IGNITE-8232: ML package cleanup for 2.5 release

this closes #3806

(cherry picked from commit 47cfdc2)


Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/a9ec54be
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/a9ec54be
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/a9ec54be

Branch: refs/heads/ignite-2.5
Commit: a9ec54be9c51fe8ff04ca5d21160c27255640007
Parents: 9be980d
Author: dmitrievanthony <dmitrievanth...@gmail.com>
Authored: Fri Apr 13 18:08:08 2018 +0300
Committer: Yury Babak <yba...@gridgain.com>
Committed: Fri Apr 13 18:08:53 2018 +0300

----------------------------------------------------------------------
 .../examples/ml/nn/MLPTrainerExample.java       |   2 +-
 ...nWithLSQRTrainerAndNormalizationExample.java | 180 ---------
 ...dLinearRegressionWithLSQRTrainerExample.java | 169 --------
 ...tedLinearRegressionWithQRTrainerExample.java | 137 -------
 ...edLinearRegressionWithSGDTrainerExample.java | 177 --------
 .../LinearRegressionLSQRTrainerExample.java     | 169 ++++++++
 ...sionLSQRTrainerWithNormalizationExample.java | 180 +++++++++
 .../LinearRegressionSGDTrainerExample.java      | 176 ++++++++
 .../main/java/org/apache/ignite/ml/Trainer.java |  36 --
 .../apache/ignite/ml/estimators/Estimators.java |  50 ---
 .../ignite/ml/estimators/package-info.java      |  22 -
 .../ml/math/functions/IgniteBiFunction.java     |   8 +-
 .../LinSysPartitionDataBuilderOnHeap.java       |  86 ----
 .../math/isolve/LinSysPartitionDataOnHeap.java  |  65 ---
 .../ml/math/isolve/lsqr/AbstractLSQR.java       |   3 +-
 .../ignite/ml/math/isolve/lsqr/LSQROnHeap.java  |  27 +-
 .../org/apache/ignite/ml/nn/MLPTrainer.java     |   1 -
 .../apache/ignite/ml/nn/UpdatesStrategy.java    |  95 +++++
 .../ignite/ml/optimization/GradientDescent.java | 202 ----------
 .../ml/optimization/GradientFunction.java       |  31 --
 .../LeastSquaresGradientFunction.java           |  33 --
 .../util/SparseDistributedMatrixMapReducer.java |  84 ----
 .../ml/optimization/util/package-info.java      |  22 -
 .../linear/LinearRegressionLSQRTrainer.java     |  10 +-
 .../linear/LinearRegressionQRTrainer.java       |  72 ----
 .../linear/LinearRegressionSGDTrainer.java      |   7 +-
 .../org/apache/ignite/ml/trainers/Trainer.java  |  33 --
 .../trainers/group/BaseLocalProcessorJob.java   | 146 -------
 .../ignite/ml/trainers/group/ConstModel.java    |  46 ---
 .../ignite/ml/trainers/group/GroupTrainer.java  | 208 ----------
 .../group/GroupTrainerBaseProcessorTask.java    | 144 -------
 .../ml/trainers/group/GroupTrainerCacheKey.java | 125 ------
 .../group/GroupTrainerEntriesProcessorTask.java |  64 ---
 .../ml/trainers/group/GroupTrainerInput.java    |  37 --
 .../group/GroupTrainerKeysProcessorTask.java    |  62 ---
 .../ml/trainers/group/GroupTrainingContext.java |  98 -----
 .../group/LocalEntriesProcessorJob.java         |  85 ----
 .../trainers/group/LocalKeysProcessorJob.java   |  78 ----
 .../ignite/ml/trainers/group/Metaoptimizer.java |  93 -----
 .../group/MetaoptimizerDistributedStep.java     |  97 -----
 .../group/MetaoptimizerGroupTrainer.java        | 132 ------
 .../ml/trainers/group/ResultAndUpdates.java     | 178 --------
 .../ml/trainers/group/UpdateStrategies.java     |  47 ---
 .../ml/trainers/group/UpdatesStrategy.java      |  95 -----
 .../ignite/ml/trainers/group/chain/Chains.java  |  56 ---
 .../trainers/group/chain/ComputationsChain.java | 246 -----------
 .../chain/DistributedEntryProcessingStep.java   |  34 --
 .../chain/DistributedKeyProcessingStep.java     |  33 --
 .../trainers/group/chain/DistributedStep.java   |  70 ----
 .../trainers/group/chain/EntryAndContext.java   |  70 ----
 .../trainers/group/chain/HasTrainingUUID.java   |  32 --
 .../ml/trainers/group/chain/KeyAndContext.java  |  67 ---
 .../ml/trainers/group/chain/package-info.java   |  22 -
 .../ignite/ml/trainers/group/package-info.java  |  22 -
 .../org/apache/ignite/ml/IgniteMLTestSuite.java |   4 -
 .../ml/math/isolve/lsqr/LSQROnHeapTest.java     |  14 +-
 .../ignite/ml/nn/MLPTrainerIntegrationTest.java |   1 -
 .../org/apache/ignite/ml/nn/MLPTrainerTest.java |   1 -
 .../MLPTrainerMnistIntegrationTest.java         |   2 +-
 .../ml/nn/performance/MLPTrainerMnistTest.java  |   2 +-
 .../ml/optimization/GradientDescentTest.java    |  64 ---
 .../ml/optimization/OptimizationTestSuite.java  |  33 --
 .../SparseDistributedMatrixMapReducerTest.java  | 135 -------
 .../ml/regressions/RegressionsTestSuite.java    |   3 -
 .../linear/ArtificialRegressionDatasets.java    | 404 -------------------
 ...istributedLinearRegressionQRTrainerTest.java |  36 --
 ...istributedLinearRegressionQRTrainerTest.java |  36 --
 .../GenericLinearRegressionTrainerTest.java     | 206 ----------
 ...wareAbstractLinearRegressionTrainerTest.java | 127 ------
 .../linear/LinearRegressionSGDTrainerTest.java  |   2 +-
 .../LocalLinearRegressionQRTrainerTest.java     |  36 --
 .../group/DistributedWorkersChainTest.java      | 189 ---------
 .../ml/trainers/group/GroupTrainerTest.java     |  90 -----
 .../trainers/group/SimpleGroupTrainerInput.java |  63 ---
 .../ml/trainers/group/TestGroupTrainer.java     | 144 -------
 .../group/TestGroupTrainerLocalContext.java     |  85 ----
 .../trainers/group/TestGroupTrainingCache.java  |  70 ----
 .../group/TestGroupTrainingSecondCache.java     |  56 ---
 .../ml/trainers/group/TestLocalContext.java     |  51 ---
 .../ml/trainers/group/TestTrainingLoopStep.java |  65 ---
 .../trainers/group/TrainersGroupTestSuite.java  |  32 --
 ...iteOLSMultipleLinearRegressionBenchmark.java |  69 ----
 .../yardstick/ml/regression/package-info.java   |  22 -
 83 files changed, 666 insertions(+), 5840 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
index ce44cc6..5d1ac38 100644
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/nn/MLPTrainerExample.java
@@ -32,7 +32,7 @@ import org.apache.ignite.ml.nn.architecture.MLPArchitecture;
 import org.apache.ignite.ml.optimization.LossFunctions;
 import 
org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate;
 import 
org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator;
-import org.apache.ignite.ml.trainers.group.UpdatesStrategy;
+import org.apache.ignite.ml.nn.UpdatesStrategy;
 import org.apache.ignite.thread.IgniteThread;
 
