Vectors

class pyspark.ml.linalg.Vectors

Factory methods for working with vectors.

Notes

Dense vectors are simply represented as NumPy array objects, so there is no need to convert 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.

Methods

dense(*elements)	Create a dense vector of 64-bit floats from a Python list or numbers.
norm(vector, p)	Find norm of the given vector.
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).
squared_distance(v1, v2)	Squared distance between two vectors.
zeros(size)

Methods Documentation

static dense(*elements: Union[float, bytes, numpy.ndarray, Iterable[float]]) → pyspark.ml.linalg.DenseVector

Create a dense vector of 64-bit floats from a Python list or numbers.

Examples

>>> Vectors.dense([1, 2, 3])
DenseVector([1.0, 2.0, 3.0])
>>> Vectors.dense(1.0, 2.0)
DenseVector([1.0, 2.0])

static norm(vector: pyspark.ml.linalg.Vector, p: NormType) → numpy.float64

Find norm of the given vector.

static sparse(size: int, *args: Union[bytes, Tuple[int, float], Iterable[float], Iterable[Tuple[int, float]], Dict[int, float]]) → pyspark.ml.linalg.SparseVector

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).

Parameters

sizeint

Size of the vector.

args

Non-zero entries, as a dictionary, list of tuples, or two sorted lists containing indices and values.

Examples

>>> 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})

static squared_distance(v1: pyspark.ml.linalg.Vector, v2: pyspark.ml.linalg.Vector) → numpy.float64

Squared distance between two vectors. a and b can be of type SparseVector, DenseVector, np.ndarray or array.array.

Examples

>>> a = Vectors.sparse(4, [(0, 1), (3, 4)])
>>> b = Vectors.dense([2, 5, 4, 1])
>>> a.squared_distance(b)
51.0

static zeros(size: int) → pyspark.ml.linalg.DenseVector

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