = squareSideLen;
+ centers.put(100, new DenseLocalOnHeapVector(new double[] {0.0, 0.0}));
+ centers.put(900, new DenseLocalOnHeapVector(new double[] {squareSideLen, 0.0}));
+ centers.put(3000, new DenseLocalOnHeapVector(new double[] {0.0, squareSideLen}));
+ centers.put(6000, new DenseLocalOnHeapVector(new double[] {squareSideLen, squareSideLen}));
+ }
+
+ /** */
+ List<Vector> generate(Matrix points) {
+ int ptsCnt = points.rowSize();
+
+ // Mass centers of dataset.
+ List<Vector> massCenters = new ArrayList<>();
+
+ int centersCnt = centers.size();
+
+ List<Integer> permutation = IntStream.range(0, ptsCnt).boxed().collect(Collectors.toList());
+ Collections.shuffle(permutation, rnd);
+
+ Vector[] mc = new Vector[centersCnt];
+ Arrays.fill(mc, VectorUtils.zeroes(2));
+
+ int totalCnt = 0;
+
+ int centIdx = 0;
+ massCenters.clear();
+
+ for (Integer count : centers.keySet()) {
+ for (int i = 0; i < count; i++) {
+ Vector pnt = getPoint(count);
+
+ mc[centIdx] = mc[centIdx].plus(pnt);
+
+ points.assignRow(permutation.get(totalCnt), pnt);
+
+ totalCnt++;
+ }
+ massCenters.add(mc[centIdx].times(1 / (double)count));
+ centIdx++;
+ }
+
+ return massCenters;
+ }
+
+ /** */
+ Map<Integer, Vector> centers() {
+ return centers;
+ }
+
+ /** */
+ private Vector getPoint(Integer cnt) {
+ Vector pnt = new DenseLocalOnHeapVector(2).assign(centers.get(cnt));
+ // Perturbate point on random value.
+ pnt.map(val -> val + rnd.nextDouble() * squareSideLen / 100);
+ return pnt;
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansExample.java
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+/*
+ * 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.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;
+import org.apache.ignite.ml.math.StorageConstants;
+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.SparseDistributedMatrix;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <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 {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) throws InterruptedException {
+ 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();
+
+ // "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;
+
+ // 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.
+ 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}};
+
+ // 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,
+ 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.
+ 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);
+
+ pntMatrix.destroy();
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/FuzzyCMeansLocalExample.java
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+/*
+ * 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/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansDistributedClustererExample.java
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+/*
+ * 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 java.util.Arrays;
+import java.util.List;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
+import org.apache.ignite.examples.ml.math.matrix.SparseDistributedMatrixExample;
+import org.apache.ignite.ml.clustering.KMeansDistributedClusterer;
+import org.apache.ignite.ml.math.StorageConstants;
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <p>
+ * Example of using {@link KMeansDistributedClusterer}.</p>
+ * <p>
+ * Note that in this example we cannot guarantee order in which nodes return results of intermediate
+ * computations and therefore algorithm can return different results.</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 class KMeansDistributedClustererExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ 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(),
+ SparseDistributedMatrixExample.class.getSimpleName(), () -> {
+
+ int ptsCnt = 10000;
+
+ SparseDistributedMatrix points = new SparseDistributedMatrix(ptsCnt, 2,
+ StorageConstants.ROW_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
+
+ DatasetWithObviousStructure dataset = new DatasetWithObviousStructure(10000);
+
+ List<Vector> massCenters = dataset.generate(points);
+
+ EuclideanDistance dist = new EuclideanDistance();
+
+ KMeansDistributedClusterer clusterer = new KMeansDistributedClusterer(dist, 3, 100, 1L);
+
+ Vector[] resCenters = clusterer.cluster(points, 4).centers();
+
+ System.out.println("Mass centers:");
+ massCenters.forEach(Tracer::showAscii);
+
+ System.out.println("Cluster centers:");
+ Arrays.asList(resCenters).forEach(Tracer::showAscii);
+
+ points.destroy();
+
+ System.out.println("\n>>> K-means distributed clusterer example completed.");
+ });
+
+ igniteThread.start();
+
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansLocalClustererExample.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansLocalClustererExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansLocalClustererExample.java
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+/*
+ * 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 java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import org.apache.ignite.ml.clustering.KMeansLocalClusterer;
+import org.apache.ignite.ml.clustering.KMeansModel;
+import org.apache.ignite.ml.math.Tracer;
+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.functions.Functions;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link KMeansLocalClusterer}.
