diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index 86d4186a2c..26f7415e1a 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -41,6 +41,14 @@ pyspark.ml.clustering module
     :undoc-members:
     :inherited-members:
 
+pyspark.ml.linalg module
+----------------------------
+
+.. automodule:: pyspark.ml.linalg
+    :members:
+    :undoc-members:
+    :inherited-members:
+
 pyspark.ml.recommendation module
 --------------------------------
 
diff --git a/python/pyspark/ml/linalg/__init__.py b/python/pyspark/ml/linalg/__init__.py
new file mode 100644
index 0000000000..f42c589b92
--- /dev/null
+++ b/python/pyspark/ml/linalg/__init__.py
@@ -0,0 +1,1145 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+MLlib utilities for linear algebra. For dense vectors, MLlib
+uses the NumPy C{array} type, so you can simply pass NumPy arrays
+around. For sparse vectors, users can construct a L{SparseVector}
+object from MLlib or pass SciPy C{scipy.sparse} column vectors if
+SciPy is available in their environment.
+"""
+
+import sys
+import array
+import struct
+
+if sys.version >= '3':
+    basestring = str
+    xrange = range
+    import copyreg as copy_reg
+    long = int
+else:
+    from itertools import izip as zip
+    import copy_reg
+
+import numpy as np
+
+from pyspark import since
+from pyspark.sql.types import UserDefinedType, StructField, StructType, ArrayType, DoubleType, \
+    IntegerType, ByteType, BooleanType
+
+
+__all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors',
+           'Matrix', 'DenseMatrix', 'SparseMatrix', 'Matrices']
+
+
+if sys.version_info[:2] == (2, 7):
+    # speed up pickling array in Python 2.7
+    def fast_pickle_array(ar):
+        return array.array, (ar.typecode, ar.tostring())
+    copy_reg.pickle(array.array, fast_pickle_array)
+
+
+# Check whether we have SciPy. MLlib works without it too, but if we have it, some methods,
+# such as _dot and _serialize_double_vector, start to support scipy.sparse matrices.
+
+try:
+    import scipy.sparse
+    _have_scipy = True
+except:
+    # No SciPy in environment, but that's okay
+    _have_scipy = False
+
+
+def _convert_to_vector(l):
+    if isinstance(l, Vector):
+        return l
+    elif type(l) in (array.array, np.array, np.ndarray, list, tuple, xrange):
+        return DenseVector(l)
+    elif _have_scipy and scipy.sparse.issparse(l):
+        assert l.shape[1] == 1, "Expected column vector"
+        csc = l.tocsc()
+        return SparseVector(l.shape[0], csc.indices, csc.data)
+    else:
+        raise TypeError("Cannot convert type %s into Vector" % type(l))
+
+
+def _vector_size(v):
+    """
+    Returns the size of the vector.
+
+    >>> _vector_size([1., 2., 3.])
+    3
+    >>> _vector_size((1., 2., 3.))
+    3
+    >>> _vector_size(array.array('d', [1., 2., 3.]))
+    3
+    >>> _vector_size(np.zeros(3))
+    3
+    >>> _vector_size(np.zeros((3, 1)))
+    3
+    >>> _vector_size(np.zeros((1, 3)))
+    Traceback (most recent call last):
+        ...
+    ValueError: Cannot treat an ndarray of shape (1, 3) as a vector
+    """
+    if isinstance(v, Vector):
+        return len(v)
+    elif type(v) in (array.array, list, tuple, xrange):
+        return len(v)
+    elif type(v) == np.ndarray:
+        if v.ndim == 1 or (v.ndim == 2 and v.shape[1] == 1):
+            return len(v)
+        else:
+            raise ValueError("Cannot treat an ndarray of shape %s as a vector" % str(v.shape))
+    elif _have_scipy and scipy.sparse.issparse(v):
+        assert v.shape[1] == 1, "Expected column vector"
+        return v.shape[0]
+    else:
+        raise TypeError("Cannot treat type %s as a vector" % type(v))
+
+
+def _format_float(f, digits=4):
+    s = str(round(f, digits))
+    if '.' in s:
+        s = s[:s.index('.') + 1 + digits]
+    return s
+
+
+def _format_float_list(l):
+    return [_format_float(x) for x in l]
+
+
+def _double_to_long_bits(value):
+    if np.isnan(value):
+        value = float('nan')
+    # pack double into 64 bits, then unpack as long int
+    return struct.unpack('Q', struct.pack('d', value))[0]
+
+
+class VectorUDT(UserDefinedType):
+    """
+    SQL user-defined type (UDT) for Vector.
+    """
+
+    @classmethod
+    def sqlType(cls):
+        return StructType([
+            StructField("type", ByteType(), False),
+            StructField("size", IntegerType(), True),
+            StructField("indices", ArrayType(IntegerType(), False), True),
+            StructField("values", ArrayType(DoubleType(), False), True)])
+
+    @classmethod
+    def module(cls):
+        return "pyspark.ml.linalg"
+
+    @classmethod
+    def scalaUDT(cls):
+        return "org.apache.spark.ml.linalg.VectorUDT"
+
+    def serialize(self, obj):
+        if isinstance(obj, SparseVector):
+            indices = [int(i) for i in obj.indices]
+            values = [float(v) for v in obj.values]
+            return (0, obj.size, indices, values)
+        elif isinstance(obj, DenseVector):
+            values = [float(v) for v in obj]
+            return (1, None, None, values)
+        else:
+            raise TypeError("cannot serialize %r of type %r" % (obj, type(obj)))
+
+    def deserialize(self, datum):
+        assert len(datum) == 4, \
+            "VectorUDT.deserialize given row with length %d but requires 4" % len(datum)
+        tpe = datum[0]
+        if tpe == 0:
+            return SparseVector(datum[1], datum[2], datum[3])
+        elif tpe == 1:
+            return DenseVector(datum[3])
+        else:
+            raise ValueError("do not recognize type %r" % tpe)
+
+    def simpleString(self):
+        return "vector"
+
+
+class MatrixUDT(UserDefinedType):
+    """
+    SQL user-defined type (UDT) for Matrix.
+    """
+
+    @classmethod
+    def sqlType(cls):
+        return StructType([
+            StructField("type", ByteType(), False),
+            StructField("numRows", IntegerType(), False),
+            StructField("numCols", IntegerType(), False),
+            StructField("colPtrs", ArrayType(IntegerType(), False), True),
+            StructField("rowIndices", ArrayType(IntegerType(), False), True),
+            StructField("values", ArrayType(DoubleType(), False), True),
+            StructField("isTransposed", BooleanType(), False)])
+
+    @classmethod
+    def module(cls):
+        return "pyspark.ml.linalg"
+
+    @classmethod
+    def scalaUDT(cls):
+        return "org.apache.spark.ml.linalg.MatrixUDT"
+
+    def serialize(self, obj):
+        if isinstance(obj, SparseMatrix):
+            colPtrs = [int(i) for i in obj.colPtrs]
+            rowIndices = [int(i) for i in obj.rowIndices]
+            values = [float(v) for v in obj.values]
+            return (0, obj.numRows, obj.numCols, colPtrs,
+                    rowIndices, values, bool(obj.isTransposed))
+        elif isinstance(obj, DenseMatrix):
+            values = [float(v) for v in obj.values]
+            return (1, obj.numRows, obj.numCols, None, None, values,
+                    bool(obj.isTransposed))
+        else:
+            raise TypeError("cannot serialize type %r" % (type(obj)))
+
+    def deserialize(self, datum):
+        assert len(datum) == 7, \
+            "MatrixUDT.deserialize given row with length %d but requires 7" % len(datum)
+        tpe = datum[0]
+        if tpe == 0:
+            return SparseMatrix(*datum[1:])
+        elif tpe == 1:
+            return DenseMatrix(datum[1], datum[2], datum[5], datum[6])
+        else:
+            raise ValueError("do not recognize type %r" % tpe)
+
+    def simpleString(self):
+        return "matrix"
+
+
+class Vector(object):
+
+    __UDT__ = VectorUDT()
+
+    """
+    Abstract class for DenseVector and SparseVector
+    """
+    def toArray(self):
+        """
+        Convert the vector into an numpy.ndarray
+
+        :return: numpy.ndarray
+        """
+        raise NotImplementedError
+
+
+class DenseVector(Vector):
+    """
+    A dense vector represented by a value array. We use numpy array for
+    storage and arithmetics will be delegated to the underlying numpy
+    array.
+
+    >>> v = Vectors.dense([1.0, 2.0])
+    >>> u = Vectors.dense([3.0, 4.0])
+    >>> v + u
+    DenseVector([4.0, 6.0])
+    >>> 2 - v
+    DenseVector([1.0, 0.0])
+    >>> v / 2
+    DenseVector([0.5, 1.0])
+    >>> v * u
+    DenseVector([3.0, 8.0])
+    >>> u / v
+    DenseVector([3.0, 2.0])
+    >>> u % 2
+    DenseVector([1.0, 0.0])
+    """
+    def __init__(self, ar):
+        if isinstance(ar, bytes):
+            ar = np.frombuffer(ar, dtype=np.float64)
+        elif not isinstance(ar, np.ndarray):
+            ar = np.array(ar, dtype=np.float64)
+        if ar.dtype != np.float64:
+            ar = ar.astype(np.float64)
+        self.array = ar
+
+    def __reduce__(self):
+        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):
+        """
+        Calculates the norm of a DenseVector.
+
+        >>> a = DenseVector([0, -1, 2, -3])
+        >>> a.norm(2)
+        3.7...
+        >>> a.norm(1)
+        6.0
+        """
+        return np.linalg.norm(self.array, p)
+
+    def dot(self, other):
+        """
+        Compute the dot product of two Vectors. We support
+        (Numpy array, list, SparseVector, or SciPy sparse)
+        and a target NumPy array that is either 1- or 2-dimensional.
+        Equivalent to calling numpy.dot of the two vectors.
+
+        >>> dense = DenseVector(array.array('d', [1., 2.]))
+        >>> dense.dot(dense)
+        5.0
+        >>> dense.dot(SparseVector(2, [0, 1], [2., 1.]))
+        4.0
+        >>> dense.dot(range(1, 3))
+        5.0
+        >>> dense.dot(np.array(range(1, 3)))
+        5.0
+        >>> dense.dot([1.,])
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> dense.dot(np.reshape([1., 2., 3., 4.], (2, 2), order='F'))
+        array([  5.,  11.])
+        >>> dense.dot(np.reshape([1., 2., 3.], (3, 1), order='F'))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        """
+        if type(other) == np.ndarray:
+            if other.ndim > 1:
+                assert len(self) == other.shape[0], "dimension mismatch"
+            return np.dot(self.array, other)
+        elif _have_scipy and scipy.sparse.issparse(other):
+            assert len(self) == other.shape[0], "dimension mismatch"
+            return other.transpose().dot(self.toArray())
+        else:
+            assert len(self) == _vector_size(other), "dimension mismatch"
+            if isinstance(other, SparseVector):
+                return other.dot(self)
+            elif isinstance(other, Vector):
+                return np.dot(self.toArray(), other.toArray())
+            else:
+                return np.dot(self.toArray(), other)
+
+    def squared_distance(self, other):
+        """
+        Squared distance of two Vectors.
+
+        >>> dense1 = DenseVector(array.array('d', [1., 2.]))
+        >>> dense1.squared_distance(dense1)
+        0.0
+        >>> dense2 = np.array([2., 1.])
+        >>> dense1.squared_distance(dense2)
+        2.0
+        >>> dense3 = [2., 1.]
+        >>> dense1.squared_distance(dense3)
+        2.0
+        >>> sparse1 = SparseVector(2, [0, 1], [2., 1.])
+        >>> dense1.squared_distance(sparse1)
+        2.0
+        >>> dense1.squared_distance([1.,])
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> dense1.squared_distance(SparseVector(1, [0,], [1.,]))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        """
+        assert len(self) == _vector_size(other), "dimension mismatch"
+        if isinstance(other, SparseVector):
+            return other.squared_distance(self)
+        elif _have_scipy and scipy.sparse.issparse(other):
+            return _convert_to_vector(other).squared_distance(self)
+
+        if isinstance(other, Vector):
+            other = other.toArray()
+        elif not isinstance(other, np.ndarray):
+            other = np.array(other)
+        diff = self.toArray() - other
+        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):
+        return self.array[item]
+
+    def __len__(self):
+        return len(self.array)
+
+    def __str__(self):
+        return "[" + ",".join([str(v) for v in self.array]) + "]"
+
+    def __repr__(self):
+        return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array))
+
+    def __eq__(self, other):
+        if isinstance(other, DenseVector):
+            return np.array_equal(self.array, other.array)
+        elif isinstance(other, SparseVector):
+            if len(self) != other.size:
+                return False
+            return Vectors._equals(list(xrange(len(self))), self.array, other.indices, other.values)
+        return False
+
+    def __ne__(self, other):
