DenseVector#

classpyspark.mllib.linalg.DenseVector(ar)[source]#

A dense vector represented by a value array. We use numpy array forstorage and arithmetics will be delegated to the underlying numpyarray.

Examples

>>>v=Vectors.dense([1.0,2.0])>>>u=Vectors.dense([3.0,4.0])>>>v+uDenseVector([4.0, 6.0])>>>2-vDenseVector([1.0, 0.0])>>>v/2DenseVector([0.5, 1.0])>>>v*uDenseVector([3.0, 8.0])>>>u/vDenseVector([3.0, 2.0])>>>u%2DenseVector([1.0, 0.0])>>>-vDenseVector([-1.0, -2.0])

Methods

asML()	Convert this vector to the new mllib-local representation.
dot(other)	Compute the dot product of two Vectors.
norm(p)	Calculates the norm of a DenseVector.
numNonzeros()	Number of nonzero elements.
parse(s)	Parse string representation back into the DenseVector.
squared_distance(other)	Squared distance of two Vectors.
toArray()	Returns an numpy.ndarray

Attributes

values

Returns a list of values

Methods Documentation

asML()[source]#

Convert this vector to the new mllib-local representation.This does NOT copy the data; it copies references.

New in version 2.0.0.

Returns

pyspark.ml.linalg.DenseVector

dot(other)[source]#

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.

Examples

>>>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

norm(p)[source]#

Calculates the norm of a DenseVector.

Examples

>>>a=DenseVector([0,-1,2,-3])>>>a.norm(2)3.7...>>>a.norm(1)6.0

numNonzeros()[source]#

Number of nonzero elements. This scans all active values and count non zeros

staticparse(s)[source]#

Parse string representation back into the DenseVector.

Examples

>>>DenseVector.parse(' [ 0.0,1.0,2.0,  3.0]')DenseVector([0.0, 1.0, 2.0, 3.0])

squared_distance(other)[source]#

Squared distance of two Vectors.

Examples

>>>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

toArray()[source]#

Returns an numpy.ndarray

Attributes Documentation

values#

Returns a list of values