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Posted to commits@ignite.apache.org by ch...@apache.org on 2017/12/28 16:13:16 UTC
[2/6] ignite git commit: IGNITE-5217: Gradient descent for OLS lin reg
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/GridAwareAbstractLinearRegressionTrainerTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/GridAwareAbstractLinearRegressionTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/GridAwareAbstractLinearRegressionTrainerTest.java
new file mode 100644
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+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/GridAwareAbstractLinearRegressionTrainerTest.java
@@ -0,0 +1,124 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.regressions.linear;
+
+import org.apache.ignite.Ignite;
+import org.apache.ignite.internal.util.IgniteUtils;
+import org.apache.ignite.ml.Trainer;
+import org.apache.ignite.ml.math.Matrix;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.functions.IgniteFunction;
+import org.apache.ignite.testframework.junits.common.GridCommonAbstractTest;
+import org.junit.Test;
+
+public abstract class GridAwareAbstractLinearRegressionTrainerTest extends GridCommonAbstractTest {
+ /** Number of nodes in grid */
+ private static final int NODE_COUNT = 3;
+
+ /**
+ * Delegate actually performs tests.
+ */
+ private final GenericLinearRegressionTrainerTest delegate;
+
+ /** */
+ private Ignite ignite;
+
+ /** */
+ public GridAwareAbstractLinearRegressionTrainerTest(
+ Trainer<LinearRegressionModel, Matrix> trainer,
+ IgniteFunction<double[][], Matrix> matrixCreator,
+ IgniteFunction<double[], Vector> vectorCreator,
+ double precision) {
+ delegate = new GenericLinearRegressionTrainerTest(trainer, matrixCreator, vectorCreator, precision);
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void beforeTestsStarted() throws Exception {
+ for (int i = 1; i <= NODE_COUNT; i++)
+ startGrid(i);
+ }
+
+ /** {@inheritDoc} */
+ @Override protected void afterTestsStopped() {
+ stopAllGrids();
+ }
+
+ /**
+ * {@inheritDoc}
+ */
+ @Override protected void beforeTest() throws Exception {
+ /* Grid instance. */
+ ignite = grid(NODE_COUNT);
+ ignite.configuration().setPeerClassLoadingEnabled(true);
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ }
+
+ /**
+ * Test trainer on regression model y = 2 * x.
+ */
+ @Test
+ public void testTrainWithoutIntercept() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainWithoutIntercept();
+ }
+
+ /**
+ * Test trainer on regression model y = -1 * x + 1.
+ */
+ @Test
+ public void testTrainWithIntercept() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainWithIntercept();
+ }
+
+ /**
+ * Tests trainer on artificial dataset with 10 observations described by 1 feature.
+ */
+ @Test
+ public void testTrainOnArtificialDataset10x1() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainOnArtificialDataset10x1();
+ }
+
+ /**
+ * Tests trainer on artificial dataset with 10 observations described by 5 features.
+ */
+ @Test
+ public void testTrainOnArtificialDataset10x5() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainOnArtificialDataset10x5();
+ }
+
+ /**
+ * Tests trainer on artificial dataset with 100 observations described by 5 features.
+ */
+ @Test
+ public void testTrainOnArtificialDataset100x5() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainOnArtificialDataset100x5();
+ }
+
+ /**
+ * Tests trainer on artificial dataset with 100 observations described by 10 features.
+ */
+ @Test
+ public void testTrainOnArtificialDataset100x10() {
+ IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
+ delegate.testTrainOnArtificialDataset100x10();
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LinearRegressionModelTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LinearRegressionModelTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LinearRegressionModelTest.java
new file mode 100644
index 0000000..aac24f4
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LinearRegressionModelTest.java
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.regressions.linear;
+
+import org.apache.ignite.ml.TestUtils;
+import org.apache.ignite.ml.math.Vector;
+import org.apache.ignite.ml.math.exceptions.CardinalityException;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+import org.junit.Test;
+
+/**
+ * Tests for {@link LinearRegressionModel}.