+        return not self == other
+
+    def __hash__(self):
+        size = len(self)
+        result = 31 + size
+        nnz = 0
+        i = 0
+        while i < size and nnz < 128:
+            if self.array[i] != 0:
+                result = 31 * result + i
+                bits = _double_to_long_bits(self.array[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
+    def __getattr__(self, item):
+        return getattr(self.array, item)
+
+    def _delegate(op):
+        def func(self, other):
+            if isinstance(other, DenseVector):
+                other = other.array
+            return DenseVector(getattr(self.array, op)(other))
+        return func
+
+    __neg__ = _delegate("__neg__")
+    __add__ = _delegate("__add__")
+    __sub__ = _delegate("__sub__")
+    __mul__ = _delegate("__mul__")
+    __div__ = _delegate("__div__")
+    __truediv__ = _delegate("__truediv__")
+    __mod__ = _delegate("__mod__")
+    __radd__ = _delegate("__radd__")
+    __rsub__ = _delegate("__rsub__")
+    __rmul__ = _delegate("__rmul__")
+    __rdiv__ = _delegate("__rdiv__")
+    __rtruediv__ = _delegate("__rtruediv__")
+    __rmod__ = _delegate("__rmod__")
+
+
+class SparseVector(Vector):
+    """
+    A simple sparse vector class for passing data to MLlib. Users may
+    alternatively pass SciPy's {scipy.sparse} data types.
+    """
+    def __init__(self, size, *args):
+        """
+        Create a sparse vector, using either a dictionary, a list of
+        (index, value) pairs, or two separate arrays of indices and
+        values (sorted by index).
+
+        :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
+          increasing indices and a list of corresponding values [index, ...],
+          [value, ...]. Inactive entries are treated as zeros.
+
+        >>> SparseVector(4, {1: 1.0, 3: 5.5})
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        >>> SparseVector(4, [(1, 1.0), (3, 5.5)])
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        >>> SparseVector(4, [1, 3], [1.0, 5.5])
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        """
+        self.size = int(size)
+        """ Size of the vector. """
+        assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
+        if len(args) == 1:
+            pairs = args[0]
+            if type(pairs) == dict:
+                pairs = pairs.items()
+            pairs = sorted(pairs)
+            self.indices = np.array([p[0] for p in pairs], dtype=np.int32)
+            """ A list of indices corresponding to active entries. """
+            self.values = np.array([p[1] for p in pairs], dtype=np.float64)
+            """ A list of values corresponding to active entries. """
+        else:
+            if isinstance(args[0], bytes):
+                assert isinstance(args[1], bytes), "values should be string too"
+                if args[0]:
+                    self.indices = np.frombuffer(args[0], np.int32)
+                    self.values = np.frombuffer(args[1], np.float64)
+                else:
+                    # np.frombuffer() doesn't work well with empty string in older version
+                    self.indices = np.array([], dtype=np.int32)
+                    self.values = np.array([], dtype=np.float64)
+            else:
+                self.indices = np.array(args[0], dtype=np.int32)
+                self.values = np.array(args[1], dtype=np.float64)
+            assert len(self.indices) == len(self.values), "index and value arrays not same length"
+            for i in xrange(len(self.indices) - 1):
+                if self.indices[i] >= self.indices[i + 1]:
+                    raise TypeError(
+                        "Indices %s and %s are not strictly increasing"
+                        % (self.indices[i], self.indices[i + 1]))
+
+    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):
+        """
+        Calculates the norm of a SparseVector.
+
+        >>> a = SparseVector(4, [0, 1], [3., -4.])
+        >>> a.norm(1)
+        7.0
+        >>> a.norm(2)
+        5.0
+        """
+        return np.linalg.norm(self.values, p)
+
+    def __reduce__(self):
+        return (
+            SparseVector,
+            (self.size, self.indices.tostring(), self.values.tostring()))
+
+    def dot(self, other):
+        """
+        Dot product with a SparseVector or 1- or 2-dimensional Numpy array.
+
+        >>> a = SparseVector(4, [1, 3], [3.0, 4.0])
+        >>> a.dot(a)
+        25.0
+        >>> a.dot(array.array('d', [1., 2., 3., 4.]))
+        22.0
+        >>> b = SparseVector(4, [2], [1.0])
+        >>> a.dot(b)
+        0.0
+        >>> a.dot(np.array([[1, 1], [2, 2], [3, 3], [4, 4]]))
+        array([ 22.,  22.])
+        >>> a.dot([1., 2., 3.])
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> a.dot(np.array([1., 2.]))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> a.dot(DenseVector([1., 2.]))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> a.dot(np.zeros((3, 2)))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        """
+
+        if isinstance(other, np.ndarray):
+            if other.ndim not in [2, 1]:
+                raise ValueError("Cannot call dot with %d-dimensional array" % other.ndim)
+            assert len(self) == other.shape[0], "dimension mismatch"
+            return np.dot(self.values, other[self.indices])
+
+        assert len(self) == _vector_size(other), "dimension mismatch"
+
+        if isinstance(other, DenseVector):
+            return np.dot(other.array[self.indices], self.values)
+
+        elif isinstance(other, SparseVector):
+            # Find out common indices.
+            self_cmind = np.in1d(self.indices, other.indices, assume_unique=True)
+            self_values = self.values[self_cmind]
+            if self_values.size == 0:
+                return 0.0
+            else:
+                other_cmind = np.in1d(other.indices, self.indices, assume_unique=True)
+                return np.dot(self_values, other.values[other_cmind])
+
+        else:
+            return self.dot(_convert_to_vector(other))
+
+    def squared_distance(self, other):
+        """
+        Squared distance from a SparseVector or 1-dimensional NumPy array.
+
+        >>> a = SparseVector(4, [1, 3], [3.0, 4.0])
+        >>> a.squared_distance(a)
+        0.0
+        >>> a.squared_distance(array.array('d', [1., 2., 3., 4.]))
+        11.0
+        >>> a.squared_distance(np.array([1., 2., 3., 4.]))
+        11.0
+        >>> b = SparseVector(4, [2], [1.0])
+        >>> a.squared_distance(b)
+        26.0
+        >>> b.squared_distance(a)
+        26.0
+        >>> b.squared_distance([1., 2.])
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        >>> b.squared_distance(SparseVector(3, [1,], [1.0,]))
+        Traceback (most recent call last):
+            ...
+        AssertionError: dimension mismatch
+        """
+        assert len(self) == _vector_size(other), "dimension mismatch"
+
+        if isinstance(other, np.ndarray) or isinstance(other, DenseVector):
+            if isinstance(other, np.ndarray) and other.ndim != 1:
+                raise Exception("Cannot call squared_distance with %d-dimensional array" %
+                                other.ndim)
+            if isinstance(other, DenseVector):
+                other = other.array
+            sparse_ind = np.zeros(other.size, dtype=bool)
+            sparse_ind[self.indices] = True
+            dist = other[sparse_ind] - self.values
+            result = np.dot(dist, dist)
+
+            other_ind = other[~sparse_ind]
+            result += np.dot(other_ind, other_ind)
+            return result
+
+        elif isinstance(other, SparseVector):
+            result = 0.0
+            i, j = 0, 0
+            while i < len(self.indices) and j < len(other.indices):
+                if self.indices[i] == other.indices[j]:
+                    diff = self.values[i] - other.values[j]
+                    result += diff * diff
+                    i += 1
+                    j += 1
+                elif self.indices[i] < other.indices[j]:
+                    result += self.values[i] * self.values[i]
+                    i += 1
+                else:
+                    result += other.values[j] * other.values[j]
+                    j += 1
+            while i < len(self.indices):
+                result += self.values[i] * self.values[i]
+                i += 1
+            while j < len(other.indices):
+                result += other.values[j] * other.values[j]
+                j += 1
+            return result
+        else:
+            return self.squared_distance(_convert_to_vector(other))
+
+    def toArray(self):
+        """
+        Returns a copy of this SparseVector as a 1-dimensional NumPy array.
+        """
+        arr = np.zeros((self.size,), dtype=np.float64)
+        arr[self.indices] = self.values
+        return arr
+
+    def __len__(self):
+        return self.size
+
+    def __str__(self):
+        inds = "[" + ",".join([str(i) for i in self.indices]) + "]"
+        vals = "[" + ",".join([str(v) for v in self.values]) + "]"
+        return "(" + ",".join((str(self.size), inds, vals)) + ")"
+
+    def __repr__(self):
+        inds = self.indices
+        vals = self.values
+        entries = ", ".join(["{0}: {1}".format(inds[i], _format_float(vals[i]))
+                             for i in xrange(len(inds))])
+        return "SparseVector({0}, {{{1}}})".format(self.size, entries)
+
+    def __eq__(self, other):
+        if isinstance(other, SparseVector):
+            return other.size == self.size and np.array_equal(other.indices, self.indices) \
+                and np.array_equal(other.values, self.values)
+        elif isinstance(other, DenseVector):
+            if self.size != len(other):
+                return False
+            return Vectors._equals(self.indices, self.values, list(xrange(len(other))), other.array)
+        return False
+
+    def __getitem__(self, index):
+        inds = self.indices
+        vals = self.values
+        if not isinstance(index, int):
+            raise TypeError(
+                "Indices must be of type integer, got type %s" % type(index))
+
+        if index >= self.size or index < -self.size:
+            raise ValueError("Index %d out of bounds." % index)
+        if index < 0:
+            index += self.size
+
+        if (inds.size == 0) or (index > inds.item(-1)):
+            return 0.
+
+        insert_index = np.searchsorted(inds, index)
+        row_ind = inds[insert_index]
+        if row_ind == index:
+            return vals[insert_index]
+        return 0.
+
+    def __ne__(self, other):
+        return not self.__eq__(other)
+
+    def __hash__(self):
+        result = 31 + self.size
+        nnz = 0
+        i = 0
+        while i < len(self.values) and nnz < 128:
+            if self.values[i] != 0:
+                result = 31 * result + int(self.indices[i])
+                bits = _double_to_long_bits(self.values[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
+
+class Vectors(object):
+
+    """
+    Factory methods for working with vectors. Note that dense vectors
+    are simply represented as NumPy array objects, so there is no need
+    to covert them for use in MLlib. For sparse vectors, the factory
+    methods in this class create an MLlib-compatible type, or users
+    can pass in SciPy's C{scipy.sparse} column vectors.
+    """
+
+    @staticmethod
+    def sparse(size, *args):
+        """
+        Create a sparse vector, using either a dictionary, a list of
+        (index, value) pairs, or two separate arrays of indices and
+        values (sorted by index).
+
+        :param size: Size of the vector.
+        :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})
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        >>> Vectors.sparse(4, [(1, 1.0), (3, 5.5)])
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        >>> Vectors.sparse(4, [1, 3], [1.0, 5.5])
+        SparseVector(4, {1: 1.0, 3: 5.5})
+        """
+        return SparseVector(size, *args)
+
+    @staticmethod
+    def dense(*elements):
+        """
+        Create a dense vector of 64-bit floats from a Python list or numbers.
+
+        >>> Vectors.dense([1, 2, 3])
+        DenseVector([1.0, 2.0, 3.0])
+        >>> Vectors.dense(1.0, 2.0)
+        DenseVector([1.0, 2.0])
+        """
+        if len(elements) == 1 and not isinstance(elements[0], (float, int, long)):
+            # it's list, numpy.array or other iterable object.
+            elements = elements[0]
+        return DenseVector(elements)
+
+    @staticmethod
+    def squared_distance(v1, v2):
+        """
+        Squared distance between two vectors.
