Hadoop/MapReduce、Spark 朴素贝叶斯分类器分类符号数据
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package cjbayesclassfier;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs; import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;import edu.umd.cloud9.io.pair.PairOfStrings;/*** * 第一步：拆分输入文件每一行，得到类输出和条件概率输出 * @author chenjie */public class CJBayesClassfier_Step1 extends Configured implements Tool { /*** * 映射器： * 输入:weather.txt * 其中一行示例如下：Sunny,Hot,High,Weak,No * 输出: * key value * (Sunny,No) 1 * (Hot,No) 1 * (High,No) 1 * (Weak,No) 1 * (CLASS,No) 1 * @author chenjie */ public static class CJBayesClassfierMapper extends Mapper<LongWritable, Text, PairOfStrings, LongWritable> { PairOfStrings outputKey = new PairOfStrings(); LongWritable outputValue = new LongWritable(1); @Override protected void map( LongWritable key, Text value, Context context) throws IOException, InterruptedException { String tokens[] = value.toString().split(","); if(tokens == null || tokens.length < 2) return; String classfier = tokens[tokens.length-1]; for(int i = 0; i < tokens.length; i++) { if(i < tokens.length-1) outputKey.set(tokens[i], classfier); else outputKey.set("CLASS", classfier); context.write(outputKey, outputValue); } } } @Deprecated public static class CJBayesClassfierReducer extends Reducer<PairOfStrings, LongWritable, PairOfStrings, LongWritable> { @Override protected void reduce( PairOfStrings key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { Long sum = 0L; for(LongWritable time : values) { sum += time.get(); } context.write(key, new LongWritable(sum)); } } public static class CJBayesClassfierReducer2 extends Reducer<PairOfStrings, LongWritable, PairOfStrings, Text> { /** * 设置多个文件输出 * */ private MultipleOutputs<PairOfStrings, Text> mos; @Override protected void setup(Context context) throws IOException, InterruptedException { mos=new MultipleOutputs<PairOfStrings, Text>(context);//初始化mos } /*** * 将key值相同的value进行累加 */ @Override protected void reduce( PairOfStrings key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { System.out.println("key =" + key ); Long sum = 0L; for(LongWritable time : values) { sum += time.get(); } String result = key.getLeftElement() + "," + key.getRightElement() + "," + sum; if(key.getLeftElement().equals("CLASS")) mos.write("CLASS", NullWritable.get(), new Text(result)); else mos.write("OTHERS", NullWritable.get(), new Text(result)); } /*** * 务必释放资源，否则不会有输出内容 */ @Override protected void cleanup( Context context) throws IOException, InterruptedException { mos.close();//释放资源 } } public static void main(String[] args) throws Exception { args = new String[2]; args[0] = "/media/chenjie/0009418200012FF3/ubuntu/weather.txt"; args[1] = "/media/chenjie/0009418200012FF3/ubuntu/CJBayes";; int jobStatus = submitJob(args); System.exit(jobStatus); } public static int submitJob(String[] args) throws Exception { int jobStatus = ToolRunner.run(new CJBayesClassfier_Step1(), args); return jobStatus; } @SuppressWarnings("deprecation") @Override public int run(String[] args) throws Exception { Configuration conf = getConf(); Job job = new Job(conf); job.setJobName("Bayes"); MultipleOutputs.addNamedOutput(job, "CLASS", TextOutputFormat.class, Text.class, Text.class); MultipleOutputs.addNamedOutput(job, "OTHERS", TextOutputFormat.class, Text.class, Text.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); job.setOutputKeyClass(PairOfStrings.class); job.setOutputValueClass(LongWritable.class); job.setMapperClass(CJBayesClassfierMapper.class); job.setReducerClass(CJBayesClassfierReducer2.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); FileSystem fs = FileSystem.get(conf); Path outPath = new Path(args[1]); if(fs.exists(outPath)) { fs.delete(outPath, true); } boolean status = job.waitForCompletion(true); return status ? 