 /**

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample.java
deleted file mode 100644
index 99e6577..0000000
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample.java
+++ /dev/null
@@ -1,180 +0,0 @@
-/*
- * 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.regression.linear;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteCache;
-import org.apache.ignite.Ignition;
-import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
-import org.apache.ignite.cache.query.QueryCursor;
-import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.configuration.CacheConfiguration;
-import 
org.apache.ignite.examples.ml.math.matrix.SparseDistributedMatrixExample;
-import org.apache.ignite.ml.math.functions.IgniteBiFunction;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import 
org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor;
-import org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
-import org.apache.ignite.thread.IgniteThread;
-
-import javax.cache.Cache;
-import java.util.Arrays;
-import java.util.UUID;
-
-/**
- * Run linear regression model over distributed matrix.
- *
- * @see LinearRegressionLSQRTrainer
- * @see NormalizationTrainer
- * @see NormalizationPreprocessor
- */
-public class DistributedLinearRegressionWithLSQRTrainerAndNormalizationExample 
{
-    /** */
-    private static final double[][] data = {
-        {8, 78, 284, 9.100000381, 109},
-        {9.300000191, 68, 433, 8.699999809, 144},
-        {7.5, 70, 739, 7.199999809, 113},
-        {8.899999619, 96, 1792, 8.899999619, 97},
-        {10.19999981, 74, 477, 8.300000191, 206},
-        {8.300000191, 111, 362, 10.89999962, 124},
-        {8.800000191, 77, 671, 10, 152},
-        {8.800000191, 168, 636, 9.100000381, 162},
-        {10.69999981, 82, 329, 8.699999809, 150},
-        {11.69999981, 89, 634, 7.599999905, 134},
-        {8.5, 149, 631, 10.80000019, 292},
-        {8.300000191, 60, 257, 9.5, 108},
-        {8.199999809, 96, 284, 8.800000191, 111},
-        {7.900000095, 83, 603, 9.5, 182},
-        {10.30000019, 130, 686, 8.699999809, 129},
-        {7.400000095, 145, 345, 11.19999981, 158},
-        {9.600000381, 112, 1357, 9.699999809, 186},
-        {9.300000191, 131, 544, 9.600000381, 177},
-        {10.60000038, 80, 205, 9.100000381, 127},
-        {9.699999809, 130, 1264, 9.199999809, 179},
-        {11.60000038, 140, 688, 8.300000191, 80},
-        {8.100000381, 154, 354, 8.399999619, 103},
-        {9.800000191, 118, 1632, 9.399999619, 101},
-        {7.400000095, 94, 348, 9.800000191, 117},
-        {9.399999619, 119, 370, 10.39999962, 88},
-        {11.19999981, 153, 648, 9.899999619, 78},
-        {9.100000381, 116, 366, 9.199999809, 102},
-        {10.5, 97, 540, 10.30000019, 95},
-        {11.89999962, 176, 680, 8.899999619, 80},
-        {8.399999619, 75, 345, 9.600000381, 92},
-        {5, 134, 525, 10.30000019, 126},
-        {9.800000191, 161, 870, 10.39999962, 108},
-        {9.800000191, 111, 669, 9.699999809, 77},
-        {10.80000019, 114, 452, 9.600000381, 60},
-        {10.10000038, 142, 430, 10.69999981, 71},
-        {10.89999962, 238, 822, 10.30000019, 86},
-        {9.199999809, 78, 190, 10.69999981, 93},
-        {8.300000191, 196, 867, 9.600000381, 106},
-        {7.300000191, 125, 969, 10.5, 162},
-        {9.399999619, 82, 499, 7.699999809, 95},
-        {9.399999619, 125, 925, 10.19999981, 91},
-        {9.800000191, 129, 353, 9.899999619, 52},
-        {3.599999905, 84, 288, 8.399999619, 110},
-        {8.399999619, 183, 718, 10.39999962, 69},
-        {10.80000019, 119, 540, 9.199999809, 57},
-        {10.10000038, 180, 668, 13, 106},
-        {9, 82, 347, 8.800000191, 40},
-        {10, 71, 345, 9.199999809, 50},
-        {11.30000019, 118, 463, 7.800000191, 35},
-        {11.30000019, 121, 728, 8.199999809, 86},
-        {12.80000019, 68, 383, 7.400000095, 57},
-        {10, 112, 316, 10.39999962, 57},
-        {6.699999809, 109, 388, 8.899999619, 94}
-    };
-
-    /** Run example. */
-    public static void main(String[] args) throws InterruptedException {
-        System.out.println();
-        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(), () -> {
-                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
-
-                System.out.println(">>> Create new normalization trainer 
object.");
-                NormalizationTrainer<Integer, double[]> normalizationTrainer = 
new NormalizationTrainer<>();
-
-                System.out.println(">>> Perform the training to get the 
normalization preprocessor.");
-                IgniteBiFunction<Integer, double[], double[]> preprocessor = 
normalizationTrainer.fit(
-                    ignite,
-                    dataCache,
-                    (k, v) -> Arrays.copyOfRange(v, 1, v.length)
-                );
-
-                System.out.println(">>> Create new linear regression trainer 
object.");
-                LinearRegressionLSQRTrainer trainer = new 
LinearRegressionLSQRTrainer();
-
-                System.out.println(">>> Perform the training to get the 
model.");
-                LinearRegressionModel mdl = trainer.fit(ignite, dataCache, 
preprocessor, (k, v) -> v[0]);
-
-                System.out.println(">>> Linear regression model: " + mdl);
-
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Ground Truth\t|");
-                System.out.println(">>> ---------------------------------");
-
-                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
-                        Integer key = observation.getKey();
-                        double[] val = observation.getValue();
-                        double groundTruth = val[0];
-
-                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(preprocessor.apply(key, val)));
-
-                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
-                    }
-                }
-
-                System.out.println(">>> ---------------------------------");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
-        }
-    }
-
-    /**
-     * Fills cache with data and returns it.
-     *
-     * @param ignite Ignite instance.
-     * @return Filled Ignite Cache.
-     */
-    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
-        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
-        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
-        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
-
-        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
-
-        for (int i = 0; i < data.length; i++)
-            cache.put(i, data[i]);
-
-        return cache;
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerExample.java
deleted file mode 100644
index 25aec0c..0000000
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithLSQRTrainerExample.java
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * 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.regression.linear;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteCache;
-import org.apache.ignite.Ignition;
-import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
-import org.apache.ignite.cache.query.QueryCursor;
-import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.configuration.CacheConfiguration;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
-import org.apache.ignite.thread.IgniteThread;
-
-import javax.cache.Cache;
-import java.util.Arrays;
-import java.util.UUID;
-
-/**
- * Run linear regression model over distributed matrix.
- *
- * @see LinearRegressionLSQRTrainer
- */
-public class DistributedLinearRegressionWithLSQRTrainerExample {
-    /** */
-    private static final double[][] data = {
-        {8, 78, 284, 9.100000381, 109},
-        {9.300000191, 68, 433, 8.699999809, 144},
-        {7.5, 70, 739, 7.199999809, 113},
-        {8.899999619, 96, 1792, 8.899999619, 97},
-        {10.19999981, 74, 477, 8.300000191, 206},
-        {8.300000191, 111, 362, 10.89999962, 124},
-        {8.800000191, 77, 671, 10, 152},
-        {8.800000191, 168, 636, 9.100000381, 162},
-        {10.69999981, 82, 329, 8.699999809, 150},
-        {11.69999981, 89, 634, 7.599999905, 134},
-        {8.5, 149, 631, 10.80000019, 292},
-        {8.300000191, 60, 257, 9.5, 108},
-        {8.199999809, 96, 284, 8.800000191, 111},
-        {7.900000095, 83, 603, 9.5, 182},
-        {10.30000019, 130, 686, 8.699999809, 129},
-        {7.400000095, 145, 345, 11.19999981, 158},
-        {9.600000381, 112, 1357, 9.699999809, 186},
-        {9.300000191, 131, 544, 9.600000381, 177},
-        {10.60000038, 80, 205, 9.100000381, 127},
-        {9.699999809, 130, 1264, 9.199999809, 179},
-        {11.60000038, 140, 688, 8.300000191, 80},
-        {8.100000381, 154, 354, 8.399999619, 103},
-        {9.800000191, 118, 1632, 9.399999619, 101},
-        {7.400000095, 94, 348, 9.800000191, 117},
-        {9.399999619, 119, 370, 10.39999962, 88},
-        {11.19999981, 153, 648, 9.899999619, 78},
-        {9.100000381, 116, 366, 9.199999809, 102},
-        {10.5, 97, 540, 10.30000019, 95},
-        {11.89999962, 176, 680, 8.899999619, 80},
-        {8.399999619, 75, 345, 9.600000381, 92},
-        {5, 134, 525, 10.30000019, 126},
-        {9.800000191, 161, 870, 10.39999962, 108},
-        {9.800000191, 111, 669, 9.699999809, 77},
-        {10.80000019, 114, 452, 9.600000381, 60},
-        {10.10000038, 142, 430, 10.69999981, 71},
-        {10.89999962, 238, 822, 10.30000019, 86},
-        {9.199999809, 78, 190, 10.69999981, 93},
-        {8.300000191, 196, 867, 9.600000381, 106},
-        {7.300000191, 125, 969, 10.5, 162},