+        a and b can be of type SparseVector, DenseVector, np.ndarray
+        or array.array.
+
+        >>> a = Vectors.sparse(4, [(0, 1), (3, 4)])
+        >>> b = Vectors.dense([2, 5, 4, 1])
+        >>> a.squared_distance(b)
+        51.0
+        """
+        v1, v2 = _convert_to_vector(v1), _convert_to_vector(v2)
+        return v1.squared_distance(v2)
+
+    @staticmethod
+    def norm(vector, p):
+        """
+        Find norm of the given vector.
+        """
+        return _convert_to_vector(vector).norm(p)
+
+    @staticmethod
+    def zeros(size):
+        return DenseVector(np.zeros(size))
+
+    @staticmethod
+    def _equals(v1_indices, v1_values, v2_indices, v2_values):
+        """
+        Check equality between sparse/dense vectors,
+        v1_indices and v2_indices assume to be strictly increasing.
+        """
+        v1_size = len(v1_values)
+        v2_size = len(v2_values)
+        k1 = 0
+        k2 = 0
+        all_equal = True
+        while all_equal:
+            while k1 < v1_size and v1_values[k1] == 0:
+                k1 += 1
+            while k2 < v2_size and v2_values[k2] == 0:
+                k2 += 1
+
+            if k1 >= v1_size or k2 >= v2_size:
+                return k1 >= v1_size and k2 >= v2_size
+
+            all_equal = v1_indices[k1] == v2_indices[k2] and v1_values[k1] == v2_values[k2]
+            k1 += 1
+            k2 += 1
+        return all_equal
+
+
+class Matrix(object):
+
+    __UDT__ = MatrixUDT()
+
+    """
+    Represents a local matrix.
+    """
+    def __init__(self, numRows, numCols, isTransposed=False):
+        self.numRows = numRows
+        self.numCols = numCols
+        self.isTransposed = isTransposed
+
+    def toArray(self):
+        """
+        Returns its elements in a NumPy ndarray.
+        """
+        raise NotImplementedError
+
+    @staticmethod
+    def _convert_to_array(array_like, dtype):
+        """
+        Convert Matrix attributes which are array-like or buffer to array.
+        """
+        if isinstance(array_like, bytes):
+            return np.frombuffer(array_like, dtype=dtype)
+        return np.asarray(array_like, dtype=dtype)
+
+
+class DenseMatrix(Matrix):
+    """
+    Column-major dense matrix.
+    """
+    def __init__(self, numRows, numCols, values, isTransposed=False):
+        Matrix.__init__(self, numRows, numCols, isTransposed)
+        values = self._convert_to_array(values, np.float64)
+        assert len(values) == numRows * numCols
+        self.values = values
+
+    def __reduce__(self):
+        return DenseMatrix, (
+            self.numRows, self.numCols, self.values.tostring(),
+            int(self.isTransposed))
+
+    def __str__(self):
+        """
+        Pretty printing of a DenseMatrix
+
+        >>> dm = DenseMatrix(2, 2, range(4))
+        >>> print(dm)
+        DenseMatrix([[ 0.,  2.],
+                     [ 1.,  3.]])
+        >>> dm = DenseMatrix(2, 2, range(4), isTransposed=True)
+        >>> print(dm)
+        DenseMatrix([[ 0.,  1.],
+                     [ 2.,  3.]])
+        """
+        # Inspired by __repr__ in scipy matrices.
+        array_lines = repr(self.toArray()).splitlines()
+
+        # We need to adjust six spaces which is the difference in number
+        # of letters between "DenseMatrix" and "array"
+        x = '\n'.join([(" " * 6 + line) for line in array_lines[1:]])
+        return array_lines[0].replace("array", "DenseMatrix") + "\n" + x
+
+    def __repr__(self):
+        """
+        Representation of a DenseMatrix
+
+        >>> dm = DenseMatrix(2, 2, range(4))
+        >>> dm
+        DenseMatrix(2, 2, [0.0, 1.0, 2.0, 3.0], False)
+        """
+        # If the number of values are less than seventeen then return as it is.
+        # Else return first eight values and last eight values.
+        if len(self.values) < 17:
+            entries = _format_float_list(self.values)
+        else:
+            entries = (
+                _format_float_list(self.values[:8]) +
+                ["..."] +
+                _format_float_list(self.values[-8:])
+            )
+
+        entries = ", ".join(entries)
+        return "DenseMatrix({0}, {1}, [{2}], {3})".format(
+            self.numRows, self.numCols, entries, self.isTransposed)
+
+    def toArray(self):
+        """
+        Return an numpy.ndarray
+
+        >>> m = DenseMatrix(2, 2, range(4))
+        >>> m.toArray()
+        array([[ 0.,  2.],
+               [ 1.,  3.]])
+        """
+        if self.isTransposed:
+            return np.asfortranarray(
+                self.values.reshape((self.numRows, self.numCols)))
+        else:
+            return self.values.reshape((self.numRows, self.numCols), order='F')
+
+    def toSparse(self):
+        """Convert to SparseMatrix"""
+        if self.isTransposed:
+            values = np.ravel(self.toArray(), order='F')
+        else:
+            values = self.values
+        indices = np.nonzero(values)[0]
+        colCounts = np.bincount(indices // self.numRows)
+        colPtrs = np.cumsum(np.hstack(
+            (0, colCounts, np.zeros(self.numCols - colCounts.size))))
+        values = values[indices]
+        rowIndices = indices % self.numRows
+
+        return SparseMatrix(self.numRows, self.numCols, colPtrs, rowIndices, values)
+
+    def __getitem__(self, indices):
+        i, j = indices
+        if i < 0 or i >= self.numRows:
+            raise ValueError("Row index %d is out of range [0, %d)"
+                             % (i, self.numRows))
+        if j >= self.numCols or j < 0:
+            raise ValueError("Column index %d is out of range [0, %d)"
+                             % (j, self.numCols))
+
+        if self.isTransposed:
+            return self.values[i * self.numCols + j]
+        else:
+            return self.values[i + j * self.numRows]
+
+    def __eq__(self, other):
+        if (not isinstance(other, DenseMatrix) or
+                self.numRows != other.numRows or
+                self.numCols != other.numCols):
+            return False
+
+        self_values = np.ravel(self.toArray(), order='F')
+        other_values = np.ravel(other.toArray(), order='F')
+        return all(self_values == other_values)
+
+
+class SparseMatrix(Matrix):
+    """Sparse Matrix stored in CSC format."""
+    def __init__(self, numRows, numCols, colPtrs, rowIndices, values,
+                 isTransposed=False):
+        Matrix.__init__(self, numRows, numCols, isTransposed)
+        self.colPtrs = self._convert_to_array(colPtrs, np.int32)
+        self.rowIndices = self._convert_to_array(rowIndices, np.int32)
+        self.values = self._convert_to_array(values, np.float64)
+
+        if self.isTransposed:
+            if self.colPtrs.size != numRows + 1:
+                raise ValueError("Expected colPtrs of size %d, got %d."
+                                 % (numRows + 1, self.colPtrs.size))
+        else:
+            if self.colPtrs.size != numCols + 1:
+                raise ValueError("Expected colPtrs of size %d, got %d."
+                                 % (numCols + 1, self.colPtrs.size))
+        if self.rowIndices.size != self.values.size:
+            raise ValueError("Expected rowIndices of length %d, got %d."
+                             % (self.rowIndices.size, self.values.size))
+
+    def __str__(self):
+        """
+        Pretty printing of a SparseMatrix
+
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
+        >>> print(sm1)
+        2 X 2 CSCMatrix
+        (0,0) 2.0
+        (1,0) 3.0
+        (1,1) 4.0
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4], True)
+        >>> print(sm1)
+        2 X 2 CSRMatrix
+        (0,0) 2.0
+        (0,1) 3.0
+        (1,1) 4.0
+        """
+        spstr = "{0} X {1} ".format(self.numRows, self.numCols)
+        if self.isTransposed:
+            spstr += "CSRMatrix\n"
+        else:
+            spstr += "CSCMatrix\n"
+
+        cur_col = 0
+        smlist = []
+
+        # Display first 16 values.
+        if len(self.values) <= 16:
+            zipindval = zip(self.rowIndices, self.values)
+        else:
+            zipindval = zip(self.rowIndices[:16], self.values[:16])
+        for i, (rowInd, value) in enumerate(zipindval):
+            if self.colPtrs[cur_col + 1] <= i:
+                cur_col += 1
+            if self.isTransposed:
+                smlist.append('({0},{1}) {2}'.format(
+                    cur_col, rowInd, _format_float(value)))
+            else:
+                smlist.append('({0},{1}) {2}'.format(
+                    rowInd, cur_col, _format_float(value)))
+        spstr += "\n".join(smlist)
+
+        if len(self.values) > 16:
+            spstr += "\n.." * 2
+        return spstr
+
+    def __repr__(self):
+        """
+        Representation of a SparseMatrix
+
+        >>> sm1 = SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2, 3, 4])
+        >>> sm1
+        SparseMatrix(2, 2, [0, 2, 3], [0, 1, 1], [2.0, 3.0, 4.0], False)
+        """
+        rowIndices = list(self.rowIndices)
+        colPtrs = list(self.colPtrs)
+
+        if len(self.values) <= 16:
+            values = _format_float_list(self.values)
+
+        else:
+            values = (
+                _format_float_list(self.values[:8]) +
+                ["..."] +
+                _format_float_list(self.values[-8:])
+            )
+            rowIndices = rowIndices[:8] + ["..."] + rowIndices[-8:]
+
+        if len(self.colPtrs) > 16:
+            colPtrs = colPtrs[:8] + ["..."] + colPtrs[-8:]
+
+        values = ", ".join(values)
+        rowIndices = ", ".join([str(ind) for ind in rowIndices])
+        colPtrs = ", ".join([str(ptr) for ptr in colPtrs])
+        return "SparseMatrix({0}, {1}, [{2}], [{3}], [{4}], {5})".format(
+            self.numRows, self.numCols, colPtrs, rowIndices,
+            values, self.isTransposed)
+
+    def __reduce__(self):
+        return SparseMatrix, (
+            self.numRows, self.numCols, self.colPtrs.tostring(),
+            self.rowIndices.tostring(), self.values.tostring(),
+            int(self.isTransposed))
+
+    def __getitem__(self, indices):
+        i, j = indices
+        if i < 0 or i >= self.numRows:
+            raise ValueError("Row index %d is out of range [0, %d)"
+                             % (i, self.numRows))
+        if j < 0 or j >= self.numCols:
+            raise ValueError("Column index %d is out of range [0, %d)"
+                             % (j, self.numCols))
+
+        # If a CSR matrix is given, then the row index should be searched
+        # for in ColPtrs, and the column index should be searched for in the
+        # corresponding slice obtained from rowIndices.
+        if self.isTransposed:
+            j, i = i, j
+
+        colStart = self.colPtrs[j]
+        colEnd = self.colPtrs[j + 1]
+        nz = self.rowIndices[colStart: colEnd]
+        ind = np.searchsorted(nz, i) + colStart
+        if ind < colEnd and self.rowIndices[ind] == i:
+            return self.values[ind]
+        else:
+            return 0.0
+
+    def toArray(self):
+        """
+        Return an numpy.ndarray
+        """
+        A = np.zeros((self.numRows, self.numCols), dtype=np.float64, order='F')
+        for k in xrange(self.colPtrs.size - 1):
+            startptr = self.colPtrs[k]
+            endptr = self.colPtrs[k + 1]
+            if self.isTransposed:
+                A[k, self.rowIndices[startptr:endptr]] = self.values[startptr:endptr]
+            else:
+                A[self.rowIndices[startptr:endptr], k] = self.values[startptr:endptr]
+        return A
+
+    def toDense(self):
+        densevals = np.ravel(self.toArray(), order='F')
+        return DenseMatrix(self.numRows, self.numCols, densevals)
+
+    # TODO: More efficient implementation:
+    def __eq__(self, other):
+        return np.all(self.toArray() == other.toArray())
+
+
+class Matrices(object):
+    @staticmethod
+    def dense(numRows, numCols, values):
+        """
+        Create a DenseMatrix
+        """
+        return DenseMatrix(numRows, numCols, values)
+
+    @staticmethod
+    def sparse(numRows, numCols, colPtrs, rowIndices, values):
+        """
+        Create a SparseMatrix
+        """
+        return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values)
+
+
+def _test():
+    import doctest
+    (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS)
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 8e56b0d6ff..c567905759 100755
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -18,7 +18,6 @@
 """
 Unit tests for Spark ML Python APIs.
 """
-import array
 import sys
 if sys.version > '3':
     xrange = range
@@ -40,15 +39,21 @@ else:
 
