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[英]Initialize multipleoutputs instance in configure method in MapReduceBase
[英]How to initialize instance variables in setup()?
這應該是一個簡單的問題,但是我正在努力。 除了初始化從配置文件中讀取的“ train_rows”和“ cols”的參數值外,我代碼中的所有內容均正常工作。
我設置了日志以在setup()方法中顯示“ train_rows”和“ cols”的值,並且這些值是正確的。 但是,當我在map()方法中嘗試相同的操作時,兩個值都顯示為0。我在做什么錯?
導入java.io.File; 導入java.io.IOException; 導入java.io.FileNotFoundException; 導入java.util.Scanner; 導入org.apache.log4j.Logger;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class KNNMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {
private static final Logger sLogger = Logger.getLogger(KNNMapper.class);
private int[][] train_vals;
private int[] train_label_vals;
private int train_rows;
private int test_rows;
private int cols;
@Override
public void setup(Context context) throws IOException, InterruptedException {
Configuration conf = context.getConfiguration();
train_rows = conf.getInt("rows", -1);
cols = conf.getInt("columns", -1);
//just changed this
//int[][] train_vals = new int[train_rows][cols];
//int[] train_label_vals = new int[train_rows];
train_vals = new int[train_rows][cols];
train_label_vals = new int[train_rows];
// read train csv, parse, and store into 2d int array
Scanner myScan;
try {
File trainfile = new File("train_sample.csv");
if (!trainfile.exists()) {
throw new IllegalArgumentException("train file didn't load");
}
myScan = new Scanner(trainfile);
//Set the delimiter used in file
myScan.useDelimiter("[,\r\n]+");
//Get all tokens and store them in some data structure
//I am just printing them
for(int row = 0; row < train_rows; row++) {
for(int col = 0; col < cols; col++) {
train_vals[row][col] = Integer.parseInt(myScan.next().toString());
}
}
myScan.close();
} catch (FileNotFoundException e) {
System.out.print("Error: Train file execution did not work.");
}
// read train_labels csv, parse, and store into 2d int array
try {
File trainlabels = new File("train_labels.csv");
if (!trainlabels.exists()) {
throw new IllegalArgumentException("train labels didn't load");
}
myScan = new Scanner(trainlabels);
//Set the delimiter used in file
myScan.useDelimiter("[,\r\n]+");
//Get all tokens and store them in some data structure
//I am just printing them
for(int row = 0; row < train_rows; row++) {
train_label_vals[row] = Integer.parseInt(myScan.next().toString());
if(row < 10) {
System.out.println(train_label_vals[row]);
}
}
myScan.close();
} catch (FileNotFoundException e) {
System.out.print("Error: Train Labels file not found.");
}
}
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// setup() gave us train_vals & train_label_vals.
// Each line in map() represents a test observation. We iterate
// through every train_val row to find nearest L2 match, then
// return a key/value pair of <observation #,
// convert from Text to String
System.out.println("I'm in the map!");
String line = value.toString();
double distance;
double best_distance = Double.POSITIVE_INFINITY;
int col_num;
int best_digit = -1;
IntWritable rowId = null;
int i;
IntWritable rowNum;
String[] pixels;
System.out.println("Number of train rows:" + train_rows);
System.out.println("Number of columns:" + cols);
// comma delimited files, split on commas
// first we find the # of rows
pixels = line.split(",");
rowId = new IntWritable(Integer.parseInt(pixels[0]));
System.out.println("working on row " + rowId);
best_distance = Double.POSITIVE_INFINITY;
for (i = 0; i < train_rows; i++) {
distance = 0.0;
col_num = 0;
for (int j = 1; j < cols; j++) {
distance += (Integer.parseInt(pixels[j]) - train_vals[i][j-1])^2;
}
if (distance < best_distance) {
best_distance = distance;
best_digit = train_label_vals[i];
}
}
System.out.println("And we're out of the loop baby yeah!");
context.write(rowId, new IntWritable(best_digit));
System.out.println("Mapper done!");
}
}
我對此表示懷疑,假設您要掃描hdfs中的文件。
您使用了:
導入java.io.File; 文件trainfile =新File(“ train_sample.csv”);
在hadoop中,這就是我們在hdfs中檢查文件的方式:
嘗試{FileSystem fs = FileSystem.get(context.getConfiguration());
if (fs.exists(new Path("/user/username/path/of/file/inhdfs"))) {
System.out.println("File exists");
}
} catch (IOException e) {
e.printStackTrace();
}
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