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Posted to commits@ignite.apache.org by ch...@apache.org on 2018/10/26 13:06:55 UTC
[1/4] ignite git commit: IGNITE-9910: [ML] Move the static
copy-pasted datasets from examples to special Util class
Repository: ignite
Updated Branches:
refs/heads/master c7449f6c6 -> 370cd3e1d
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/boston_housing_dataset.txt
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diff --git a/examples/src/main/resources/datasets/boston_housing_dataset.txt b/examples/src/main/resources/datasets/boston_housing_dataset.txt
new file mode 100644
index 0000000..654a340
--- /dev/null
+++ b/examples/src/main/resources/datasets/boston_housing_dataset.txt
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/cleared_machines.csv
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/cleared_machines.csv b/examples/src/main/resources/datasets/cleared_machines.csv
new file mode 100644
index 0000000..e22aac8
--- /dev/null
+++ b/examples/src/main/resources/datasets/cleared_machines.csv
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/cleared_machines.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/cleared_machines.txt b/examples/src/main/resources/datasets/cleared_machines.txt
deleted file mode 100644
index cf8b6b0..0000000
--- a/examples/src/main/resources/datasets/cleared_machines.txt
+++ /dev/null
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/glass_identification.csv
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/glass_identification.csv b/examples/src/main/resources/datasets/glass_identification.csv
new file mode 100644
index 0000000..ae1d6d1
--- /dev/null
+++ b/examples/src/main/resources/datasets/glass_identification.csv
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/mortalitydata.csv
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/mortalitydata.csv b/examples/src/main/resources/datasets/mortalitydata.csv
new file mode 100644
index 0000000..e4f3e41
--- /dev/null
+++ b/examples/src/main/resources/datasets/mortalitydata.csv
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/two_classed_iris.csv
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/two_classed_iris.csv b/examples/src/main/resources/datasets/two_classed_iris.csv
new file mode 100644
index 0000000..872c699
--- /dev/null
+++ b/examples/src/main/resources/datasets/two_classed_iris.csv
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http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/resources/datasets/wine.txt
----------------------------------------------------------------------
diff --git a/examples/src/main/resources/datasets/wine.txt b/examples/src/main/resources/datasets/wine.txt
new file mode 100644
index 0000000..a0b3962
--- /dev/null
+++ b/examples/src/main/resources/datasets/wine.txt
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+2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
+2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
+2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
+2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
+2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
+2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
+2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
+2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
+2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
+2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
+2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
+3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
+3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
+3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
+3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
+3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
+3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
+3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
+3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
+3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
+3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
+3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
+3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
+3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
+3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
+3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
+3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
+3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
+3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
+3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
+3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
+3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
+3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
+3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
+3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
+3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
+3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
+3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
+3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
+3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
+3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
+3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
+3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
+3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
+3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
+3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
+3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
+3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
+3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
+3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
+3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
+3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
+3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
+3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
+3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
+3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
+3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
+3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
+3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java b/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
index 0761ff5..f854177 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/knn/regression/KNNRegressionModel.java
@@ -77,6 +77,8 @@ public class KNNRegressionModel extends KNNClassificationModel {
sum += neighbor.label() * distance;
div += distance;
}
+ if (div == 0.0) // when all neighbours are equal to the given point
+ return simpleRegression(neighbors);
return sum / div;
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/AbstractVector.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/AbstractVector.java b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/AbstractVector.java
index 343ebf1..8e544bd 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/AbstractVector.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/AbstractVector.java
@@ -841,6 +841,15 @@ public abstract class AbstractVector implements Vector {
return like(size()).assign(this);
}
+ /** {@inheritDoc} */
+ @Override public Vector copyOfRange(int from, int to) {
+ Vector copiedVector = like(to - from);
+ for (int i = from, j = 0; i < to; i++, j++)
+ copiedVector.set(j, this.get(i));
+
+ return copiedVector;
+ }
+
/**
* @return Result of dot with self.
*/
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/Vector.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/Vector.java b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/Vector.java
index f544405..c505d2f 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/Vector.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/Vector.java
@@ -127,6 +127,15 @@ public interface Vector extends MetaAttributes, Externalizable, StorageOpsMetric
public Vector sort();
/**
+ * Copies the specified range of the vector into a new vector.
+ * @param from the initial index of the range to be copied, inclusive
+ * @param to the final index of the range to be copied, exclusive.
+ * (This index may lie outside the array.)
+ * @return A new vector containing the specified range from the original vector
+ */
+ public Vector copyOfRange(int from, int to);
+
+ /**
* Gets element at the given index.
*
* NOTE: implementation can choose to reuse {@link Element} instance so you need to copy it
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/impl/DelegatingVector.java
----------------------------------------------------------------------
diff --git a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/impl/DelegatingVector.java b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/impl/DelegatingVector.java
index 3a44373..ff2dc7a 100644
--- a/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/impl/DelegatingVector.java
+++ b/modules/ml/src/main/java/org/apache/ignite/ml/math/primitives/vector/impl/DelegatingVector.java
@@ -151,6 +151,11 @@ public class DelegatingVector implements Vector {
}
/** {@inheritDoc} */
+ @Override public Vector copyOfRange(int from, int to) {
+ return dlg.copyOfRange(from, to);
+ }
+
+ /** {@inheritDoc} */
@Override public Spliterator<Double> allSpliterator() {
return dlg.allSpliterator();
}
[4/4] ignite git commit: IGNITE-9910: [ML] Move the static
copy-pasted datasets from examples to special Util class
Posted by ch...@apache.org.
IGNITE-9910: [ML] Move the static copy-pasted datasets from examples
to special Util class
this closes #5028
Project: http://git-wip-us.apache.org/repos/asf/ignite/repo
Commit: http://git-wip-us.apache.org/repos/asf/ignite/commit/370cd3e1
Tree: http://git-wip-us.apache.org/repos/asf/ignite/tree/370cd3e1
Diff: http://git-wip-us.apache.org/repos/asf/ignite/diff/370cd3e1
Branch: refs/heads/master
Commit: 370cd3e1d60237e4a238c1c789cddbd1164e57e6
Parents: c7449f6
Author: zaleslaw <za...@gmail.com>
Authored: Fri Oct 26 16:06:42 2018 +0300
Committer: Yury Babak <yb...@gridgain.com>
Committed: Fri Oct 26 16:06:42 2018 +0300
----------------------------------------------------------------------
.../clustering/KMeansClusterizationExample.java | 150 +----
.../ml/knn/ANNClassificationExample.java | 2 +-
.../ml/knn/KNNClassificationExample.java | 183 +------
.../examples/ml/knn/KNNRegressionExample.java | 221 +-------
.../examples/ml/nn/MLPTrainerExample.java | 2 +-
.../LinearRegressionLSQRTrainerExample.java | 86 +--
...ssionLSQRTrainerWithMinMaxScalerExample.java | 85 +--
.../LinearRegressionSGDTrainerExample.java | 86 +--
.../LogisticRegressionSGDTrainerExample.java | 132 +----
...gressionMultiClassClassificationExample.java | 158 +-----
.../ml/selection/cv/CrossValidationExample.java | 2 +-
.../split/TrainTestDatasetSplitterExample.java | 90 +--
.../binary/SVMBinaryClassificationExample.java | 132 +----
.../SVMMultiClassClassificationExample.java | 189 ++-----
...ecisionTreeClassificationTrainerExample.java | 2 +-
.../DecisionTreeRegressionTrainerExample.java | 2 +-
.../GDBOnTreesClassificationTrainerExample.java | 2 +-
.../RandomForestClassificationExample.java | 216 +-------
.../RandomForestRegressionExample.java | 543 +------------------
.../examples/ml/util/MLSandboxDatasets.java | 87 +++
.../ignite/examples/ml/util/SandboxMLCache.java | 144 +++++
.../ignite/examples/ml/util/TestCache.java | 77 ---
.../datasets/boston_housing_dataset.txt | 505 +++++++++++++++++
.../resources/datasets/cleared_machines.csv | 209 +++++++
.../resources/datasets/cleared_machines.txt | 209 -------
.../resources/datasets/glass_identification.csv | 116 ++++
.../main/resources/datasets/mortalitydata.csv | 53 ++
.../resources/datasets/two_classed_iris.csv | 100 ++++
examples/src/main/resources/datasets/wine.txt | 178 ++++++
.../ml/knn/regression/KNNRegressionModel.java | 2 +
.../math/primitives/vector/AbstractVector.java | 9 +
.../ml/math/primitives/vector/Vector.java | 9 +
.../vector/impl/DelegatingVector.java | 5 +
33 files changed, 1643 insertions(+), 2343 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
index 567775b..3c8eeaa 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/clustering/KMeansClusterizationExample.java
@@ -17,19 +17,19 @@
package org.apache.ignite.examples.ml.clustering;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.clustering.kmeans.KMeansModel;
import org.apache.ignite.ml.clustering.kmeans.KMeansTrainer;
import org.apache.ignite.ml.math.Tracer;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
/**
* Run KMeans clustering algorithm ({@link KMeansTrainer}) over distributed dataset.
@@ -47,14 +47,15 @@ import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
*/
public class KMeansClusterizationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> KMeans clustering algorithm over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
KMeansTrainer trainer = new KMeansTrainer()
.withSeed(7867L);
@@ -62,8 +63,8 @@ public class KMeansClusterizationExample {
KMeansModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> KMeans centroids");
@@ -71,139 +72,24 @@ public class KMeansClusterizationExample {
Tracer.showAscii(mdl.getCenters()[1]);
System.out.println(">>>");
- System.out.println(">>> -----------------------------------");
- System.out.println(">>> | Predicted cluster\t| Real Label\t|");
- System.out.println(">>> -----------------------------------");
+ System.out.println(">>> --------------------------------------------");
+ System.out.println(">>> | Predicted cluster\t| Erased class label\t|");
+ System.out.println(">>> --------------------------------------------");
- int amountOfErrors = 0;
- int totalAmount = 0;
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- 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 DenseVector(inputs));
-
- totalAmount++;
- if (groundTruth != prediction)
- amountOfErrors++;
+ double prediction = mdl.apply(inputs);
System.out.printf(">>> | %.4f\t\t\t| %.4f\t\t|\n", prediction, groundTruth);
}
System.out.println(">>> ---------------------------------");
-
- System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
- System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
-
System.out.println(">>> KMeans clustering algorithm over cached dataset usage example completed.");
}
}
}
-
- /** The Iris dataset. */
- private static final double[][] data = {
- {0, 5.1, 3.5, 1.4, 0.2},
- {0, 4.9, 3, 1.4, 0.2},
- {0, 4.7, 3.2, 1.3, 0.2},
- {0, 4.6, 3.1, 1.5, 0.2},
- {0, 5, 3.6, 1.4, 0.2},
- {0, 5.4, 3.9, 1.7, 0.4},
- {0, 4.6, 3.4, 1.4, 0.3},
- {0, 5, 3.4, 1.5, 0.2},
- {0, 4.4, 2.9, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5.4, 3.7, 1.5, 0.2},
- {0, 4.8, 3.4, 1.6, 0.2},
- {0, 4.8, 3, 1.4, 0.1},
- {0, 4.3, 3, 1.1, 0.1},
- {0, 5.8, 4, 1.2, 0.2},
- {0, 5.7, 4.4, 1.5, 0.4},
- {0, 5.4, 3.9, 1.3, 0.4},
- {0, 5.1, 3.5, 1.4, 0.3},
- {0, 5.7, 3.8, 1.7, 0.3},
- {0, 5.1, 3.8, 1.5, 0.3},
- {0, 5.4, 3.4, 1.7, 0.2},
- {0, 5.1, 3.7, 1.5, 0.4},
- {0, 4.6, 3.6, 1, 0.2},
- {0, 5.1, 3.3, 1.7, 0.5},
- {0, 4.8, 3.4, 1.9, 0.2},
- {0, 5, 3, 1.6, 0.2},
- {0, 5, 3.4, 1.6, 0.4},
- {0, 5.2, 3.5, 1.5, 0.2},
- {0, 5.2, 3.4, 1.4, 0.2},
- {0, 4.7, 3.2, 1.6, 0.2},
- {0, 4.8, 3.1, 1.6, 0.2},
- {0, 5.4, 3.4, 1.5, 0.4},
- {0, 5.2, 4.1, 1.5, 0.1},
- {0, 5.5, 4.2, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5, 3.2, 1.2, 0.2},
- {0, 5.5, 3.5, 1.3, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 4.4, 3, 1.3, 0.2},
- {0, 5.1, 3.4, 1.5, 0.2},
- {0, 5, 3.5, 1.3, 0.3},
- {0, 4.5, 2.3, 1.3, 0.3},
- {0, 4.4, 3.2, 1.3, 0.2},
- {0, 5, 3.5, 1.6, 0.6},
- {0, 5.1, 3.8, 1.9, 0.4},
- {0, 4.8, 3, 1.4, 0.3},
- {0, 5.1, 3.8, 1.6, 0.2},
- {0, 4.6, 3.2, 1.4, 0.2},
- {0, 5.3, 3.7, 1.5, 0.2},
- {0, 5, 3.3, 1.4, 0.2},
- {1, 7, 3.2, 4.7, 1.4},
- {1, 6.4, 3.2, 4.5, 1.5},
- {1, 6.9, 3.1, 4.9, 1.5},
- {1, 5.5, 2.3, 4, 1.3},
- {1, 6.5, 2.8, 4.6, 1.5},
- {1, 5.7, 2.8, 4.5, 1.3},
- {1, 6.3, 3.3, 4.7, 1.6},
- {1, 4.9, 2.4, 3.3, 1},
- {1, 6.6, 2.9, 4.6, 1.3},
- {1, 5.2, 2.7, 3.9, 1.4},
- {1, 5, 2, 3.5, 1},
- {1, 5.9, 3, 4.2, 1.5},
- {1, 6, 2.2, 4, 1},
- {1, 6.1, 2.9, 4.7, 1.4},
- {1, 5.6, 2.9, 3.6, 1.3},
- {1, 6.7, 3.1, 4.4, 1.4},
- {1, 5.6, 3, 4.5, 1.5},
- {1, 5.8, 2.7, 4.1, 1},
- {1, 6.2, 2.2, 4.5, 1.5},
- {1, 5.6, 2.5, 3.9, 1.1},
- {1, 5.9, 3.2, 4.8, 1.8},
- {1, 6.1, 2.8, 4, 1.3},
- {1, 6.3, 2.5, 4.9, 1.5},
- {1, 6.1, 2.8, 4.7, 1.2},
- {1, 6.4, 2.9, 4.3, 1.3},
- {1, 6.6, 3, 4.4, 1.4},
- {1, 6.8, 2.8, 4.8, 1.4},
- {1, 6.7, 3, 5, 1.7},
- {1, 6, 2.9, 4.5, 1.5},
- {1, 5.7, 2.6, 3.5, 1},
- {1, 5.5, 2.4, 3.8, 1.1},
- {1, 5.5, 2.4, 3.7, 1},
- {1, 5.8, 2.7, 3.9, 1.2},
- {1, 6, 2.7, 5.1, 1.6},
- {1, 5.4, 3, 4.5, 1.5},
- {1, 6, 3.4, 4.5, 1.6},
- {1, 6.7, 3.1, 4.7, 1.5},
- {1, 6.3, 2.3, 4.4, 1.3},
- {1, 5.6, 3, 4.1, 1.3},
- {1, 5.5, 2.5, 4, 1.3},
- {1, 5.5, 2.6, 4.4, 1.2},
- {1, 6.1, 3, 4.6, 1.4},
- {1, 5.8, 2.6, 4, 1.2},
- {1, 5, 2.3, 3.3, 1},
- {1, 5.6, 2.7, 4.2, 1.3},
- {1, 5.7, 3, 4.2, 1.2},
- {1, 5.7, 2.9, 4.2, 1.3},
- {1, 6.2, 2.9, 4.3, 1.3},
- {1, 5.1, 2.5, 3, 1.1},
- {1, 5.7, 2.8, 4.1, 1.3},
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
index c9490fc..419eccb 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/ANNClassificationExample.java
@@ -51,7 +51,7 @@ import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
*/
public class ANNClassificationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) {
System.out.println();
System.out.println(">>> ANN multi-class classification algorithm over cached dataset usage example started.");
// Start ignite grid.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
index 5cbb2ad..31ecdac 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNClassificationExample.java
@@ -17,20 +17,20 @@
package org.apache.ignite.examples.ml.knn;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.knn.NNClassificationModel;
import org.apache.ignite.ml.knn.classification.KNNClassificationTrainer;
import org.apache.ignite.ml.knn.classification.NNStrategy;
import org.apache.ignite.ml.math.distances.EuclideanDistance;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
/**
* Run kNN multi-class classification trainer ({@link KNNClassificationTrainer}) over distributed dataset.