+ */
+public class KMeansLocalClustererExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ // IMPL NOTE based on KMeansDistributedClustererTestSingleNode#testClusterizationOnDatasetWithObviousStructure
+ System.out.println(">>> K-means local clusterer example started.");
+
+ int ptsCnt = 10000;
+ DenseLocalOnHeapMatrix points = new DenseLocalOnHeapMatrix(ptsCnt, 2);
+
+ DatasetWithObviousStructure dataset = new DatasetWithObviousStructure(10000);
+
+ List<Vector> massCenters = dataset.generate(points);
+
+ EuclideanDistance dist = new EuclideanDistance();
+ OrderedNodesComparator comp = new OrderedNodesComparator(
+ dataset.centers().values().toArray(new Vector[] {}), dist);
+
+ massCenters.sort(comp);
+
+ KMeansLocalClusterer clusterer = new KMeansLocalClusterer(dist, 100, 1L);
+
+ KMeansModel mdl = clusterer.cluster(points, 4);
+ Vector[] resCenters = mdl.centers();
+ Arrays.sort(resCenters, comp);
+
+ System.out.println("Mass centers:");
+ massCenters.forEach(Tracer::showAscii);
+
+ System.out.println("Cluster centers:");
+ Arrays.asList(resCenters).forEach(Tracer::showAscii);
+
+ System.out.println("\n>>> K-means local clusterer example completed.");
+ }
+
+ /** */
+ private static class OrderedNodesComparator implements Comparator<Vector> {
+ /** */
+ private final DistanceMeasure measure;
+
+ /** */
+ List<Vector> orderedNodes;
+
+ /** */
+ OrderedNodesComparator(Vector[] orderedNodes, DistanceMeasure measure) {
+ this.orderedNodes = Arrays.asList(orderedNodes);
+ this.measure = measure;
+ }
+
+ /** */
+ private int findClosestNodeIndex(Vector v) {
+ return Functions.argmin(orderedNodes, v1 -> measure.compute(v1, v)).get1();
+ }
+
+ /** */
+ @Override public int compare(Vector v1, Vector v2) {
+ int ind1 = findClosestNodeIndex(v1);
+ int ind2 = findClosestNodeIndex(v2);
+
+ int signum = (int)Math.signum(ind1 - ind2);
+
+ if (signum != 0)
+ return signum;
+
+ return (int)Math.signum(orderedNodes.get(ind1).minus(v1).kNorm(2) -
+ orderedNodes.get(ind2).minus(v2).kNorm(2));
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/clustering/package-info.java
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diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/package-info.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/package-info.java
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index 0000000..872aa16
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * ML clustering examples.
+ */
+package org.apache.ignite.examples.ml.clustering;
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
new file mode 100644
index 0000000..0e1a52f
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/KNNClassificationExample.java
@@ -0,0 +1,155 @@
+/*
+ * 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.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;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
+import org.apache.ignite.ml.knn.models.KNNModel;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.math.distances.EuclideanDistance;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.ml.structures.LabeledDatasetTestTrainPair;
+import org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader;
+import org.apache.ignite.ml.structures.preprocessing.LabellingMachine;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <p>
+ * Example of using {@link KNNModel} with iris dataset.</p>
+ * <p>
+ * Note that in this example we cannot guarantee order in which nodes return results of intermediate
+ * computations and therefore algorithm can return different results.</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 class KNNClassificationExample {
+ /** Separator. */
+ private static final String SEPARATOR = "\t";
+
+ /** Path to the Iris dataset. */
+ private static final String KNN_IRIS_TXT = "../datasets/iris.txt";
+
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) throws InterruptedException {
+ System.out.println(">>> kNN classification example started.");
+ // Start ignite grid.
