[SPARK-10631] [DOCUMENTATION, MLLIB, PYSPARK] Added documentation for few APIs

There are some missing API docs in pyspark.mllib.linalg.Vector (including DenseVector and SparseVector). We should add them based on their Scala counterparts.

Author: vinodkc <vinod.kc.in@gmail.com>

Closes #8834 from vinodkc/fix_SPARK-10631.

1 files changed, 17 insertions, 5 deletions

diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py
index 4829acb16e..f929e3e96f 100644
--- a/python/pyspark/mllib/linalg/__init__.py
+++ b/python/pyspark/mllib/linalg/__init__.py
@@ -301,11 +301,14 @@ class DenseVector(Vector):
         return DenseVector, (self.array.tostring(),)
 
     def numNonzeros(self):
+        """
+        Number of nonzero elements. This scans all active values and count non zeros
+        """
         return np.count_nonzero(self.array)
 
     def norm(self, p):
         """
-        Calculte the norm of a DenseVector.
+        Calculates the norm of a DenseVector.
 
         >>> a = DenseVector([0, -1, 2, -3])
         >>> a.norm(2)
@@ -397,10 +400,16 @@ class DenseVector(Vector):
         return np.dot(diff, diff)
 
     def toArray(self):
+        """
+        Returns an numpy.ndarray
+        """
         return self.array
 
     @property
     def values(self):
+        """
+        Returns a list of values
+        """
         return self.array
 
     def __getitem__(self, item):
@@ -479,8 +488,8 @@ class SparseVector(Vector):
 
         :param size: Size of the vector.
         :param args: Active entries, as a dictionary {index: value, ...},
-          a list of tuples [(index, value), ...], or a list of strictly i
-          ncreasing indices and a list of corresponding values [index, ...],
+          a list of tuples [(index, value), ...], or a list of strictly
+          increasing indices and a list of corresponding values [index, ...],
           [value, ...]. Inactive entries are treated as zeros.
 
         >>> SparseVector(4, {1: 1.0, 3: 5.5})
@@ -521,11 +530,14 @@ class SparseVector(Vector):
                     raise TypeError("indices array must be sorted")
 
     def numNonzeros(self):
+        """
+        Number of nonzero elements. This scans all active values and count non zeros.
+        """
         return np.count_nonzero(self.values)
 
     def norm(self, p):
         """
-        Calculte the norm of a SparseVector.
+        Calculates the norm of a SparseVector.
 
         >>> a = SparseVector(4, [0, 1], [3., -4.])
         >>> a.norm(1)
@@ -797,7 +809,7 @@ class Vectors(object):
         values (sorted by index).
 
         :param size: Size of the vector.
-        :param args: Non-zero entries, as a dictionary, list of tupes,
+        :param args: Non-zero entries, as a dictionary, list of tuples,
                      or two sorted lists containing indices and values.
 
         >>> Vectors.sparse(4, {1: 1.0, 3: 5.5})