@@ -48,22 +48,23 @@ import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
*/
public class KNNClassificationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> kNN multi-class classification algorithm over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.IRIS);
KNNClassificationTrainer trainer = new KNNClassificationTrainer();
NNClassificationModel knnMdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
).withK(3)
.withDistanceMeasure(new EuclideanDistance())
.withStrategy(NNStrategy.WEIGHTED);
@@ -75,13 +76,13 @@ public class KNNClassificationExample {
int amountOfErrors = 0;
int totalAmount = 0;
- 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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = knnMdl.apply(new DenseVector(inputs));
+ double prediction = knnMdl.apply(inputs);
totalAmount++;
if (groundTruth != prediction)
@@ -99,158 +100,4 @@ public class KNNClassificationExample {
}
}
}
-
- /** The Iris dataset. */
- private static final double[][] data = {
- {1, 5.1, 3.5, 1.4, 0.2},
- {1, 4.9, 3, 1.4, 0.2},
- {1, 4.7, 3.2, 1.3, 0.2},
- {1, 4.6, 3.1, 1.5, 0.2},
- {1, 5, 3.6, 1.4, 0.2},
- {1, 5.4, 3.9, 1.7, 0.4},
- {1, 4.6, 3.4, 1.4, 0.3},
- {1, 5, 3.4, 1.5, 0.2},
- {1, 4.4, 2.9, 1.4, 0.2},
- {1, 4.9, 3.1, 1.5, 0.1},
- {1, 5.4, 3.7, 1.5, 0.2},
- {1, 4.8, 3.4, 1.6, 0.2},
- {1, 4.8, 3, 1.4, 0.1},
- {1, 4.3, 3, 1.1, 0.1},
- {1, 5.8, 4, 1.2, 0.2},
- {1, 5.7, 4.4, 1.5, 0.4},
- {1, 5.4, 3.9, 1.3, 0.4},
- {1, 5.1, 3.5, 1.4, 0.3},
- {1, 5.7, 3.8, 1.7, 0.3},
- {1, 5.1, 3.8, 1.5, 0.3},
- {1, 5.4, 3.4, 1.7, 0.2},
- {1, 5.1, 3.7, 1.5, 0.4},
- {1, 4.6, 3.6, 1, 0.2},
- {1, 5.1, 3.3, 1.7, 0.5},
- {1, 4.8, 3.4, 1.9, 0.2},
- {1, 5, 3, 1.6, 0.2},
- {1, 5, 3.4, 1.6, 0.4},
- {1, 5.2, 3.5, 1.5, 0.2},
- {1, 5.2, 3.4, 1.4, 0.2},
- {1, 4.7, 3.2, 1.6, 0.2},
- {1, 4.8, 3.1, 1.6, 0.2},
- {1, 5.4, 3.4, 1.5, 0.4},
- {1, 5.2, 4.1, 1.5, 0.1},
- {1, 5.5, 4.2, 1.4, 0.2},
- {1, 4.9, 3.1, 1.5, 0.1},
- {1, 5, 3.2, 1.2, 0.2},
- {1, 5.5, 3.5, 1.3, 0.2},
- {1, 4.9, 3.1, 1.5, 0.1},
- {1, 4.4, 3, 1.3, 0.2},
- {1, 5.1, 3.4, 1.5, 0.2},
- {1, 5, 3.5, 1.3, 0.3},
- {1, 4.5, 2.3, 1.3, 0.3},
- {1, 4.4, 3.2, 1.3, 0.2},
- {1, 5, 3.5, 1.6, 0.6},
- {1, 5.1, 3.8, 1.9, 0.4},
- {1, 4.8, 3, 1.4, 0.3},
- {1, 5.1, 3.8, 1.6, 0.2},
- {1, 4.6, 3.2, 1.4, 0.2},
- {1, 5.3, 3.7, 1.5, 0.2},
- {1, 5, 3.3, 1.4, 0.2},
- {2, 7, 3.2, 4.7, 1.4},
- {2, 6.4, 3.2, 4.5, 1.5},
- {2, 6.9, 3.1, 4.9, 1.5},
- {2, 5.5, 2.3, 4, 1.3},
- {2, 6.5, 2.8, 4.6, 1.5},
- {2, 5.7, 2.8, 4.5, 1.3},
- {2, 6.3, 3.3, 4.7, 1.6},
- {2, 4.9, 2.4, 3.3, 1},
- {2, 6.6, 2.9, 4.6, 1.3},
- {2, 5.2, 2.7, 3.9, 1.4},
- {2, 5, 2, 3.5, 1},
- {2, 5.9, 3, 4.2, 1.5},
- {2, 6, 2.2, 4, 1},
- {2, 6.1, 2.9, 4.7, 1.4},
- {2, 5.6, 2.9, 3.6, 1.3},
- {2, 6.7, 3.1, 4.4, 1.4},
- {2, 5.6, 3, 4.5, 1.5},
- {2, 5.8, 2.7, 4.1, 1},
- {2, 6.2, 2.2, 4.5, 1.5},
- {2, 5.6, 2.5, 3.9, 1.1},
- {2, 5.9, 3.2, 4.8, 1.8},
- {2, 6.1, 2.8, 4, 1.3},
- {2, 6.3, 2.5, 4.9, 1.5},
- {2, 6.1, 2.8, 4.7, 1.2},
- {2, 6.4, 2.9, 4.3, 1.3},
- {2, 6.6, 3, 4.4, 1.4},
- {2, 6.8, 2.8, 4.8, 1.4},
- {2, 6.7, 3, 5, 1.7},
- {2, 6, 2.9, 4.5, 1.5},
- {2, 5.7, 2.6, 3.5, 1},
- {2, 5.5, 2.4, 3.8, 1.1},
- {2, 5.5, 2.4, 3.7, 1},
- {2, 5.8, 2.7, 3.9, 1.2},
- {2, 6, 2.7, 5.1, 1.6},
- {2, 5.4, 3, 4.5, 1.5},
- {2, 6, 3.4, 4.5, 1.6},
- {2, 6.7, 3.1, 4.7, 1.5},
- {2, 6.3, 2.3, 4.4, 1.3},
- {2, 5.6, 3, 4.1, 1.3},
- {2, 5.5, 2.5, 4, 1.3},
- {2, 5.5, 2.6, 4.4, 1.2},
- {2, 6.1, 3, 4.6, 1.4},
- {2, 5.8, 2.6, 4, 1.2},
- {2, 5, 2.3, 3.3, 1},
- {2, 5.6, 2.7, 4.2, 1.3},
- {2, 5.7, 3, 4.2, 1.2},
- {2, 5.7, 2.9, 4.2, 1.3},
- {2, 6.2, 2.9, 4.3, 1.3},
- {2, 5.1, 2.5, 3, 1.1},
- {2, 5.7, 2.8, 4.1, 1.3},
- {3, 6.3, 3.3, 6, 2.5},
- {3, 5.8, 2.7, 5.1, 1.9},
- {3, 7.1, 3, 5.9, 2.1},
- {3, 6.3, 2.9, 5.6, 1.8},
- {3, 6.5, 3, 5.8, 2.2},
- {3, 7.6, 3, 6.6, 2.1},
- {3, 4.9, 2.5, 4.5, 1.7},
- {3, 7.3, 2.9, 6.3, 1.8},
- {3, 6.7, 2.5, 5.8, 1.8},
- {3, 7.2, 3.6, 6.1, 2.5},
- {3, 6.5, 3.2, 5.1, 2},
- {3, 6.4, 2.7, 5.3, 1.9},
- {3, 6.8, 3, 5.5, 2.1},
- {3, 5.7, 2.5, 5, 2},
- {3, 5.8, 2.8, 5.1, 2.4},
- {3, 6.4, 3.2, 5.3, 2.3},
- {3, 6.5, 3, 5.5, 1.8},
- {3, 7.7, 3.8, 6.7, 2.2},
- {3, 7.7, 2.6, 6.9, 2.3},
- {3, 6, 2.2, 5, 1.5},
- {3, 6.9, 3.2, 5.7, 2.3},
- {3, 5.6, 2.8, 4.9, 2},
- {3, 7.7, 2.8, 6.7, 2},
- {3, 6.3, 2.7, 4.9, 1.8},
- {3, 6.7, 3.3, 5.7, 2.1},
- {3, 7.2, 3.2, 6, 1.8},
- {3, 6.2, 2.8, 4.8, 1.8},
- {3, 6.1, 3, 4.9, 1.8},
- {3, 6.4, 2.8, 5.6, 2.1},
- {3, 7.2, 3, 5.8, 1.6},
- {3, 7.4, 2.8, 6.1, 1.9},
- {3, 7.9, 3.8, 6.4, 2},
- {3, 6.4, 2.8, 5.6, 2.2},
- {3, 6.3, 2.8, 5.1, 1.5},
- {3, 6.1, 2.6, 5.6, 1.4},
- {3, 7.7, 3, 6.1, 2.3},
- {3, 6.3, 3.4, 5.6, 2.4},
- {3, 6.4, 3.1, 5.5, 1.8},
- {3, 6, 3, 4.8, 1.8},
- {3, 6.9, 3.1, 5.4, 2.1},
- {3, 6.7, 3.1, 5.6, 2.4},
- {3, 6.9, 3.1, 5.1, 2.3},
- {3, 5.8, 2.7, 5.1, 1.9},
- {3, 6.8, 3.2, 5.9, 2.3},
- {3, 6.7, 3.3, 5.7, 2.5},
- {3, 6.7, 3, 5.2, 2.3},
- {3, 6.3, 2.5, 5, 1.9},
- {3, 6.5, 3, 5.2, 2},
- {3, 6.2, 3.4, 5.4, 2.3},
- {3, 5.9, 3, 5.1, 1.8}
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
index 3969f0c..9917e80 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/knn/KNNRegressionExample.java
@@ -17,20 +17,20 @@
package org.apache.ignite.examples.ml.knn;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.knn.classification.NNStrategy;
import org.apache.ignite.ml.knn.regression.KNNRegressionModel;
import org.apache.ignite.ml.knn.regression.KNNRegressionTrainer;
import org.apache.ignite.ml.math.distances.ManhattanDistance;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
/**
* Run kNN regression trainer ({@link KNNRegressionTrainer}) over distributed dataset.
@@ -49,22 +49,23 @@ import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
*/
public class KNNRegressionExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> kNN regression over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.CLEARED_MACHINES);
KNNRegressionTrainer trainer = new KNNRegressionTrainer();
KNNRegressionModel knnMdl = (KNNRegressionModel) trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
).withK(5)
.withDistanceMeasure(new ManhattanDistance())
.withStrategy(NNStrategy.WEIGHTED);
@@ -79,13 +80,13 @@ public class KNNRegressionExample {
// Calculate mean absolute error (MAE)
double mae = 0.0;
- 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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = knnMdl.apply(new DenseVector(inputs));
+ double prediction = knnMdl.apply(inputs);
mse += Math.pow(prediction - groundTruth, 2.0);
mae += Math.abs(prediction - groundTruth);
@@ -107,196 +108,4 @@ public class KNNRegressionExample {
}
}
}
-
- /** The Iris dataset. */
- private static final double[][] data = {
- {199, 125, 256, 6000, 256, 16, 128},
- {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},
- {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},
- {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},
- {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},
- {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},
- {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},
- {42, 180, 2000, 8000, 8, 1, 6},
- {76, 185, 2000, 16000, 16, 1, 6},
- {76, 180, 2000, 16000, 16, 1, 6},
- {26, 225, 1000, 4000, 2, 3, 6},
- {59, 25, 2000, 12000, 8, 1, 4},
- {65, 25, 2000, 12000, 16, 3, 5},
- {101, 17, 4000, 16000, 8, 6, 12},
- {116, 17, 4000, 16000, 32, 6, 12},
- {18, 1500, 768, 1000, 0, 0, 0},
- {20, 1500, 768, 2000, 0, 0, 0},
- {20, 800, 768, 2000, 0, 0, 0},
- {30, 50, 2000, 4000, 0, 3, 6},
- {44, 50, 2000, 8000, 8, 3, 6},
- {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},
- {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},
- {41, 92, 2000, 8000, 4, 1, 6},
- {86, 75, 4000, 16000, 16, 1, 6},
- {95, 60, 4000, 16000, 32, 1, 6},
- {107, 60, 2000, 16000, 64, 5, 8},
- {117, 60, 4000, 16000, 64, 5, 8},
- {119, 50, 4000, 16000, 64, 5, 10},
- {120, 72, 4000, 16000, 64, 8, 16},
- {48, 72, 2000, 8000, 16, 6, 8},
- {126, 40, 8000, 16000, 32, 8, 16},
- {266, 40, 8000, 32000, 64, 8, 24},
- {270, 35, 8000, 32000, 64, 8, 24},
- {426, 38, 16000, 32000, 128, 16, 32},
- {151, 48, 4000, 24000, 32, 8, 24},
- {267, 38, 8000, 32000, 64, 8, 24},
- {603, 30, 16000, 32000, 256, 16, 24},
- {19, 112, 1000, 1000, 0, 1, 4},
- {21, 84, 1000, 2000, 0, 1, 6},
- {26, 56, 1000, 4000, 0, 1, 6},
- {35, 56, 2000, 6000, 0, 1, 8},
- {41, 56, 2000, 8000, 0, 1, 8},
- {47, 56, 4000, 8000, 0, 1, 8},
- {62, 56, 4000, 12000, 0, 1, 8},
- {78, 56, 4000, 16000, 0, 1, 8},
- {80, 38, 4000, 8000, 32, 16, 32},
- {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},
- {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/370cd3e1/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 6d5745e..dc67aa1 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
@@ -61,7 +61,7 @@ public class MLPTrainerExample {
*
* @param args Command line arguments, none required.