+ try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
+ System.out.println(">>> Ignite grid started.");
+
+ IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+ KNNClassificationExample.class.getSimpleName(), () -> {
+
+ try {
+ // Prepare path to read
+ 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);
+
+ // Random splitting of iris data as 70% train and 30% test datasets
+ LabeledDatasetTestTrainPair split = new LabeledDatasetTestTrainPair(dataset, 0.3);
+
+ 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();
+
+ KNNModel knnMdl = new KNNModel(5, new EuclideanDistance(), KNNStrategy.SIMPLE, train);
+
+ // Clone labels
+ final double[] labels = test.labels();
+
+ // Save predicted classes to test dataset
+ LabellingMachine.assignLabels(test, knnMdl);
+
+ // Calculate amount of errors on test dataset
+ int amountOfErrors = 0;
+ for (int i = 0; i < test.rowSize(); i++) {
+ if (test.label(i) != labels[i])
+ amountOfErrors++;
+ }
+
+ System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
+ System.out.println("\n>>> Accuracy " + amountOfErrors / (double)test.rowSize());
+
+ // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
+ int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
+ for (int i = 0; i < test.rowSize(); i++) {
+ int idx1 = (int)test.label(i);
+ int idx2 = (int)labels[i];
+ confusionMtx[idx1 - 1][idx2 - 1]++;
+ }
+ System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
+
+ // Calculate precision, recall and F-metric for each class
+ for (int i = 0; i < 3; i++) {
+ double precision = 0.0;
+ for (int j = 0; j < 3; j++)
+ precision += confusionMtx[i][j];
+ precision = confusionMtx[i][i] / precision;
+
+ double clsLb = (double)(i + 1);
+
+ System.out.println("\n>>> Precision for class " + clsLb + " is " + precision);
+
+ double recall = 0.0;
+ for (int j = 0; j < 3; j++)
+ recall += confusionMtx[j][i];
+ recall = confusionMtx[i][i] / recall;
+ System.out.println("\n>>> Recall for class " + clsLb + " is " + recall);
+
+ double fScore = 2 * precision * recall / (precision + recall);
+ System.out.println("\n>>> F-score for class " + clsLb + " is " + fScore);
+ }
+
+ }
+ catch (URISyntaxException | IOException e) {
+ e.printStackTrace();
+ System.out.println("\n>>> Unexpected exception, check resources: " + e);
+ }
+ finally {
+ System.out.println("\n>>> kNN classification example completed.");
+ }
+
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/package-info.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/package-info.java
new file mode 100644
index 0000000..d853f0d
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/classification/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN classification examples.
+ */
+package org.apache.ignite.examples.ml.knn.classification;
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/README.md
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/README.md b/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/README.md
new file mode 100644
index 0000000..2f9c5ec
--- /dev/null
+++ b/examples/src/main/java/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/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt b/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/cleared_machines.txt
new file mode 100644
index 0000000..cf8b6b0
--- /dev/null
+++ b/examples/src/main/java/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
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+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
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+426,38,16000,32000,128,16,32
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+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/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/iris.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/iris.txt b/examples/src/main/java/org/apache/ignite/examples/ml/knn/datasets/iris.txt
new file mode 100644
index 0000000..18f5f7c
--- /dev/null
+++ b/examples/src/main/java/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/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/package-info.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/package-info.java
new file mode 100644
index 0000000..8de4656
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN examples.
+ */
+package org.apache.ignite.examples.ml.knn;
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
new file mode 100644
index 0000000..b52613a
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/KNNRegressionExample.java
@@ -0,0 +1,157 @@
+/*
+ * 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.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;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.examples.ExampleNodeStartup;
+import org.apache.ignite.examples.ml.knn.classification.KNNClassificationExample;
+import org.apache.ignite.ml.knn.models.KNNStrategy;
+import org.apache.ignite.ml.knn.regression.KNNMultipleLinearRegression;
+import org.apache.ignite.ml.math.distances.ManhattanDistance;
+import org.apache.ignite.ml.structures.LabeledDataset;
+import org.apache.ignite.ml.structures.LabeledDatasetTestTrainPair;
+import org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader;
+import org.apache.ignite.ml.structures.preprocessing.LabellingMachine;
+import org.apache.ignite.ml.structures.preprocessing.Normalizer;
+import org.apache.ignite.thread.IgniteThread;
+
+/**
+ * <p>
+ * Example of using {@link KNNMultipleLinearRegression} with iris dataset.</p>
+ * <p>
+ * Note that in this example we cannot guarantee order in which nodes return results of intermediate
+ * computations and therefore algorithm can return different results.</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 class KNNRegressionExample {
+ /** Separator. */
+ private static final String SEPARATOR = ",";
+
+ /** */
+ private static final String KNN_CLEARED_MACHINES_TXT = "../datasets/cleared_machines.txt";
+
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) throws InterruptedException {
+ System.out.println(">>> kNN regression example started.");
+ // Start ignite grid.