0 : 1; } }package cjbayesclassfier;import java.io.BufferedReader;import java.io.FileReader;import java.io.IOException;import java.net.URI;import java.util.HashMap;import java.util.Map;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.DoubleWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;import edu.umd.cloud9.io.pair.PairOfStrings;/*** * 第二步：计算概率 * @author chenjie * */public class CJBayesClassfier_Step2 extends Configured implements Tool { public static class CJBayesClassfierMapper2 extends Mapper<LongWritable, Text, PairOfStrings, DoubleWritable> { PairOfStrings outputKey = new PairOfStrings(); DoubleWritable outputValue = new DoubleWritable(1); private Map<String,Integer> classMap = new HashMap<String,Integer>(); @Override protected void setup(Context context) throws IOException, InterruptedException { FileReader fr = new FileReader("CLASS"); BufferedReader br = new BufferedReader(fr); String line = null; while((line = br.readLine()) != null) { String tokens[] = line.split(","); String classfier = tokens[1]; String count = tokens[2]; classMap.put(classfier, Integer.parseInt(count)); } fr.close(); br.close(); int sum = 0; for(Map.Entry<String,Integer> entry : classMap.entrySet()) { sum += entry.getValue(); } for(Map.Entry<String,Integer> entry : classMap.entrySet()) { double poss = entry.getValue() * 1.0 / sum; context.write(new PairOfStrings("CLASS", entry.getKey()), new DoubleWritable(poss)); } } @Override protected void map( LongWritable key, Text value, Context context) throws IOException, InterruptedException { String tokens[] = value.toString().split(","); if(tokens == null || tokens.length < 3) return; String X = tokens[0]; String classfier = tokens[1]; Integer count = Integer.valueOf(tokens[2]); outputKey.set(X, classfier); Integer classCount = classMap.get(classfier); outputValue.set(count * 1.0 / classCount); context.write(outputKey, outputValue); } } public static class CJBayesClassfierReducer2 extends Reducer<PairOfStrings, DoubleWritable, NullWritable, Text> { @Override protected void reduce( PairOfStrings key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException { for(DoubleWritable dw : values) context.write(NullWritable.get(), new Text(key.getLeftElement() + "," + key.getRightElement() + "," + dw)); } } public static void main(String[] args) throws Exception { args = new String[2]; args[0] = "/media/chenjie/0009418200012FF3/ubuntu/CJBayes/OTHERS-r-00000"; args[1] = "/media/chenjie/0009418200012FF3/ubuntu/CJBayes2"; int jobStatus = submitJob(args); System.exit(jobStatus); } public static int submitJob(String[] args) throws Exception { int jobStatus = ToolRunner.run(new CJBayesClassfier_Step2(), args); return jobStatus; } @SuppressWarnings("deprecation") @Override public int run(String[] args) throws Exception { Configuration conf = getConf(); Job job = new Job(conf); job.setJobName("Bayes"); job.addCacheArchive(new URI("/media/chenjie/0009418200012FF3/ubuntu/CJBayes/CLASS-r-00000" + "#CLASS")); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); job.setOutputKeyClass(PairOfStrings.class); job.setOutputValueClass(DoubleWritable.class); job.setMapperClass(CJBayesClassfierMapper2.class); job.setReducerClass(CJBayesClassfierReducer2.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); FileSystem fs = FileSystem.get(conf); Path outPath = new Path(args[1]); if(fs.exists(outPath)) { fs.delete(outPath, true); } boolean status = job.waitForCompletion(true); return status ? 