+ */
+public class LinearRegressionModelTest {
+ /** */
+ private static final double PRECISION = 1e-6;
+
+ /** */
+ @Test
+ public void testPredict() {
+ Vector weights = new DenseLocalOnHeapVector(new double[]{2.0, 3.0});
+ LinearRegressionModel mdl = new LinearRegressionModel(weights, 1.0);
+
+ Vector observation = new DenseLocalOnHeapVector(new double[]{1.0, 1.0});
+ TestUtils.assertEquals(1.0 + 2.0 * 1.0 + 3.0 * 1.0, mdl.apply(observation), PRECISION);
+
+ observation = new DenseLocalOnHeapVector(new double[]{2.0, 1.0});
+ TestUtils.assertEquals(1.0 + 2.0 * 2.0 + 3.0 * 1.0, mdl.apply(observation), PRECISION);
+
+ observation = new DenseLocalOnHeapVector(new double[]{1.0, 2.0});
+ TestUtils.assertEquals(1.0 + 2.0 * 1.0 + 3.0 * 2.0, mdl.apply(observation), PRECISION);
+
+ observation = new DenseLocalOnHeapVector(new double[]{-2.0, 1.0});
+ TestUtils.assertEquals(1.0 - 2.0 * 2.0 + 3.0 * 1.0, mdl.apply(observation), PRECISION);
+
+ observation = new DenseLocalOnHeapVector(new double[]{1.0, -2.0});
+ TestUtils.assertEquals(1.0 + 2.0 * 1.0 - 3.0 * 2.0, mdl.apply(observation), PRECISION);
+ }
+
+ /** */
+ @Test(expected = CardinalityException.class)
+ public void testPredictOnAnObservationWithWrongCardinality() {
+ Vector weights = new DenseLocalOnHeapVector(new double[]{2.0, 3.0});
+
+ LinearRegressionModel mdl = new LinearRegressionModel(weights, 1.0);
+
+ Vector observation = new DenseLocalOnHeapVector(new double[]{1.0});
+
+ mdl.apply(observation);
+ }
+}
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionQRTrainerTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionQRTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionQRTrainerTest.java
new file mode 100644
index 0000000..f37d71d
--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionQRTrainerTest.java
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.regressions.linear;
+
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+
+/**
+ * Tests for {@link LinearRegressionQRTrainer} on {@link DenseLocalOnHeapMatrix}.
+ */
+public class LocalLinearRegressionQRTrainerTest extends GenericLinearRegressionTrainerTest {
+ /** */
+ public LocalLinearRegressionQRTrainerTest() {
+ super(
+ new LinearRegressionQRTrainer(),
+ DenseLocalOnHeapMatrix::new,
+ DenseLocalOnHeapVector::new,
+ 1e-6
+ );
+ }
+}
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http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionSGDTrainerTest.java
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diff --git a/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionSGDTrainerTest.java b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionSGDTrainerTest.java
new file mode 100644
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--- /dev/null
+++ b/modules/ml/src/test/java/org/apache/ignite/ml/regressions/linear/LocalLinearRegressionSGDTrainerTest.java
@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.ignite.ml.regressions.linear;
+
+import org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix;
+import org.apache.ignite.ml.math.impls.vector.DenseLocalOnHeapVector;
+
+/**
+ * Tests for {@link LinearRegressionSGDTrainer} on {@link DenseLocalOnHeapMatrix}.
+ */
+public class LocalLinearRegressionSGDTrainerTest extends GenericLinearRegressionTrainerTest {
+ /** */
+ public LocalLinearRegressionSGDTrainerTest() {
+ super(
+ new LinearRegressionSGDTrainer(100_000, 1e-12),
+ DenseLocalOnHeapMatrix::new,
+ DenseLocalOnHeapVector::new,
+ 1e-2);
+ }
+}
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/resources/datasets/regression/README.md
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diff --git a/modules/ml/src/test/resources/datasets/regression/README.md b/modules/ml/src/test/resources/datasets/regression/README.md
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+++ b/modules/ml/src/test/resources/datasets/regression/README.md
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+This package contains two datasets:
+
+Boston House Prices dataset
+===========================
+
+Notes
+------
+Data Set Characteristics:
+
+ :Number of Instances: 506
+
+ :Number of Attributes: 13 numeric/categorical predictive
+
+ :Median Value (attribute 14) is usually the target
+
+ :Attribute Information (in order):
+ - CRIM per capita crime rate by town
+ - ZN proportion of residential land zoned for lots over 25,000 sq.ft.
+ - INDUS proportion of non-retail business acres per town
+ - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
+ - NOX nitric oxides concentration (parts per 10 million)
+ - RM average number of rooms per dwelling
+ - AGE proportion of owner-occupied units built prior to 1940
+ - DIS weighted distances to five Boston employment centres
+ - RAD index of accessibility to radial highways
+ - TAX full-value property-tax rate per $10,000
+ - PTRATIO pupil-teacher ratio by town
+ - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
+ - LSTAT % lower status of the population
+ - MEDV Median value of owner-occupied homes in $1000's
+
+ :Missing Attribute Values: None
+
+ :Creator: Harrison, D. and Rubinfeld, D.L.
