我为我的道歉Java noobness但我想使用Weka从控制台，由于某种原因，我得到以下错误：
Error: Could not find or load main class weka.classifiers.trees.J48
我想下面的命令：
java weka.classifiers.trees.J48 -l C:\xampp\htdocs\frequencyreplyallwords.arff -T C:\xampp\htdocs\testfreqrep.arff -p 0 > C:\xampp\htdocs\output.txt
我怀疑一些问题的classpath但因为我真的不明白Java是有检查，如果一切正常的任何简单的方法？
感谢您的任何帮助
我想，你使用windows，所以这是Windows命令行的例子。 如果你得到
SET WEKA_HOME=C:\Program Files\Weka-3-7
SET CLASSPATH=%CLASPATH%;%WEKA_HOME%\weka.jar
SET HEAP_OPTION=-Xms4096m -Xmx8192m
SET JAVA_COMMAND=java %HEAP_OPTION%
%JAVA_COMMAND% weka.core.SystemInfo
你应该让你的系统值与秧鸡值一起，像weka.version：3.7.9
Linux的/ MacOS的解决方案
下载相关版本，如开发Linux版本的在这里 ，在3.9.1版本中，从这个目录这里
添加以下行到~/.bash_profile
的命令输出cat ~/.bash.profile
export R_HOME="/Applications/R.app/Contents/MacOS/R" #for WEKA MLR R plugin
export CLASSPATH="/Applications/weka-3-9-1/weka.jar" #for WEKA commandline
export WEKAINSTALL="/Applications/weka-3-9-1"
export WEKA_HOME="/Applications/weka-3-9-1"
export CLASSPATH=$CLASSPATH;$WEKA_HOME/weka.jar
export HEAP_OPTION=-Xms4096m -Xmx8192m
export JAVA_COMMAND java $HEAP_OPTION
之后，你应该能够运行
java weka.classifiers.trees.J48 -t $WEKAINSTALL/data/iris.arff
输出
J48 pruned tree
------------------
petalwidth <= 0.6: Iris-setosa (50.0)
petalwidth > 0.6
| petalwidth <= 1.7
| | petallength <= 4.9: Iris-versicolor (48.0/1.0)
| | petallength > 4.9
| | | petalwidth <= 1.5: Iris-virginica (3.0)
| | | petalwidth > 1.5: Iris-versicolor (3.0/1.0)
| petalwidth > 1.7: Iris-virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Time taken to build model: 0.44 seconds
Time taken to test model on training data: 0.01 seconds
=== Error on training data ===
Correctly Classified Instances 147 98 %
Incorrectly Classified Instances 3 2 %
Kappa statistic 0.97
Mean absolute error 0.0233
Root mean squared error 0.108
Relative absolute error 5.2482 %
Root relative squared error 22.9089 %
Total Number of Instances 150
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 Iris-setosa
0.980 0.020 0.961 0.980 0.970 0.955 0.990 0.969 Iris-versicolor
0.960 0.010 0.980 0.960 0.970 0.955 0.990 0.970 Iris-virginica
Weighted Avg. 0.980 0.010 0.980 0.980 0.980 0.970 0.993 0.980
=== Confusion Matrix ===
a b c <-- classified as
50 0 0 | a = Iris-setosa
0 49 1 | b = Iris-versicolor
0 2 48 | c = Iris-virginica
=== Stratified cross-validation ===
Correctly Classified Instances 144 96 %
Incorrectly Classified Instances 6 4 %
Kappa statistic 0.94
Mean absolute error 0.035
Root mean squared error 0.1586
Relative absolute error 7.8705 %
Root relative squared error 33.6353 %
Total Number of Instances 150
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.980 0.000 1.000 0.980 0.990 0.985 0.990 0.987 Iris-setosa
0.940 0.030 0.940 0.940 0.940 0.910 0.952 0.880 Iris-versicolor
0.960 0.030 0.941 0.960 0.950 0.925 0.961 0.905 Iris-virginica
Weighted Avg. 0.960 0.020 0.960 0.960 0.960 0.940 0.968 0.924
=== Confusion Matrix ===
a b c <-- classified as
49 1 0 | a = Iris-setosa
0 47 3 | b = Iris-versicolor
0 2 48 | c = Iris-virginica
您可以为用户提供类路径-cp PARAM：
java -cp /path/to/weka/weka.jar weka.classifiers.trees.J48 ...
# on Windows, this is probably something like
java -cp C:\path\to\weka\weka.jar weka.classifiers.trees.J48 ...