0 : 1; } }package cjbayesclassfier;import java.io.BufferedReader;import java.io.FileNotFoundException;import java.io.FileReader;import java.io.IOException;import java.net.URI;import java.util.ArrayList;import java.util.List;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.DoubleWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;import edu.umd.cloud9.io.pair.PairOfStrings;/*** * 第三步：根据上一步计算的概率进行贝叶斯推断 * @author chenjie * */public class CJBayesClassfier_Step3 extends Configured implements Tool { public static class CJBayesClassfierMapper3 extends Mapper<LongWritable, Text, Text, LongWritable> { LongWritable outputValue = new LongWritable(1); @Override protected void map( LongWritable key, Text value, Context context) throws IOException, InterruptedException { context.write(value, outputValue); } } public static class CJBayesClassfierReducer3 extends Reducer<Text, LongWritable, Text, Text> { private List<String> classfications; @Override protected void setup( Reducer<Text, LongWritable, Text, Text>.Context context) throws IOException, InterruptedException { classfications = buildClassfications(); for(String classfication : classfications) { System.out.println("分类:" + classfication); } buildCJGLTable(); CJGLTable.show(); } private List<String> buildClassfications() throws IOException { List<String> list = new ArrayList<String>(); FileReader fr = new FileReader("CLASS"); BufferedReader br = new BufferedReader(fr); String line = null; while((line = br.readLine()) != null) { String tokens[] = line.split(","); String classfier = tokens[1]; list.add(classfier); } fr.close(); br.close(); return list; } private void buildCJGLTable() throws IOException { FileReader fr = new FileReader("GL"); BufferedReader br = new BufferedReader(fr); String line = null; while((line = br.readLine()) != null) { String tokens[] = line.split(","); PairOfStrings key = new PairOfStrings(tokens[0],tokens[1]); CJGLTable.add(key, Double.valueOf(tokens[2])); } fr.close(); br.close(); } @Override protected void reduce( Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException { System.out.println("key=" + key); System.out.println("values:"); for(LongWritable lw : values) { System.out.println(lw); } String [] attributes = key.toString().split(","); String selectedClass = null; double maxPosterior = 0.0; for(String aClass : classfications) { System.out.println("对于类别:" + aClass); double posterior = CJGLTable.getClassGL(aClass); System.out.println("其概率为:" + posterior); for(String attr : attributes) { System.out.println("\t对于条件：" + attr); double conGL = CJGLTable.getConditionalGL(attr, aClass); System.out.println("\t其概率为:" + conGL); posterior *= CJGLTable.getConditionalGL(attr, aClass); } if(selectedClass == null) { selectedClass = aClass; maxPosterior = posterior; } else { if(posterior > maxPosterior) { selectedClass = aClass; maxPosterior = posterior; } } context.write(key, new Text("贝叶斯分类：" + selectedClass + ",其概率为" + maxPosterior)); } context.write(key, new Text("最终结果:贝叶斯分类为" + selectedClass + ",其概率为" + maxPosterior)); } } public static void main(String[] args) throws Exception { args = new String[2]; args[0] = "/media/chenjie/0009418200012FF3/ubuntu/weather_predict.txt"; args[1] = "/media/chenjie/0009418200012FF3/ubuntu/CJBayes3"; int jobStatus = submitJob(args); System.exit(jobStatus); } public static int submitJob(String[] args) throws Exception { int jobStatus = ToolRunner.run(new CJBayesClassfier_Step3(), args); return jobStatus; } @SuppressWarnings("deprecation") @Override public int run(String[] args) throws Exception { Configuration conf = getConf(); Job job = new Job(conf); job.setJobName("Bayes"); job.addCacheArchive(new URI("/media/chenjie/0009418200012FF3/ubuntu/CJBayes/CLASS-r-00000" + "#CLASS")); job.addCacheArchive(new URI("/media/chenjie/0009418200012FF3/ubuntu/CJBayes2/part-r-00000" + "#GL")); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); job.setMapperClass(CJBayesClassfierMapper3.class); job.setReducerClass(CJBayesClassfierReducer3.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); FileSystem fs = FileSystem.get(conf); Path outPath = new Path(args[1]); if(fs.exists(outPath)) { fs.delete(outPath, true); } boolean status = job.waitForCompletion(true); return status ? 