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/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.classification.LinearSVC
// $example off$
import org.apache.spark.sql.SparkSession
object LinearSVCExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("LinearSVCExample")
.getOrCreate()
// $example on$
// Load training data
val training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val lsvc = new LinearSVC()
.setMaxIter(10)
.setRegParam(0.1)
// Fit the model
val lsvcModel = lsvc.fit(training)
// Print the coefficients and intercept for linear svc
println(s"Coefficients: ${lsvcModel.coefficients} Intercept: ${lsvcModel.intercept}")
// $example off$
spark.stop()
}
}
// scalastyle:on println
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