[jvm-packages] change class to object in examples (#1703)

* change class to object in examples

* fix compilation error
This commit is contained in:
Nan Zhu 2016-10-26 14:54:56 -04:00 committed by GitHub
parent 016ab89484
commit f801c22710
9 changed files with 9 additions and 13 deletions

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@ -17,7 +17,6 @@
package ml.dmlc.xgboost4j.scala.example
import java.io.File
import java.util
import scala.collection.mutable
@ -25,9 +24,8 @@ import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
import ml.dmlc.xgboost4j.java.example.util.DataLoader
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class BasicWalkThrough {
object BasicWalkThrough {
def main(args: Array[String]): Unit = {
import BasicWalkThrough._
val trainMax = new DMatrix("../../demo/data/agaricus.txt.train")
val testMax = new DMatrix("../../demo/data/agaricus.txt.test")
@ -82,9 +80,7 @@ class BasicWalkThrough {
val predicts3 = booster3.predict(testMax2)
println(checkPredicts(predicts, predicts3))
}
}
object BasicWalkThrough {
def checkPredicts(fPredicts: Array[Array[Float]], sPredicts: Array[Array[Float]]): Boolean = {
require(fPredicts.length == sPredicts.length, "the comparing predicts must be with the same " +
"length")

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@ -20,7 +20,7 @@ import scala.collection.mutable
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class BoostFromPrediction {
object BoostFromPrediction {
def main(args: Array[String]): Unit = {
println("start running example to start from a initial prediction")

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@ -19,7 +19,7 @@ import scala.collection.mutable
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class CrossValidation {
object CrossValidation {
def main(args: Array[String]): Unit = {
val trainMat: DMatrix = new DMatrix("../../demo/data/agaricus.txt.train")

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@ -32,7 +32,7 @@ import org.apache.commons.logging.{LogFactory, Log}
* function
*
*/
class CustomObjective {
object CustomObjective {
/**
* loglikelihoode loss obj function

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@ -20,7 +20,7 @@ import scala.collection.mutable
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class ExternalMemory {
object ExternalMemory {
def main(args: Array[String]): Unit = {
// this is the only difference, add a # followed by a cache prefix name
// several cache file with the prefix will be generated

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@ -25,7 +25,7 @@ import ml.dmlc.xgboost4j.scala.example.util.CustomEval
* this is an example of fit generalized linear model in xgboost
* basically, we are using linear model, instead of tree for our boosters
*/
class GeneralizedLinearModel {
object GeneralizedLinearModel {
def main(args: Array[String]): Unit = {
val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
val testMat = new DMatrix("../../demo/data/agaricus.txt.test")

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@ -20,7 +20,7 @@ import scala.collection.mutable
import ml.dmlc.xgboost4j.scala.example.util.CustomEval
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class PredictFirstNTree {
object PredictFirstNTree {
def main(args: Array[String]): Unit = {
val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")

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@ -22,7 +22,7 @@ import scala.collection.mutable
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
class PredictLeafIndices {
object PredictLeafIndices {
def main(args: Array[String]): Unit = {
val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")

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@ -35,7 +35,7 @@ case class Store(storeId: Int, storeType: String, assortment: String, competitio
competitionOpenSinceMonth: Int, competitionOpenSinceYear: Int, promo2: Int,
promo2SinceWeek: Int, promo2SinceYear: Int, promoInterval: String)
object Main {
object SparkModelTuningTool {
private def parseStoreFile(storeFilePath: String): List[Store] = {
var isHeader = true