*/
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) {
// IMPL NOTE based on MLPGroupTrainerTest#testXOR
System.out.println(">>> Distributed multilayer perceptron example started.");
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/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
index 862a37f..aeb7a0d 100644
--- 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
@@ -17,16 +17,16 @@
package org.apache.ignite.examples.ml.regression.linear;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
@@ -44,72 +44,16 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
* You can change the test data used in this example and re-run it to explore this algorithm further.</p>
*/
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 {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Linear regression model over cache based dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
System.out.println(">>> Create new linear regression trainer object.");
LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer();
@@ -118,8 +62,8 @@ public class LinearRegressionLSQRTrainerExample {
LinearRegressionModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> Linear regression model: " + mdl);
@@ -128,13 +72,13 @@ public class LinearRegressionLSQRTrainerExample {
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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
index 5692cb3..873cefb 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/linear/LinearRegressionLSQRTrainerWithMinMaxScalerExample.java
@@ -17,17 +17,17 @@
package org.apache.ignite.examples.ml.regression.linear;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.math.functions.IgniteBiFunction;
import org.apache.ignite.ml.math.primitives.vector.Vector;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor;
import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer;
import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
@@ -50,84 +50,25 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
* You can change the test data used in this example and re-run it to explore this algorithm further.</p>
*/
public class LinearRegressionLSQRTrainerWithMinMaxScalerExample {
- /** */
- 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 {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
- System.out.println(">>> Linear regression model with minmaxscaling preprocessor over cached dataset usage example started.");
+ System.out.println(">>> Linear regression model with Min Max Scaling preprocessor over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
- System.out.println(">>> Create new minmaxscaling trainer object.");
- MinMaxScalerTrainer<Integer, Vector> normalizationTrainer = new MinMaxScalerTrainer<>();
+ System.out.println(">>> Create new MinMaxScaler trainer object.");
+ MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>();
- System.out.println(">>> Perform the training to get the minmaxscaling preprocessor.");
- IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit(
+ System.out.println(">>> Perform the training to get the MinMaxScaler preprocessor.");
+ IgniteBiFunction<Integer, Vector, Vector> preprocessor = minMaxScalerTrainer.fit(
ignite,
dataCache,
- (k, v) -> {
- double[] arr = v.asArray();
- return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
- }
+ (k, v) -> v.copyOfRange(1, v.size())
);
System.out.println(">>> Create new linear regression trainer object.");
@@ -155,7 +96,7 @@ public class LinearRegressionLSQRTrainerWithMinMaxScalerExample {
}
System.out.println(">>> ---------------------------------");
- System.out.println(">>> Linear regression model with minmaxscaling preprocessor over cache based dataset usage example completed.");
+ System.out.println(">>> Linear regression model with MinMaxScaler preprocessor over cache based dataset usage example completed.");
}
}
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/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
index 1e9bd5a..1dad08b 100644
--- 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
@@ -17,16 +17,16 @@
package org.apache.ignite.examples.ml.regression.linear;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.nn.UpdatesStrategy;
import org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate;
import org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator;
@@ -49,72 +49,16 @@ import org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer;
* You can change the test data used in this example and re-run it to explore this algorithm further.</p>
*/
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 {
+ public static void main(String[] args) throws FileNotFoundException {
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.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
System.out.println(">>> Create new linear regression trainer object.");
LinearRegressionSGDTrainer<?> trainer = new LinearRegressionSGDTrainer<>(new UpdatesStrategy<>(
@@ -127,8 +71,8 @@ public class LinearRegressionSGDTrainerExample {
LinearRegressionModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> Linear regression model: " + mdl);
@@ -137,13 +81,13 @@ public class LinearRegressionSGDTrainerExample {
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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
index 15330d0..52ee330 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/binary/LogisticRegressionSGDTrainerExample.java
@@ -17,6 +17,7 @@
package org.apache.ignite.examples.ml.regression.logistic.binary;
+import java.io.FileNotFoundException;
import java.util.Arrays;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
@@ -24,9 +25,9 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.nn.UpdatesStrategy;
import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate;
import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator;
@@ -50,14 +51,15 @@ import org.apache.ignite.ml.regressions.logistic.binomial.LogisticRegressionSGDT
*/
public class LogisticRegressionSGDTrainerExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Logistic regression model over partitioned dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
System.out.println(">>> Create new logistic regression trainer object.");
LogisticRegressionSGDTrainer<?> trainer = new LogisticRegressionSGDTrainer<>()
@@ -75,8 +77,8 @@ public class LogisticRegressionSGDTrainerExample {
LogisticRegressionModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> Logistic regression model: " + mdl);
@@ -87,13 +89,13 @@ public class LogisticRegressionSGDTrainerExample {
// Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
int[][] confusionMtx = {{0, 0}, {0, 0}};
- 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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
totalAmount++;
if(groundTruth != prediction)
@@ -119,108 +121,4 @@ public class LogisticRegressionSGDTrainerExample {
System.out.println(">>> Logistic regression model over partitioned dataset usage example completed.");
}
}
-
- /** The 1st and 2nd classes from the Iris dataset. */
- private static final double[][] data = {
- {0, 5.1, 3.5, 1.4, 0.2},
- {0, 4.9, 3, 1.4, 0.2},
- {0, 4.7, 3.2, 1.3, 0.2},
- {0, 4.6, 3.1, 1.5, 0.2},
- {0, 5, 3.6, 1.4, 0.2},
- {0, 5.4, 3.9, 1.7, 0.4},
- {0, 4.6, 3.4, 1.4, 0.3},
- {0, 5, 3.4, 1.5, 0.2},
- {0, 4.4, 2.9, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5.4, 3.7, 1.5, 0.2},
- {0, 4.8, 3.4, 1.6, 0.2},
- {0, 4.8, 3, 1.4, 0.1},
- {0, 4.3, 3, 1.1, 0.1},
- {0, 5.8, 4, 1.2, 0.2},
- {0, 5.7, 4.4, 1.5, 0.4},
- {0, 5.4, 3.9, 1.3, 0.4},
- {0, 5.1, 3.5, 1.4, 0.3},
- {0, 5.7, 3.8, 1.7, 0.3},
- {0, 5.1, 3.8, 1.5, 0.3},
- {0, 5.4, 3.4, 1.7, 0.2},
- {0, 5.1, 3.7, 1.5, 0.4},
- {0, 4.6, 3.6, 1, 0.2},
- {0, 5.1, 3.3, 1.7, 0.5},
- {0, 4.8, 3.4, 1.9, 0.2},
- {0, 5, 3, 1.6, 0.2},
- {0, 5, 3.4, 1.6, 0.4},
- {0, 5.2, 3.5, 1.5, 0.2},
- {0, 5.2, 3.4, 1.4, 0.2},
- {0, 4.7, 3.2, 1.6, 0.2},
- {0, 4.8, 3.1, 1.6, 0.2},
- {0, 5.4, 3.4, 1.5, 0.4},
- {0, 5.2, 4.1, 1.5, 0.1},
- {0, 5.5, 4.2, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5, 3.2, 1.2, 0.2},
- {0, 5.5, 3.5, 1.3, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 4.4, 3, 1.3, 0.2},
- {0, 5.1, 3.4, 1.5, 0.2},
- {0, 5, 3.5, 1.3, 0.3},
- {0, 4.5, 2.3, 1.3, 0.3},
- {0, 4.4, 3.2, 1.3, 0.2},
- {0, 5, 3.5, 1.6, 0.6},
- {0, 5.1, 3.8, 1.9, 0.4},
- {0, 4.8, 3, 1.4, 0.3},
- {0, 5.1, 3.8, 1.6, 0.2},
- {0, 4.6, 3.2, 1.4, 0.2},
- {0, 5.3, 3.7, 1.5, 0.2},
- {0, 5, 3.3, 1.4, 0.2},
- {1, 7, 3.2, 4.7, 1.4},
- {1, 6.4, 3.2, 4.5, 1.5},
- {1, 6.9, 3.1, 4.9, 1.5},
- {1, 5.5, 2.3, 4, 1.3},
- {1, 6.5, 2.8, 4.6, 1.5},
- {1, 5.7, 2.8, 4.5, 1.3},
- {1, 6.3, 3.3, 4.7, 1.6},
- {1, 4.9, 2.4, 3.3, 1},
- {1, 6.6, 2.9, 4.6, 1.3},
- {1, 5.2, 2.7, 3.9, 1.4},
- {1, 5, 2, 3.5, 1},
- {1, 5.9, 3, 4.2, 1.5},
- {1, 6, 2.2, 4, 1},
- {1, 6.1, 2.9, 4.7, 1.4},
- {1, 5.6, 2.9, 3.6, 1.3},
- {1, 6.7, 3.1, 4.4, 1.4},
- {1, 5.6, 3, 4.5, 1.5},
- {1, 5.8, 2.7, 4.1, 1},
- {1, 6.2, 2.2, 4.5, 1.5},
- {1, 5.6, 2.5, 3.9, 1.1},
- {1, 5.9, 3.2, 4.8, 1.8},
- {1, 6.1, 2.8, 4, 1.3},
- {1, 6.3, 2.5, 4.9, 1.5},
- {1, 6.1, 2.8, 4.7, 1.2},
- {1, 6.4, 2.9, 4.3, 1.3},
- {1, 6.6, 3, 4.4, 1.4},
- {1, 6.8, 2.8, 4.8, 1.4},
- {1, 6.7, 3, 5, 1.7},
- {1, 6, 2.9, 4.5, 1.5},
- {1, 5.7, 2.6, 3.5, 1},
- {1, 5.5, 2.4, 3.8, 1.1},
- {1, 5.5, 2.4, 3.7, 1},
- {1, 5.8, 2.7, 3.9, 1.2},
- {1, 6, 2.7, 5.1, 1.6},
- {1, 5.4, 3, 4.5, 1.5},
- {1, 6, 3.4, 4.5, 1.6},
- {1, 6.7, 3.1, 4.7, 1.5},
- {1, 6.3, 2.3, 4.4, 1.3},
- {1, 5.6, 3, 4.1, 1.3},
- {1, 5.5, 2.5, 4, 1.3},
- {1, 5.5, 2.6, 4.4, 1.2},
- {1, 6.1, 3, 4.6, 1.4},
- {1, 5.8, 2.6, 4, 1.2},
- {1, 5, 2.3, 3.3, 1},
- {1, 5.6, 2.7, 4.2, 1.3},
- {1, 5.7, 3, 4.2, 1.2},
- {1, 5.7, 2.9, 4.2, 1.3},
- {1, 6.2, 2.9, 4.3, 1.3},
- {1, 5.1, 2.5, 3, 1.1},
- {1, 5.7, 2.8, 4.1, 1.3},
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java
index ff2761a..962fdac 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/regression/logistic/multiclass/LogRegressionMultiClassClassificationExample.java
@@ -17,6 +17,7 @@
package org.apache.ignite.examples.ml.regression.logistic.multiclass;
+import java.io.FileNotFoundException;
import java.util.Arrays;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
@@ -24,11 +25,10 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.math.functions.IgniteBiFunction;
import org.apache.ignite.ml.math.primitives.vector.Vector;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
import org.apache.ignite.ml.nn.UpdatesStrategy;
import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate;
import org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator;
@@ -54,14 +54,15 @@ import org.apache.ignite.ml.regressions.logistic.multiclass.LogRegressionMultiCl
*/
public class LogRegressionMultiClassClassificationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Logistic Regression Multi-class classification model over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.GLASS_IDENTIFICATION);
LogRegressionMultiClassTrainer<?> trainer = new LogRegressionMultiClassTrainer<>()
.withUpdatesStgy(new UpdatesStrategy<>(
@@ -77,10 +78,7 @@ public class LogRegressionMultiClassClassificationExample {
LogRegressionMultiClassModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> {
- double[] arr = v.asArray();
- return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
- },
+ (k, v) -> v.copyOfRange(1, v.size()),
(k, v) -> v.get(0)
);
@@ -92,10 +90,7 @@ public class LogRegressionMultiClassClassificationExample {
IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit(
ignite,
dataCache,
- (k, v) -> {
- double[] arr = v.asArray();
- return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
- }
+ (k, v) -> v.copyOfRange(1, v.size())
);
LogRegressionMultiClassModel mdlWithNormalization = trainer.fit(
@@ -105,7 +100,7 @@ public class LogRegressionMultiClassClassificationExample {
(k, v) -> v.get(0)
);
- System.out.println(">>> Logistic Regression Multi-class model with minmaxscaling");
+ System.out.println(">>> Logistic Regression Multi-class model with normalization");
System.out.println(mdlWithNormalization.toString());
System.out.println(">>> ----------------------------------------------------------------");
@@ -122,12 +117,12 @@ public class LogRegressionMultiClassClassificationExample {
try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
for (Cache.Entry<Integer, Vector> observation : observations) {
- double[] val = observation.getValue().asArray();
- double[] inputs = Arrays.copyOfRange(val, 1, val.length);
- double groundTruth = val[0];
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
- double predictionWithNormalization = mdlWithNormalization.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
+ double predictionWithNormalization = mdlWithNormalization.apply(inputs);
totalAmount++;
@@ -140,7 +135,7 @@ public class LogRegressionMultiClassClassificationExample {
confusionMtx[idx1][idx2]++;
- // Collect data for model with minmaxscaling
+ // Collect data for model with normalization
if(groundTruth != predictionWithNormalization)
amountOfErrorsWithNormalization++;
@@ -166,127 +161,4 @@ public class LogRegressionMultiClassClassificationExample {
}
}
}
-
- /** The preprocessed Glass dataset from the Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Glass+Identification
- * There are 3 classes with labels: 1 {building_windows_float_processed}, 3 {vehicle_windows_float_processed}, 7 {headlamps}.
- * Feature names: 'Na-Sodium', 'Mg-Magnesium', 'Al-Aluminum', 'Ba-Barium', 'Fe-Iron'.