-        {9.399999619, 82, 499, 7.699999809, 95},
-        {9.399999619, 125, 925, 10.19999981, 91},
-        {9.800000191, 129, 353, 9.899999619, 52},
-        {3.599999905, 84, 288, 8.399999619, 110},
-        {8.399999619, 183, 718, 10.39999962, 69},
-        {10.80000019, 119, 540, 9.199999809, 57},
-        {10.10000038, 180, 668, 13, 106},
-        {9, 82, 347, 8.800000191, 40},
-        {10, 71, 345, 9.199999809, 50},
-        {11.30000019, 118, 463, 7.800000191, 35},
-        {11.30000019, 121, 728, 8.199999809, 86},
-        {12.80000019, 68, 383, 7.400000095, 57},
-        {10, 112, 316, 10.39999962, 57},
-        {6.699999809, 109, 388, 8.899999619, 94}
-    };
-
-    /** Run example. */
-    public static void main(String[] args) throws InterruptedException {
-        System.out.println();
-        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(),
-                
DistributedLinearRegressionWithLSQRTrainerExample.class.getSimpleName(), () -> {
-                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
-
-                System.out.println(">>> Create new linear regression trainer 
object.");
-                LinearRegressionLSQRTrainer trainer = new 
LinearRegressionLSQRTrainer();
-
-                System.out.println(">>> Perform the training to get the 
model.");
-                LinearRegressionModel mdl = trainer.fit(
-                    ignite,
-                    dataCache,
-                    (k, v) -> Arrays.copyOfRange(v, 1, v.length),
-                    (k, v) -> v[0]
-                );
-
-                System.out.println(">>> Linear regression model: " + mdl);
-
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Ground Truth\t|");
-                System.out.println(">>> ---------------------------------");
-
-                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
-                        double[] val = observation.getValue();
-                        double[] inputs = Arrays.copyOfRange(val, 1, 
val.length);
-                        double groundTruth = val[0];
-
-                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(inputs));
-
-                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
-                    }
-                }
-
-                System.out.println(">>> ---------------------------------");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
-        }
-    }
-
-    /**
-     * Fills cache with data and returns it.
-     *
-     * @param ignite Ignite instance.
-     * @return Filled Ignite Cache.
-     */
-    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
-        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
-        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
-        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
-
-        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
-
-        for (int i = 0; i < data.length; i++)
-            cache.put(i, data[i]);
-
-        return cache;
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithQRTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithQRTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithQRTrainerExample.java
deleted file mode 100644
index 98d5e4e..0000000
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithQRTrainerExample.java
+++ /dev/null
@@ -1,137 +0,0 @@
-/*
- * 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.regression.linear;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.Ignition;
-import 
org.apache.ignite.examples.ml.math.matrix.SparseDistributedMatrixExample;
-import org.apache.ignite.ml.Trainer;
-import org.apache.ignite.ml.math.Matrix;
-import org.apache.ignite.ml.math.Vector;
-import org.apache.ignite.ml.math.impls.matrix.SparseDistributedMatrix;
-import org.apache.ignite.ml.math.impls.vector.SparseDistributedVector;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionQRTrainer;
-import org.apache.ignite.thread.IgniteThread;
-
-import java.util.Arrays;
-
-/**
- * Run linear regression model over distributed matrix.
- *
- * @see LinearRegressionQRTrainer
- */
-public class DistributedLinearRegressionWithQRTrainerExample {
-    /** */
-    private static final double[][] data = {
-        {8, 78, 284, 9.100000381, 109},
-        {9.300000191, 68, 433, 8.699999809, 144},
-        {7.5, 70, 739, 7.199999809, 113},
-        {8.899999619, 96, 1792, 8.899999619, 97},
-        {10.19999981, 74, 477, 8.300000191, 206},
-        {8.300000191, 111, 362, 10.89999962, 124},
-        {8.800000191, 77, 671, 10, 152},
-        {8.800000191, 168, 636, 9.100000381, 162},
-        {10.69999981, 82, 329, 8.699999809, 150},
-        {11.69999981, 89, 634, 7.599999905, 134},
-        {8.5, 149, 631, 10.80000019, 292},
-        {8.300000191, 60, 257, 9.5, 108},
-        {8.199999809, 96, 284, 8.800000191, 111},
-        {7.900000095, 83, 603, 9.5, 182},
-        {10.30000019, 130, 686, 8.699999809, 129},
-        {7.400000095, 145, 345, 11.19999981, 158},
-        {9.600000381, 112, 1357, 9.699999809, 186},
-        {9.300000191, 131, 544, 9.600000381, 177},
-        {10.60000038, 80, 205, 9.100000381, 127},
-        {9.699999809, 130, 1264, 9.199999809, 179},
-        {11.60000038, 140, 688, 8.300000191, 80},
-        {8.100000381, 154, 354, 8.399999619, 103},
-        {9.800000191, 118, 1632, 9.399999619, 101},
-        {7.400000095, 94, 348, 9.800000191, 117},
-        {9.399999619, 119, 370, 10.39999962, 88},
-        {11.19999981, 153, 648, 9.899999619, 78},
-        {9.100000381, 116, 366, 9.199999809, 102},
-        {10.5, 97, 540, 10.30000019, 95},
-        {11.89999962, 176, 680, 8.899999619, 80},
-        {8.399999619, 75, 345, 9.600000381, 92},
-        {5, 134, 525, 10.30000019, 126},
-        {9.800000191, 161, 870, 10.39999962, 108},
-        {9.800000191, 111, 669, 9.699999809, 77},
-        {10.80000019, 114, 452, 9.600000381, 60},
-        {10.10000038, 142, 430, 10.69999981, 71},
-        {10.89999962, 238, 822, 10.30000019, 86},
-        {9.199999809, 78, 190, 10.69999981, 93},
-        {8.300000191, 196, 867, 9.600000381, 106},
-        {7.300000191, 125, 969, 10.5, 162},
-        {9.399999619, 82, 499, 7.699999809, 95},
-        {9.399999619, 125, 925, 10.19999981, 91},
-        {9.800000191, 129, 353, 9.899999619, 52},
-        {3.599999905, 84, 288, 8.399999619, 110},
-        {8.399999619, 183, 718, 10.39999962, 69},
-        {10.80000019, 119, 540, 9.199999809, 57},
-        {10.10000038, 180, 668, 13, 106},
-        {9, 82, 347, 8.800000191, 40},
-        {10, 71, 345, 9.199999809, 50},
-        {11.30000019, 118, 463, 7.800000191, 35},
-        {11.30000019, 121, 728, 8.199999809, 86},
-        {12.80000019, 68, 383, 7.400000095, 57},
-        {10, 112, 316, 10.39999962, 57},
-        {6.699999809, 109, 388, 8.899999619, 94}
-    };
-
-    /** Run example. */
-    public static void main(String[] args) throws InterruptedException {
-        System.out.println();
-        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(), () -> {
-
-                // Create SparseDistributedMatrix, new cache will be created 
automagically.
-                System.out.println(">>> Create new SparseDistributedMatrix 
inside IgniteThread.");
-                SparseDistributedMatrix distributedMatrix = new 
SparseDistributedMatrix(data);
-
-                System.out.println(">>> Create new linear regression trainer 
object.");
-                Trainer<LinearRegressionModel, Matrix> trainer = new 
LinearRegressionQRTrainer();
-
-                System.out.println(">>> Perform the training to get the 
model.");
-                LinearRegressionModel mdl = trainer.train(distributedMatrix);
-                System.out.println(">>> Linear regression model: " + mdl);
-
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Ground Truth\t|");
-                System.out.println(">>> ---------------------------------");
-                for (double[] observation : data) {
-                    Vector inputs = new 
SparseDistributedVector(Arrays.copyOfRange(observation, 1, observation.length));
-                    double prediction = mdl.apply(inputs);
-                    double groundTruth = observation[0];
-                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
-                }
-                System.out.println(">>> ---------------------------------");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
-        }
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithSGDTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithSGDTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithSGDTrainerExample.java
deleted file mode 100644
index 44366e1..0000000
--- 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/DistributedLinearRegressionWithSGDTrainerExample.java
+++ /dev/null
@@ -1,177 +0,0 @@
-/*
- * 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.regression.linear;
-
-import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteCache;
-import org.apache.ignite.Ignition;
-import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
-import org.apache.ignite.cache.query.QueryCursor;
-import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.configuration.CacheConfiguration;
-import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
-import 
org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate;
-import 
org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionQRTrainer;
-import org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer;
-import org.apache.ignite.ml.trainers.group.UpdatesStrategy;
