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author | vectorijk <jiangkai@gmail.com> | 2015-11-02 16:12:04 -0800 |
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committer | DB Tsai <dbt@netflix.com> | 2015-11-02 16:12:04 -0800 |
commit | c020f7d9d43548d27ae4a9564ba38981fd530cb1 (patch) | |
tree | 8dc46ed1b48d88852323747b2d86aedd1c770b64 /mllib/src/test/java | |
parent | ec03866a7ef2d0826520755d47c8c9480148a76c (diff) | |
download | spark-c020f7d9d43548d27ae4a9564ba38981fd530cb1.tar.gz spark-c020f7d9d43548d27ae4a9564ba38981fd530cb1.tar.bz2 spark-c020f7d9d43548d27ae4a9564ba38981fd530cb1.zip |
[SPARK-10592] [ML] [PySpark] Deprecate weights and use coefficients instead in ML models
Deprecated in `LogisticRegression` and `LinearRegression`
Author: vectorijk <jiangkai@gmail.com>
Closes #9311 from vectorijk/spark-10592.
Diffstat (limited to 'mllib/src/test/java')
-rw-r--r-- | mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java index 253cabf013..cbabafe1b5 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java @@ -47,16 +47,16 @@ public class JavaOneVsRestSuite implements Serializable { jsql = new SQLContext(jsc); int nPoints = 3; - // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2. + // The following coefficients and xMean/xVariance are computed from iris dataset with lambda=0.2. // As a result, we are drawing samples from probability distribution of an actual model. - double[] weights = { + double[] coefficients = { -0.57997, 0.912083, -0.371077, -0.819866, 2.688191, -0.16624, -0.84355, -0.048509, -0.301789, 4.170682 }; double[] xMean = {5.843, 3.057, 3.758, 1.199}; double[] xVariance = {0.6856, 0.1899, 3.116, 0.581}; List<LabeledPoint> points = JavaConverters.seqAsJavaListConverter( - generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42) + generateMultinomialLogisticInput(coefficients, xMean, xVariance, true, nPoints, 42) ).asJava(); datasetRDD = jsc.parallelize(points, 2); dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class); |