 from shutil import rmtree
 import tempfile
+import array as pyarray
 import numpy as np
+from numpy import (
+    array, array_equal, zeros, inf, random, exp, dot, all, mean, abs, arange, tile, ones)
+from numpy import sum as array_sum
 import inspect
 
-from pyspark import keyword_only
+from pyspark import keyword_only, SparkContext
 from pyspark.ml import Estimator, Model, Pipeline, PipelineModel, Transformer
 from pyspark.ml.classification import *
 from pyspark.ml.clustering import *
 from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
 from pyspark.ml.feature import *
+from pyspark.ml.linalg import Vector, SparseVector, DenseVector, VectorUDT,\
+    DenseMatrix, SparseMatrix, Vectors, Matrices, MatrixUDT, _convert_to_vector
 from pyspark.ml.param import Param, Params, TypeConverters
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
 from pyspark.ml.recommendation import ALS
@@ -57,13 +62,28 @@ from pyspark.ml.regression import LinearRegression, DecisionTreeRegressor, \
 from pyspark.ml.tuning import *
 from pyspark.ml.wrapper import JavaParams
 from pyspark.mllib.common import _java2py
-from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector
+from pyspark.mllib.linalg import SparseVector as OldSparseVector, DenseVector as OldDenseVector,\
+    DenseMatrix as OldDenseMatrix, MatrixUDT as OldMatrixUDT, SparseMatrix as OldSparseMatrix,\
+    Vectors as OldVectors, VectorUDT as OldVectorUDT
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.serializers import PickleSerializer
 from pyspark.sql import DataFrame, Row, SparkSession
 from pyspark.sql.functions import rand
 from pyspark.sql.utils import IllegalArgumentException
 from pyspark.storagelevel import *
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 
+ser = PickleSerializer()
+
+
+class MLlibTestCase(unittest.TestCase):
+    def setUp(self):
+        self.sc = SparkContext('local[4]', "MLlib tests")
+        self.spark = SparkSession(self.sc)
+
+    def tearDown(self):
+        self.spark.stop()
+
 