-import org.apache.ignite.thread.IgniteThread;
-
-import javax.cache.Cache;
-import java.util.Arrays;
-import java.util.UUID;
-
-/**
- * Run linear regression model over distributed matrix.
- *
- * @see LinearRegressionQRTrainer
- */
-public class DistributedLinearRegressionWithSGDTrainerExample {
-    /** */
-    private static final double[][] data = {
-        {8, 78, 284, 9.100000381, 109},
-        {9.300000191, 68, 433, 8.699999809, 144},
-        {7.5, 70, 739, 7.199999809, 113},
-        {8.899999619, 96, 1792, 8.899999619, 97},
-        {10.19999981, 74, 477, 8.300000191, 206},
-        {8.300000191, 111, 362, 10.89999962, 124},
-        {8.800000191, 77, 671, 10, 152},
-        {8.800000191, 168, 636, 9.100000381, 162},
-        {10.69999981, 82, 329, 8.699999809, 150},
-        {11.69999981, 89, 634, 7.599999905, 134},
-        {8.5, 149, 631, 10.80000019, 292},
-        {8.300000191, 60, 257, 9.5, 108},
-        {8.199999809, 96, 284, 8.800000191, 111},
-        {7.900000095, 83, 603, 9.5, 182},
-        {10.30000019, 130, 686, 8.699999809, 129},
-        {7.400000095, 145, 345, 11.19999981, 158},
-        {9.600000381, 112, 1357, 9.699999809, 186},
-        {9.300000191, 131, 544, 9.600000381, 177},
-        {10.60000038, 80, 205, 9.100000381, 127},
-        {9.699999809, 130, 1264, 9.199999809, 179},
-        {11.60000038, 140, 688, 8.300000191, 80},
-        {8.100000381, 154, 354, 8.399999619, 103},
-        {9.800000191, 118, 1632, 9.399999619, 101},
-        {7.400000095, 94, 348, 9.800000191, 117},
-        {9.399999619, 119, 370, 10.39999962, 88},
-        {11.19999981, 153, 648, 9.899999619, 78},
-        {9.100000381, 116, 366, 9.199999809, 102},
-        {10.5, 97, 540, 10.30000019, 95},
-        {11.89999962, 176, 680, 8.899999619, 80},
-        {8.399999619, 75, 345, 9.600000381, 92},
-        {5, 134, 525, 10.30000019, 126},
-        {9.800000191, 161, 870, 10.39999962, 108},
-        {9.800000191, 111, 669, 9.699999809, 77},
-        {10.80000019, 114, 452, 9.600000381, 60},
-        {10.10000038, 142, 430, 10.69999981, 71},
-        {10.89999962, 238, 822, 10.30000019, 86},
-        {9.199999809, 78, 190, 10.69999981, 93},
-        {8.300000191, 196, 867, 9.600000381, 106},
-        {7.300000191, 125, 969, 10.5, 162},
-        {9.399999619, 82, 499, 7.699999809, 95},
-        {9.399999619, 125, 925, 10.19999981, 91},
-        {9.800000191, 129, 353, 9.899999619, 52},
-        {3.599999905, 84, 288, 8.399999619, 110},
-        {8.399999619, 183, 718, 10.39999962, 69},
-        {10.80000019, 119, 540, 9.199999809, 57},
-        {10.10000038, 180, 668, 13, 106},
-        {9, 82, 347, 8.800000191, 40},
-        {10, 71, 345, 9.199999809, 50},
-        {11.30000019, 118, 463, 7.800000191, 35},
-        {11.30000019, 121, 728, 8.199999809, 86},
-        {12.80000019, 68, 383, 7.400000095, 57},
-        {10, 112, 316, 10.39999962, 57},
-        {6.699999809, 109, 388, 8.899999619, 94}
-    };
-
-    /** Run example. */
-    public static void main(String[] args) throws InterruptedException {
-        System.out.println();
-        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(),
-                
DistributedLinearRegressionWithSGDTrainerExample.class.getSimpleName(), () -> {
-
-                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
-
-                System.out.println(">>> Create new linear regression trainer 
object.");
-                LinearRegressionSGDTrainer<?> trainer = new 
LinearRegressionSGDTrainer<>(new UpdatesStrategy<>(
-                    new RPropUpdateCalculator(),
-                    RPropParameterUpdate::sumLocal,
-                    RPropParameterUpdate::avg
-                ), 100000,  10, 100, 123L);
-
-                System.out.println(">>> Perform the training to get the 
model.");
-                LinearRegressionModel mdl = trainer.fit(
-                    ignite,
-                    dataCache,
-                    (k, v) -> Arrays.copyOfRange(v, 1, v.length),
-                    (k, v) -> v[0]
-                );
-
-                System.out.println(">>> Linear regression model: " + mdl);
-
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Ground Truth\t|");
-                System.out.println(">>> ---------------------------------");
-
-                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
-                        double[] val = observation.getValue();
-                        double[] inputs = Arrays.copyOfRange(val, 1, 
val.length);
-                        double groundTruth = val[0];
-
-                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(inputs));
-
-                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
-                    }
-                }
-
-                System.out.println(">>> ---------------------------------");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
-        }
-    }
-
-    /**
-     * Fills cache with data and returns it.
-     *
-     * @param ignite Ignite instance.
-     * @return Filled Ignite Cache.
-     */
-    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
-        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
-        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
-        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
-
-        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
-
-        for (int i = 0; i < data.length; i++)
-            cache.put(i, data[i]);
-
-        return cache;
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
new file mode 100644
index 0000000..276d43f
--- /dev/null
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerExample.java
@@ -0,0 +1,169 @@
+/*
+ * 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.regression.linear;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.cache.query.QueryCursor;
+import org.apache.ignite.cache.query.ScanQuery;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
+import org.apache.ignite.thread.IgniteThread;
+
+import javax.cache.Cache;
+import java.util.Arrays;
+import java.util.UUID;
+
+/**
+ * Run linear regression model over distributed matrix.
+ *
+ * @see LinearRegressionLSQRTrainer
+ */
+public class LinearRegressionLSQRTrainerExample {
+    /** */
+    private static final double[][] data = {
+        {8, 78, 284, 9.100000381, 109},
+        {9.300000191, 68, 433, 8.699999809, 144},
+        {7.5, 70, 739, 7.199999809, 113},
+        {8.899999619, 96, 1792, 8.899999619, 97},
+        {10.19999981, 74, 477, 8.300000191, 206},
+        {8.300000191, 111, 362, 10.89999962, 124},
+        {8.800000191, 77, 671, 10, 152},
+        {8.800000191, 168, 636, 9.100000381, 162},
+        {10.69999981, 82, 329, 8.699999809, 150},
+        {11.69999981, 89, 634, 7.599999905, 134},
+        {8.5, 149, 631, 10.80000019, 292},
+        {8.300000191, 60, 257, 9.5, 108},
+        {8.199999809, 96, 284, 8.800000191, 111},
+        {7.900000095, 83, 603, 9.5, 182},
+        {10.30000019, 130, 686, 8.699999809, 129},
+        {7.400000095, 145, 345, 11.19999981, 158},
+        {9.600000381, 112, 1357, 9.699999809, 186},
+        {9.300000191, 131, 544, 9.600000381, 177},
+        {10.60000038, 80, 205, 9.100000381, 127},
+        {9.699999809, 130, 1264, 9.199999809, 179},
+        {11.60000038, 140, 688, 8.300000191, 80},
+        {8.100000381, 154, 354, 8.399999619, 103},
+        {9.800000191, 118, 1632, 9.399999619, 101},
+        {7.400000095, 94, 348, 9.800000191, 117},
+        {9.399999619, 119, 370, 10.39999962, 88},
+        {11.19999981, 153, 648, 9.899999619, 78},
+        {9.100000381, 116, 366, 9.199999809, 102},
+        {10.5, 97, 540, 10.30000019, 95},
+        {11.89999962, 176, 680, 8.899999619, 80},
+        {8.399999619, 75, 345, 9.600000381, 92},
+        {5, 134, 525, 10.30000019, 126},
+        {9.800000191, 161, 870, 10.39999962, 108},
+        {9.800000191, 111, 669, 9.699999809, 77},
+        {10.80000019, 114, 452, 9.600000381, 60},
+        {10.10000038, 142, 430, 10.69999981, 71},
+        {10.89999962, 238, 822, 10.30000019, 86},
+        {9.199999809, 78, 190, 10.69999981, 93},
+        {8.300000191, 196, 867, 9.600000381, 106},
+        {7.300000191, 125, 969, 10.5, 162},
+        {9.399999619, 82, 499, 7.699999809, 95},
+        {9.399999619, 125, 925, 10.19999981, 91},
+        {9.800000191, 129, 353, 9.899999619, 52},
+        {3.599999905, 84, 288, 8.399999619, 110},
+        {8.399999619, 183, 718, 10.39999962, 69},
+        {10.80000019, 119, 540, 9.199999809, 57},
+        {10.10000038, 180, 668, 13, 106},
+        {9, 82, 347, 8.800000191, 40},
+        {10, 71, 345, 9.199999809, 50},
+        {11.30000019, 118, 463, 7.800000191, 35},
+        {11.30000019, 121, 728, 8.199999809, 86},
+        {12.80000019, 68, 383, 7.400000095, 57},
+        {10, 112, 316, 10.39999962, 57},
+        {6.699999809, 109, 388, 8.899999619, 94}
+    };
+
+    /** Run example. */
+    public static void main(String[] args) throws InterruptedException {
+        System.out.println();
+        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(),
+                LinearRegressionLSQRTrainerExample.class.getSimpleName(), () 
-> {
+                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
+
+                System.out.println(">>> Create new linear regression trainer 
object.");
+                LinearRegressionLSQRTrainer trainer = new 
LinearRegressionLSQRTrainer();
+
+                System.out.println(">>> Perform the training to get the 
model.");
+                LinearRegressionModel mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    (k, v) -> Arrays.copyOfRange(v, 1, v.length),