+ try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
+ System.out.println(">>> Ignite grid started.");
+
+ IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
+ KNNRegressionExample.class.getSimpleName(), () -> {
+
+ try {
+ // Prepare path to read
+ 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);
+
+ // Normalize dataset
+ Normalizer.normalizeWithMiniMax(dataset);
+
+ // 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());
+
+ 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);
+
+ // Clone labels
+ final double[] labels = test.labels();
+
+ // Save predicted classes to test dataset
+ LabellingMachine.assignLabels(test, knnMdl);
+
+ // Calculate mean squared error (MSE)
+ double mse = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ mse += Math.pow(test.label(i) - labels[i], 2.0);
+ mse = mse / test.rowSize();
+
+ System.out.println("\n>>> Mean squared error (MSE) " + mse);
+
+ // Calculate mean absolute error (MAE)
+ double mae = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ mae += Math.abs(test.label(i) - labels[i]);
+ mae = mae / test.rowSize();
+
+ System.out.println("\n>>> Mean absolute error (MAE) " + mae);
+
+ // Calculate R^2 as 1 - RSS/TSS
+ double avg = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++)
+ avg += test.label(i);
+
+ avg = avg / test.rowSize();
+
+ double detCf = 0.0;
+ double tss = 0.0;
+
+ for (int i = 0; i < test.rowSize(); i++) {
+ detCf += Math.pow(test.label(i) - labels[i], 2.0);
+ tss += Math.pow(test.label(i) - avg, 2.0);
+ }
+
+ detCf = 1 - detCf / tss;
+
+ System.out.println("\n>>> R^2 " + detCf);
+ }
+ catch (URISyntaxException | IOException e) {
+ e.printStackTrace();
+ System.out.println("\n>>> Unexpected exception, check resources: " + e);
+ }
+ finally {
+ System.out.println("\n>>> kNN regression example completed.");
+ }
+ });
+
+ igniteThread.start();
+ igniteThread.join();
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/package-info.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/package-info.java
new file mode 100644
index 0000000..e7ac336
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/regression/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * kNN regression examples.
+ */
+package org.apache.ignite.examples.ml.knn.regression;
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/CholeskyDecompositionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/CholeskyDecompositionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/CholeskyDecompositionExample.java
new file mode 100644
index 0000000..ebac2b1
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/CholeskyDecompositionExample.java
@@ -0,0 +1,80 @@
+/*
+ * 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.math.decompositions;
+
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.decompositions.CholeskyDecomposition;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link CholeskyDecomposition}.
+ */
+public class CholeskyDecompositionExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ System.out.println(">>> Cholesky decomposition example started.");
+ // Let's compute a Cholesky decomposition of Hermitian matrix m:
+ // m = l l^{*}, where
+ // l is a lower triangular matrix
+ // l^{*} is its conjugate transpose
+
+ DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] {
+ {2.0d, -1.0d, 0.0d},
+ {-1.0d, 2.0d, -1.0d},
+ {0.0d, -1.0d, 2.0d}
+ });
+ System.out.println("\n>>> Matrix m for decomposition: ");
+ Tracer.showAscii(m);
+
+ // This decomposition is useful when dealing with systems of linear equations of the form
+ // m x = b where m is a Hermitian matrix.
+ // For such systems Cholesky decomposition provides
+ // more effective method of solving compared to LU decomposition.
+ // Suppose we want to solve system
+ // m x = b for various bs. Then after we computed Cholesky decomposition, we can feed various bs
+ // as a matrix of the form
+ // (b1, b2, ..., bm)
+ // to the method Cholesky::solve which returns solutions in the form
+ // (sol1, sol2, ..., solm)
+ CholeskyDecomposition dec = new CholeskyDecomposition(m);
+ System.out.println("\n>>> Made decomposition m = l * l^{*}.");
+ System.out.println(">>> Matrix l is ");
+ Tracer.showAscii(dec.getL());
+ System.out.println(">>> Matrix l^{*} is ");
+ Tracer.showAscii(dec.getLT());
+
+ Matrix bs = new DenseLocalOnHeapMatrix(new double[][] {
+ {4.0, -6.0, 7.0},
+ {1.0, 1.0, 1.0}
+ }).transpose();
+ System.out.println("\n>>> Solving systems of linear equations of the form m x = b for various bs represented by columns of matrix");
+ Tracer.showAscii(bs);
+ Matrix sol = dec.solve(bs);
+
+ System.out.println("\n>>> List of solutions: ");
+ for (int i = 0; i < sol.columnSize(); i++)
+ Tracer.showAscii(sol.viewColumn(i));
+
+ System.out.println("\n>>> Cholesky decomposition example completed.");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/EigenDecompositionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/EigenDecompositionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/EigenDecompositionExample.java
new file mode 100644
index 0000000..cda37f4
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/EigenDecompositionExample.java
@@ -0,0 +1,69 @@
+/*
+ * 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.math.decompositions;
+
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.decompositions.EigenDecomposition;
+import org.apache.ignite.ml.math.functions.Functions;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link EigenDecomposition}.