 class SparkSessionTestCase(PySparkTestCase):
     @classmethod
@@ -142,23 +162,23 @@ class ParamTypeConversionTests(PySparkTestCase):
 
     def test_vector(self):
         ewp = ElementwiseProduct(scalingVec=[1, 3])
-        self.assertEqual(ewp.getScalingVec(), DenseVector([1.0, 3.0]))
+        self.assertEqual(ewp.getScalingVec(), OldDenseVector([1.0, 3.0]))
         ewp = ElementwiseProduct(scalingVec=np.array([1.2, 3.4]))
-        self.assertEqual(ewp.getScalingVec(), DenseVector([1.2, 3.4]))
+        self.assertEqual(ewp.getScalingVec(), OldDenseVector([1.2, 3.4]))
         self.assertRaises(TypeError, lambda: ElementwiseProduct(scalingVec=["a", "b"]))
 
     def test_list(self):
         l = [0, 1]
-        for lst_like in [l, np.array(l), DenseVector(l), SparseVector(len(l), range(len(l)), l),
-                         array.array('l', l), xrange(2), tuple(l)]:
+        for lst_like in [l, np.array(l), OldDenseVector(l), OldSparseVector(len(l),
+                         range(len(l)), l), pyarray.array('l', l), xrange(2), tuple(l)]:
             converted = TypeConverters.toList(lst_like)
             self.assertEqual(type(converted), list)
             self.assertListEqual(converted, l)
 
