GaussianMixture¶
-
class
pyspark.mllib.clustering.GaussianMixture[source]¶ Learning algorithm for Gaussian Mixtures using the expectation-maximization algorithm.
New in version 1.3.0.
Methods
train(rdd, k[, convergenceTol, …])Train a Gaussian Mixture clustering model.
Methods Documentation
-
classmethod
train(rdd: pyspark.rdd.RDD[VectorLike], k: int, convergenceTol: float = 0.001, maxIterations: int = 100, seed: Optional[int] = None, initialModel: Optional[pyspark.mllib.clustering.GaussianMixtureModel] = None) → pyspark.mllib.clustering.GaussianMixtureModel[source]¶ Train a Gaussian Mixture clustering model.
New in version 1.3.0.
- Parameters
- rdd:
pyspark.RDD Training points as an RDD of
pyspark.mllib.linalg.Vectoror convertible sequence types.- kint
Number of independent Gaussians in the mixture model.
- convergenceTolfloat, optional
Maximum change in log-likelihood at which convergence is considered to have occurred. (default: 1e-3)
- maxIterationsint, optional
Maximum number of iterations allowed. (default: 100)
- seedint, optional
Random seed for initial Gaussian distribution. Set as None to generate seed based on system time. (default: None)
- initialModelGaussianMixtureModel, optional
Initial GMM starting point, bypassing the random initialization. (default: None)
- rdd:
-
classmethod