+ */
+public class EigenDecompositionExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ System.out.println(">>> Eigen decomposition example started.");
+
+ // Let's compute EigenDecomposition for some square (n x n) matrix m with real eigenvalues:
+ // m = v d v^{-1}, where d is diagonal matrix having eigenvalues of m on diagonal
+ // and v is matrix where i-th column is eigenvector for i-th eigenvalue (i from 0 to n - 1)
+ DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] {
+ {1.0d, 0.0d, 0.0d, 0.0d},
+ {0.0d, 1.0d, 0.0d, 0.0d},
+ {0.0d, 0.0d, 2.0d, 0.0d},
+ {1.0d, 1.0d, 0.0d, 2.0d}
+ });
+ System.out.println("\n>>> Matrix m for decomposition: ");
+ Tracer.showAscii(m);
+
+ EigenDecomposition dec = new EigenDecomposition(m);
+ System.out.println("\n>>> Made decomposition.");
+ System.out.println(">>> Matrix getV is ");
+ Tracer.showAscii(dec.getV());
+ System.out.println(">>> Matrix getD is ");
+ Tracer.showAscii(dec.getD());
+
+ // From this decomposition we, for example, can easily compute determinant of matrix m
+ // det (m) = det (v d v^{-1}) =
+ // det(v) det (d) det(v^{-1}) =
+ // det(v) det(v)^{-1} det(d) =
+ // det (d) =
+ // product of diagonal elements of d =
+ // product of eigenvalues
+ double det = dec.getRealEigenValues().foldMap(Functions.MULT, Functions.IDENTITY, 1.0);
+ System.out.println("\n>>> Determinant is " + det);
+
+ System.out.println("\n>>> Eigen decomposition example completed.");
+ }
+
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/LUDecompositionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/LUDecompositionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/LUDecompositionExample.java
new file mode 100644
index 0000000..a815047
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/LUDecompositionExample.java
@@ -0,0 +1,83 @@
+/*
+ * 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.math.decompositions;
+
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.decompositions.LUDecomposition;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link LUDecomposition}.
+ */
+public class LUDecompositionExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ System.out.println(">>> LU decomposition example started.");
+ // Let's compute a LU decomposition for some (n x n) matrix m:
+ // m = p l u, where
+ // p is an (n x n) is a row-permutation matrix
+ // l is a (n x n) lower triangular matrix
+ // u is a (n x n) upper triangular matrix
+
+ DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] {
+ {1.0d, 1.0d, -1.0d},
+ {1.0d, -2.0d, 3.0d},
+ {2.0d, 3.0d, 1.0d}
+ });
+ System.out.println("\n>>> Matrix m for decomposition: ");
+ Tracer.showAscii(m);
+
+ // This decomposition is useful when dealing with systems of linear equations.
+ // (see https://en.wikipedia.org/wiki/LU_decomposition)
+ // suppose we want to solve system
+ // m x = b for various bs. Then after we computed LU decomposition, we can feed various bs
+ // as a matrix of the form
+ // (b1, b2, ..., bm)
+ // to the method LUDecomposition::solve which returns solutions in the form
+ // (sol1, sol2, ..., solm)
+
+ LUDecomposition dec = new LUDecomposition(m);
+ System.out.println("\n>>> Made decomposition.");
+ System.out.println(">>> Matrix getL is ");
+ Tracer.showAscii(dec.getL());
+ System.out.println(">>> Matrix getU is ");
+ Tracer.showAscii(dec.getU());
+ System.out.println(">>> Matrix getP is ");
+ Tracer.showAscii(dec.getP());
+
+ Matrix bs = new DenseLocalOnHeapMatrix(new double[][] {
+ {4.0, -6.0, 7.0},
+ {1.0, 1.0, 1.0}
+ });
+ System.out.println("\n>>> Matrix to solve: ");
+ Tracer.showAscii(bs);
+
+ Matrix sol = dec.solve(bs.transpose());
+
+ System.out.println("\n>>> List of solutions: ");
+ for (int i = 0; i < sol.columnSize(); i++)
+ Tracer.showAscii(sol.viewColumn(i));
+
+ System.out.println("\n>>> LU decomposition example completed.");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/QRDecompositionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/QRDecompositionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/QRDecompositionExample.java
new file mode 100644
index 0000000..bed99d1
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/QRDecompositionExample.java
@@ -0,0 +1,82 @@
+/*
+ * 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.math.decompositions;
+
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.decompositions.QRDecomposition;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link QRDecomposition}.