     def test_list_int(self):
-        for indices in [[1.0, 2.0], np.array([1.0, 2.0]), DenseVector([1.0, 2.0]),
-                        SparseVector(2, {0: 1.0, 1: 2.0}), xrange(1, 3), (1.0, 2.0),
-                        array.array('d', [1.0, 2.0])]:
+        for indices in [[1.0, 2.0], np.array([1.0, 2.0]), OldDenseVector([1.0, 2.0]),
+                        OldSparseVector(2, {0: 1.0, 1: 2.0}), xrange(1, 3), (1.0, 2.0),
+                        pyarray.array('d', [1.0, 2.0])]:
             vs = VectorSlicer(indices=indices)
             self.assertListEqual(vs.getIndices(), [1, 2])
             self.assertTrue(all([type(v) == int for v in vs.getIndices()]))
@@ -390,9 +410,9 @@ class FeatureTests(SparkSessionTestCase):
 
     def test_idf(self):
         dataset = self.spark.createDataFrame([
-            (DenseVector([1.0, 2.0]),),
-            (DenseVector([0.0, 1.0]),),
-            (DenseVector([3.0, 0.2]),)], ["tf"])
+            (OldDenseVector([1.0, 2.0]),),
+            (OldDenseVector([0.0, 1.0]),),
+            (OldDenseVector([3.0, 0.2]),)], ["tf"])
         idf0 = IDF(inputCol="tf")
         self.assertListEqual(idf0.params, [idf0.inputCol, idf0.minDocFreq, idf0.outputCol])
         idf0m = idf0.fit(dataset, {idf0.outputCol: "idf"})
@@ -437,10 +457,10 @@ class FeatureTests(SparkSessionTestCase):
 
     def test_count_vectorizer_with_binary(self):
         dataset = self.spark.createDataFrame([
-            (0, "a a a b b c".split(' '), SparseVector(3, {0: 1.0, 1: 1.0, 2: 1.0}),),
-            (1, "a a".split(' '), SparseVector(3, {0: 1.0}),),
-            (2, "a b".split(' '), SparseVector(3, {0: 1.0, 1: 1.0}),),
-            (3, "c".split(' '), SparseVector(3, {2: 1.0}),)], ["id", "words", "expected"])
+            (0, "a a a b b c".split(' '), OldSparseVector(3, {0: 1.0, 1: 1.0, 2: 1.0}),),
+            (1, "a a".split(' '), OldSparseVector(3, {0: 1.0}),),
+            (2, "a b".split(' '), OldSparseVector(3, {0: 1.0, 1: 1.0}),),
+            (3, "c".split(' '), OldSparseVector(3, {2: 1.0}),)], ["id", "words", "expected"])
         cv = CountVectorizer(binary=True, inputCol="words", outputCol="features")
         model = cv.fit(dataset)
 
@@ -561,11 +581,11 @@ class CrossValidatorTests(SparkSessionTestCase):
         # Save/load for CrossValidator will be added later: SPARK-13786
         temp_path = tempfile.mkdtemp()
         dataset = self.spark.createDataFrame(
-            [(Vectors.dense([0.0]), 0.0),
-             (Vectors.dense([0.4]), 1.0),
-             (Vectors.dense([0.5]), 0.0),
-             (Vectors.dense([0.6]), 1.0),
-             (Vectors.dense([1.0]), 1.0)] * 10,
+            [(OldVectors.dense([0.0]), 0.0),
+             (OldVectors.dense([0.4]), 1.0),
+             (OldVectors.dense([0.5]), 0.0),
+             (OldVectors.dense([0.6]), 1.0),
+             (OldVectors.dense([1.0]), 1.0)] * 10,
             ["features", "label"])
         lr = LogisticRegression()
         grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
@@ -634,11 +654,11 @@ class TrainValidationSplitTests(SparkSessionTestCase):
         # Save/load for TrainValidationSplit will be added later: SPARK-13786
         temp_path = tempfile.mkdtemp()
         dataset = self.spark.createDataFrame(
-            [(Vectors.dense([0.0]), 0.0),
-             (Vectors.dense([0.4]), 1.0),
-             (Vectors.dense([0.5]), 0.0),
-             (Vectors.dense([0.6]), 1.0),
-             (Vectors.dense([1.0]), 1.0)] * 10,
+            [(OldVectors.dense([0.0]), 0.0),
+             (OldVectors.dense([0.4]), 1.0),
+             (OldVectors.dense([0.5]), 0.0),
+             (OldVectors.dense([0.6]), 1.0),
+             (OldVectors.dense([1.0]), 1.0)] * 10,
             ["features", "label"])
         lr = LogisticRegression()
         grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
@@ -837,8 +857,8 @@ class LDATest(SparkSessionTestCase):
     def test_persistence(self):
         # Test save/load for LDA, LocalLDAModel, DistributedLDAModel.
         df = self.spark.createDataFrame([
-            [1, Vectors.dense([0.0, 1.0])],
-            [2, Vectors.sparse(2, {0: 1.0})],
+            [1, OldVectors.dense([0.0, 1.0])],
+            [2, OldVectors.sparse(2, {0: 1.0})],
         ], ["id", "features"])
         # Fit model
         lda = LDA(k=2, seed=1, optimizer="em")
@@ -873,9 +893,8 @@ class LDATest(SparkSessionTestCase):
 class TrainingSummaryTest(SparkSessionTestCase):
 
     def test_linear_regression_summary(self):
-        from pyspark.mllib.linalg import Vectors
-        df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
-                                         (0.0, 2.0, Vectors.sparse(1, [], []))],
+        df = self.spark.createDataFrame([(1.0, 2.0, OldVectors.dense(1.0)),
+                                         (0.0, 2.0, OldVectors.sparse(1, [], []))],
                                         ["label", "weight", "features"])
         lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal", weightCol="weight",
                               fitIntercept=False)
@@ -947,9 +966,8 @@ class TrainingSummaryTest(SparkSessionTestCase):
         self.assertAlmostEqual(sameSummary.deviance, s.deviance)
 
     def test_logistic_regression_summary(self):
-        from pyspark.mllib.linalg import Vectors
-        df = self.spark.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
-                                         (0.0, 2.0, Vectors.sparse(1, [], []))],
+        df = self.spark.createDataFrame([(1.0, 2.0, OldVectors.dense(1.0)),
+                                         (0.0, 2.0, OldVectors.sparse(1, [], []))],
                                         ["label", "weight", "features"])
         lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight", fitIntercept=False)
         model = lr.fit(df)
@@ -978,9 +996,9 @@ class TrainingSummaryTest(SparkSessionTestCase):
 class OneVsRestTests(SparkSessionTestCase):
 
     def test_copy(self):
-        df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
-                                         (1.0, Vectors.sparse(2, [], [])),
-                                         (2.0, Vectors.dense(0.5, 0.5))],
+        df = self.spark.createDataFrame([(0.0, OldVectors.dense(1.0, 0.8)),
+                                         (1.0, OldVectors.sparse(2, [], [])),
+                                         (2.0, OldVectors.dense(0.5, 0.5))],
                                         ["label", "features"])
         lr = LogisticRegression(maxIter=5, regParam=0.01)
         ovr = OneVsRest(classifier=lr)
@@ -992,9 +1010,9 @@ class OneVsRestTests(SparkSessionTestCase):
         self.assertEqual(model1.getPredictionCol(), "indexed")
 
     def test_output_columns(self):
-        df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
-                                         (1.0, Vectors.sparse(2, [], [])),
-                                         (2.0, Vectors.dense(0.5, 0.5))],
+        df = self.spark.createDataFrame([(0.0, OldVectors.dense(1.0, 0.8)),
+                                         (1.0, OldVectors.sparse(2, [], [])),
+                                         (2.0, OldVectors.dense(0.5, 0.5))],
                                         ["label", "features"])
         lr = LogisticRegression(maxIter=5, regParam=0.01)
         ovr = OneVsRest(classifier=lr)
@@ -1004,9 +1022,9 @@ class OneVsRestTests(SparkSessionTestCase):
 