+                    (k, v) -> v[0]
+                );
+
+                System.out.println(">>> Linear regression model: " + mdl);
+
+                System.out.println(">>> ---------------------------------");
+                System.out.println(">>> | Prediction\t| Ground Truth\t|");
+                System.out.println(">>> ---------------------------------");
+
+                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
+                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
+                        double[] val = observation.getValue();
+                        double[] inputs = Arrays.copyOfRange(val, 1, 
val.length);
+                        double groundTruth = val[0];
+
+                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(inputs));
+
+                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
+                    }
+                }
+
+                System.out.println(">>> ---------------------------------");
+            });
+
+            igniteThread.start();
+
+            igniteThread.join();
+        }
+    }
+
+    /**
+     * Fills cache with data and returns it.
+     *
+     * @param ignite Ignite instance.
+     * @return Filled Ignite Cache.
+     */
+    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
+        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
+        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
+        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
+
+        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
+
+        for (int i = 0; i < data.length; i++)
+            cache.put(i, data[i]);
+
+        return cache;
+    }
+}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithNormalizationExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithNormalizationExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithNormalizationExample.java
new file mode 100644
index 0000000..0358f44
--- /dev/null
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithNormalizationExample.java
@@ -0,0 +1,180 @@
+/*
+ * 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.regression.linear;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.cache.query.QueryCursor;
+import org.apache.ignite.cache.query.ScanQuery;
+import org.apache.ignite.configuration.CacheConfiguration;
+import 
org.apache.ignite.examples.ml.math.matrix.SparseDistributedMatrixExample;
+import org.apache.ignite.ml.math.functions.IgniteBiFunction;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import 
org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor;
+import org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
+import org.apache.ignite.thread.IgniteThread;
+
+import javax.cache.Cache;
+import java.util.Arrays;
+import java.util.UUID;
+
+/**
+ * Run linear regression model over distributed matrix.
+ *
+ * @see LinearRegressionLSQRTrainer
+ * @see NormalizationTrainer
+ * @see NormalizationPreprocessor
+ */
+public class LinearRegressionLSQRTrainerWithNormalizationExample {
+    /** */
+    private static final double[][] data = {
+        {8, 78, 284, 9.100000381, 109},
+        {9.300000191, 68, 433, 8.699999809, 144},
+        {7.5, 70, 739, 7.199999809, 113},
+        {8.899999619, 96, 1792, 8.899999619, 97},
+        {10.19999981, 74, 477, 8.300000191, 206},
+        {8.300000191, 111, 362, 10.89999962, 124},
+        {8.800000191, 77, 671, 10, 152},
+        {8.800000191, 168, 636, 9.100000381, 162},
+        {10.69999981, 82, 329, 8.699999809, 150},
+        {11.69999981, 89, 634, 7.599999905, 134},
+        {8.5, 149, 631, 10.80000019, 292},
+        {8.300000191, 60, 257, 9.5, 108},
+        {8.199999809, 96, 284, 8.800000191, 111},
+        {7.900000095, 83, 603, 9.5, 182},
+        {10.30000019, 130, 686, 8.699999809, 129},
+        {7.400000095, 145, 345, 11.19999981, 158},
+        {9.600000381, 112, 1357, 9.699999809, 186},
+        {9.300000191, 131, 544, 9.600000381, 177},
+        {10.60000038, 80, 205, 9.100000381, 127},
+        {9.699999809, 130, 1264, 9.199999809, 179},
+        {11.60000038, 140, 688, 8.300000191, 80},
+        {8.100000381, 154, 354, 8.399999619, 103},
+        {9.800000191, 118, 1632, 9.399999619, 101},
+        {7.400000095, 94, 348, 9.800000191, 117},
+        {9.399999619, 119, 370, 10.39999962, 88},
+        {11.19999981, 153, 648, 9.899999619, 78},
+        {9.100000381, 116, 366, 9.199999809, 102},
+        {10.5, 97, 540, 10.30000019, 95},
+        {11.89999962, 176, 680, 8.899999619, 80},
+        {8.399999619, 75, 345, 9.600000381, 92},
+        {5, 134, 525, 10.30000019, 126},
+        {9.800000191, 161, 870, 10.39999962, 108},
+        {9.800000191, 111, 669, 9.699999809, 77},
+        {10.80000019, 114, 452, 9.600000381, 60},
+        {10.10000038, 142, 430, 10.69999981, 71},
+        {10.89999962, 238, 822, 10.30000019, 86},
+        {9.199999809, 78, 190, 10.69999981, 93},
+        {8.300000191, 196, 867, 9.600000381, 106},
+        {7.300000191, 125, 969, 10.5, 162},
+        {9.399999619, 82, 499, 7.699999809, 95},
+        {9.399999619, 125, 925, 10.19999981, 91},
+        {9.800000191, 129, 353, 9.899999619, 52},
+        {3.599999905, 84, 288, 8.399999619, 110},
+        {8.399999619, 183, 718, 10.39999962, 69},
+        {10.80000019, 119, 540, 9.199999809, 57},
+        {10.10000038, 180, 668, 13, 106},
+        {9, 82, 347, 8.800000191, 40},
+        {10, 71, 345, 9.199999809, 50},
+        {11.30000019, 118, 463, 7.800000191, 35},
+        {11.30000019, 121, 728, 8.199999809, 86},
+        {12.80000019, 68, 383, 7.400000095, 57},
+        {10, 112, 316, 10.39999962, 57},
+        {6.699999809, 109, 388, 8.899999619, 94}
+    };
+
+    /** Run example. */
+    public static void main(String[] args) throws InterruptedException {
+        System.out.println();
+        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(), () -> {
+                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
+
+                System.out.println(">>> Create new normalization trainer 
object.");
+                NormalizationTrainer<Integer, double[]> normalizationTrainer = 
new NormalizationTrainer<>();
+
+                System.out.println(">>> Perform the training to get the 
normalization preprocessor.");
+                IgniteBiFunction<Integer, double[], double[]> preprocessor = 
normalizationTrainer.fit(
+                    ignite,
+                    dataCache,
+                    (k, v) -> Arrays.copyOfRange(v, 1, v.length)
+                );
+
+                System.out.println(">>> Create new linear regression trainer 
object.");
+                LinearRegressionLSQRTrainer trainer = new 
LinearRegressionLSQRTrainer();
+
+                System.out.println(">>> Perform the training to get the 
model.");
+                LinearRegressionModel mdl = trainer.fit(ignite, dataCache, 
preprocessor, (k, v) -> v[0]);
+
+                System.out.println(">>> Linear regression model: " + mdl);
+
+                System.out.println(">>> ---------------------------------");
+                System.out.println(">>> | Prediction\t| Ground Truth\t|");
+                System.out.println(">>> ---------------------------------");
+
+                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
+                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
+                        Integer key = observation.getKey();
+                        double[] val = observation.getValue();
+                        double groundTruth = val[0];
+
+                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(preprocessor.apply(key, val)));
+
+                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
+                    }
+                }
+
+                System.out.println(">>> ---------------------------------");
+            });
+
+            igniteThread.start();
+
+            igniteThread.join();
+        }
+    }
+
+    /**
+     * Fills cache with data and returns it.
+     *
+     * @param ignite Ignite instance.
+     * @return Filled Ignite Cache.
+     */
+    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
+        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
+        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
+        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
+
+        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
+
+        for (int i = 0; i < data.length; i++)
+            cache.put(i, data[i]);
+
+        return cache;
+    }
+}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
----------------------------------------------------------------------
diff --git 
a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
new file mode 100644
index 0000000..ce6ad3b
--- /dev/null
+++ 
b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionSGDTrainerExample.java
@@ -0,0 +1,176 @@
+/*
+ * 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.regression.linear;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.Ignition;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.cache.query.QueryCursor;
+import org.apache.ignite.cache.query.ScanQuery;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import 
org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate;
+import 
org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
+import org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer;
+import org.apache.ignite.ml.nn.UpdatesStrategy;
+import org.apache.ignite.thread.IgniteThread;