- */
- private static final double[][] data = {
- {1, 1.52101, 4.49, 1.10, 0.00, 0.00},
- {1, 1.51761, 3.60, 1.36, 0.00, 0.00},
- {1, 1.51618, 3.55, 1.54, 0.00, 0.00},
- {1, 1.51766, 3.69, 1.29, 0.00, 0.00},
- {1, 1.51742, 3.62, 1.24, 0.00, 0.00},
- {1, 1.51596, 3.61, 1.62, 0.00, 0.26},
- {1, 1.51743, 3.60, 1.14, 0.00, 0.00},
- {1, 1.51756, 3.61, 1.05, 0.00, 0.00},
- {1, 1.51918, 3.58, 1.37, 0.00, 0.00},
- {1, 1.51755, 3.60, 1.36, 0.00, 0.11},
- {1, 1.51571, 3.46, 1.56, 0.00, 0.24},
- {1, 1.51763, 3.66, 1.27, 0.00, 0.00},
- {1, 1.51589, 3.43, 1.40, 0.00, 0.24},
- {1, 1.51748, 3.56, 1.27, 0.00, 0.17},
- {1, 1.51763, 3.59, 1.31, 0.00, 0.00},
- {1, 1.51761, 3.54, 1.23, 0.00, 0.00},
- {1, 1.51784, 3.67, 1.16, 0.00, 0.00},
- {1, 1.52196, 3.85, 0.89, 0.00, 0.00},
- {1, 1.51911, 3.73, 1.18, 0.00, 0.00},
- {1, 1.51735, 3.54, 1.69, 0.00, 0.07},
- {1, 1.51750, 3.55, 1.49, 0.00, 0.19},
- {1, 1.51966, 3.75, 0.29, 0.00, 0.00},
- {1, 1.51736, 3.62, 1.29, 0.00, 0.00},
- {1, 1.51751, 3.57, 1.35, 0.00, 0.00},
- {1, 1.51720, 3.50, 1.15, 0.00, 0.00},
- {1, 1.51764, 3.54, 1.21, 0.00, 0.00},
- {1, 1.51793, 3.48, 1.41, 0.00, 0.00},
- {1, 1.51721, 3.48, 1.33, 0.00, 0.00},
- {1, 1.51768, 3.52, 1.43, 0.00, 0.00},
- {1, 1.51784, 3.49, 1.28, 0.00, 0.00},
- {1, 1.51768, 3.56, 1.30, 0.00, 0.14},
- {1, 1.51747, 3.50, 1.14, 0.00, 0.00},
- {1, 1.51775, 3.48, 1.23, 0.09, 0.22},
- {1, 1.51753, 3.47, 1.38, 0.00, 0.06},
- {1, 1.51783, 3.54, 1.34, 0.00, 0.00},
- {1, 1.51567, 3.45, 1.21, 0.00, 0.00},
- {1, 1.51909, 3.53, 1.32, 0.11, 0.00},
- {1, 1.51797, 3.48, 1.35, 0.00, 0.00},
- {1, 1.52213, 3.82, 0.47, 0.00, 0.00},
- {1, 1.52213, 3.82, 0.47, 0.00, 0.00},
- {1, 1.51793, 3.50, 1.12, 0.00, 0.00},
- {1, 1.51755, 3.42, 1.20, 0.00, 0.00},
- {1, 1.51779, 3.39, 1.33, 0.00, 0.00},
- {1, 1.52210, 3.84, 0.72, 0.00, 0.00},
- {1, 1.51786, 3.43, 1.19, 0.00, 0.30},
- {1, 1.51900, 3.48, 1.35, 0.00, 0.00},
- {1, 1.51869, 3.37, 1.18, 0.00, 0.16},
- {1, 1.52667, 3.70, 0.71, 0.00, 0.10},
- {1, 1.52223, 3.77, 0.79, 0.00, 0.00},
- {1, 1.51898, 3.35, 1.23, 0.00, 0.00},
- {1, 1.52320, 3.72, 0.51, 0.00, 0.16},
- {1, 1.51926, 3.33, 1.28, 0.00, 0.11},
- {1, 1.51808, 2.87, 1.19, 0.00, 0.00},
- {1, 1.51837, 2.84, 1.28, 0.00, 0.00},
- {1, 1.51778, 2.81, 1.29, 0.00, 0.09},
- {1, 1.51769, 2.71, 1.29, 0.00, 0.24},
- {1, 1.51215, 3.47, 1.12, 0.00, 0.31},
- {1, 1.51824, 3.48, 1.29, 0.00, 0.00},
- {1, 1.51754, 3.74, 1.17, 0.00, 0.00},
- {1, 1.51754, 3.66, 1.19, 0.00, 0.11},
- {1, 1.51905, 3.62, 1.11, 0.00, 0.00},
- {1, 1.51977, 3.58, 1.32, 0.69, 0.00},
- {1, 1.52172, 3.86, 0.88, 0.00, 0.11},
- {1, 1.52227, 3.81, 0.78, 0.00, 0.00},
- {1, 1.52172, 3.74, 0.90, 0.00, 0.07},
- {1, 1.52099, 3.59, 1.12, 0.00, 0.00},
- {1, 1.52152, 3.65, 0.87, 0.00, 0.17},
- {1, 1.52152, 3.65, 0.87, 0.00, 0.17},
- {1, 1.52152, 3.58, 0.90, 0.00, 0.16},
- {1, 1.52300, 3.58, 0.82, 0.00, 0.03},
- {3, 1.51769, 3.66, 1.11, 0.00, 0.00},
- {3, 1.51610, 3.53, 1.34, 0.00, 0.00},
- {3, 1.51670, 3.57, 1.38, 0.00, 0.10},
- {3, 1.51643, 3.52, 1.35, 0.00, 0.00},
- {3, 1.51665, 3.45, 1.76, 0.00, 0.17},
- {3, 1.52127, 3.90, 0.83, 0.00, 0.00},
- {3, 1.51779, 3.65, 0.65, 0.00, 0.00},
- {3, 1.51610, 3.40, 1.22, 0.00, 0.00},
- {3, 1.51694, 3.58, 1.31, 0.00, 0.00},
- {3, 1.51646, 3.40, 1.26, 0.00, 0.00},
- {3, 1.51655, 3.39, 1.28, 0.00, 0.00},
- {3, 1.52121, 3.76, 0.58, 0.00, 0.00},
- {3, 1.51776, 3.41, 1.52, 0.00, 0.00},
- {3, 1.51796, 3.36, 1.63, 0.00, 0.09},
- {3, 1.51832, 3.34, 1.54, 0.00, 0.00},
- {3, 1.51934, 3.54, 0.75, 0.15, 0.24},
- {3, 1.52211, 3.78, 0.91, 0.00, 0.37},
- {7, 1.51131, 3.20, 1.81, 1.19, 0.00},
- {7, 1.51838, 3.26, 2.22, 1.63, 0.00},
- {7, 1.52315, 3.34, 1.23, 0.00, 0.00},
- {7, 1.52247, 2.20, 2.06, 0.00, 0.00},
- {7, 1.52365, 1.83, 1.31, 1.68, 0.00},
- {7, 1.51613, 1.78, 1.79, 0.76, 0.00},
- {7, 1.51602, 0.00, 2.38, 0.64, 0.09},
- {7, 1.51623, 0.00, 2.79, 0.40, 0.09},
- {7, 1.51719, 0.00, 2.00, 1.59, 0.08},
- {7, 1.51683, 0.00, 1.98, 1.57, 0.07},
- {7, 1.51545, 0.00, 2.68, 0.61, 0.05},
- {7, 1.51556, 0.00, 2.54, 0.81, 0.01},
- {7, 1.51727, 0.00, 2.34, 0.66, 0.00},
- {7, 1.51531, 0.00, 2.66, 0.64, 0.00},
- {7, 1.51609, 0.00, 2.51, 0.53, 0.00},
- {7, 1.51508, 0.00, 2.25, 0.63, 0.00},
- {7, 1.51653, 0.00, 1.19, 0.00, 0.00},
- {7, 1.51514, 0.00, 2.42, 0.56, 0.00},
- {7, 1.51658, 0.00, 1.99, 1.71, 0.00},
- {7, 1.51617, 0.00, 2.27, 0.67, 0.00},
- {7, 1.51732, 0.00, 1.80, 1.55, 0.00},
- {7, 1.51645, 0.00, 1.87, 1.38, 0.00},
- {7, 1.51831, 0.00, 1.82, 2.88, 0.00},
- {7, 1.51640, 0.00, 2.74, 0.54, 0.00},
- {7, 1.51623, 0.00, 2.88, 1.06, 0.00},
- {7, 1.51685, 0.00, 1.99, 1.59, 0.00},
- {7, 1.52065, 0.00, 2.02, 1.64, 0.00},
- {7, 1.51651, 0.00, 1.94, 1.57, 0.00},
- {7, 1.51711, 0.00, 2.08, 1.67, 0.00},
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
index 25ce156..552bcd2 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/selection/cv/CrossValidationExample.java
@@ -45,7 +45,7 @@ public class CrossValidationExample {
*
* @param args Command line arguments, none required.
*/
- public static void main(String... args) throws InterruptedException {
+ public static void main(String... args) {
System.out.println(">>> Cross validation score calculator example started.");
// Start ignite grid.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/selection/split/TrainTestDatasetSplitterExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/selection/split/TrainTestDatasetSplitterExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/selection/split/TrainTestDatasetSplitterExample.java
index 8b104f5..4bfd993 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/selection/split/TrainTestDatasetSplitterExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/selection/split/TrainTestDatasetSplitterExample.java
@@ -17,16 +17,16 @@
package org.apache.ignite.examples.ml.selection.split;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer;
import org.apache.ignite.ml.regressions.linear.LinearRegressionModel;
import org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter;
@@ -47,78 +47,22 @@ import org.apache.ignite.ml.selection.split.TrainTestSplit;
* further.</p>
*/
public class TrainTestDatasetSplitterExample {
- /** */
- 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 {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Linear regression model over cache based dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.MORTALITY_DATA);
System.out.println(">>> Create new linear regression trainer object.");
LinearRegressionLSQRTrainer trainer = new LinearRegressionLSQRTrainer();
System.out.println(">>> Create new training dataset splitter object.");
- TrainTestSplit<Integer, double[]> split = new TrainTestDatasetSplitter<Integer, double[]>()
+ TrainTestSplit<Integer, Vector> split = new TrainTestDatasetSplitter<Integer, Vector>()
.split(0.75);
System.out.println(">>> Perform the training to get the model.");
@@ -126,8 +70,8 @@ public class TrainTestDatasetSplitterExample {
ignite,
dataCache,
split.getTrainFilter(),
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> Linear regression model: " + mdl);
@@ -136,16 +80,16 @@ public class TrainTestDatasetSplitterExample {
System.out.println(">>> | Prediction\t| Ground Truth\t|");
System.out.println(">>> ---------------------------------");
- ScanQuery<Integer, double[]> qry = new ScanQuery<>();
+ ScanQuery<Integer, Vector> qry = new ScanQuery<>();
qry.setFilter(split.getTestFilter());
- try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(qry)) {
- for (Cache.Entry<Integer, double[]> observation : observations) {
- double[] val = observation.getValue();
- double[] inputs = Arrays.copyOfRange(val, 1, val.length);
- double groundTruth = val[0];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(qry)) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
}
[3/4] ignite git commit: IGNITE-9910: [ML] Move the static
copy-pasted datasets from examples to special Util class
Posted by ch...@apache.org.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java
index c219441..679bd77 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/svm/binary/SVMBinaryClassificationExample.java
@@ -17,6 +17,7 @@
package org.apache.ignite.examples.ml.svm.binary;
+import java.io.FileNotFoundException;
import java.util.Arrays;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
@@ -24,9 +25,9 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationModel;
import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer;
@@ -46,22 +47,23 @@ import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer;
*/
public class SVMBinaryClassificationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> SVM Binary classification model over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.TWO_CLASSED_IRIS);
SVMLinearBinaryClassificationTrainer trainer = new SVMLinearBinaryClassificationTrainer();
SVMLinearBinaryClassificationModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
System.out.println(">>> SVM model " + mdl);
@@ -76,13 +78,13 @@ public class SVMBinaryClassificationExample {
// Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
int[][] confusionMtx = {{0, 0}, {0, 0}};
- 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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
totalAmount++;
if(groundTruth != prediction)
@@ -107,108 +109,4 @@ public class SVMBinaryClassificationExample {
System.out.println(">>> Linear regression model over cache based dataset usage example completed.");
}
}
-
- /** The 1st and 2nd classes from the Iris dataset. */
- private static final double[][] data = {
- {0, 5.1, 3.5, 1.4, 0.2},
- {0, 4.9, 3, 1.4, 0.2},
- {0, 4.7, 3.2, 1.3, 0.2},
- {0, 4.6, 3.1, 1.5, 0.2},
- {0, 5, 3.6, 1.4, 0.2},
- {0, 5.4, 3.9, 1.7, 0.4},
- {0, 4.6, 3.4, 1.4, 0.3},
- {0, 5, 3.4, 1.5, 0.2},
- {0, 4.4, 2.9, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5.4, 3.7, 1.5, 0.2},
- {0, 4.8, 3.4, 1.6, 0.2},
- {0, 4.8, 3, 1.4, 0.1},
- {0, 4.3, 3, 1.1, 0.1},
- {0, 5.8, 4, 1.2, 0.2},
- {0, 5.7, 4.4, 1.5, 0.4},
- {0, 5.4, 3.9, 1.3, 0.4},
- {0, 5.1, 3.5, 1.4, 0.3},
- {0, 5.7, 3.8, 1.7, 0.3},
- {0, 5.1, 3.8, 1.5, 0.3},
- {0, 5.4, 3.4, 1.7, 0.2},
- {0, 5.1, 3.7, 1.5, 0.4},
- {0, 4.6, 3.6, 1, 0.2},
- {0, 5.1, 3.3, 1.7, 0.5},
- {0, 4.8, 3.4, 1.9, 0.2},
- {0, 5, 3, 1.6, 0.2},
- {0, 5, 3.4, 1.6, 0.4},
- {0, 5.2, 3.5, 1.5, 0.2},
- {0, 5.2, 3.4, 1.4, 0.2},
- {0, 4.7, 3.2, 1.6, 0.2},
- {0, 4.8, 3.1, 1.6, 0.2},
- {0, 5.4, 3.4, 1.5, 0.4},
- {0, 5.2, 4.1, 1.5, 0.1},
- {0, 5.5, 4.2, 1.4, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 5, 3.2, 1.2, 0.2},
- {0, 5.5, 3.5, 1.3, 0.2},
- {0, 4.9, 3.1, 1.5, 0.1},
- {0, 4.4, 3, 1.3, 0.2},
- {0, 5.1, 3.4, 1.5, 0.2},
- {0, 5, 3.5, 1.3, 0.3},
- {0, 4.5, 2.3, 1.3, 0.3},
- {0, 4.4, 3.2, 1.3, 0.2},
- {0, 5, 3.5, 1.6, 0.6},
- {0, 5.1, 3.8, 1.9, 0.4},
- {0, 4.8, 3, 1.4, 0.3},
- {0, 5.1, 3.8, 1.6, 0.2},
- {0, 4.6, 3.2, 1.4, 0.2},
- {0, 5.3, 3.7, 1.5, 0.2},
- {0, 5, 3.3, 1.4, 0.2},
- {1, 7, 3.2, 4.7, 1.4},
- {1, 6.4, 3.2, 4.5, 1.5},
- {1, 6.9, 3.1, 4.9, 1.5},
- {1, 5.5, 2.3, 4, 1.3},
- {1, 6.5, 2.8, 4.6, 1.5},
- {1, 5.7, 2.8, 4.5, 1.3},
- {1, 6.3, 3.3, 4.7, 1.6},
- {1, 4.9, 2.4, 3.3, 1},
- {1, 6.6, 2.9, 4.6, 1.3},
- {1, 5.2, 2.7, 3.9, 1.4},
- {1, 5, 2, 3.5, 1},
- {1, 5.9, 3, 4.2, 1.5},
- {1, 6, 2.2, 4, 1},
- {1, 6.1, 2.9, 4.7, 1.4},
- {1, 5.6, 2.9, 3.6, 1.3},
- {1, 6.7, 3.1, 4.4, 1.4},
- {1, 5.6, 3, 4.5, 1.5},
- {1, 5.8, 2.7, 4.1, 1},
- {1, 6.2, 2.2, 4.5, 1.5},
- {1, 5.6, 2.5, 3.9, 1.1},
- {1, 5.9, 3.2, 4.8, 1.8},
- {1, 6.1, 2.8, 4, 1.3},
- {1, 6.3, 2.5, 4.9, 1.5},
- {1, 6.1, 2.8, 4.7, 1.2},
- {1, 6.4, 2.9, 4.3, 1.3},
- {1, 6.6, 3, 4.4, 1.4},
- {1, 6.8, 2.8, 4.8, 1.4},
- {1, 6.7, 3, 5, 1.7},
- {1, 6, 2.9, 4.5, 1.5},
- {1, 5.7, 2.6, 3.5, 1},
- {1, 5.5, 2.4, 3.8, 1.1},
- {1, 5.5, 2.4, 3.7, 1},
- {1, 5.8, 2.7, 3.9, 1.2},
- {1, 6, 2.7, 5.1, 1.6},
- {1, 5.4, 3, 4.5, 1.5},
- {1, 6, 3.4, 4.5, 1.6},
- {1, 6.7, 3.1, 4.7, 1.5},
- {1, 6.3, 2.3, 4.4, 1.3},
- {1, 5.6, 3, 4.1, 1.3},
- {1, 5.5, 2.5, 4, 1.3},
- {1, 5.5, 2.6, 4.4, 1.2},
- {1, 6.1, 3, 4.6, 1.4},
- {1, 5.8, 2.6, 4, 1.2},
- {1, 5, 2.3, 3.3, 1},
- {1, 5.6, 2.7, 4.2, 1.3},
- {1, 5.7, 3, 4.2, 1.2},
- {1, 5.7, 2.9, 4.2, 1.3},
- {1, 6.2, 2.9, 4.3, 1.3},
- {1, 5.1, 2.5, 3, 1.1},
- {1, 5.7, 2.8, 4.1, 1.3},
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
index 520b8cc..987ac41 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
@@ -17,6 +17,7 @@
package org.apache.ignite.examples.ml.svm.multiclass;
+import java.io.FileNotFoundException;
import java.util.Arrays;
import javax.cache.Cache;
import org.apache.ignite.Ignite;
@@ -24,11 +25,10 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.math.functions.IgniteBiFunction;
import org.apache.ignite.ml.math.primitives.vector.Vector;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer;
import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationModel;
import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationTrainer;
@@ -48,74 +48,70 @@ import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationTrainer;
* <a href="https://en.wikipedia.org/wiki/Confusion_matrix">confusion matrix</a>.</p>
* <p>
* You can change the test data used in this example and re-run it to explore this algorithm further.</p>
+ * NOTE: the smallest 3rd class could be classified via linear SVM here.