     def test_save_load(self):
         temp_path = tempfile.mkdtemp()
-        df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
-                                         (1.0, Vectors.sparse(2, [], [])),
-                                         (2.0, Vectors.dense(0.5, 0.5))],
+        df = self.spark.createDataFrame([(0.0, OldVectors.dense(1.0, 0.8)),
+                                         (1.0, OldVectors.sparse(2, [], [])),
+                                         (2.0, OldVectors.dense(0.5, 0.5))],
                                         ["label", "features"])
         lr = LogisticRegression(maxIter=5, regParam=0.01)
         ovr = OneVsRest(classifier=lr)
@@ -1034,7 +1052,7 @@ class HashingTFTest(SparkSessionTestCase):
         hashingTF.setInputCol("words").setOutputCol("features").setNumFeatures(n).setBinary(True)
         output = hashingTF.transform(df)
         features = output.select("features").first().features.toArray()
-        expected = Vectors.dense([1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).toArray()
+        expected = OldVectors.dense([1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).toArray()
         for i in range(0, n):
             self.assertAlmostEqual(features[i], expected[i], 14, "Error at " + str(i) +
                                    ": expected " + str(expected[i]) + ", got " + str(features[i]))
@@ -1109,6 +1127,354 @@ class DefaultValuesTests(PySparkTestCase):
                     self.check_params(cls())
 