+
+import javax.cache.Cache;
+import java.util.Arrays;
+import java.util.UUID;
+
+/**
+ * Run linear regression model over distributed matrix.
+ *
+ * @see LinearRegressionSGDTrainer
+ */
+public class LinearRegressionSGDTrainerExample {
+    /** */
+    private static final double[][] data = {
+        {8, 78, 284, 9.100000381, 109},
+        {9.300000191, 68, 433, 8.699999809, 144},
+        {7.5, 70, 739, 7.199999809, 113},
+        {8.899999619, 96, 1792, 8.899999619, 97},
+        {10.19999981, 74, 477, 8.300000191, 206},
+        {8.300000191, 111, 362, 10.89999962, 124},
+        {8.800000191, 77, 671, 10, 152},
+        {8.800000191, 168, 636, 9.100000381, 162},
+        {10.69999981, 82, 329, 8.699999809, 150},
+        {11.69999981, 89, 634, 7.599999905, 134},
+        {8.5, 149, 631, 10.80000019, 292},
+        {8.300000191, 60, 257, 9.5, 108},
+        {8.199999809, 96, 284, 8.800000191, 111},
+        {7.900000095, 83, 603, 9.5, 182},
+        {10.30000019, 130, 686, 8.699999809, 129},
+        {7.400000095, 145, 345, 11.19999981, 158},
+        {9.600000381, 112, 1357, 9.699999809, 186},
+        {9.300000191, 131, 544, 9.600000381, 177},
+        {10.60000038, 80, 205, 9.100000381, 127},
+        {9.699999809, 130, 1264, 9.199999809, 179},
+        {11.60000038, 140, 688, 8.300000191, 80},
+        {8.100000381, 154, 354, 8.399999619, 103},
+        {9.800000191, 118, 1632, 9.399999619, 101},
+        {7.400000095, 94, 348, 9.800000191, 117},
+        {9.399999619, 119, 370, 10.39999962, 88},
+        {11.19999981, 153, 648, 9.899999619, 78},
+        {9.100000381, 116, 366, 9.199999809, 102},
+        {10.5, 97, 540, 10.30000019, 95},
+        {11.89999962, 176, 680, 8.899999619, 80},
+        {8.399999619, 75, 345, 9.600000381, 92},
+        {5, 134, 525, 10.30000019, 126},
+        {9.800000191, 161, 870, 10.39999962, 108},
+        {9.800000191, 111, 669, 9.699999809, 77},
+        {10.80000019, 114, 452, 9.600000381, 60},
+        {10.10000038, 142, 430, 10.69999981, 71},
+        {10.89999962, 238, 822, 10.30000019, 86},
+        {9.199999809, 78, 190, 10.69999981, 93},
+        {8.300000191, 196, 867, 9.600000381, 106},
+        {7.300000191, 125, 969, 10.5, 162},
+        {9.399999619, 82, 499, 7.699999809, 95},
+        {9.399999619, 125, 925, 10.19999981, 91},
+        {9.800000191, 129, 353, 9.899999619, 52},
+        {3.599999905, 84, 288, 8.399999619, 110},
+        {8.399999619, 183, 718, 10.39999962, 69},
+        {10.80000019, 119, 540, 9.199999809, 57},
+        {10.10000038, 180, 668, 13, 106},
+        {9, 82, 347, 8.800000191, 40},
+        {10, 71, 345, 9.199999809, 50},
+        {11.30000019, 118, 463, 7.800000191, 35},
+        {11.30000019, 121, 728, 8.199999809, 86},
+        {12.80000019, 68, 383, 7.400000095, 57},
+        {10, 112, 316, 10.39999962, 57},
+        {6.699999809, 109, 388, 8.899999619, 94}
+    };
+
+    /** Run example. */
+    public static void main(String[] args) throws InterruptedException {
+        System.out.println();
+        System.out.println(">>> Linear regression model over sparse 
distributed matrix API usage 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(),
+                LinearRegressionSGDTrainerExample.class.getSimpleName(), () -> 
{
+
+                IgniteCache<Integer, double[]> dataCache = 
getTestCache(ignite);
+
+                System.out.println(">>> Create new linear regression trainer 
object.");
+                LinearRegressionSGDTrainer<?> trainer = new 
LinearRegressionSGDTrainer<>(new UpdatesStrategy<>(
+                    new RPropUpdateCalculator(),
+                    RPropParameterUpdate::sumLocal,
+                    RPropParameterUpdate::avg
+                ), 100000,  10, 100, 123L);
+
+                System.out.println(">>> Perform the training to get the 
model.");
+                LinearRegressionModel mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    (k, v) -> Arrays.copyOfRange(v, 1, v.length),
+                    (k, v) -> v[0]
+                );
+
+                System.out.println(">>> Linear regression model: " + mdl);
+
+                System.out.println(">>> ---------------------------------");
+                System.out.println(">>> | Prediction\t| Ground Truth\t|");
+                System.out.println(">>> ---------------------------------");
+
+                try (QueryCursor<Cache.Entry<Integer, double[]>> observations 
= dataCache.query(new ScanQuery<>())) {
+                    for (Cache.Entry<Integer, double[]> observation : 
observations) {
+                        double[] val = observation.getValue();
+                        double[] inputs = Arrays.copyOfRange(val, 1, 
val.length);
+                        double groundTruth = val[0];
+
+                        double prediction = mdl.apply(new 
DenseLocalOnHeapVector(inputs));
+
+                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", 
prediction, groundTruth);
+                    }
+                }
+
+                System.out.println(">>> ---------------------------------");
+            });
+
+            igniteThread.start();
+
+            igniteThread.join();
+        }
+    }
+
+    /**
+     * Fills cache with data and returns it.
+     *
+     * @param ignite Ignite instance.
+     * @return Filled Ignite Cache.
+     */
+    private static IgniteCache<Integer, double[]> getTestCache(Ignite ignite) {
+        CacheConfiguration<Integer, double[]> cacheConfiguration = new 
CacheConfiguration<>();
+        cacheConfiguration.setName("TEST_" + UUID.randomUUID());
+        cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 
10));
+
+        IgniteCache<Integer, double[]> cache = 
ignite.createCache(cacheConfiguration);
+
+        for (int i = 0; i < data.length; i++)
+            cache.put(i, data[i]);
+
+        return cache;
+    }
+}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/Trainer.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/Trainer.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/Trainer.java
deleted file mode 100644
index f53b801..0000000
--- a/modules/ml/src/main/java/org/apache/ignite/ml/Trainer.java
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * 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.ml;
-
-/**
- * Interface for Trainers. Trainer is just a function which produces model 
from the data.
- *
- * @param <M> Type of produced model.
- * @param <T> Type of data needed for model producing.
- */
-// TODO: IGNITE-7659: Reduce multiple Trainer interfaces to one
-@Deprecated
-public interface Trainer<M extends Model, T> {
-    /**
-     * Returns model based on data
-     *
-     * @param data data to build model
-     * @return model
-     */
-    M train(T data);
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/estimators/Estimators.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/estimators/Estimators.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/estimators/Estimators.java
deleted file mode 100644
index b2731ff..0000000
--- a/modules/ml/src/main/java/org/apache/ignite/ml/estimators/Estimators.java
+++ /dev/null
@@ -1,50 +0,0 @@
-/*
- * 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.ml.estimators;
-
-import java.util.concurrent.atomic.AtomicLong;
-import java.util.function.Function;
-import java.util.stream.Stream;
-import org.apache.ignite.lang.IgniteBiTuple;
-import org.apache.ignite.ml.Model;
-import org.apache.ignite.ml.math.functions.IgniteTriFunction;
-
-/** Estimators. */
-public class Estimators {
-    /** Simple implementation of mean squared error estimator. */
-    public static <T, V> IgniteTriFunction<Model<T, V>, 
Stream<IgniteBiTuple<T, V>>, Function<V, Double>, Double> MSE() {
-        return (model, stream, f) -> stream.mapToDouble(dp -> {
-            double diff = f.apply(dp.get2()) - f.apply(model.apply(dp.get1()));
-            return diff * diff;
-        }).average().orElse(0);
-    }
-
-    /** Simple implementation of errors percentage estimator. */
-    public static <T, V> IgniteTriFunction<Model<T, V>, 
Stream<IgniteBiTuple<T, V>>, Function<V, Double>, Double> errorsPercentage() {
-        return (model, stream, f) -> {
-            AtomicLong total = new AtomicLong(0);
-
-            long cnt = stream.
-                peek((ib) -> total.incrementAndGet()).
-                filter(dp -> !model.apply(dp.get1()).equals(dp.get2())).
-                count();
-
-            return (double)cnt / total.get();
-        };
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/estimators/package-info.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/estimators/package-info.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/estimators/package-info.java
deleted file mode 100644
index c03827f..0000000
--- a/modules/ml/src/main/java/org/apache/ignite/ml/estimators/package-info.java
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- * 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. -->
- * Contains estimation algorithms.
- */
-package org.apache.ignite.ml.estimators;
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/math/functions/IgniteBiFunction.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/functions/IgniteBiFunction.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/functions/IgniteBiFunction.java
index dc49739..45fd035 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/functions/IgniteBiFunction.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/functions/IgniteBiFunction.java
@@ -18,6 +18,7 @@
 package org.apache.ignite.ml.math.functions;
 