*/
public class SVMMultiClassClassificationExample {
/** Run example. */
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> SVM Multi-class classification model over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.GLASS_IDENTIFICATION);
SVMLinearMultiClassClassificationTrainer trainer = new SVMLinearMultiClassClassificationTrainer();
SVMLinearMultiClassClassificationModel mdl = trainer.fit(
ignite,
dataCache,
- (k, v) -> {
- double[] arr = v.asArray();
- return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
- },
+ (k, v) -> v.copyOfRange(1, v.size()),
(k, v) -> v.get(0)
);
System.out.println(">>> SVM Multi-class model");
System.out.println(mdl.toString());
- MinMaxScalerTrainer<Integer, Vector> normalizationTrainer = new MinMaxScalerTrainer<>();
+ MinMaxScalerTrainer<Integer, Vector> minMaxScalerTrainer = new MinMaxScalerTrainer<>();
- IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit(
+ IgniteBiFunction<Integer, Vector, Vector> preprocessor = minMaxScalerTrainer.fit(
ignite,
dataCache,
- (k, v) -> {
- double[] arr = v.asArray();
- return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
- }
+ (k, v) -> v.copyOfRange(1, v.size())
);
- SVMLinearMultiClassClassificationModel mdlWithNormalization = trainer.fit(
+ SVMLinearMultiClassClassificationModel mdlWithScaling = trainer.fit(
ignite,
dataCache,
preprocessor,
(k, v) -> v.get(0)
);
- System.out.println(">>> SVM Multi-class model with minmaxscaling");
- System.out.println(mdlWithNormalization.toString());
+ System.out.println(">>> SVM Multi-class model with MinMaxScaling");
+ System.out.println(mdlWithScaling.toString());
System.out.println(">>> ----------------------------------------------------------------");
- System.out.println(">>> | Prediction\t| Prediction with Normalization\t| Ground Truth\t|");
+ System.out.println(">>> | Prediction\t| Prediction with MinMaxScaling\t| Ground Truth\t|");
System.out.println(">>> ----------------------------------------------------------------");
int amountOfErrors = 0;
- int amountOfErrorsWithNormalization = 0;
+ int amountOfErrorsWithMinMaxScaling = 0;
int totalAmount = 0;
// Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
- int[][] confusionMtxWithNormalization = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
+ int[][] confusionMtxWithMinMaxScaling = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
for (Cache.Entry<Integer, Vector> observation : observations) {
- double[] val = observation.getValue().asArray();
- double[] inputs = Arrays.copyOfRange(val, 1, val.length);
- double groundTruth = val[0];
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = mdl.apply(new DenseVector(inputs));
- double predictionWithNormalization = mdlWithNormalization.apply(new DenseVector(inputs));
+ double prediction = mdl.apply(inputs);
+ double predictionWithMinMaxScaling = mdlWithScaling.apply(inputs);
totalAmount++;
@@ -129,15 +125,15 @@ public class SVMMultiClassClassificationExample {
confusionMtx[idx1][idx2]++;
// Collect data for model with minmaxscaling
- if(groundTruth != predictionWithNormalization)
- amountOfErrorsWithNormalization++;
+ if (groundTruth != predictionWithMinMaxScaling)
+ amountOfErrorsWithMinMaxScaling++;
- idx1 = (int)predictionWithNormalization == 1 ? 0 : ((int)predictionWithNormalization == 3 ? 1 : 2);
+ idx1 = (int)predictionWithMinMaxScaling == 1 ? 0 : ((int)predictionWithMinMaxScaling == 3 ? 1 : 2);
idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2);
- confusionMtxWithNormalization[idx1][idx2]++;
+ confusionMtxWithMinMaxScaling[idx1][idx2]++;
- System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithNormalization, groundTruth);
+ System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithMinMaxScaling, groundTruth);
}
System.out.println(">>> ----------------------------------------------------------------");
System.out.println("\n>>> -----------------SVM model-------------");
@@ -145,136 +141,13 @@ public class SVMMultiClassClassificationExample {
System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
- System.out.println("\n>>> -----------------SVM model with Normalization-------------");
- System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithNormalization);
- System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithNormalization / (double)totalAmount));
- System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithNormalization));
+ System.out.println("\n>>> -----------------SVM model with MinMaxScaling-------------");
+ System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithMinMaxScaling);
+ System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithMinMaxScaling / (double)totalAmount));
+ System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithMinMaxScaling));
System.out.println(">>> Linear regression model over cache based dataset usage example completed.");
}
}
}
-
- /** The preprocessed Glass dataset from the Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Glass+Identification
- * There are 3 classes with labels: 1 {building_windows_float_processed}, 3 {vehicle_windows_float_processed}, 7 {headlamps}.
- * Feature names: 'Na-Sodium', 'Mg-Magnesium', 'Al-Aluminum', 'Ba-Barium', 'Fe-Iron'.
- */
- private static final double[][] data = {
- {1, 1.52101, 4.49, 1.10, 0.00, 0.00},
- {1, 1.51761, 3.60, 1.36, 0.00, 0.00},
- {1, 1.51618, 3.55, 1.54, 0.00, 0.00},
- {1, 1.51766, 3.69, 1.29, 0.00, 0.00},
- {1, 1.51742, 3.62, 1.24, 0.00, 0.00},
- {1, 1.51596, 3.61, 1.62, 0.00, 0.26},
- {1, 1.51743, 3.60, 1.14, 0.00, 0.00},
- {1, 1.51756, 3.61, 1.05, 0.00, 0.00},
- {1, 1.51918, 3.58, 1.37, 0.00, 0.00},
- {1, 1.51755, 3.60, 1.36, 0.00, 0.11},
- {1, 1.51571, 3.46, 1.56, 0.00, 0.24},
- {1, 1.51763, 3.66, 1.27, 0.00, 0.00},
- {1, 1.51589, 3.43, 1.40, 0.00, 0.24},
- {1, 1.51748, 3.56, 1.27, 0.00, 0.17},
- {1, 1.51763, 3.59, 1.31, 0.00, 0.00},
- {1, 1.51761, 3.54, 1.23, 0.00, 0.00},
- {1, 1.51784, 3.67, 1.16, 0.00, 0.00},
- {1, 1.52196, 3.85, 0.89, 0.00, 0.00},
- {1, 1.51911, 3.73, 1.18, 0.00, 0.00},
- {1, 1.51735, 3.54, 1.69, 0.00, 0.07},
- {1, 1.51750, 3.55, 1.49, 0.00, 0.19},
- {1, 1.51966, 3.75, 0.29, 0.00, 0.00},
- {1, 1.51736, 3.62, 1.29, 0.00, 0.00},
- {1, 1.51751, 3.57, 1.35, 0.00, 0.00},
- {1, 1.51720, 3.50, 1.15, 0.00, 0.00},
- {1, 1.51764, 3.54, 1.21, 0.00, 0.00},
- {1, 1.51793, 3.48, 1.41, 0.00, 0.00},
- {1, 1.51721, 3.48, 1.33, 0.00, 0.00},
- {1, 1.51768, 3.52, 1.43, 0.00, 0.00},
- {1, 1.51784, 3.49, 1.28, 0.00, 0.00},
- {1, 1.51768, 3.56, 1.30, 0.00, 0.14},
- {1, 1.51747, 3.50, 1.14, 0.00, 0.00},
- {1, 1.51775, 3.48, 1.23, 0.09, 0.22},
- {1, 1.51753, 3.47, 1.38, 0.00, 0.06},
- {1, 1.51783, 3.54, 1.34, 0.00, 0.00},
- {1, 1.51567, 3.45, 1.21, 0.00, 0.00},
- {1, 1.51909, 3.53, 1.32, 0.11, 0.00},
- {1, 1.51797, 3.48, 1.35, 0.00, 0.00},
- {1, 1.52213, 3.82, 0.47, 0.00, 0.00},
- {1, 1.52213, 3.82, 0.47, 0.00, 0.00},
- {1, 1.51793, 3.50, 1.12, 0.00, 0.00},
- {1, 1.51755, 3.42, 1.20, 0.00, 0.00},
- {1, 1.51779, 3.39, 1.33, 0.00, 0.00},
- {1, 1.52210, 3.84, 0.72, 0.00, 0.00},
- {1, 1.51786, 3.43, 1.19, 0.00, 0.30},
- {1, 1.51900, 3.48, 1.35, 0.00, 0.00},
- {1, 1.51869, 3.37, 1.18, 0.00, 0.16},
- {1, 1.52667, 3.70, 0.71, 0.00, 0.10},
- {1, 1.52223, 3.77, 0.79, 0.00, 0.00},
- {1, 1.51898, 3.35, 1.23, 0.00, 0.00},
- {1, 1.52320, 3.72, 0.51, 0.00, 0.16},
- {1, 1.51926, 3.33, 1.28, 0.00, 0.11},
- {1, 1.51808, 2.87, 1.19, 0.00, 0.00},
- {1, 1.51837, 2.84, 1.28, 0.00, 0.00},
- {1, 1.51778, 2.81, 1.29, 0.00, 0.09},
- {1, 1.51769, 2.71, 1.29, 0.00, 0.24},
- {1, 1.51215, 3.47, 1.12, 0.00, 0.31},
- {1, 1.51824, 3.48, 1.29, 0.00, 0.00},
- {1, 1.51754, 3.74, 1.17, 0.00, 0.00},
- {1, 1.51754, 3.66, 1.19, 0.00, 0.11},
- {1, 1.51905, 3.62, 1.11, 0.00, 0.00},
- {1, 1.51977, 3.58, 1.32, 0.69, 0.00},
- {1, 1.52172, 3.86, 0.88, 0.00, 0.11},
- {1, 1.52227, 3.81, 0.78, 0.00, 0.00},
- {1, 1.52172, 3.74, 0.90, 0.00, 0.07},
- {1, 1.52099, 3.59, 1.12, 0.00, 0.00},
- {1, 1.52152, 3.65, 0.87, 0.00, 0.17},
- {1, 1.52152, 3.65, 0.87, 0.00, 0.17},
- {1, 1.52152, 3.58, 0.90, 0.00, 0.16},
- {1, 1.52300, 3.58, 0.82, 0.00, 0.03},
- {3, 1.51769, 3.66, 1.11, 0.00, 0.00},
- {3, 1.51610, 3.53, 1.34, 0.00, 0.00},
- {3, 1.51670, 3.57, 1.38, 0.00, 0.10},
- {3, 1.51643, 3.52, 1.35, 0.00, 0.00},
- {3, 1.51665, 3.45, 1.76, 0.00, 0.17},
- {3, 1.52127, 3.90, 0.83, 0.00, 0.00},
- {3, 1.51779, 3.65, 0.65, 0.00, 0.00},
- {3, 1.51610, 3.40, 1.22, 0.00, 0.00},
- {3, 1.51694, 3.58, 1.31, 0.00, 0.00},
- {3, 1.51646, 3.40, 1.26, 0.00, 0.00},
- {3, 1.51655, 3.39, 1.28, 0.00, 0.00},
- {3, 1.52121, 3.76, 0.58, 0.00, 0.00},
- {3, 1.51776, 3.41, 1.52, 0.00, 0.00},
- {3, 1.51796, 3.36, 1.63, 0.00, 0.09},
- {3, 1.51832, 3.34, 1.54, 0.00, 0.00},
- {3, 1.51934, 3.54, 0.75, 0.15, 0.24},
- {3, 1.52211, 3.78, 0.91, 0.00, 0.37},
- {7, 1.51131, 3.20, 1.81, 1.19, 0.00},
- {7, 1.51838, 3.26, 2.22, 1.63, 0.00},
- {7, 1.52315, 3.34, 1.23, 0.00, 0.00},
- {7, 1.52247, 2.20, 2.06, 0.00, 0.00},
- {7, 1.52365, 1.83, 1.31, 1.68, 0.00},
- {7, 1.51613, 1.78, 1.79, 0.76, 0.00},
- {7, 1.51602, 0.00, 2.38, 0.64, 0.09},
- {7, 1.51623, 0.00, 2.79, 0.40, 0.09},
- {7, 1.51719, 0.00, 2.00, 1.59, 0.08},
- {7, 1.51683, 0.00, 1.98, 1.57, 0.07},
- {7, 1.51545, 0.00, 2.68, 0.61, 0.05},
- {7, 1.51556, 0.00, 2.54, 0.81, 0.01},
- {7, 1.51727, 0.00, 2.34, 0.66, 0.00},
- {7, 1.51531, 0.00, 2.66, 0.64, 0.00},
- {7, 1.51609, 0.00, 2.51, 0.53, 0.00},
- {7, 1.51508, 0.00, 2.25, 0.63, 0.00},
- {7, 1.51653, 0.00, 1.19, 0.00, 0.00},
- {7, 1.51514, 0.00, 2.42, 0.56, 0.00},
- {7, 1.51658, 0.00, 1.99, 1.71, 0.00},
- {7, 1.51617, 0.00, 2.27, 0.67, 0.00},
- {7, 1.51732, 0.00, 1.80, 1.55, 0.00},
- {7, 1.51645, 0.00, 1.87, 1.38, 0.00},
- {7, 1.51831, 0.00, 1.82, 2.88, 0.00},
- {7, 1.51640, 0.00, 2.74, 0.54, 0.00},
- {7, 1.51623, 0.00, 2.88, 1.06, 0.00},
- {7, 1.51685, 0.00, 1.99, 1.59, 0.00},
- {7, 1.52065, 0.00, 2.02, 1.64, 0.00},
- {7, 1.51651, 0.00, 1.94, 1.57, 0.00},
- {7, 1.51711, 0.00, 2.08, 1.67, 0.00},
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
index 652b293..cc212e6 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
@@ -45,7 +45,7 @@ public class DecisionTreeClassificationTrainerExample {
*
* @param args Command line arguments, none required.
*/
- public static void main(String... args) throws InterruptedException {
+ public static void main(String... args) {
System.out.println(">>> Decision tree classification trainer example started.");
// Start ignite grid.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
index 2a89c7e..2338522 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
@@ -45,7 +45,7 @@ public class DecisionTreeRegressionTrainerExample {
*
* @param args Command line arguments, none required.
*/
- public static void main(String... args) throws InterruptedException {
+ public static void main(String... args) {
System.out.println(">>> Decision tree regression trainer example started.");
// Start ignite grid.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
index 5beb954..c478407 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
@@ -42,7 +42,7 @@ public class GDBOnTreesClassificationTrainerExample {
*
* @param args Command line arguments, none required.
*/
- public static void main(String... args) throws InterruptedException {
+ public static void main(String... args) {
System.out.println();
System.out.println(">>> GDB classification trainer example started.");
// Start ignite grid.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
index ea235ee..83a78d3 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
@@ -17,7 +17,7 @@
package org.apache.ignite.examples.ml.tree.randomforest;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
@@ -27,10 +27,11 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.composition.ModelsComposition;
import org.apache.ignite.ml.dataset.feature.FeatureMeta;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer;
import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
@@ -54,18 +55,19 @@ public class RandomForestClassificationExample {
/**
* Run example.
