编写一个mapreduce的成绩统计学生平均成绩，输入文件的格式为 学号:课程号:成绩分数，要求输出为学号:平均绩点GPA。绩点GPA计算方式按上海理工大学规定。Java

好的，这是一个典型的MapReduce程序，需要分为两个阶段：Map阶段和Reduce阶段。以下是Java代码实现：

Map阶段：

public static class Map extends Mapper<LongWritable, Text, Text, FloatWritable> {
    private Text outputKey = new Text();
    private FloatWritable outputValue = new FloatWritable();

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split(":");
        if (fields.length != 3) {
            return;
        }

        String studentId = fields[0];
        String courseId = fields[1];
        float score = Float.parseFloat(fields[2]);

        // 计算绩点GPA
        float gpa = 0;
        if (score >= 90) {
            gpa = 4.0f;
        } else if (score >= 85) {
            gpa = 3.7f;
        } else if (score >= 82) {
            gpa = 3.3f;
        } else if (score >= 78) {
            gpa = 3.0f;
        } else if (score >= 75) {
            gpa = 2.7f;
        } else if (score >= 72) {
            gpa = 2.3f;
        } else if (score >= 68) {
            gpa = 2.0f;
        } else if (score >= 64) {
            gpa = 1.5f;
        } else if (score >= 60) {
            gpa = 1.0f;
        }

        // 计算学号:课程号的组合作为Map输出的key
        outputKey.set(studentId + ":" + courseId);
        outputValue.set(gpa);
        context.write(outputKey, outputValue);
    }
}

Reduce阶段：

public static class Reduce extends Reducer<Text, FloatWritable, Text, FloatWritable> {
    private FloatWritable outputValue = new FloatWritable();

    public void reduce(Text key, Iterable<FloatWritable> values, Context context) throws IOException, InterruptedException {
        float totalGPA = 0;
        int count = 0;
        for (FloatWritable value : values) {
            totalGPA += value.get();
            count++;
        }

        // 计算平均绩点GPA
        float avgGPA = totalGPA / count;

        // 输出学号:平均绩点GPA的组合作为Reduce输出的key
        context.write(new Text(key.toString().split(":")[0]), new FloatWritable(avgGPA));
    }
}

最后，在Driver类中设置Job的输入路径、输出路径、Mapper和Reducer，然后运行Job即可完成统计。

完整代码如下：

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.FloatWritable;
import org.apache.hadoop.io.LongWritable;
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.output.FileOutputFormat;

public class ScoreGPA {
    public static class Map extends Mapper<LongWritable, Text, Text, FloatWritable> {
        private Text outputKey = new Text();
        private FloatWritable outputValue = new FloatWritable();

        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(":");
            if (fields.length != 3) {
                return;
            }

            String studentId = fields[0];
            String courseId = fields[1];
            float score = Float.parseFloat(fields[2]);

            // 计算绩点GPA
            float gpa = 0;
            if (score >= 90) {
                gpa = 4.0f;
            } else if (score >= 85) {
                gpa = 3.7f;
            } else if (score >= 82) {
                gpa = 3.3f;
            } else if (score >= 78) {
                gpa = 3.0f;
            } else if (score >= 75) {
                gpa = 2.7f;
            } else if (score >= 72) {
                gpa = 2.3f;
            } else if (score >= 68) {
                gpa = 2.0f;
            } else if (score >= 64) {
                gpa = 1.5f;
            } else if (score >= 60) {
                gpa = 1.0f;
            }

            // 计算学号:课程号的组合作为Map输出的key
            outputKey.set(studentId + ":" + courseId);
            outputValue.set(gpa);
            context.write(outputKey, outputValue);
        }
    }

    public static class Reduce extends Reducer<Text, FloatWritable, Text, FloatWritable> {
        private FloatWritable outputValue = new FloatWritable();

        public void reduce(Text key, Iterable<FloatWritable> values, Context context) throws IOException, InterruptedException {
            float totalGPA = 0;
            int count = 0;
            for (FloatWritable value : values) {
                totalGPA += value.get();
                count++;
            }

            // 计算平均绩点GPA
            float avgGPA = totalGPA / count;

            // 输出学号:平均绩点GPA的组合作为Reduce输出的key
            context.write(new Text(key.toString().split(":")[0]), new FloatWritable(avgGPA));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "ScoreGPA");
        job.setJarByClass(ScoreGPA.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FloatWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}