1 files changed, 65 insertions, 1 deletions
diff --git a/python/pyspark/mllib/stat.py b/python/pyspark/mllib/stat.py
index 982906b9d0..a73abc5ff9 100644
--- a/python/pyspark/mllib/stat.py
+++ b/python/pyspark/mllib/stat.py
@@ -22,12 +22,76 @@ Python package for statistical functions in MLlib.
 from pyspark.mllib._common import \
     _get_unmangled_double_vector_rdd, _get_unmangled_rdd, \
     _serialize_double, _serialize_double_vector, \
-    _deserialize_double, _deserialize_double_matrix
+    _deserialize_double, _deserialize_double_matrix, _deserialize_double_vector
+
+
+class MultivariateStatisticalSummary(object):
+
+    """
+    Trait for multivariate statistical summary of a data matrix.
+    """
+
+    def __init__(self, sc, java_summary):
+        """
+        :param sc:  Spark context
+        :param java_summary:  Handle to Java summary object
+        """
+        self._sc = sc
+        self._java_summary = java_summary
+
+    def __del__(self):
+        self._sc._gateway.detach(self._java_summary)
+
+    def mean(self):
+        return _deserialize_double_vector(self._java_summary.mean())
+
+    def variance(self):
+        return _deserialize_double_vector(self._java_summary.variance())
+
+    def count(self):
+        return self._java_summary.count()
+
+    def numNonzeros(self):
+        return _deserialize_double_vector(self._java_summary.numNonzeros())
+
+    def max(self):
+        return _deserialize_double_vector(self._java_summary.max())
+
+    def min(self):
+        return _deserialize_double_vector(self._java_summary.min())
 
 
 class Statistics(object):
 
     @staticmethod
+    def colStats(X):
+        """
+        Computes column-wise summary statistics for the input RDD[Vector].
+
+        >>> from linalg import Vectors
+        >>> rdd = sc.parallelize([Vectors.dense([2, 0, 0, -2]),
+        ...                       Vectors.dense([4, 5, 0,  3]),
+        ...                       Vectors.dense([6, 7, 0,  8])])
+        >>> cStats = Statistics.colStats(rdd)
+        >>> cStats.mean()
+        array([ 4.,  4.,  0.,  3.])
+        >>> cStats.variance()
+        array([  4.,  13.,   0.,  25.])
+        >>> cStats.count()
+        3L
+        >>> cStats.numNonzeros()
+        array([ 3.,  2.,  0.,  3.])
+        >>> cStats.max()
+        array([ 6.,  7.,  0.,  8.])
+        >>> cStats.min()
+        array([ 2.,  0.,  0., -2.])
+        """
+        sc = X.ctx
+        Xser = _get_unmangled_double_vector_rdd(X)
+        cStats = sc._jvm.PythonMLLibAPI().colStats(Xser._jrdd)
+        return MultivariateStatisticalSummary(sc, cStats)
+
+    @staticmethod
     def corr(x, y=None, method=None):
         """
         Compute the correlation (matrix) for the input RDD(s) using the