*/
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.WINE_RECOGNITION);
AtomicInteger idx = new AtomicInteger(0);
RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer(
- IntStream.range(0, data[0].length - 1).mapToObj(
+ IntStream.range(0, dataCache.get(1).size() - 1).mapToObj(
x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
).withAmountOfTrees(101)
.withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
@@ -76,23 +78,23 @@ public class RandomForestClassificationExample {
System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName());
- ModelsComposition randomForest = classifier.fit(ignite, dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
- (k, v) -> v[0]
+ ModelsComposition randomForestMdl = classifier.fit(ignite, dataCache,
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
- System.out.println(">>> Trained model: " + randomForest.toString(true));
+ System.out.println(">>> Trained model: " + randomForestMdl.toString(true));
int amountOfErrors = 0;
int totalAmount = 0;
- 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];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = randomForest.apply(VectorUtils.of(inputs));
+ double prediction = randomForestMdl.apply(inputs);
totalAmount++;
if (groundTruth != prediction)
@@ -107,186 +109,4 @@ public class RandomForestClassificationExample {
}
}
}
-
- /** The Wine recognition dataset. */
- private static final double[][] data = {
- {1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065},
- {1, 13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, .26, 1.28, 4.38, 1.05, 3.4, 1050},
- {1, 13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, .3, 2.81, 5.68, 1.03, 3.17, 1185},
- {1, 14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, .24, 2.18, 7.8, .86, 3.45, 1480},
- {1, 13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, .39, 1.82, 4.32, 1.04, 2.93, 735},
- {1, 14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, .34, 1.97, 6.75, 1.05, 2.85, 1450},
- {1, 14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, .3, 1.98, 5.25, 1.02, 3.58, 1290},
- {1, 14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, .31, 1.25, 5.05, 1.06, 3.58, 1295},
- {1, 14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, .29, 1.98, 5.2, 1.08, 2.85, 1045},
- {1, 13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, .22, 1.85, 7.22, 1.01, 3.55, 1045},
- {1, 14.1, 2.16, 2.3, 18, 105, 2.95, 3.32, .22, 2.38, 5.75, 1.25, 3.17, 1510},
- {1, 14.12, 1.48, 2.32, 16.8, 95, 2.2, 2.43, .26, 1.57, 5, 1.17, 2.82, 1280},
- {1, 13.75, 1.73, 2.41, 16, 89, 2.6, 2.76, .29, 1.81, 5.6, 1.15, 2.9, 1320},
- {1, 14.75, 1.73, 2.39, 11.4, 91, 3.1, 3.69, .43, 2.81, 5.4, 1.25, 2.73, 1150},
- {1, 14.38, 1.87, 2.38, 12, 102, 3.3, 3.64, .29, 2.96, 7.5, 1.2, 3, 1547},
- {1, 13.63, 1.81, 2.7, 17.2, 112, 2.85, 2.91, .3, 1.46, 7.3, 1.28, 2.88, 1310},
- {1, 14.3, 1.92, 2.72, 20, 120, 2.8, 3.14, .33, 1.97, 6.2, 1.07, 2.65, 1280},
- {1, 13.83, 1.57, 2.62, 20, 115, 2.95, 3.4, .4, 1.72, 6.6, 1.13, 2.57, 1130},
- {1, 14.19, 1.59, 2.48, 16.5, 108, 3.3, 3.93, .32, 1.86, 8.7, 1.23, 2.82, 1680},
- {1, 13.64, 3.1, 2.56, 15.2, 116, 2.7, 3.03, .17, 1.66, 5.1, .96, 3.36, 845},
- {1, 14.06, 1.63, 2.28, 16, 126, 3, 3.17, .24, 2.1, 5.65, 1.09, 3.71, 780},
- {1, 12.93, 3.8, 2.65, 18.6, 102, 2.41, 2.41, .25, 1.98, 4.5, 1.03, 3.52, 770},
- {1, 13.71, 1.86, 2.36, 16.6, 101, 2.61, 2.88, .27, 1.69, 3.8, 1.11, 4, 1035},
- {1, 12.85, 1.6, 2.52, 17.8, 95, 2.48, 2.37, .26, 1.46, 3.93, 1.09, 3.63, 1015},
- {1, 13.5, 1.81, 2.61, 20, 96, 2.53, 2.61, .28, 1.66, 3.52, 1.12, 3.82, 845},
- {1, 13.05, 2.05, 3.22, 25, 124, 2.63, 2.68, .47, 1.92, 3.58, 1.13, 3.2, 830},
- {1, 13.39, 1.77, 2.62, 16.1, 93, 2.85, 2.94, .34, 1.45, 4.8, .92, 3.22, 1195},
- {1, 13.3, 1.72, 2.14, 17, 94, 2.4, 2.19, .27, 1.35, 3.95, 1.02, 2.77, 1285},
- {1, 13.87, 1.9, 2.8, 19.4, 107, 2.95, 2.97, .37, 1.76, 4.5, 1.25, 3.4, 915},
- {1, 14.02, 1.68, 2.21, 16, 96, 2.65, 2.33, .26, 1.98, 4.7, 1.04, 3.59, 1035},
- {1, 13.73, 1.5, 2.7, 22.5, 101, 3, 3.25, .29, 2.38, 5.7, 1.19, 2.71, 1285},
- {1, 13.58, 1.66, 2.36, 19.1, 106, 2.86, 3.19, .22, 1.95, 6.9, 1.09, 2.88, 1515},
- {1, 13.68, 1.83, 2.36, 17.2, 104, 2.42, 2.69, .42, 1.97, 3.84, 1.23, 2.87, 990},
- {1, 13.76, 1.53, 2.7, 19.5, 132, 2.95, 2.74, .5, 1.35, 5.4, 1.25, 3, 1235},
- {1, 13.51, 1.8, 2.65, 19, 110, 2.35, 2.53, .29, 1.54, 4.2, 1.1, 2.87, 1095},
- {1, 13.48, 1.81, 2.41, 20.5, 100, 2.7, 2.98, .26, 1.86, 5.1, 1.04, 3.47, 920},
- {1, 13.28, 1.64, 2.84, 15.5, 110, 2.6, 2.68, .34, 1.36, 4.6, 1.09, 2.78, 880},
- {1, 13.05, 1.65, 2.55, 18, 98, 2.45, 2.43, .29, 1.44, 4.25, 1.12, 2.51, 1105},
- {1, 13.07, 1.5, 2.1, 15.5, 98, 2.4, 2.64, .28, 1.37, 3.7, 1.18, 2.69, 1020},
- {1, 14.22, 3.99, 2.51, 13.2, 128, 3, 3.04, .2, 2.08, 5.1, .89, 3.53, 760},
- {1, 13.56, 1.71, 2.31, 16.2, 117, 3.15, 3.29, .34, 2.34, 6.13, .95, 3.38, 795},
- {1, 13.41, 3.84, 2.12, 18.8, 90, 2.45, 2.68, .27, 1.48, 4.28, .91, 3, 1035},
- {1, 13.88, 1.89, 2.59, 15, 101, 3.25, 3.56, .17, 1.7, 5.43, .88, 3.56, 1095},
- {1, 13.24, 3.98, 2.29, 17.5, 103, 2.64, 2.63, .32, 1.66, 4.36, .82, 3, 680},
- {1, 13.05, 1.77, 2.1, 17, 107, 3, 3, .28, 2.03, 5.04, .88, 3.35, 885},
- {1, 14.21, 4.04, 2.44, 18.9, 111, 2.85, 2.65, .3, 1.25, 5.24, .87, 3.33, 1080},
- {1, 14.38, 3.59, 2.28, 16, 102, 3.25, 3.17, .27, 2.19, 4.9, 1.04, 3.44, 1065},
- {1, 13.9, 1.68, 2.12, 16, 101, 3.1, 3.39, .21, 2.14, 6.1, .91, 3.33, 985},
- {1, 14.1, 2.02, 2.4, 18.8, 103, 2.75, 2.92, .32, 2.38, 6.2, 1.07, 2.75, 1060},
- {1, 13.94, 1.73, 2.27, 17.4, 108, 2.88, 3.54, .32, 2.08, 8.90, 1.12, 3.1, 1260},
- {1, 13.05, 1.73, 2.04, 12.4, 92, 2.72, 3.27, .17, 2.91, 7.2, 1.12, 2.91, 1150},
- {1, 13.83, 1.65, 2.6, 17.2, 94, 2.45, 2.99, .22, 2.29, 5.6, 1.24, 3.37, 1265},
- {1, 13.82, 1.75, 2.42, 14, 111, 3.88, 3.74, .32, 1.87, 7.05, 1.01, 3.26, 1190},
- {1, 13.77, 1.9, 2.68, 17.1, 115, 3, 2.79, .39, 1.68, 6.3, 1.13, 2.93, 1375},
- {1, 13.74, 1.67, 2.25, 16.4, 118, 2.6, 2.9, .21, 1.62, 5.85, .92, 3.2, 1060},
- {1, 13.56, 1.73, 2.46, 20.5, 116, 2.96, 2.78, .2, 2.45, 6.25, .98, 3.03, 1120},
- {1, 14.22, 1.7, 2.3, 16.3, 118, 3.2, 3, .26, 2.03, 6.38, .94, 3.31, 970},
- {1, 13.29, 1.97, 2.68, 16.8, 102, 3, 3.23, .31, 1.66, 6, 1.07, 2.84, 1270},
- {1, 13.72, 1.43, 2.5, 16.7, 108, 3.4, 3.67, .19, 2.04, 6.8, .89, 2.87, 1285},
- {2, 12.37, .94, 1.36, 10.6, 88, 1.98, .57, .28, .42, 1.95, 1.05, 1.82, 520},
- {2, 12.33, 1.1, 2.28, 16, 101, 2.05, 1.09, .63, .41, 3.27, 1.25, 1.67, 680},
- {2, 12.64, 1.36, 2.02, 16.8, 100, 2.02, 1.41, .53, .62, 5.75, .98, 1.59, 450},
- {2, 13.67, 1.25, 1.92, 18, 94, 2.1, 1.79, .32, .73, 3.8, 1.23, 2.46, 630},
- {2, 12.37, 1.13, 2.16, 19, 87, 3.5, 3.1, .19, 1.87, 4.45, 1.22, 2.87, 420},
- {2, 12.17, 1.45, 2.53, 19, 104, 1.89, 1.75, .45, 1.03, 2.95, 1.45, 2.23, 355},
- {2, 12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, .37, 2.08, 4.6, 1.19, 2.3, 678},
- {2, 13.11, 1.01, 1.7, 15, 78, 2.98, 3.18, .26, 2.28, 5.3, 1.12, 3.18, 502},
- {2, 12.37, 1.17, 1.92, 19.6, 78, 2.11, 2, .27, 1.04, 4.68, 1.12, 3.48, 510},
- {2, 13.34, .94, 2.36, 17, 110, 2.53, 1.3, .55, .42, 3.17, 1.02, 1.93, 750},
- {2, 12.21, 1.19, 1.75, 16.8, 151, 1.85, 1.28, .14, 2.5, 2.85, 1.28, 3.07, 718},
- {2, 12.29, 1.61, 2.21, 20.4, 103, 1.1, 1.02, .37, 1.46, 3.05, .906, 1.82, 870},
- {2, 13.86, 1.51, 2.67, 25, 86, 2.95, 2.86, .21, 1.87, 3.38, 1.36, 3.16, 410},
- {2, 13.49, 1.66, 2.24, 24, 87, 1.88, 1.84, .27, 1.03, 3.74, .98, 2.78, 472},
- {2, 12.99, 1.67, 2.6, 30, 139, 3.3, 2.89, .21, 1.96, 3.35, 1.31, 3.5, 985},
- {2, 11.96, 1.09, 2.3, 21, 101, 3.38, 2.14, .13, 1.65, 3.21, .99, 3.13, 886},
- {2, 11.66, 1.88, 1.92, 16, 97, 1.61, 1.57, .34, 1.15, 3.8, 1.23, 2.14, 428},
- {2, 13.03, .9, 1.71, 16, 86, 1.95, 2.03, .24, 1.46, 4.6, 1.19, 2.48, 392},
- {2, 11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, .43, .95, 2.65, .96, 2.52, 500},
- {2, 12.33, .99, 1.95, 14.8, 136, 1.9, 1.85, .35, 2.76, 3.4, 1.06, 2.31, 750},
- {2, 12.7, 3.87, 2.4, 23, 101, 2.83, 2.55, .43, 1.95, 2.57, 1.19, 3.13, 463},
- {2, 12, .92, 2, 19, 86, 2.42, 2.26, .3, 1.43, 2.5, 1.38, 3.12, 278},
- {2, 12.72, 1.81, 2.2, 18.8, 86, 2.2, 2.53, .26, 1.77, 3.9, 1.16, 3.14, 714},
- {2, 12.08, 1.13, 2.51, 24, 78, 2, 1.58, .4, 1.4, 2.2, 1.31, 2.72, 630},
- {2, 13.05, 3.86, 2.32, 22.5, 85, 1.65, 1.59, .61, 1.62, 4.8, .84, 2.01, 515},
- {2, 11.84, .89, 2.58, 18, 94, 2.2, 2.21, .22, 2.35, 3.05, .79, 3.08, 520},
- {2, 12.67, .98, 2.24, 18, 99, 2.2, 1.94, .3, 1.46, 2.62, 1.23, 3.16, 450},
- {2, 12.16, 1.61, 2.31, 22.8, 90, 1.78, 1.69, .43, 1.56, 2.45, 1.33, 2.26, 495},
- {2, 11.65, 1.67, 2.62, 26, 88, 1.92, 1.61, .4, 1.34, 2.6, 1.36, 3.21, 562},
- {2, 11.64, 2.06, 2.46, 21.6, 84, 1.95, 1.69, .48, 1.35, 2.8, 1, 2.75, 680},
- {2, 12.08, 1.33, 2.3, 23.6, 70, 2.2, 1.59, .42, 1.38, 1.74, 1.07, 3.21, 625},
- {2, 12.08, 1.83, 2.32, 18.5, 81, 1.6, 1.5, .52, 1.64, 2.4, 1.08, 2.27, 480},
- {2, 12, 1.51, 2.42, 22, 86, 1.45, 1.25, .5, 1.63, 3.6, 1.05, 2.65, 450},
- {2, 12.69, 1.53, 2.26, 20.7, 80, 1.38, 1.46, .58, 1.62, 3.05, .96, 2.06, 495},
- {2, 12.29, 2.83, 2.22, 18, 88, 2.45, 2.25, .25, 1.99, 2.15, 1.15, 3.3, 290},
- {2, 11.62, 1.99, 2.28, 18, 98, 3.02, 2.26, .17, 1.35, 3.25, 1.16, 2.96, 345},
- {2, 12.47, 1.52, 2.2, 19, 162, 2.5, 2.27, .32, 3.28, 2.6, 1.16, 2.63, 937},
- {2, 11.81, 2.12, 2.74, 21.5, 134, 1.6, .99, .14, 1.56, 2.5, .95, 2.26, 625},
- {2, 12.29, 1.41, 1.98, 16, 85, 2.55, 2.5, .29, 1.77, 2.9, 1.23, 2.74, 428},
- {2, 12.37, 1.07, 2.1, 18.5, 88, 3.52, 3.75, .24, 1.95, 4.5, 1.04, 2.77, 660},
- {2, 12.29, 3.17, 2.21, 18, 88, 2.85, 2.99, .45, 2.81, 2.3, 1.42, 2.83, 406},
- {2, 12.08, 2.08, 1.7, 17.5, 97, 2.23, 2.17, .26, 1.4, 3.3, 1.27, 2.96, 710},
- {2, 12.6, 1.34, 1.9, 18.5, 88, 1.45, 1.36, .29, 1.35, 2.45, 1.04, 2.77, 562},
- {2, 12.34, 2.45, 2.46, 21, 98, 2.56, 2.11, .34, 1.31, 2.8, .8, 3.38, 438},
- {2, 11.82, 1.72, 1.88, 19.5, 86, 2.5, 1.64, .37, 1.42, 2.06, .94, 2.44, 415},
- {2, 12.51, 1.73, 1.98, 20.5, 85, 2.2, 1.92, .32, 1.48, 2.94, 1.04, 3.57, 672},
- {2, 12.42, 2.55, 2.27, 22, 90, 1.68, 1.84, .66, 1.42, 2.7, .86, 3.3, 315},
- {2, 12.25, 1.73, 2.12, 19, 80, 1.65, 2.03, .37, 1.63, 3.4, 1, 3.17, 510},
- {2, 12.72, 1.75, 2.28, 22.5, 84, 1.38, 1.76, .48, 1.63, 3.3, .88, 2.42, 488},
- {2, 12.22, 1.29, 1.94, 19, 92, 2.36, 2.04, .39, 2.08, 2.7, .86, 3.02, 312},
- {2, 11.61, 1.35, 2.7, 20, 94, 2.74, 2.92, .29, 2.49, 2.65, .96, 3.26, 680},
- {2, 11.46, 3.74, 1.82, 19.5, 107, 3.18, 2.58, .24, 3.58, 2.9, .75, 2.81, 562},
- {2, 12.52, 2.43, 2.17, 21, 88, 2.55, 2.27, .26, 1.22, 2, .9, 2.78, 325},
- {2, 11.76, 2.68, 2.92, 20, 103, 1.75, 2.03, .6, 1.05, 3.8, 1.23, 2.5, 607},
- {2, 11.41, .74, 2.5, 21, 88, 2.48, 2.01, .42, 1.44, 3.08, 1.1, 2.31, 434},
- {2, 12.08, 1.39, 2.5, 22.5, 84, 2.56, 2.29, .43, 1.04, 2.9, .93, 3.19, 385},
- {2, 11.03, 1.51, 2.2, 21.5, 85, 2.46, 2.17, .52, 2.01, 1.9, 1.71, 2.87, 407},
- {2, 11.82, 1.47, 1.99, 20.8, 86, 1.98, 1.6, .3, 1.53, 1.95, .95, 3.3423, 495},
- {2, 12.42, 1.61, 2.19, 22.5, 108, 2, 2.09, .34, 1.61, 2.06, 1.06, 2.96, 345},
- {2, 12.77, 3.43, 1.98, 16, 80, 1.63, 1.25, .43, .83, 3.4, .7, 2.12, 372},
- {2, 12, 3.43, 2, 19, 87, 2, 1.64, .37, 1.87, 1.28, .93, 3.05, 564},
- {2, 11.45, 2.4, 2.42, 20, 96, 2.9, 2.79, .32, 1.83, 3.25, .8, 3.39, 625},
- {2, 11.56, 2.05, 3.23, 28.5, 119, 3.18, 5.08, .47, 1.87, 6, .93, 3.69, 465},
- {2, 12.42, 4.43, 2.73, 26.5, 102, 2.2, 2.13, .43, 1.71, 2.08, .92, 3.12, 365},
- {2, 13.05, 5.8, 2.13, 21.5, 86, 2.62, 2.65, .3, 2.01, 2.6, .73, 3.1, 380},
- {2, 11.87, 4.31, 2.39, 21, 82, 2.86, 3.03, .21, 2.91, 2.8, .75, 3.64, 380},
- {2, 12.07, 2.16, 2.17, 21, 85, 2.6, 2.65, .37, 1.35, 2.76, .86, 3.28, 378},
- {2, 12.43, 1.53, 2.29, 21.5, 86, 2.74, 3.15, .39, 1.77, 3.94, .69, 2.84, 352},
- {2, 11.79, 2.13, 2.78, 28.5, 92, 2.13, 2.24, .58, 1.76, 3, .97, 2.44, 466},
- {2, 12.37, 1.63, 2.3, 24.5, 88, 2.22, 2.45, .4, 1.9, 2.12, .89, 2.78, 342},
- {2, 12.04, 4.3, 2.38, 22, 80, 2.1, 1.75, .42, 1.35, 2.6, .79, 2.57, 580},
- {3, 12.86, 1.35, 2.32, 18, 122, 1.51, 1.25, .21, .94, 4.1, .76, 1.29, 630},
- {3, 12.88, 2.99, 2.4, 20, 104, 1.3, 1.22, .24, .83, 5.4, .74, 1.42, 530},
- {3, 12.81, 2.31, 2.4, 24, 98, 1.15, 1.09, .27, .83, 5.7, .66, 1.36, 560},
- {3, 12.7, 3.55, 2.36, 21.5, 106, 1.7, 1.2, .17, .84, 5, .78, 1.29, 600},