+
+This is a copy of UCI ML housing dataset.
+http://archive.ics.uci.edu/ml/datasets/Housing
+
+
+This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
+
+The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
+prices and the demand for clean air', J. Environ. Economics & Management,
+vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
+...', Wiley, 1980. N.B. Various transformations are used in the table on
+pages 244-261 of the latter.
+
+The Boston house-price data has been used in many machine learning papers that address regression
+problems.
+
+**References**
+
+ - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
+ - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.
+ - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)
+
+
+Diabetes dataset
+================
+
+Notes
+-----
+
+Ten baseline variables, age, sex, body mass index, average blood
+pressure, and six blood serum measurements were obtained for each of n =
+442 diabetes patients, as well as the response of interest, a
+quantitative measure of disease progression one year after baseline.
+
+Data Set Characteristics:
+
+ :Number of Instances: 442
+
+ :Number of Attributes: First 10 columns are numeric predictive values
+
+ :Target: Column 11 is a quantitative measure of disease progression one year after baseline
+
+ :Attributes:
+ :Age:
+ :Sex:
+ :Body mass index:
+ :Average blood pressure:
+ :S1:
+ :S2:
+ :S3:
+ :S4:
+ :S5:
+ :S6:
+
+Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times
+n_samples
+ (i.e. the sum of squares of each column totals 1).
+
+Source URL:
+http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html
+
+For more information see:
+Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499.
+(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/ignite/blob/b2060855/modules/ml/src/test/resources/datasets/regression/boston.csv
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diff --git a/modules/ml/src/test/resources/datasets/regression/boston.csv b/modules/ml/src/test/resources/datasets/regression/boston.csv
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+24.0, 0.00632, 18.0, 2.31, 0.0, 0.538, 6.575, 65.2, 4.09, 1.0, 296.0, 15.3, 396.9, 4.98
+21.6, 0.02731, 0.0, 7.07, 0.0, 0.469, 6.421, 78.9, 4.9671, 2.0, 242.0, 17.8, 396.9, 9.14
+34.7, 0.02729, 0.0, 7.07, 0.0, 0.469, 7.185, 61.1, 4.9671, 2.0, 242.0, 17.8, 392.83, 4.03
+33.4, 0.03237, 0.0, 2.18, 0.0, 0.458, 6.998, 45.8, 6.0622, 3.0, 222.0, 18.7, 394.63, 2.94
+36.2, 0.06905, 0.0, 2.18, 0.0, 0.458, 7.147, 54.2, 6.0622, 3.0, 222.0, 18.7, 396.9, 5.33
+28.7, 0.02985, 0.0, 2.18, 0.0, 0.458, 6.43, 58.7, 6.0622, 3.0, 222.0, 18.7, 394.12, 5.21
+22.9, 0.08829, 12.5, 7.87, 0.0, 0.524, 6.012, 66.6, 5.5605, 5.0, 311.0, 15.2, 395.6, 12.43
+27.1, 0.14455, 12.5, 7.87, 0.0, 0.524, 6.172, 96.1, 5.9505, 5.0, 311.0, 15.2, 396.9, 19.15
+16.5, 0.21124, 12.5, 7.87, 0.0, 0.524, 5.631, 100.0, 6.0821, 5.0, 311.0, 15.2, 386.63, 29.93
