【发布时间】:2013-12-18 20:03:25
【问题描述】:
我已经问了一些关于 Weka 和 C# 以及 WekaSharp 的操作的广泛问题,所以我想我会尝试问一个更集中的问题,以尝试自己进一步取得进展。作为从我使用的 C# 执行 weka 的 weka 站点给出的示例,我想使用并行操作运行部分计算,但不知道如何在此处编写原始代码:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using weka.classifiers.meta;
using weka.classifiers.functions;
using weka.core;
using java.io;
using weka.clusterers;
using System.Diagnostics;
using System.Threading;
// From http://weka.wikispaces.com/IKVM+with+Weka+tutorial
class MainClass
{
public static void Main(string[] args)
{
System.Console.WriteLine("J48 in C#");
classifyTest();
}
const int percentSplit = 66;
public static void classifyTest()
{
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(@"C:\Users\Deines\Documents\School\Software\WekaSharp2012\data\iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.trees.J48();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
cl.buildClassifier(train);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
numCorrect++;
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
}
我想跑步:
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
if (predictedClass == insts.instance(i).classValue())
numCorrect++;
}
为了比较费率,顺序和并发。我知道命令是 System.Linq.ParallelExecutionMode() ,但我不确定在这种情况下如何应用它。非常感谢。
【问题讨论】:
标签: c# linq classification weka