0 : 1; } }package cjbayesclassfier;import java.util.HashMap;import java.util.Map;import edu.umd.cloud9.io.pair.PairOfStrings;/*** * 保存概率表 * @author chenjie */public class CJGLTable { private static Map<PairOfStrings,Double> map = new HashMap<PairOfStrings,Double>(); public static void add(PairOfStrings key,Double gl) { map.put(key, gl); } public static double getClassGL(String aClass) { PairOfStrings pos = new PairOfStrings("CLASS",aClass); return map.get(pos)==null ? 0 : map.get(pos); } public static double getConditionalGL(String conditional,String aClass) { PairOfStrings pos = new PairOfStrings(conditional,aClass); return map.get(pos)==null ? 0 : map.get(pos); } public static void show() { for(Map.Entry<PairOfStrings,Double> entry : map.entrySet()) { System.out.println(entry); } }}第一步：输入:weather.txt--------------------------Sunny,Hot,High,Weak,NoSunny,Hot,High,Strong,NoOvercast,Hot,High,Weak,YesRain,Mild,High,Weak,YesRain,Cool,Normal,Weak,YesRain,Cool,Normal,Strong,NoOvercast,Cool,Normal,Strong,YesSunny,Mild,High,Weak,NoSunny,Cool,Normal,Weak,YesRain,Mild,Normal,Weak,YesSunny,Mild,Normal,Strong,YesOvercast,Mild,High,Strong,YesOvercast,Hot,Normal,Weak,YesRain,Mild,High,Strong,No输出：CLASS-r-00000----------------------CLASS,No,5CLASS,Yes,9OTHERS-r-00000--------------------------Cool,No,1Cool,Yes,3High,No,4High,Yes,3Hot,No,2Hot,Yes,2Mild,No,2Mild,Yes,4Normal,No,1Normal,Yes,6Overcast,Yes,4Rain,No,2Rain,Yes,3Strong,No,3Strong,Yes,3Sunny,No,3Sunny,Yes,2Weak,No,2Weak,Yes,6第二步：缓存：CLASS-r-00000-----------------------CLASS,No,5CLASS,Yes,9输入：OTHERS-r-00000------------------------Cool,No,1Cool,Yes,3High,No,4High,Yes,3Hot,No,2Hot,Yes,2Mild,No,2Mild,Yes,4Normal,No,1Normal,Yes,6Overcast,Yes,4Rain,No,2Rain,Yes,3Strong,No,3Strong,Yes,3Sunny,No,3Sunny,Yes,2Weak,No,2Weak,Yes,6输出：part-r-00000----------------------------------CLASS,No,0.35714285714285715CLASS,Yes,0.6428571428571429Cool,No,0.2Cool,Yes,0.3333333333333333High,No,0.8High,Yes,0.3333333333333333Hot,No,0.4Hot,Yes,0.2222222222222222Mild,No,0.4Mild,Yes,0.4444444444444444Normal,No,0.2Normal,Yes,0.6666666666666666Overcast,Yes,0.4444444444444444Rain,No,0.4Rain,Yes,0.3333333333333333Strong,No,0.6Strong,Yes,0.3333333333333333Sunny,No,0.6Sunny,Yes,0.2222222222222222Weak,No,0.4Weak,Yes,0.6666666666666666第三步：缓存：CLASS-r-00000-------------------------------CLASS,No,5CLASS,Yes,9缓存：part-r-00000------------------------------------CLASS,No,0.35714285714285715CLASS,Yes,0.6428571428571429Cool,No,0.2Cool,Yes,0.3333333333333333High,No,0.8High,Yes,0.3333333333333333Hot,No,0.4Hot,Yes,0.2222222222222222Mild,No,0.4Mild,Yes,0.4444444444444444Normal,No,0.2Normal,Yes,0.6666666666666666Overcast,Yes,0.4444444444444444Rain,No,0.4Rain,Yes,0.3333333333333333Strong,No,0.6Strong,Yes,0.3333333333333333Sunny,No,0.6Sunny,Yes,0.2222222222222222Weak,No,0.4Weak,Yes,0.6666666666666666输入：weather_predict.txt---------------------------------Overcast,Hot,High,Strong过程：---------------------------------------------分类:No分类:Yes(High, No)=0.8(Strong, No)=0.6(Normal, No)=0.2(Normal, Yes)=0.6666666666666666(Strong, Yes)=0.3333333333333333(CLASS, No)=0.35714285714285715(CLASS, Yes)=0.6428571428571429(Cool, No)=0.2(High, Yes)=0.3333333333333333(Hot, No)=0.4(Sunny, No)=0.6(Weak, No)=0.4(Cool, Yes)=0.3333333333333333(Mild, No)=0.4(Overcast, Yes)=0.4444444444444444(Rain, No)=0.4(Rain, Yes)=0.3333333333333333(Weak, Yes)=0.6666666666666666(Hot, Yes)=0.2222222222222222(Sunny, Yes)=0.2222222222222222(Mild, Yes)=0.4444444444444444key=Overcast,Hot,High,Strongvalues:1对于类别:No其概率为:0.35714285714285715 对于条件：Overcast 其概率为:0.0 对于条件：Hot 其概率为:0.4 对于条件：High 其概率为:0.8 对于条件：Strong 其概率为:0.6对于类别:Yes其概率为:0.6428571428571429 对于条件：Overcast 其概率为:0.4444444444444444 对于条件：Hot 其概率为:0.2222222222222222 对于条件：High 其概率为:0.3333333333333333 对于条件：Strong 其概率为:0.3333333333333333输出：Overcast,Hot,High,Strong 贝叶斯分类：No,其概率为0.0Overcast,Hot,High,Strong 贝叶斯分类：Yes,其概率为0.007054673721340388Overcast,Hot,High,Strong 最终结果:贝叶斯分类为Yes,其概率为0.007054673721340388