 import java.io.Serializable;
+import java.util.Objects;
 import java.util.function.BiFunction;
 
 /**
@@ -25,5 +26,10 @@ import java.util.function.BiFunction;
  *
  * @see java.util.function.BiFunction
  */
-public interface IgniteBiFunction<A, B, T> extends BiFunction<A, B, T>, 
Serializable {
+public interface IgniteBiFunction<T, U, R> extends BiFunction<T, U, R>, 
Serializable {
+    /** {@inheritDoc} */
+    default <V> IgniteBiFunction<T, U, V> andThen(IgniteFunction<? super R, ? 
extends V> after) {
+        Objects.requireNonNull(after);
+        return (T t, U u) -> after.apply(apply(t, u));
+    }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataBuilderOnHeap.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataBuilderOnHeap.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataBuilderOnHeap.java
deleted file mode 100644
index e80b935..0000000
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataBuilderOnHeap.java
+++ /dev/null
@@ -1,86 +0,0 @@
-/*
- * 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.ml.math.isolve;
-
-import java.io.Serializable;
-import java.util.Iterator;
-import org.apache.ignite.ml.dataset.PartitionDataBuilder;
-import org.apache.ignite.ml.dataset.UpstreamEntry;
-import org.apache.ignite.ml.math.functions.IgniteBiFunction;
-
-/**
- * Linear system partition data builder that builds {@link 
LinSysPartitionDataOnHeap}.
- *
- * @param <K> Type of a key in <tt>upstream</tt> data.
- * @param <V> Type of a value in <tt>upstream</tt> data.
- * @param <C> Type of a partition <tt>context</tt>.
- */
-public class LinSysPartitionDataBuilderOnHeap<K, V, C extends Serializable>
-    implements PartitionDataBuilder<K, V, C, LinSysPartitionDataOnHeap> {
-    /** */
-    private static final long serialVersionUID = -7820760153954269227L;
-
-    /** Extractor of X matrix row. */
-    private final IgniteBiFunction<K, V, double[]> xExtractor;
-
-    /** Extractor of Y vector value. */
-    private final IgniteBiFunction<K, V, Double> yExtractor;
-
-    /**
-     * Constructs a new instance of linear system partition data builder.
-     *
-     * @param xExtractor Extractor of X matrix row.
-     * @param yExtractor Extractor of Y vector value.
-     */
-    public LinSysPartitionDataBuilderOnHeap(IgniteBiFunction<K, V, double[]> 
xExtractor,
-        IgniteBiFunction<K, V, Double> yExtractor) {
-        this.xExtractor = xExtractor;
-        this.yExtractor = yExtractor;
-    }
-
-    /** {@inheritDoc} */
-    @Override public LinSysPartitionDataOnHeap build(Iterator<UpstreamEntry<K, 
V>> upstreamData, long upstreamDataSize,
-        C ctx) {
-        // Prepares the matrix of features in flat column-major format.
-        int xCols = -1;
-        double[] x = null;//new double[Math.toIntExact(upstreamDataSize * 
cols)];
-        double[] y = new double[Math.toIntExact(upstreamDataSize)];
-
-        int ptr = 0;
-        while (upstreamData.hasNext()) {
-            UpstreamEntry<K, V> entry = upstreamData.next();
-            double[] row = xExtractor.apply(entry.getKey(), entry.getValue());
-
-            if (xCols < 0) {
-                xCols = row.length;
-                x = new double[Math.toIntExact(upstreamDataSize * xCols)];
-            }
-            else
-                assert row.length == xCols : "X extractor must return exactly 
" + xCols + " columns";
-
-            for (int i = 0; i < xCols; i++)
-                x[Math.toIntExact(i * upstreamDataSize) + ptr] = row[i];
-
-            y[ptr] = yExtractor.apply(entry.getKey(), entry.getValue());
-
-            ptr++;
-        }
-
-        return new LinSysPartitionDataOnHeap(x, y, 
Math.toIntExact(upstreamDataSize));
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataOnHeap.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataOnHeap.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataOnHeap.java
deleted file mode 100644
index 89c8e44..0000000
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/LinSysPartitionDataOnHeap.java
+++ /dev/null
@@ -1,65 +0,0 @@
-/*
- * 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.ml.math.isolve;
-
-/**
- * On Heap partition data that keeps part of a linear system.
- */
-public class LinSysPartitionDataOnHeap implements AutoCloseable {
-    /** Part of X matrix. */
-    private final double[] x;
-
-    /** Part of Y vector. */
-    private final double[] y;
-
-    /** Number of rows. */
-    private final int rows;
-
-    /**
-     * Constructs a new instance of linear system partition data.
-     *
-     * @param x Part of X matrix.
-     * @param y Part of Y vector.
-     * @param rows Number of rows.
-     */
-    public LinSysPartitionDataOnHeap(double[] x, double[] y, int rows) {
-        this.x = x;
-        this.rows = rows;
-        this.y = y;
-    }
-
-    /** */
-    public double[] getX() {
-        return x;
-    }
-
-    /** */
-    public int getRows() {
-        return rows;
-    }
-
-    /** */
-    public double[] getY() {
-        return y;
-    }
-
-    /** {@inheritDoc} */
-    @Override public void close() {
-        // Do nothing, GC will clean up.
-    }
-}

