spark GaussianMixtureModelWrapper 源码
spark GaussianMixtureModelWrapper 代码
文件路径：/mllib/src/main/scala/org/apache/spark/mllib/api/python/GaussianMixtureModelWrapper.scala
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.mllib.api.python
import scala.collection.JavaConverters
import org.apache.spark.SparkContext
import org.apache.spark.mllib.clustering.GaussianMixtureModel
import org.apache.spark.mllib.linalg.{Vector, Vectors}
/**
* Wrapper around GaussianMixtureModel to provide helper methods in Python
*/
private[python] class GaussianMixtureModelWrapper(model: GaussianMixtureModel) {
val weights: Vector = Vectors.dense(model.weights)
val k: Int = weights.size
/**
* Returns gaussians as a List of Vectors and Matrices corresponding each MultivariateGaussian
*/
val gaussians: Array[Byte] = {
val modelGaussians = model.gaussians.map { gaussian =>
Array[Any](gaussian.mu, gaussian.sigma)
}
SerDe.dumps(JavaConverters.seqAsJavaListConverter(modelGaussians).asJava)
}
def predictSoft(point: Vector): Vector = {
Vectors.dense(model.predictSoft(point))
}
def save(sc: SparkContext, path: String): Unit = model.save(sc, path)
}
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