- {3, 12.51, 1.24, 2.25, 17.5, 85, 2, .58, .6, 1.25, 5.45, .75, 1.51, 650},
- {3, 12.6, 2.46, 2.2, 18.5, 94, 1.62, .66, .63, .94, 7.1, .73, 1.58, 695},
- {3, 12.25, 4.72, 2.54, 21, 89, 1.38, .47, .53, .8, 3.85, .75, 1.27, 720},
- {3, 12.53, 5.51, 2.64, 25, 96, 1.79, .6, .63, 1.1, 5, .82, 1.69, 515},
- {3, 13.49, 3.59, 2.19, 19.5, 88, 1.62, .48, .58, .88, 5.7, .81, 1.82, 580},
- {3, 12.84, 2.96, 2.61, 24, 101, 2.32, .6, .53, .81, 4.92, .89, 2.15, 590},
- {3, 12.93, 2.81, 2.7, 21, 96, 1.54, .5, .53, .75, 4.6, .77, 2.31, 600},
- {3, 13.36, 2.56, 2.35, 20, 89, 1.4, .5, .37, .64, 5.6, .7, 2.47, 780},
- {3, 13.52, 3.17, 2.72, 23.5, 97, 1.55, .52, .5, .55, 4.35, .89, 2.06, 520},
- {3, 13.62, 4.95, 2.35, 20, 92, 2, .8, .47, 1.02, 4.4, .91, 2.05, 550},
- {3, 12.25, 3.88, 2.2, 18.5, 112, 1.38, .78, .29, 1.14, 8.21, .65, 2, 855},
- {3, 13.16, 3.57, 2.15, 21, 102, 1.5, .55, .43, 1.3, 4, .6, 1.68, 830},
- {3, 13.88, 5.04, 2.23, 20, 80, .98, .34, .4, .68, 4.9, .58, 1.33, 415},
- {3, 12.87, 4.61, 2.48, 21.5, 86, 1.7, .65, .47, .86, 7.65, .54, 1.86, 625},
- {3, 13.32, 3.24, 2.38, 21.5, 92, 1.93, .76, .45, 1.25, 8.42, .55, 1.62, 650},
- {3, 13.08, 3.9, 2.36, 21.5, 113, 1.41, 1.39, .34, 1.14, 9.40, .57, 1.33, 550},
- {3, 13.5, 3.12, 2.62, 24, 123, 1.4, 1.57, .22, 1.25, 8.60, .59, 1.3, 500},
- {3, 12.79, 2.67, 2.48, 22, 112, 1.48, 1.36, .24, 1.26, 10.8, .48, 1.47, 480},
- {3, 13.11, 1.9, 2.75, 25.5, 116, 2.2, 1.28, .26, 1.56, 7.1, .61, 1.33, 425},
- {3, 13.23, 3.3, 2.28, 18.5, 98, 1.8, .83, .61, 1.87, 10.52, .56, 1.51, 675},
- {3, 12.58, 1.29, 2.1, 20, 103, 1.48, .58, .53, 1.4, 7.6, .58, 1.55, 640},
- {3, 13.17, 5.19, 2.32, 22, 93, 1.74, .63, .61, 1.55, 7.9, .6, 1.48, 725},
- {3, 13.84, 4.12, 2.38, 19.5, 89, 1.8, .83, .48, 1.56, 9.01, .57, 1.64, 480},
- {3, 12.45, 3.03, 2.64, 27, 97, 1.9, .58, .63, 1.14, 7.5, .67, 1.73, 880},
- {3, 14.34, 1.68, 2.7, 25, 98, 2.8, 1.31, .53, 2.7, 13, .57, 1.96, 660},
- {3, 13.48, 1.67, 2.64, 22.5, 89, 2.6, 1.1, .52, 2.29, 11.75, .57, 1.78, 620},
- {3, 12.36, 3.83, 2.38, 21, 88, 2.3, .92, .5, 1.04, 7.65, .56, 1.58, 520},
- {3, 13.69, 3.26, 2.54, 20, 107, 1.83, .56, .5, .8, 5.88, .96, 1.82, 680},
- {3, 12.85, 3.27, 2.58, 22, 106, 1.65, .6, .6, .96, 5.58, .87, 2.11, 570},
- {3, 12.96, 3.45, 2.35, 18.5, 106, 1.39, .7, .4, .94, 5.28, .68, 1.75, 675},
- {3, 13.78, 2.76, 2.3, 22, 90, 1.35, .68, .41, 1.03, 9.58, .7, 1.68, 615},
- {3, 13.73, 4.36, 2.26, 22.5, 88, 1.28, .47, .52, 1.15, 6.62, .78, 1.75, 520},
- {3, 13.45, 3.7, 2.6, 23, 111, 1.7, .92, .43, 1.46, 10.68, .85, 1.56, 695},
- {3, 12.82, 3.37, 2.3, 19.5, 88, 1.48, .66, .4, .97, 10.26, .72, 1.75, 685},
- {3, 13.58, 2.58, 2.69, 24.5, 105, 1.55, .84, .39, 1.54, 8.66, .74, 1.8, 750},
- {3, 13.4, 4.6, 2.86, 25, 112, 1.98, .96, .27, 1.11, 8.5, .67, 1.92, 630},
- {3, 12.2, 3.03, 2.32, 19, 96, 1.25, .49, .4, .73, 5.5, .66, 1.83, 510},
- {3, 12.77, 2.39, 2.28, 19.5, 86, 1.39, .51, .48, .64, 9.899999, .57, 1.63, 470},
- {3, 14.16, 2.51, 2.48, 20, 91, 1.68, .7, .44, 1.24, 9.7, .62, 1.71, 660},
- {3, 13.71, 5.65, 2.45, 20.5, 95, 1.68, .61, .52, 1.06, 7.7, .64, 1.74, 740},
- {3, 13.4, 3.91, 2.48, 23, 102, 1.8, .75, .43, 1.41, 7.3, .7, 1.56, 750},
- {3, 13.27, 4.28, 2.26, 20, 120, 1.59, .69, .43, 1.35, 10.2, .59, 1.56, 835},
- {3, 13.17, 2.59, 2.37, 20, 120, 1.65, .68, .53, 1.46, 9.3, .6, 1.62, 840},
- {3, 14.13, 4.1, 2.74, 24.5, 96, 2.05, .76, .56, 1.35, 9.2, .61, 1.6, 560}
- };
}
[2/4] ignite git commit: IGNITE-9910: [ML] Move the static
copy-pasted datasets from examples to special Util class
Posted by ch...@apache.org.
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
index 9b4aece..3bf2c8e 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
@@ -17,7 +17,7 @@
package org.apache.ignite.examples.ml.tree.randomforest;
-import java.util.Arrays;
+import java.io.FileNotFoundException;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
@@ -27,14 +27,15 @@ import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.query.QueryCursor;
import org.apache.ignite.cache.query.ScanQuery;
-import org.apache.ignite.examples.ml.util.TestCache;
+import org.apache.ignite.examples.ml.util.MLSandboxDatasets;
+import org.apache.ignite.examples.ml.util.SandboxMLCache;
import org.apache.ignite.ml.composition.ModelsComposition;
import org.apache.ignite.ml.dataset.feature.FeatureMeta;
import org.apache.ignite.ml.environment.LearningEnvironment;
import org.apache.ignite.ml.environment.logging.ConsoleLogger;
import org.apache.ignite.ml.environment.logging.MLLogger;
import org.apache.ignite.ml.environment.parallelism.ParallelismStrategy;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer;
import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
@@ -58,18 +59,19 @@ public class RandomForestRegressionExample {
/**
* Run example.
*/
- public static void main(String[] args) throws InterruptedException {
+ public static void main(String[] args) throws FileNotFoundException {
System.out.println();
System.out.println(">>> Random Forest regression algorithm over cached dataset usage example started.");
// Start ignite grid.
try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
System.out.println(">>> Ignite grid started.");
- IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+ IgniteCache<Integer, Vector> dataCache = new SandboxMLCache(ignite)
+ .fillCacheWith(MLSandboxDatasets.BOSTON_HOUSE_PRICES);
AtomicInteger idx = new AtomicInteger(0);
RandomForestRegressionTrainer trainer = new RandomForestRegressionTrainer(
- IntStream.range(0, data[0].length - 1).mapToObj(
+ IntStream.range(0, dataCache.get(1).size() - 1).mapToObj(
x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
).withAmountOfTrees(101)
.withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
@@ -86,24 +88,24 @@ public class RandomForestRegressionExample {
System.out.println(">>> Configured trainer: " + trainer.getClass().getSimpleName());
- ModelsComposition randomForest = trainer.fit(ignite, dataCache,
- (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
- (k, v) -> v[v.length - 1]
+ ModelsComposition randomForestMdl = trainer.fit(ignite, dataCache,
+ (k, v) -> v.copyOfRange(1, v.size()),
+ (k, v) -> v.get(0)
);
- System.out.println(">>> Trained model: " + randomForest.toString(true));
+ System.out.println(">>> Trained model: " + randomForestMdl.toString(true));
double mse = 0.0;
double mae = 0.0;
int totalAmount = 0;
- 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, 0, val.length - 1);
- double groundTruth = val[val.length - 1];
+ try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+ for (Cache.Entry<Integer, Vector> observation : observations) {
+ Vector val = observation.getValue();
+ Vector inputs = val.copyOfRange(1, val.size());
+ double groundTruth = val.get(0);
- double prediction = randomForest.apply(VectorUtils.of(inputs));
+ double prediction = randomForestMdl.apply(inputs);
mse += Math.pow(prediction - groundTruth, 2.0);
mae += Math.abs(prediction - groundTruth);
@@ -123,513 +125,4 @@ public class RandomForestRegressionExample {
}
}
}
-
- /** The Boston housing dataset. */
- private static final double[][] data = {
- {0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60},
- {0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70},
- {0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40},
- {0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20},
- {0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70},
- {0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90},
- {0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10},
- {0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50},
- {0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90},
- {0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45,15.00},
- {0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90},
- {0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70},
- {0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40},
- {0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20},
- {0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47,19.90},
- {1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10},
- {0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67,17.50},
- {0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69,20.20},
- {0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28,18.20},
- {1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02,13.60},
- {0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83,19.60},
- {1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72,15.20},
- {0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88,14.50},
- {0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30,15.60},
- {0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51,13.90},
- {0.67191,0.00,8.140,0,0.5380,5.8130,90.30,4.6820,4,307.0,21.00,376.88,14.81,16.60},
- {0.95577,0.00,8.140,0,0.5380,6.0470,88.80,4.4534,4,307.0,21.00,306.38,17.28,14.80},
- {0.77299,0.00,8.140,0,0.5380,6.4950,94.40,4.4547,4,307.0,21.00,387.94,12.80,18.40},
- {1.00245,0.00,8.140,0,0.5380,6.6740,87.30,4.2390,4,307.0,21.00,380.23,11.98,21.00},
- {1.13081,0.00,8.140,0,0.5380,5.7130,94.10,4.2330,4,307.0,21.00,360.17,22.60,12.70},
- {1.35472,0.00,8.140,0,0.5380,6.0720,100.00,4.1750,4,307.0,21.00,376.73,13.04,14.50},
- {1.38799,0.00,8.140,0,0.5380,5.9500,82.00,3.9900,4,307.0,21.00,232.60,27.71,13.20},
- {1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35,13.10},
- {1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34,13.50},
- {0.06417,0.00,5.960,0,0.4990,5.9330,68.20,3.3603,5,279.0,19.20,396.90,9.68,18.90},
- {0.09744,0.00,5.960,0,0.4990,5.8410,61.40,3.3779,5,279.0,19.20,377.56,11.41,20.00},
- {0.08014,0.00,5.960,0,0.4990,5.8500,41.50,3.9342,5,279.0,19.20,396.90,8.77,21.00},
- {0.17505,0.00,5.960,0,0.4990,5.9660,30.20,3.8473,5,279.0,19.20,393.43,10.13,24.70},
- {0.02763,75.00,2.950,0,0.4280,6.5950,21.80,5.4011,3,252.0,18.30,395.63,4.32,30.80},
- {0.03359,75.00,2.950,0,0.4280,7.0240,15.80,5.4011,3,252.0,18.30,395.62,1.98,34.90},
- {0.12744,0.00,6.910,0,0.4480,6.7700,2.90,5.7209,3,233.0,17.90,385.41,4.84,26.60},
- {0.14150,0.00,6.910,0,0.4480,6.1690,6.60,5.7209,3,233.0,17.90,383.37,5.81,25.30},
- {0.15936,0.00,6.910,0,0.4480,6.2110,6.50,5.7209,3,233.0,17.90,394.46,7.44,24.70},
- {0.12269,0.00,6.910,0,0.4480,6.0690,40.00,5.7209,3,233.0,17.90,389.39,9.55,21.20},
- {0.17142,0.00,6.910,0,0.4480,5.6820,33.80,5.1004,3,233.0,17.90,396.90,10.21,19.30},
- {0.18836,0.00,6.910,0,0.4480,5.7860,33.30,5.1004,3,233.0,17.90,396.90,14.15,20.00},
- {0.22927,0.00,6.910,0,0.4480,6.0300,85.50,5.6894,3,233.0,17.90,392.74,18.80,16.60},
- {0.25387,0.00,6.910,0,0.4480,5.3990,95.30,5.8700,3,233.0,17.90,396.90,30.81,14.40},
- {0.21977,0.00,6.910,0,0.4480,5.6020,62.00,6.0877,3,233.0,17.90,396.90,16.20,19.40},
- {0.08873,21.00,5.640,0,0.4390,5.9630,45.70,6.8147,4,243.0,16.80,395.56,13.45,19.70},
- {0.04337,21.00,5.640,0,0.4390,6.1150,63.00,6.8147,4,243.0,16.80,393.97,9.43,20.50},
- {0.05360,21.00,5.640,0,0.4390,6.5110,21.10,6.8147,4,243.0,16.80,396.90,5.28,25.00},
- {0.04981,21.00,5.640,0,0.4390,5.9980,21.40,6.8147,4,243.0,16.80,396.90,8.43,23.40},
- {0.01360,75.00,4.000,0,0.4100,5.8880,47.60,7.3197,3,469.0,21.10,396.90,14.80,18.90},
- {0.01311,90.00,1.220,0,0.4030,7.2490,21.90,8.6966,5,226.0,17.90,395.93,4.81,35.40},
- {0.02055,85.00,0.740,0,0.4100,6.3830,35.70,9.1876,2,313.0,17.30,396.90,5.77,24.70},
- {0.01432,100.00,1.320,0,0.4110,6.8160,40.50,8.3248,5,256.0,15.10,392.90,3.95,31.60},
- {0.15445,25.00,5.130,0,0.4530,6.1450,29.20,7.8148,8,284.0,19.70,390.68,6.86,23.30},
- {0.10328,25.00,5.130,0,0.4530,5.9270,47.20,6.9320,8,284.0,19.70,396.90,9.22,19.60},
- {0.14932,25.00,5.130,0,0.4530,5.7410,66.20,7.2254,8,284.0,19.70,395.11,13.15,18.70},
- {0.17171,25.00,5.130,0,0.4530,5.9660,93.40,6.8185,8,284.0,19.70,378.08,14.44,16.00},
- {0.11027,25.00,5.130,0,0.4530,6.4560,67.80,7.2255,8,284.0,19.70,396.90,6.73,22.20},
- {0.12650,25.00,5.130,0,0.4530,6.7620,43.40,7.9809,8,284.0,19.70,395.58,9.50,25.00},
- {0.01951,17.50,1.380,0,0.4161,7.1040,59.50,9.2229,3,216.0,18.60,393.24,8.05,33.00},
- {0.03584,80.00,3.370,0,0.3980,6.2900,17.80,6.6115,4,337.0,16.10,396.90,4.67,23.50},
- {0.04379,80.00,3.370,0,0.3980,5.7870,31.10,6.6115,4,337.0,16.10,396.90,10.24,19.40},
