本文整理匯總了Java中org.apache.spark.ml.regression.LinearRegressionModel類的典型用法代碼示例。如果您正苦於以下問題：Java LinearRegressionModel類的具體用法？Java LinearRegressionModel怎麽用？Java LinearRegressionModel使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
LinearRegressionModel類屬於org.apache.spark.ml.regression包，在下文中一共展示了LinearRegressionModel類的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: LinearRegressionModelConverter
import org.apache.spark.ml.regression.LinearRegressionModel; //導入依賴的package包/類
public LinearRegressionModelConverter(LinearRegressionModel model){
super(model);
}
示例2: encodeModel
import org.apache.spark.ml.regression.LinearRegressionModel; //導入依賴的package包/類
@Override
public RegressionModel encodeModel(Schema schema){
LinearRegressionModel model = getTransformer();
return RegressionModelUtil.createRegression(schema.getFeatures(), VectorUtil.toList(model.coefficients()), model.intercept(), null, schema);
}
示例3: start
import org.apache.spark.ml.regression.LinearRegressionModel; //導入依賴的package包/類
private void start() {
SparkSession spark = SparkSession.builder().appName("Simple prediction from Text File").master("local").getOrCreate();
spark.udf().register("vectorBuilder", new VectorBuilder(), new VectorUDT());
String filename = "data/tuple-data-file.csv";
StructType schema = new StructType(
new StructField[] { new StructField("_c0", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("_c1", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), true, Metadata.empty()), });
Dataset<Row> df = spark.read().format("csv").schema(schema).option("header", "false")
.load(filename);
df = df.withColumn("valuefeatures", df.col("_c0")).drop("_c0");
df = df.withColumn("label", df.col("_c1")).drop("_c1");
df.printSchema();
df = df.withColumn("features", callUDF("vectorBuilder", df.col("valuefeatures")));
df.printSchema();
df.show();
LinearRegression lr = new LinearRegression().setMaxIter(20);// .setRegParam(1).setElasticNetParam(1);
// Fit the model to the data.
LinearRegressionModel model = lr.fit(df);
// Given a dataset, predict each point's label, and show the results.
model.transform(df).show();
LinearRegressionTrainingSummary trainingSummary = model.summary();
System.out.println("numIterations: " + trainingSummary.totalIterations());
System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
trainingSummary.residuals().show();
System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
System.out.println("r2: " + trainingSummary.r2());
double intercept = model.intercept();
System.out.println("Interesection: " + intercept);
double regParam = model.getRegParam();
System.out.println("Regression parameter: " + regParam);
double tol = model.getTol();
System.out.println("Tol: " + tol);
Double feature = 7.0;
Vector features = Vectors.dense(feature);
double p = model.predict(features);
System.out.println("Prediction for feature " + feature + " is " + p);
System.out.println(8 * regParam + intercept);
}