+ */
+public class QRDecompositionExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ System.out.println(">>> QR decomposition example started.");
+ Matrix m = new DenseLocalOnHeapMatrix(new double[][] {
+ {2.0d, -1.0d, 0.0d},
+ {-1.0d, 2.0d, -1.0d},
+ {0.0d, -1.0d, 2.0d}
+ });
+
+ System.out.println("\n>>> Input matrix:");
+ Tracer.showAscii(m);
+
+ QRDecomposition dec = new QRDecomposition(m);
+ System.out.println("\n>>> Value for full rank in decomposition: [" + dec.hasFullRank() + "].");
+
+ Matrix q = dec.getQ();
+ Matrix r = dec.getR();
+
+ System.out.println("\n>>> Orthogonal matrix Q:");
+ Tracer.showAscii(q);
+ System.out.println("\n>>> Upper triangular matrix R:");
+ Tracer.showAscii(r);
+
+ Matrix qSafeCp = safeCopy(q);
+
+ Matrix identity = qSafeCp.times(qSafeCp.transpose());
+
+ System.out.println("\n>>> Identity matrix obtained from Q:");
+ Tracer.showAscii(identity);
+
+ Matrix recomposed = qSafeCp.times(r);
+
+ System.out.println("\n>>> Recomposed input matrix:");
+ Tracer.showAscii(recomposed);
+
+ Matrix sol = dec.solve(new DenseLocalOnHeapMatrix(3, 10));
+
+ System.out.println("\n>>> Solved matrix:");
+ Tracer.showAscii(sol);
+
+ dec.destroy();
+
+ System.out.println("\n>>> QR decomposition example completed.");
+ }
+
+ /** */
+ private static Matrix safeCopy(Matrix orig) {
+ return new DenseLocalOnHeapMatrix(orig.rowSize(), orig.columnSize()).assign(orig);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/SingularValueDecompositionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/SingularValueDecompositionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/SingularValueDecompositionExample.java
new file mode 100644
index 0000000..81406ae
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/SingularValueDecompositionExample.java
@@ -0,0 +1,70 @@
+/*
+ * 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.math.decompositions;
+
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.decompositions.SingularValueDecomposition;
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+
+/**
+ * Example of using {@link SingularValueDecomposition}.
+ */
+public class SingularValueDecompositionExample {
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ System.out.println(">>> Singular value decomposition (SVD) example started.");
+
+ // Let's compute a SVD of (l x k) matrix m. This decomposition can be thought as extension of EigenDecomposition to
+ // rectangular matrices. The factorization we get is following:
+ // m = u * s * v^{*}, where
+ // u is a real or complex unitary matrix
+ // s is a rectangular diagonal matrix with non-negative real numbers on diagonal (this numbers are singular values of m)
+ // v is a real or complex unitary matrix
+ // If m is real then u and v are also real.
+ // Complex case is not supported for the moment.
+ DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] {
+ {1.0d, 0.0d, 0.0d, 0.0d, 2.0d},
+ {0.0d, 0.0d, 3.0d, 0.0d, 0.0d},
+ {0.0d, 0.0d, 0.0d, 0.0d, 0.0d},
+ {0.0d, 2.0d, 0.0d, 0.0d, 0.0d}
+ });
+ System.out.println("\n>>> Matrix m for decomposition: ");
+ Tracer.showAscii(m);
+
+ SingularValueDecomposition dec = new SingularValueDecomposition(m);
+ System.out.println("\n>>> Made decomposition m = u * s * v^{*}.");
+ System.out.println(">>> Matrix u is ");
+ Tracer.showAscii(dec.getU());
+ System.out.println(">>> Matrix s is ");
+ Tracer.showAscii(dec.getS());
+ System.out.println(">>> Matrix v is ");
+ Tracer.showAscii(dec.getV());
+
+ // This decomposition can in particular help with solving problem of finding x minimizing 2-norm of m x such
+ // that 2-norm of x is 1. It appears that it is the right singular vector corresponding to minimal singular
+ // value, which is always last.
