Package pyspark :: Package mllib :: Module linalg :: Class Vectors

Class Vectors

object --+
         |
        Vectors

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 scipy.sparse column vectors.

Instance Methods
Inherited from `object`: `__delattr__`, `__format__`, `__getattribute__`, `__hash__`, `__init__`, `__new__`, `__reduce__`, `__reduce_ex__`, `__repr__`, `__setattr__`, `__sizeof__`, `__str__`, `__subclasshook__`

Static Methods

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).

source code

dense(elements)
Create a dense vector of 64-bit floats from a Python list.

source code

Properties
Inherited from `object`: `__class__`

Method Details

sparse(size, args)
Static Method*

source code


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 tupes,
             or two sorted lists containing indices and values.

>>> print Vectors.sparse(4, {1: 1.0, 3: 5.5})
[1: 1.0, 3: 5.5]
>>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)])
[1: 1.0, 3: 5.5]
>>> print Vectors.sparse(4, [1, 3], [1.0, 5.5])
[1: 1.0, 3: 5.5]

dense(elements)
Static Method

source code

Create a dense vector of 64-bit floats from a Python list. Always returns a NumPy array.

>>> Vectors.dense([1, 2, 3])
array([ 1.,  2.,  3.])

Class Vectors

sparse(size, *args) Static Method

dense(elements) Static Method

sparse(size, args)
Static Method*

dense(elements)
Static Method