[SPARK-11473][ML] R-like summary statistics with intercept for OLS via normal equation solver

Follow up [SPARK-9836](https://issues.apache.org/jira/browse/SPARK-9836), we should also support summary statistics for ```intercept```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9485 from yanboliang/spark-11473.

1 files changed, 8 insertions, 8 deletions

diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index d7b4fd92c3..7648bf1326 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -55,15 +55,15 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     >>> lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal")
     >>> model = lr.fit(df)
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
-    >>> model.transform(test0).head().prediction
-    -1.0
-    >>> model.weights
-    DenseVector([1.0])
-    >>> model.intercept
-    0.0
+    >>> abs(model.transform(test0).head().prediction - (-1.0)) < 0.001
+    True
+    >>> abs(model.coefficients[0] - 1.0) < 0.001
+    True
+    >>> abs(model.intercept - 0.0) < 0.001
+    True
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
-    >>> model.transform(test1).head().prediction
-    1.0
+    >>> abs(model.transform(test1).head().prediction - 1.0) < 0.001
+    True
     >>> lr.setParams("vector")
     Traceback (most recent call last):
         ...