+ System.out.println("\n>>> Vector x minimizing 2-norm of m x such that 2 norm of x is 1: ");
+ Tracer.showAscii(dec.getV().viewColumn(dec.getSingularValues().length - 1));
+
+ System.out.println("\n>>> Singular value decomposition (SVD) example completed.");
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/package-info.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/package-info.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/package-info.java
new file mode 100644
index 0000000..644f8ba
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/decompositions/package-info.java
@@ -0,0 +1,22 @@
+/*
+ * 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 description. -->
+ * Core algebra decomposition examples.
+ */
+package org.apache.ignite.examples.ml.math.decompositions;
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/CacheMatrixExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/CacheMatrixExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/CacheMatrixExample.java
new file mode 100644
index 0000000..a7cbaab
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/CacheMatrixExample.java
@@ -0,0 +1,102 @@
+/*
+ * 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.math.matrix;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.examples.ml.math.vector.CacheVectorExample;
+import org.apache.ignite.ml.math.IdentityValueMapper;
+import org.apache.ignite.ml.math.Tracer;
+import org.apache.ignite.ml.math.distributed.MatrixKeyMapper;
+import org.apache.ignite.ml.math.distributed.ValueMapper;
+import org.apache.ignite.ml.math.functions.Functions;
+import org.apache.ignite.ml.math.impls.matrix.CacheMatrix;
+
+/**
+ * Example that demonstrates how to use {@link CacheMatrix}.
+ *
+ * Basically CacheMatrix is view over existing data in cache. So we have {@link MatrixKeyMapper} and {@link ValueMapper}
+ * for this purpose. A {@link MatrixKeyMapper} allows us to map matrix indices to cache keys. And a {@link ValueMapper}
+ * allows us map cache object to matrix elements - doubles.
+ *
+ * In this example we use simple flat mapping for keys and {@link IdentityValueMapper} for cache objects
+ * because they are Doubles.
+ *
+ * @see CacheVectorExample
+ */
+public class CacheMatrixExample {
+ /** */ private static final String CACHE_NAME = CacheMatrixExample.class.getSimpleName();
+ /** */ private static final int ROWS = 3;
+ /** */ private static final int COLS = 3;
+
+ /**
+ * Executes example.
+ *
+ * @param args Command line arguments, none required.
+ */
+ public static void main(String[] args) {
+ try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
+ System.out.println();
+ System.out.println(">>> CacheMatrix example started.");
+
+ CacheConfiguration<Integer, Double> cfg = new CacheConfiguration<>();
+
+ cfg.setName(CACHE_NAME);
+
+ try (IgniteCache<Integer, Double> cache = ignite.getOrCreateCache(cfg)) {
+ double[][] testValues = {{1.0, 0.0, 0.0}, {1.0, 1.0, 0.0}, {1.0, 1.0, 1.0}};
+
+ ValueMapper valMapper = new IdentityValueMapper();
+
+ // Map matrix element indices to cache keys.
+ MatrixKeyMapper<Integer> keyMapper = new MatrixKeyMapper<Integer>() {
+ @Override public Integer apply(int x, int y) {
+ return x * COLS + y;
+ }
+
+ @Override public boolean isValid(Integer integer) {
+ return integer >= 0 && integer < COLS * ROWS;
+ }
+ };
+
+ // Create cache matrix.
+ CacheMatrix<Integer, Double> cacheMatrix = new CacheMatrix<>(ROWS, COLS, cache, keyMapper, valMapper);
+
+ cacheMatrix.assign(testValues);
+
+ Tracer.showAscii(cacheMatrix);
+
+ // Find all positive elements.