- {0.05789,12.50,6.070,0,0.4090,5.8780,21.40,6.4980,4,345.0,18.90,396.21,8.10,22.00},
- {0.13554,12.50,6.070,0,0.4090,5.5940,36.80,6.4980,4,345.0,18.90,396.90,13.09,17.40},
- {0.12816,12.50,6.070,0,0.4090,5.8850,33.00,6.4980,4,345.0,18.90,396.90,8.79,20.90},
- {0.08826,0.00,10.810,0,0.4130,6.4170,6.60,5.2873,4,305.0,19.20,383.73,6.72,24.20},
- {0.15876,0.00,10.810,0,0.4130,5.9610,17.50,5.2873,4,305.0,19.20,376.94,9.88,21.70},
- {0.09164,0.00,10.810,0,0.4130,6.0650,7.80,5.2873,4,305.0,19.20,390.91,5.52,22.80},
- {0.19539,0.00,10.810,0,0.4130,6.2450,6.20,5.2873,4,305.0,19.20,377.17,7.54,23.40},
- {0.07896,0.00,12.830,0,0.4370,6.2730,6.00,4.2515,5,398.0,18.70,394.92,6.78,24.10},
- {0.09512,0.00,12.830,0,0.4370,6.2860,45.00,4.5026,5,398.0,18.70,383.23,8.94,21.40},
- {0.10153,0.00,12.830,0,0.4370,6.2790,74.50,4.0522,5,398.0,18.70,373.66,11.97,20.00},
- {0.08707,0.00,12.830,0,0.4370,6.1400,45.80,4.0905,5,398.0,18.70,386.96,10.27,20.80},
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- {10.23300,0.00,18.100,0,0.6140,6.1850,96.70,2.1705,24,666.0,20.20,379.70,18.03,14.60},
- {14.33370,0.00,18.100,0,0.6140,6.2290,88.00,1.9512,24,666.0,20.20,383.32,13.11,21.40},
- {5.82401,0.00,18.100,0,0.5320,6.2420,64.70,3.4242,24,666.0,20.20,396.90,10.74,23.00},
- {5.70818,0.00,18.100,0,0.5320,6.7500,74.90,3.3317,24,666.0,20.20,393.07,7.74,23.70},
- {5.73116,0.00,18.100,0,0.5320,7.0610,77.00,3.4106,24,666.0,20.20,395.28,7.01,25.00},
- {2.81838,0.00,18.100,0,0.5320,5.7620,40.30,4.0983,24,666.0,20.20,392.92,10.42,21.80},
- {2.37857,0.00,18.100,0,0.5830,5.8710,41.90,3.7240,24,666.0,20.20,370.73,13.34,20.60},
- {3.67367,0.00,18.100,0,0.5830,6.3120,51.90,3.9917,24,666.0,20.20,388.62,10.58,21.20},
- {5.69175,0.00,18.100,0,0.5830,6.1140,79.80,3.5459,24,666.0,20.20,392.68,14.98,19.10},
- {4.83567,0.00,18.100,0,0.5830,5.9050,53.20,3.1523,24,666.0,20.20,388.22,11.45,20.60},
- {0.15086,0.00,27.740,0,0.6090,5.4540,92.70,1.8209,4,711.0,20.10,395.09,18.06,15.20},
- {0.18337,0.00,27.740,0,0.6090,5.4140,98.30,1.7554,4,711.0,20.10,344.05,23.97,7.00},
- {0.20746,0.00,27.740,0,0.6090,5.0930,98.00,1.8226,4,711.0,20.10,318.43,29.68,8.10},
- {0.10574,0.00,27.740,0,0.6090,5.9830,98.80,1.8681,4,711.0,20.10,390.11,18.07,13.60},
- {0.11132,0.00,27.740,0,0.6090,5.9830,83.50,2.1099,4,711.0,20.10,396.90,13.35,20.10},
- {0.17331,0.00,9.690,0,0.5850,5.7070,54.00,2.3817,6,391.0,19.20,396.90,12.01,21.80},
- {0.27957,0.00,9.690,0,0.5850,5.9260,42.60,2.3817,6,391.0,19.20,396.90,13.59,24.50},
- {0.17899,0.00,9.690,0,0.5850,5.6700,28.80,2.7986,6,391.0,19.20,393.29,17.60,23.10},
- {0.28960,0.00,9.690,0,0.5850,5.3900,72.90,2.7986,6,391.0,19.20,396.90,21.14,19.70},
- {0.26838,0.00,9.690,0,0.5850,5.7940,70.60,2.8927,6,391.0,19.20,396.90,14.10,18.30},
- {0.23912,0.00,9.690,0,0.5850,6.0190,65.30,2.4091,6,391.0,19.20,396.90,12.92,21.20},
- {0.17783,0.00,9.690,0,0.5850,5.5690,73.50,2.3999,6,391.0,19.20,395.77,15.10,17.50},
- {0.22438,0.00,9.690,0,0.5850,6.0270,79.70,2.4982,6,391.0,19.20,396.90,14.33,16.80},
- {0.06263,0.00,11.930,0,0.5730,6.5930,69.10,2.4786,1,273.0,21.00,391.99,9.67,22.40},
- {0.04527,0.00,11.930,0,0.5730,6.1200,76.70,2.2875,1,273.0,21.00,396.90,9.08,20.60},
- {0.06076,0.00,11.930,0,0.5730,6.9760,91.00,2.1675,1,273.0,21.00,396.90,5.64,23.90},
- {0.10959,0.00,11.930,0,0.5730,6.7940,89.30,2.3889,1,273.0,21.00,393.45,6.48,22.00},
- {0.04741,0.00,11.930,0,0.5730,6.0300,80.80,2.5050,1,273.0,21.00,396.90,7.88,11.90}
- };
}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/util/MLSandboxDatasets.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/util/MLSandboxDatasets.java b/examples/src/main/java/org/apache/ignite/examples/ml/util/MLSandboxDatasets.java
new file mode 100644
index 0000000..b7380e0
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/util/MLSandboxDatasets.java
@@ -0,0 +1,87 @@
+/*
+ * 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.util;
+
+/**
+ * The names of popular datasets used in examples.
+ */
+public enum MLSandboxDatasets {
+ /** The full Iris dataset from Machine Learning Repository. */
+ IRIS("examples/src/main/resources/datasets/iris.txt", false, "\t"),
+
+ /** The Titanic dataset from Kaggle competition. */
+ TITANIC("examples/src/main/resources/datasets/titanic.csv", true, ";"),
+
+ /** The 1st and 2nd classes from the Iris dataset. */
+ TWO_CLASSED_IRIS("examples/src/main/resources/datasets/two_classed_iris.csv", false, "\t"),
+
+ /** The dataset is about different computers' properties based on https://archive.ics.uci.edu/ml/datasets/Computer+Hardware. */
+ CLEARED_MACHINES("examples/src/main/resources/datasets/cleared_machines.csv", false, ";"),
+
+ /**
+ * The health data is related to death rate based on; doctor availability, hospital availability,
+ * annual per capita income, and population density people per square mile.
+ */
+ MORTALITY_DATA("examples/src/main/resources/datasets/mortalitydata.csv", false, ";"),
+
+ /**
+ * The preprocessed Glass dataset from the Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Glass+Identification
+ * There are 3 classes with labels: 1 {building_windows_float_processed}, 3 {vehicle_windows_float_processed}, 7 {headlamps}.
+ * Feature names: 'Na-Sodium', 'Mg-Magnesium', 'Al-Aluminum', 'Ba-Barium', 'Fe-Iron'.
+ */
+ GLASS_IDENTIFICATION("examples/src/main/resources/datasets/glass_identification.csv", false, ";"),
+
+ /** The Wine recognition data. Could be found <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/wine/">here</a>. */
+ WINE_RECOGNITION("examples/src/main/resources/datasets/wine.txt", false, ","),
+
+ /** The Boston house-prices dataset. Could be found <a href="https://archive.ics.uci.edu/ml/machine-learning-databases/housing/">here</a>. */
+ BOSTON_HOUSE_PRICES("examples/src/main/resources/datasets/boston_housing_dataset.txt", false, ",");
+
+ /** Filename. */
+ private final String filename;
+
+ /** The csv file has header. */
+ private final boolean hasHeader;
+
+ /** The separator between words. */
+ private final String separator;
+
+ /**
+ * @param filename Filename.
+ * @param hasHeader The csv file has header.
+ * @param separator The special sign to separate the line on words.
+ */
+ MLSandboxDatasets(final String filename, boolean hasHeader, String separator) {
+ this.filename = filename;
+ this.hasHeader = hasHeader;
+ this.separator = separator;
+ }
+
+ /** */
+ public String getFileName() { return filename; }
+
+ /** */
+ public boolean hasHeader() {
+ return hasHeader;
+ }
+
+ /** */
+ public String getSeparator() {
+ return separator;
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/util/SandboxMLCache.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/util/SandboxMLCache.java b/examples/src/main/java/org/apache/ignite/examples/ml/util/SandboxMLCache.java
new file mode 100644
index 0000000..cc607be
--- /dev/null
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/util/SandboxMLCache.java
@@ -0,0 +1,144 @@
+/*
+ * 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.util;
+
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.nio.file.Paths;
+import java.text.NumberFormat;
+import java.text.ParseException;
+import java.util.Locale;
+import java.util.Scanner;
+import java.util.UUID;
+import org.apache.ignite.Ignite;
+import org.apache.ignite.IgniteCache;
+import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
+import org.apache.ignite.configuration.CacheConfiguration;
+import org.apache.ignite.ml.math.exceptions.knn.FileParsingException;
+import org.apache.ignite.ml.math.primitives.vector.Vector;
+import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
+
+
+/**
+ * Common utility code used in some ML examples to set up test cache.
+ */
+public class SandboxMLCache {
+ /** */
+ private final Ignite ignite;
+
+ /** */
+ public SandboxMLCache(Ignite ignite) {
+ this.ignite = ignite;
+ }
+
+ /**
+ * Fills cache with data and returns it.
+ *
+ * @param data Data to fill the cache with.
+ * @return Filled Ignite Cache.
+ */
+ public IgniteCache<Integer, double[]> fillCacheWith(double[][] data) {
+ 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;
+ }
+
+ /**
+ * Fills cache with data and returns it.
+ *
+ * @param data Data to fill the cache with.
+ * @return Filled Ignite Cache.
+ */
+ public IgniteCache<Integer, Vector> getVectors(double[][] data) {
+ CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();
+ cacheConfiguration.setName("TEST_" + UUID.randomUUID());
+ cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 10));
+
+ IgniteCache<Integer, Vector> cache = ignite.createCache(cacheConfiguration);
+
+ for (int i = 0; i < data.length; i++)
+ cache.put(i, VectorUtils.of(data[i]));
+
+ return cache;
+ }
+
+ /**
+ * Fills cache with data and returns it.
+ *
+ * @param dataset The chosen dataset.
+ * @return Filled Ignite Cache.
+ * @throws FileNotFoundException If file not found.
+ */
+ public IgniteCache<Integer, Vector> fillCacheWith(MLSandboxDatasets dataset) throws FileNotFoundException {
+
+ IgniteCache<Integer, Vector> cache = getCache();
+
+ Scanner scanner = new Scanner(new File(dataset.getFileName()));
+
+ int cnt = 0;
+ while (scanner.hasNextLine()) {
+ String row = scanner.nextLine();
+ if(dataset.hasHeader() && cnt == 0) {
+ cnt++;
+ continue;
+ }
+
+ String[] cells = row.split(dataset.getSeparator());
+
+ double[] data = new double[cells.length];
+ NumberFormat format = NumberFormat.getInstance(Locale.FRANCE);
+
+ for (int i = 0; i < cells.length; i++)
+ try{
+ if(cells[i].equals("")) data[i] = Double.NaN;
+ else data[i] = Double.valueOf(cells[i]);
+ } catch (java.lang.NumberFormatException e) {
+ try {
+ data[i] = format.parse(cells[i]).doubleValue();
+ }
+ catch (ParseException e1) {
+ throw new FileParsingException(cells[i], i, Paths.get(dataset.getFileName()));
+ }
+ }
+ cache.put(cnt++, VectorUtils.of(data));
+ }
+ return cache;
+
+ }
+
+ /**
+ * Fills cache with data and returns it.
+ *
+ * @return Filled Ignite Cache.
+ */
+ private IgniteCache<Integer, Vector> getCache() {
+
+ CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();
+ cacheConfiguration.setName("TUTORIAL_" + UUID.randomUUID());
+ cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 10));
+
+ return ignite.createCache(cacheConfiguration);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/370cd3e1/examples/src/main/java/org/apache/ignite/examples/ml/util/TestCache.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/util/TestCache.java b/examples/src/main/java/org/apache/ignite/examples/ml/util/TestCache.java
deleted file mode 100644
index 454aa76..0000000
--- a/examples/src/main/java/org/apache/ignite/examples/ml/util/TestCache.java
+++ /dev/null
@@ -1,77 +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.util;
-
-import java.util.UUID;
-import org.apache.ignite.Ignite;
-import org.apache.ignite.IgniteCache;
-import org.apache.ignite.cache.affinity.rendezvous.RendezvousAffinityFunction;
-import org.apache.ignite.configuration.CacheConfiguration;
-import org.apache.ignite.ml.math.primitives.vector.Vector;
-import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
-
-/**
- * Common utility code used in some ML examples to set up test cache.
- */
-public class TestCache {
- /** */
- private final Ignite ignite;
-
- /** */
- public TestCache(Ignite ignite) {
- this.ignite = ignite;
- }
-
- /**
- * Fills cache with data and returns it.
- *
- * @param data Data to fill the cache with.
- * @return Filled Ignite Cache.
- */
- public IgniteCache<Integer, double[]> fillCacheWith(double[][] data) {
- 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;
- }
-
- /**
- * Fills cache with data and returns it.
- *
- * @param data Data to fill the cache with.
- * @return Filled Ignite Cache.
- */
- public IgniteCache<Integer, Vector> getVectors(double[][] data) {
- CacheConfiguration<Integer, Vector> cacheConfiguration = new CacheConfiguration<>();
- cacheConfiguration.setName("TEST_" + UUID.randomUUID());
- cacheConfiguration.setAffinity(new RendezvousAffinityFunction(false, 10));
-
- IgniteCache<Integer, Vector> cache = ignite.createCache(cacheConfiguration);
-
- for (int i = 0; i < data.length; i++)
- cache.put(i, VectorUtils.of(data[i]));
-
- return cache;
- }
-}