+18.9, 0.17004, 12.5, 7.87, 0.0, 0.524, 6.004, 85.9, 6.5921, 5.0, 311.0, 15.2, 386.71, 17.1
+15.0, 0.22489, 12.5, 7.87, 0.0, 0.524, 6.377, 94.3, 6.3467, 5.0, 311.0, 15.2, 392.52, 20.45
+18.9, 0.11747, 12.5, 7.87, 0.0, 0.524, 6.009, 82.9, 6.2267, 5.0, 311.0, 15.2, 396.9, 13.27
+21.7, 0.09378, 12.5, 7.87, 0.0, 0.524, 5.889, 39.0, 5.4509, 5.0, 311.0, 15.2, 390.5, 15.71
+20.4, 0.62976, 0.0, 8.14, 0.0, 0.538, 5.949, 61.8, 4.7075, 4.0, 307.0, 21.0, 396.9, 8.26
+18.2, 0.63796, 0.0, 8.14, 0.0, 0.538, 6.096, 84.5, 4.4619, 4.0, 307.0, 21.0, 380.02, 10.26
+19.9, 0.62739, 0.0, 8.14, 0.0, 0.538, 5.834, 56.5, 4.4986, 4.0, 307.0, 21.0, 395.62, 8.47
+23.1, 1.05393, 0.0, 8.14, 0.0, 0.538, 5.935, 29.3, 4.4986, 4.0, 307.0, 21.0, 386.85, 6.58
+17.5, 0.7842, 0.0, 8.14, 0.0, 0.538, 5.99, 81.7, 4.2579, 4.0, 307.0, 21.0, 386.75, 14.67
+20.2, 0.80271, 0.0, 8.14, 0.0, 0.538, 5.456, 36.6, 3.7965, 4.0, 307.0, 21.0, 288.99, 11.69
+18.2, 0.7258, 0.0, 8.14, 0.0, 0.538, 5.727, 69.5, 3.7965, 4.0, 307.0, 21.0, 390.95, 11.28
+13.6, 1.25179, 0.0, 8.14, 0.0, 0.538, 5.57, 98.1, 3.7979, 4.0, 307.0, 21.0, 376.57, 21.02
+19.6, 0.85204, 0.0, 8.14, 0.0, 0.538, 5.965, 89.2, 4.0123, 4.0, 307.0, 21.0, 392.53, 13.83
+15.2, 1.23247, 0.0, 8.14, 0.0, 0.538, 6.142, 91.7, 3.9769, 4.0, 307.0, 21.0, 396.9, 18.72
+14.5, 0.98843, 0.0, 8.14, 0.0, 0.538, 5.813, 100.0, 4.0952, 4.0, 307.0, 21.0, 394.54, 19.88
+15.6, 0.75026, 0.0, 8.14, 0.0, 0.538, 5.924, 94.1, 4.3996, 4.0, 307.0, 21.0, 394.33, 16.3
+13.9, 0.84054, 0.0, 8.14, 0.0, 0.538, 5.599, 85.7, 4.4546, 4.0, 307.0, 21.0, 303.42, 16.51
+16.6, 0.67191, 0.0, 8.14, 0.0, 0.538, 5.813, 90.3, 4.682, 4.0, 307.0, 21.0, 376.88, 14.81
+14.8, 0.95577, 0.0, 8.14, 0.0, 0.538, 6.047, 88.8, 4.4534, 4.0, 307.0, 21.0, 306.38, 17.28
+18.4, 0.77299, 0.0, 8.14, 0.0, 0.538, 6.495, 94.4, 4.4547, 4.0, 307.0, 21.0, 387.94, 12.8
+21.0, 1.00245, 0.0, 8.14, 0.0, 0.538, 6.674, 87.3, 4.239, 4.0, 307.0, 21.0, 380.23, 11.98
+12.7, 1.13081, 0.0, 8.14, 0.0, 0.538, 5.713, 94.1, 4.233, 4.0, 307.0, 21.0, 360.17, 22.6
+14.5, 1.35472, 0.0, 8.14, 0.0, 0.538, 6.072, 100.0, 4.175, 4.0, 307.0, 21.0, 376.73, 13.04
+13.2, 1.38799, 0.0, 8.14, 0.0, 0.538, 5.95, 82.0, 3.99, 4.0, 307.0, 21.0, 232.6, 27.71
+13.1, 1.15172, 0.0, 8.14, 0.0, 0.538, 5.701, 95.0, 3.7872, 4.0, 307.0, 21.0, 358.77, 18.35
+13.5, 1.61282, 0.0, 8.14, 0.0, 0.538, 6.096, 96.9, 3.7598, 4.0, 307.0, 21.0, 248.31, 20.34
+18.9, 0.06417, 0.0, 5.96, 0.0, 0.499, 5.933, 68.2, 3.3603, 5.0, 279.0, 19.2, 396.9, 9.68
+20.0, 0.09744, 0.0, 5.96, 0.0, 0.499, 5.841, 61.4, 3.3779, 5.0, 279.0, 19.2, 377.56, 11.41
+21.0, 0.08014, 0.0, 5.96, 0.0, 0.499, 5.85, 41.5, 3.9342, 5.0, 279.0, 19.2, 396.9, 8.77
+24.7, 0.17505, 0.0, 5.96, 0.0, 0.499, 5.966, 30.2, 3.8473, 5.0, 279.0, 19.2, 393.43, 10.13
+30.8, 0.02763, 75.0, 2.95, 0.0, 0.428, 6.595, 21.8, 5.4011, 3.0, 252.0, 18.3, 395.63, 4.32
+34.9, 0.03359, 75.0, 2.95, 0.0, 0.428, 7.024, 15.8, 5.4011, 3.0, 252.0, 18.3, 395.62, 1.98
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\ No newline at end of file