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/AbstractLSQR.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/AbstractLSQR.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/AbstractLSQR.java
index 8d190cd..d1d3219 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/AbstractLSQR.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/AbstractLSQR.java
@@ -19,6 +19,7 @@ package org.apache.ignite.ml.math.isolve.lsqr;
 
 import com.github.fommil.netlib.BLAS;
 import java.util.Arrays;
+import org.apache.ignite.ml.math.Precision;
 
 /**
  * Basic implementation of the LSQR algorithm without assumptions about 
dataset storage format or data processing
@@ -30,7 +31,7 @@ import java.util.Arrays;
 // TODO: IGNITE-7660: Refactor LSQR algorithm
 public abstract class AbstractLSQR {
     /** The smallest representable positive number such that 1.0 + eps != 1.0. 
*/
-    private static final double eps = 
Double.longBitsToDouble(Double.doubleToLongBits(1.0) | 1) - 1.0;
+    private static final double eps = Precision.EPSILON;
 
     /** BLAS (Basic Linear Algebra Subprograms) instance. */
     private static BLAS blas = BLAS.getInstance();

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/LSQROnHeap.java
----------------------------------------------------------------------
diff --git 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/LSQROnHeap.java
 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/LSQROnHeap.java
index b1cc4c9..e138cf3 100644
--- 
a/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/LSQROnHeap.java
+++ 
b/modules/ml/src/main/java/org/apache/ignite/ml/math/isolve/lsqr/LSQROnHeap.java
@@ -22,14 +22,14 @@ import java.util.Arrays;
 import org.apache.ignite.ml.dataset.Dataset;
 import org.apache.ignite.ml.dataset.DatasetBuilder;
 import org.apache.ignite.ml.dataset.PartitionDataBuilder;
-import org.apache.ignite.ml.math.isolve.LinSysPartitionDataOnHeap;
+import org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData;
 
 /**
  * Distributed implementation of LSQR algorithm based on {@link AbstractLSQR} 
and {@link Dataset}.
  */
 public class LSQROnHeap<K, V> extends AbstractLSQR implements AutoCloseable {
     /** Dataset. */
-    private final Dataset<LSQRPartitionContext, LinSysPartitionDataOnHeap> 
dataset;
+    private final Dataset<LSQRPartitionContext, SimpleLabeledDatasetData> 
dataset;
 
     /**
      * Constructs a new instance of OnHeap LSQR algorithm implementation.
@@ -38,7 +38,7 @@ public class LSQROnHeap<K, V> extends AbstractLSQR implements 
AutoCloseable {
      * @param partDataBuilder Partition data builder.
      */
     public LSQROnHeap(DatasetBuilder<K, V> datasetBuilder,
-        PartitionDataBuilder<K, V, LSQRPartitionContext, 
LinSysPartitionDataOnHeap> partDataBuilder) {
+        PartitionDataBuilder<K, V, LSQRPartitionContext, 
SimpleLabeledDatasetData> partDataBuilder) {
         this.dataset = datasetBuilder.build(
             (upstream, upstreamSize) -> new LSQRPartitionContext(),
             partDataBuilder
@@ -48,20 +48,20 @@ public class LSQROnHeap<K, V> extends AbstractLSQR 
implements AutoCloseable {
     /** {@inheritDoc} */
     @Override protected double bnorm() {
         return dataset.computeWithCtx((ctx, data) -> {
-            ctx.setU(Arrays.copyOf(data.getY(), data.getY().length));
+            ctx.setU(Arrays.copyOf(data.getLabels(), data.getLabels().length));
 
-            return BLAS.getInstance().dnrm2(data.getY().length, data.getY(), 
1);
+            return BLAS.getInstance().dnrm2(data.getLabels().length, 
data.getLabels(), 1);
         }, (a, b) -> a == null ? b : b == null ? a : Math.sqrt(a * a + b * b));
     }
 
     /** {@inheritDoc} */
     @Override protected double beta(double[] x, double alfa, double beta) {
         return dataset.computeWithCtx((ctx, data) -> {
-            if (data.getX() == null)
+            if (data.getFeatures() == null)
                 return null;
 
-            int cols = data.getX().length / data.getRows();
-            BLAS.getInstance().dgemv("N", data.getRows(), cols, alfa, 
data.getX(),
+            int cols = data.getFeatures().length / data.getRows();
+            BLAS.getInstance().dgemv("N", data.getRows(), cols, alfa, 
data.getFeatures(),
                 Math.max(1, data.getRows()), x, 1, beta, ctx.getU(), 1);
 
             return BLAS.getInstance().dnrm2(ctx.getU().length, ctx.getU(), 1);
@@ -71,13 +71,13 @@ public class LSQROnHeap<K, V> extends AbstractLSQR 
implements AutoCloseable {
     /** {@inheritDoc} */
     @Override protected double[] iter(double bnorm, double[] target) {
         double[] res = dataset.computeWithCtx((ctx, data) -> {
-            if (data.getX() == null)
+            if (data.getFeatures() == null)
                 return null;
 
-            int cols =  data.getX().length / data.getRows();
+            int cols =  data.getFeatures().length / data.getRows();
             BLAS.getInstance().dscal(ctx.getU().length, 1 / bnorm, ctx.getU(), 
1);
             double[] v = new double[cols];
-            BLAS.getInstance().dgemv("T", data.getRows(), cols, 1.0, 
data.getX(),
+            BLAS.getInstance().dgemv("T", data.getRows(), cols, 1.0, 
data.getFeatures(),
                 Math.max(1, data.getRows()), ctx.getU(), 1, 0, v, 1);
 
             return v;
@@ -101,7 +101,10 @@ public class LSQROnHeap<K, V> extends AbstractLSQR 
implements AutoCloseable {
      * @return number of columns
      */
     @Override protected int getColumns() {
-        return dataset.compute(data -> data.getX() == null ? null :  
data.getX().length / data.getRows(), (a, b) -> a == null ? b : a);
+        return dataset.compute(
+            data -> data.getFeatures() == null ? null : 
data.getFeatures().length / data.getRows(),
+            (a, b) -> a == null ? b : a
+        );
     }
 
     /** {@inheritDoc} */

http://git-wip-us.apache.org/repos/asf/ignite/blob/a9ec54be/modules/ml/src/main/java/org/apache/ignite/ml/nn/MLPTrainer.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/nn/MLPTrainer.java 
b/modules/ml/src/main/java/org/apache/ignite/ml/nn/MLPTrainer.java
index fe955cb..d12a276 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/nn/MLPTrainer.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/nn/MLPTrainer.java
@@ -33,7 +33,6 @@ import org.apache.ignite.ml.nn.architecture.MLPArchitecture;
 import org.apache.ignite.ml.nn.initializers.RandomInitializer;
 import 
org.apache.ignite.ml.optimization.updatecalculators.ParameterUpdateCalculator;
 import org.apache.ignite.ml.trainers.MultiLabelDatasetTrainer;
-import org.apache.ignite.ml.trainers.group.UpdatesStrategy;
 import org.apache.ignite.ml.util.Utils;
 
 import java.io.Serializable;

Reply via email to