Normalizer#

classpyspark.mllib.feature.Normalizer(p=2.0)[source]#

Normalizes samples individually to unit L^p norm

For any 1 <=p < float(‘inf’), normalizes samples usingsum(abs(vector)^p)^(1/p) as norm.

Forp = float(‘inf’), max(abs(vector)) will be used as norm fornormalization.

New in version 1.2.0.

Parameters

pfloat, optional

Normalization in L^p^ space, p = 2 by default.

Examples

>>>frompyspark.mllib.linalgimportVectors>>>v=Vectors.dense(range(3))>>>nor=Normalizer(1)>>>nor.transform(v)DenseVector([0.0, 0.3333, 0.6667])

>>>rdd=sc.parallelize([v])>>>nor.transform(rdd).collect()[DenseVector([0.0, 0.3333, 0.6667])]

>>>nor2=Normalizer(float("inf"))>>>nor2.transform(v)DenseVector([0.0, 0.5, 1.0])

Methods

transform(vector)

Applies unit length normalization on a vector.

Methods Documentation

transform(vector)[source]#

Applies unit length normalization on a vector.

New in version 1.2.0.

Parameters

vectorpyspark.mllib.linalg.Vector orpyspark.RDD

vector or RDD of vector to be normalized.

Returns

pyspark.mllib.linalg.Vector orpyspark.RDD

normalized vector(s). If the norm of the input is zero, itwill return the input vector.