使用Spark（原生API）import org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutableimport scala.collection.mutable.ArrayBufferobject CJBayes { def main(args: Array[String]): Unit = { val sparkConf = new SparkConf().setAppName("cjbayes").setMaster("local") val sc = new SparkContext(sparkConf) val input = "file:///media/chenjie/0009418200012FF3/ubuntu/weather.txt" val predictFile = "file:///media/chenjie/0009418200012FF3/ubuntu/weather_predict.txt" val output = "file:///media/chenjie/0009418200012FF3/ubuntu/weather" val inputRDD = sc.textFile(input) val trainDataSize = inputRDD.count() val mapRDD = inputRDD.flatMap{line=> val result = ArrayBuffer[Tuple2[Tuple2[String,String],Integer]]() val tokens = line.split(",") val classfier = tokens(tokens.length-1) for(i <- 0 until tokens.length-1){ result += (Tuple2(Tuple2(tokens(i),classfier),1)) } result += (Tuple2(Tuple2("CLASS",classfier),1)) result } val reduceRDD = mapRDD.reduceByKey(_+_) val countsMap = reduceRDD.collectAsMap() val PT = new mutable.HashMap[Tuple2[String,String],Double]() val CLASSFICATIONS = new mutable.ArrayBuffer[String]() countsMap.foreach(item=>{ val k = item._1 val v:Integer = item._2 val condition = k._1 val classfication = k._2 if(condition.equals("CLASS")){ PT.put(k,v.toDouble/trainDataSize.toDouble) CLASSFICATIONS += k._2 } else{ val k2 = new Tuple2[String,String]("CLASS",classfication) val count = countsMap.get(k2) if(count==null){ PT.put(k,0.0) } else{ PT.put(k,v.toDouble/count.get) } } }) PT.foreach(println) val predict = sc.textFile(predictFile) predict.map(line=>{ val attributes = line.split(",") var selectedClass = "" var maxPosterior = 0.0 for(aClass <- CLASSFICATIONS){ println("对于类:" + aClass) var posterior: Double = if (PT.get(Tuple2("CLASS", aClass)) == None) 0 else PT.get(Tuple2("CLASS", aClass)).get println("其概率为:" + posterior) for(attr <- attributes){ println("\t对于条件:" + attr) val probability:Double = if (PT.get(Tuple2(attr,aClass)) == None) 0 else PT.get(Tuple2(attr,aClass)).get println("\t其概率为:" + probability) posterior *= probability if(selectedClass == null){ selectedClass = aClass maxPosterior = posterior } else{ if(posterior > maxPosterior){ selectedClass = aClass maxPosterior = posterior } } } } line + "," + selectedClass + ":" + maxPosterior }).foreach(println)}
使用Spark（mllib机器学习库）
import org.apache.spark.mllib.classification.NaiveBayesimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutableimport scala.collection.mutable.ArrayBufferobject CJBayes { def main(args: Array[String]): Unit = { val sparkConf = new SparkConf().setAppName("cjbayes").setMaster("local") val sc = new SparkContext(sparkConf) val input = "file:///media/chenjie/0009418200012FF3/ubuntu/weather1.txt" val predictFile = "file:///media/chenjie/0009418200012FF3/ubuntu/weather_predict.txt" val data = sc.textFile(input) val parsedData =data.map { line => val parts =line.split(',') LabeledPoint(parts(1).toDouble,Vectors.dense(parts(0).split(' ').map(_.toDouble))) } // 把数据的100%作为训练集，0%作为测试集. val splits = parsedData.randomSplit(Array(1.0,0.0),seed = 11L) val training =splits(0) val test =splits(1) //获得训练模型,第一个参数为数据，第二个参数为平滑参数，默认为1，可改 val model =NaiveBayes.train(training,lambda = 1.0) //对模型进行准确度分析 val predictionAndLabel= test.map(p => (model.predict(p.features),p.label)) val accuracy =1.0 *predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() println("accuracy-->"+accuracy) println("Predictionof (2.0,1.0,1.0,2.0):"+model.predict(Vectors.dense(2.0,1.0,1.0,2.0))) }}
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