 
+def _squared_distance(a, b):
+    if isinstance(a, Vector):
+        return a.squared_distance(b)
+    else:
+        return b.squared_distance(a)
+
+
+class VectorTests(MLlibTestCase):
+
+    def _test_serialize(self, v):
+        self.assertEqual(v, ser.loads(ser.dumps(v)))
+        jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v)))
+        nv = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvec)))
+        self.assertEqual(v, nv)
+        vs = [v] * 100
+        jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs)))
+        nvs = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvecs)))
+        self.assertEqual(vs, nvs)
+
+    def test_serialize(self):
+        # Because pickle path still uses old vector/matrix
+        # TODO: Change this to new vector/matrix when pickle for new vector/matrix is ready.
+        self._test_serialize(OldDenseVector(range(10)))
+        self._test_serialize(OldDenseVector(array([1., 2., 3., 4.])))
+        self._test_serialize(OldDenseVector(pyarray.array('d', range(10))))
+        self._test_serialize(OldSparseVector(4, {1: 1, 3: 2}))
+        self._test_serialize(OldSparseVector(3, {}))
+        self._test_serialize(OldDenseMatrix(2, 3, range(6)))
+        sm1 = OldSparseMatrix(
+            3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0])
+        self._test_serialize(sm1)
+
+    def test_dot(self):
+        sv = SparseVector(4, {1: 1, 3: 2})
+        dv = DenseVector(array([1., 2., 3., 4.]))
+        lst = DenseVector([1, 2, 3, 4])
+        mat = array([[1., 2., 3., 4.],
+                     [1., 2., 3., 4.],
+                     [1., 2., 3., 4.],
+                     [1., 2., 3., 4.]])
+        arr = pyarray.array('d', [0, 1, 2, 3])
+        self.assertEqual(10.0, sv.dot(dv))
+        self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
+        self.assertEqual(30.0, dv.dot(dv))
+        self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
+        self.assertEqual(30.0, lst.dot(dv))
+        self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))
+        self.assertEqual(7.0, sv.dot(arr))
+
+    def test_squared_distance(self):
+        sv = SparseVector(4, {1: 1, 3: 2})
+        dv = DenseVector(array([1., 2., 3., 4.]))
+        lst = DenseVector([4, 3, 2, 1])
+        lst1 = [4, 3, 2, 1]
+        arr = pyarray.array('d', [0, 2, 1, 3])
+        narr = array([0, 2, 1, 3])
+        self.assertEqual(15.0, _squared_distance(sv, dv))
+        self.assertEqual(25.0, _squared_distance(sv, lst))
+        self.assertEqual(20.0, _squared_distance(dv, lst))
+        self.assertEqual(15.0, _squared_distance(dv, sv))
+        self.assertEqual(25.0, _squared_distance(lst, sv))
+        self.assertEqual(20.0, _squared_distance(lst, dv))
+        self.assertEqual(0.0, _squared_distance(sv, sv))
+        self.assertEqual(0.0, _squared_distance(dv, dv))
+        self.assertEqual(0.0, _squared_distance(lst, lst))
+        self.assertEqual(25.0, _squared_distance(sv, lst1))
+        self.assertEqual(3.0, _squared_distance(sv, arr))
+        self.assertEqual(3.0, _squared_distance(sv, narr))
+
+    def test_hash(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEqual(hash(v1), hash(v2))
+        self.assertEqual(hash(v1), hash(v3))
+        self.assertEqual(hash(v2), hash(v3))
+        self.assertFalse(hash(v1) == hash(v4))
+        self.assertFalse(hash(v2) == hash(v4))
+
+    def test_eq(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
+        v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
+        v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEqual(v1, v2)
+        self.assertEqual(v1, v3)
+        self.assertFalse(v2 == v4)
+        self.assertFalse(v1 == v5)
+        self.assertFalse(v1 == v6)
+
+    def test_equals(self):
+        indices = [1, 2, 4]
+        values = [1., 3., 2.]
+        self.assertTrue(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 1., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 2., 2.]))
+
+    def test_conversion(self):
+        # numpy arrays should be automatically upcast to float64
+        # tests for fix of [SPARK-5089]
+        v = array([1, 2, 3, 4], dtype='float64')
+        dv = DenseVector(v)
+        self.assertTrue(dv.array.dtype == 'float64')
+        v = array([1, 2, 3, 4], dtype='float32')
+        dv = DenseVector(v)
+        self.assertTrue(dv.array.dtype == 'float64')
+
+    def test_sparse_vector_indexing(self):
+        sv = SparseVector(5, {1: 1, 3: 2})
+        self.assertEqual(sv[0], 0.)
+        self.assertEqual(sv[3], 2.)
+        self.assertEqual(sv[1], 1.)
+        self.assertEqual(sv[2], 0.)
+        self.assertEqual(sv[4], 0.)
+        self.assertEqual(sv[-1], 0.)
+        self.assertEqual(sv[-2], 2.)
+        self.assertEqual(sv[-3], 0.)
+        self.assertEqual(sv[-5], 0.)
+        for ind in [5, -6]:
+            self.assertRaises(ValueError, sv.__getitem__, ind)
+        for ind in [7.8, '1']:
+            self.assertRaises(TypeError, sv.__getitem__, ind)
+
+        zeros = SparseVector(4, {})
+        self.assertEqual(zeros[0], 0.0)
+        self.assertEqual(zeros[3], 0.0)
+        for ind in [4, -5]:
+            self.assertRaises(ValueError, zeros.__getitem__, ind)
+
+        empty = SparseVector(0, {})
+        for ind in [-1, 0, 1]:
+            self.assertRaises(ValueError, empty.__getitem__, ind)
+
+    def test_matrix_indexing(self):
+        mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10])
+        expected = [[0, 6], [1, 8], [4, 10]]
+        for i in range(3):
+            for j in range(2):
+                self.assertEqual(mat[i, j], expected[i][j])
+
+    def test_repr_dense_matrix(self):
+        mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10])
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
+
+        mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10], True)
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
+
+        mat = DenseMatrix(6, 3, zeros(18))
+        self.assertTrue(
+            repr(mat),
+            'DenseMatrix(6, 3, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ..., \
+                0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], False)')
+
+    def test_repr_sparse_matrix(self):
+        sm1t = SparseMatrix(
+            3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0],
+            isTransposed=True)
+        self.assertTrue(
+            repr(sm1t),
+            'SparseMatrix(3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], True)')
+
+        indices = tile(arange(6), 3)
+        values = ones(18)
+        sm = SparseMatrix(6, 3, [0, 6, 12, 18], indices, values)
+        self.assertTrue(
+            repr(sm), "SparseMatrix(6, 3, [0, 6, 12, 18], \
+                [0, 1, 2, 3, 4, 5, 0, 1, ..., 4, 5, 0, 1, 2, 3, 4, 5], \
+                [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ..., \
+                1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], False)")
+
+        self.assertTrue(
+            str(sm),
+            "6 X 3 CSCMatrix\n\
+            (0,0) 1.0\n(1,0) 1.0\n(2,0) 1.0\n(3,0) 1.0\n(4,0) 1.0\n(5,0) 1.0\n\
+            (0,1) 1.0\n(1,1) 1.0\n(2,1) 1.0\n(3,1) 1.0\n(4,1) 1.0\n(5,1) 1.0\n\
+            (0,2) 1.0\n(1,2) 1.0\n(2,2) 1.0\n(3,2) 1.0\n..\n..")
+
+        sm = SparseMatrix(1, 18, zeros(19), [], [])
+        self.assertTrue(
+            repr(sm),
+            'SparseMatrix(1, 18, \
+                [0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0], [], [], False)')
+
+    def test_sparse_matrix(self):
+        # Test sparse matrix creation.
+        sm1 = SparseMatrix(
+            3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0])
+        self.assertEqual(sm1.numRows, 3)
+        self.assertEqual(sm1.numCols, 4)
+        self.assertEqual(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4])
+        self.assertEqual(sm1.rowIndices.tolist(), [1, 2, 1, 2])
+        self.assertEqual(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0])
+        self.assertTrue(
+            repr(sm1),
+            'SparseMatrix(3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0], False)')
+
+        # Test indexing
+        expected = [
+            [0, 0, 0, 0],
+            [1, 0, 4, 0],
+            [2, 0, 5, 0]]
+
+        for i in range(3):
+            for j in range(4):
+                self.assertEqual(expected[i][j], sm1[i, j])
+        self.assertTrue(array_equal(sm1.toArray(), expected))
+
+        # Test conversion to dense and sparse.
+        smnew = sm1.toDense().toSparse()
+        self.assertEqual(sm1.numRows, smnew.numRows)
+        self.assertEqual(sm1.numCols, smnew.numCols)
+        self.assertTrue(array_equal(sm1.colPtrs, smnew.colPtrs))
+        self.assertTrue(array_equal(sm1.rowIndices, smnew.rowIndices))
+        self.assertTrue(array_equal(sm1.values, smnew.values))
+
+        sm1t = SparseMatrix(
+            3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0],
+            isTransposed=True)
+        self.assertEqual(sm1t.numRows, 3)
+        self.assertEqual(sm1t.numCols, 4)
+        self.assertEqual(sm1t.colPtrs.tolist(), [0, 2, 3, 5])
+        self.assertEqual(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2])
+        self.assertEqual(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0])
+
+        expected = [
+            [3, 2, 0, 0],
+            [0, 0, 4, 0],
+            [9, 0, 8, 0]]
+
+        for i in range(3):
+            for j in range(4):
+                self.assertEqual(expected[i][j], sm1t[i, j])
+        self.assertTrue(array_equal(sm1t.toArray(), expected))
+
+    def test_dense_matrix_is_transposed(self):
+        mat1 = DenseMatrix(3, 2, [0, 4, 1, 6, 3, 9], isTransposed=True)
+        mat = DenseMatrix(3, 2, [0, 1, 3, 4, 6, 9])
+        self.assertEqual(mat1, mat)
+
+        expected = [[0, 4], [1, 6], [3, 9]]
+        for i in range(3):
+            for j in range(2):
+                self.assertEqual(mat1[i, j], expected[i][j])
+        self.assertTrue(array_equal(mat1.toArray(), expected))
+
+        sm = mat1.toSparse()
+        self.assertTrue(array_equal(sm.rowIndices, [1, 2, 0, 1, 2]))
+        self.assertTrue(array_equal(sm.colPtrs, [0, 2, 5]))
+        self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9]))
+
+    def test_norms(self):
+        a = DenseVector([0, 2, 3, -1])
+        self.assertAlmostEqual(a.norm(2), 3.742, 3)
+        self.assertTrue(a.norm(1), 6)
+        self.assertTrue(a.norm(inf), 3)
+        a = SparseVector(4, [0, 2], [3, -4])
+        self.assertAlmostEqual(a.norm(2), 5)
+        self.assertTrue(a.norm(1), 7)
+        self.assertTrue(a.norm(inf), 4)
+
+        tmp = SparseVector(4, [0, 2], [3, 0])
+        self.assertEqual(tmp.numNonzeros(), 1)
+
+
+class VectorUDTTests(MLlibTestCase):
+
+    dv0 = DenseVector([])
+    dv1 = DenseVector([1.0, 2.0])
+    sv0 = SparseVector(2, [], [])
+    sv1 = SparseVector(2, [1], [2.0])
+    udt = VectorUDT()
+
+    old_dv0 = OldDenseVector([])
+    old_dv1 = OldDenseVector([1.0, 2.0])
+    old_sv0 = OldSparseVector(2, [], [])
+    old_sv1 = OldSparseVector(2, [1], [2.0])
+    old_udt = OldVectorUDT()
+
+    def test_json_schema(self):
+        self.assertEqual(VectorUDT.fromJson(self.udt.jsonValue()), self.udt)
+
+    def test_serialization(self):
+        for v in [self.dv0, self.dv1, self.sv0, self.sv1]:
+            self.assertEqual(v, self.udt.deserialize(self.udt.serialize(v)))
+
+    def test_infer_schema(self):
+        rdd = self.sc.parallelize([LabeledPoint(1.0, self.old_dv1),
+                                   LabeledPoint(0.0, self.old_sv1)])
+        df = rdd.toDF()
+        schema = df.schema
+        field = [f for f in schema.fields if f.name == "features"][0]
+        self.assertEqual(field.dataType, self.old_udt)
+        vectors = df.rdd.map(lambda p: p.features).collect()
+        self.assertEqual(len(vectors), 2)
+        for v in vectors:
+            if isinstance(v, OldSparseVector):
+                self.assertEqual(v, self.old_sv1)
+            elif isinstance(v, OldDenseVector):
+                self.assertEqual(v, self.old_dv1)
+            else:
+                raise TypeError("expecting a vector but got %r of type %r" % (v, type(v)))
+
+
+class MatrixUDTTests(MLlibTestCase):
+
+    dm1 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10])
+    dm2 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10], isTransposed=True)
+    sm1 = SparseMatrix(1, 1, [0, 1], [0], [2.0])
+    sm2 = SparseMatrix(2, 1, [0, 0, 1], [0], [5.0], isTransposed=True)
+    udt = MatrixUDT()
+
+    old_dm1 = OldDenseMatrix(3, 2, [0, 1, 4, 5, 9, 10])
+    old_dm2 = OldDenseMatrix(3, 2, [0, 1, 4, 5, 9, 10], isTransposed=True)
+    old_sm1 = OldSparseMatrix(1, 1, [0, 1], [0], [2.0])
+    old_sm2 = OldSparseMatrix(2, 1, [0, 0, 1], [0], [5.0], isTransposed=True)
+    old_udt = OldMatrixUDT()
+
+    def test_json_schema(self):
+        self.assertEqual(MatrixUDT.fromJson(self.udt.jsonValue()), self.udt)
+
+    def test_serialization(self):
+        for m in [self.dm1, self.dm2, self.sm1, self.sm2]:
+            self.assertEqual(m, self.udt.deserialize(self.udt.serialize(m)))
+
+    def test_infer_schema(self):
+        rdd = self.sc.parallelize([("dense", self.old_dm1), ("sparse", self.old_sm1)])
+        df = rdd.toDF()
+        schema = df.schema
+        self.assertTrue(schema.fields[1].dataType, self.old_udt)
+        matrices = df.rdd.map(lambda x: x._2).collect()
+        self.assertEqual(len(matrices), 2)
+        for m in matrices:
+            if isinstance(m, OldDenseMatrix):
+                self.assertTrue(m, self.old_dm1)
+            elif isinstance(m, OldSparseMatrix):
+                self.assertTrue(m, self.old_sm1)
+            else:
+                raise ValueError("Expected a matrix but got type %r" % type(m))
+
+
 if __name__ == "__main__":
     from pyspark.ml.tests import *
     if xmlrunner: