LinearDataGenerator¶

class pyspark.mllib.util.LinearDataGenerator[source]¶

Utils for generating linear data.

New in version 1.5.0.

Methods

generateLinearInput(intercept, weights, …)

New in version 1.5.0.

generateLinearRDD(sc, nexamples, nfeatures, eps)

Generate an RDD of LabeledPoints.

Methods Documentation

static generateLinearInput(intercept: float, weights: VectorLike, xMean: VectorLike, xVariance: VectorLike, nPoints: int, seed: int, eps: float) → List[LabeledPoint][source]¶

New in version 1.5.0.

Parameters

interceptfloat: bias factor, the term c in X’w + c
weightspyspark.mllib.linalg.Vector or convertible: feature vector, the term w in X’w + c
xMeanpyspark.mllib.linalg.Vector or convertible: Point around which the data X is centered.
xVariancepyspark.mllib.linalg.Vector or convertible: Variance of the given data
nPointsint: Number of points to be generated
seedint: Random Seed
epsfloat: Used to scale the noise. If eps is set high, the amount of gaussian noise added is more.

Returns

list: of pyspark.mllib.regression.LabeledPoints of length nPoints

static generateLinearRDD(sc: pyspark.context.SparkContext, nexamples: int, nfeatures: int, eps: float, nParts: int = 2, intercept: float = 0.0) → pyspark.rdd.RDD[LabeledPoint][source]¶: Generate an RDD of LabeledPoints.

New in version 1.5.0.

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