[SPARK-5652][Mllib] Use broadcasted weights in LogisticRegressionModel

`LogisticRegressionModel`'s `predictPoint` should directly use broadcasted weights. This pr also fixes the compilation errors of two unit test suite: `JavaLogisticRegressionSuite ` and `JavaLinearRegressionSuite`.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #4429 from viirya/use_bcvalue and squashes the following commits:

5a797e5 [Liang-Chi Hsieh] Use broadcasted weights. Fix compilation error.

2 files changed, 4 insertions, 4 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 26284023b0..d4b6644792 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -84,7 +84,7 @@ public class JavaLogisticRegressionSuite implements Serializable {
       .setThreshold(0.6)
       .setProbabilityCol("myProbability");
     LogisticRegressionModel model = lr.fit(dataset);
-    assert(model.fittingParamMap().apply(lr.maxIter()) == 10);
+    assert(model.fittingParamMap().apply(lr.maxIter()).equals(10));
     assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0));
     assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6));
     assert(model.getThreshold() == 0.6);
@@ -109,7 +109,7 @@ public class JavaLogisticRegressionSuite implements Serializable {
     // Call fit() with new params, and check as many params as we can.
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
         lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
-    assert(model2.fittingParamMap().apply(lr.maxIter()) == 5);
+    assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5));
     assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1));
     assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4));
     assert(model2.getThreshold() == 0.4);
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index 5bd616e74d..40d5a92bb3 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -76,13 +76,13 @@ public class JavaLinearRegressionSuite implements Serializable {
         .setMaxIter(10)
         .setRegParam(1.0);
     LinearRegressionModel model = lr.fit(dataset);
-    assert(model.fittingParamMap().apply(lr.maxIter()) == 10);
+    assert(model.fittingParamMap().apply(lr.maxIter()).equals(10));
     assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0));
 
     // Call fit() with new params, and check as many params as we can.
     LinearRegressionModel model2 =
         lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred"));
-    assert(model2.fittingParamMap().apply(lr.maxIter()) == 5);
+    assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5));
     assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1));
     assert(model2.getPredictionCol().equals("thePred"));
   }