apply google-java-style indentation and impose import orders....
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@@ -19,10 +19,11 @@ import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import ml.dmlc.xgboost4j.*;
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import org.apache.commons.logging.Log;
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import org.apache.commons.logging.LogFactory;
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import ml.dmlc.xgboost4j.*;
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/**
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* an example user define objective and eval
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* NOTE: when you do customized loss function, the default prediction value is margin
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@@ -1,15 +1,9 @@
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package ml.dmlc.xgboost4j.demo;
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import java.io.File;
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import java.io.IOException;
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import java.util.Arrays;
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import java.util.HashMap;
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import ml.dmlc.xgboost4j.Rabit;
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import ml.dmlc.xgboost4j.Booster;
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import ml.dmlc.xgboost4j.DMatrix;
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import ml.dmlc.xgboost4j.XGBoost;
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import ml.dmlc.xgboost4j.XGBoostError;
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import ml.dmlc.xgboost4j.*;
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/**
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* Distributed training example, used to quick test distributed training.
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@@ -15,14 +15,14 @@
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*/
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package ml.dmlc.xgboost4j.demo;
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import java.util.HashMap;
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import ml.dmlc.xgboost4j.Booster;
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import ml.dmlc.xgboost4j.DMatrix;
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import ml.dmlc.xgboost4j.XGBoost;
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import ml.dmlc.xgboost4j.XGBoostError;
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import ml.dmlc.xgboost4j.demo.util.CustomEval;
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import java.util.HashMap;
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/**
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* this is an example of fit generalized linear model in xgboost
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* basically, we are using linear model, instead of tree for our boosters
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@@ -17,6 +17,7 @@ package ml.dmlc.xgboost4j.demo.util;
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import org.apache.commons.logging.Log;
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import org.apache.commons.logging.LogFactory;
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import ml.dmlc.xgboost4j.DMatrix;
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import ml.dmlc.xgboost4j.IEvaluation;
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import ml.dmlc.xgboost4j.XGBoostError;
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@@ -15,12 +15,12 @@
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*/
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package ml.dmlc.xgboost4j.demo.util;
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import org.apache.commons.lang3.ArrayUtils;
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import java.io.*;
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import java.util.ArrayList;
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import java.util.List;
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import org.apache.commons.lang3.ArrayUtils;
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/**
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* util class for loading data
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*
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