+ Integer nonZeroes = cacheMatrix.foldMap((o, aDouble) -> {
+ if (aDouble > 0)
+ return o + 1;
+ return o;
+ }, Functions.IDENTITY, 0);
+
+ System.out.println("Quantity of non zeroes elements is " + nonZeroes.intValue());
+
+ System.out.println(">>>");
+ System.out.println(">>> Finished executing Ignite \"CacheMatrix\" example.");
+ System.out.println(">>> Lower triangular matrix 3x3 have only 6 positive elements.");
+ System.out.println(">>>");
+ }
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/43a62cdb/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/ExampleMatrixStorage.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/ExampleMatrixStorage.java b/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/ExampleMatrixStorage.java
new file mode 100644
index 0000000..542fbdc
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/math/matrix/ExampleMatrixStorage.java
@@ -0,0 +1,175 @@
+/*
+ * 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.math.matrix;
+
+import java.io.IOException;
+import java.io.ObjectInput;
+import java.io.ObjectOutput;
+import java.util.Arrays;
+import org.apache.ignite.ml.math.MatrixStorage;
+import org.apache.ignite.ml.math.StorageConstants;
+import org.apache.ignite.ml.math.impls.storage.matrix.ArrayMatrixStorage;
+import org.apache.ignite.ml.math.util.MatrixUtil;
+
+/**
+ * Example matrix storage implementation, modeled after {@link ArrayMatrixStorage}.
+ */
+class ExampleMatrixStorage implements MatrixStorage {
+ /** Backing data array. */
+ private double[][] data;
+ /** Amount of rows in a matrix storage. */
+ private int rows;
+ /** Amount of columns in a matrix storage. */
+ private int cols;
+
+ /**
+ *
+ */
+ public ExampleMatrixStorage() {
+ // No-op.
+ }
+
+ /**
+ * @param rows Amount of rows in a matrix storage.
+ * @param cols Amount of columns in a matrix storage.
+ */
+ ExampleMatrixStorage(int rows, int cols) {
+ assert rows > 0;
+ assert cols > 0;
+
+ this.data = new double[rows][cols];
+ this.rows = rows;
+ this.cols = cols;
+ }
+
+ /**
+ * @param data Backing data array.
+ */
+ ExampleMatrixStorage(double[][] data) {
+ assert data != null;
+ assert data[0] != null;
+
+ this.data = data;
+ this.rows = data.length;
+ this.cols = data[0].length;
+
+ assert rows > 0;
+ assert cols > 0;
+ }
+
+ /** {@inheritDoc} */
+ @Override public double get(int x, int y) {
+ return data[x][y];
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean isSequentialAccess() {
+ return false;
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean isDense() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean isRandomAccess() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean isDistributed() {
+ return false;
+ }
+
+ /** {@inheritDoc} */
+ @Override public void set(int x, int y, double v) {
+ data[x][y] = v;
+ }
+
+ /** {@inheritDoc} */
+ @Override public int columnSize() {
+ return cols;
+ }
+
+ /** {@inheritDoc} */
+ @Override public int rowSize() {
+ return rows;
+ }
+
+ /** {@inheritDoc} */
+ @Override public int storageMode() {
+ // This value indicate that we store matrix data by rows.
+ return StorageConstants.ROW_STORAGE_MODE;
+ }
+
+ /** {@inheritDoc} */
+ @Override public int accessMode() {
+ return StorageConstants.RANDOM_ACCESS_MODE;
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean isArrayBased() {
+ return true;
+ }
+
+ /** {@inheritDoc} */
+ @Override public double[] data() {
+ return MatrixUtil.flatten(data, StorageConstants.ROW_STORAGE_MODE);
+ }
+
+ /** {@inheritDoc */
+ @Override public void writeExternal(ObjectOutput out) throws IOException {
+ out.writeInt(rows);
+ out.writeInt(cols);
+
+ out.writeObject(data);
+ }
+
+ /** {@inheritDoc */
+ @Override public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
+ rows = in.readInt();
+ cols = in.readInt();
+
+ data = (double[][])in.readObject();
+ }
+
+ /** {@inheritDoc} */
+ @Override public int hashCode() {
+ int res = 1;
+
+ res += res * 37 + rows;
+ res += res * 37 + cols;
+ res += res * 37 + Arrays.deepHashCode(data);
+
+ return res;
+ }
+
+ /** {@inheritDoc} */
+ @Override public boolean equals(Object o) {
+ if (this == o)
+ return true;
+
+ if (o == null || getClass() != o.getClass())
+ return false;
+
+ ExampleMatrixStorage that = (ExampleMatrixStorage)o;
+
+ return Arrays.deepEquals(data, that.data);
+ }
+}