本文整理汇总了Python中weka.classifiers.Classifier.options方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.options方法的具体用法？Python Classifier.options怎么用？Python Classifier.options使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.Classifier的用法示例。
在下文中一共展示了Classifier.options方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Classifier
# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import options [as 别名]
import weka.core.jvm as jvm
jvm.start()
jvm.start(system_cp=True, packages=True)
jvm.start(packages="/usr/local/lib/python2.7/dist-packages/weka")
jvm.start(max_heap_size="512m")
data_dir="CSDMC2010_SPAM/CSDMC2010_SPAM/TRAINING"
from weka.classifiers import Classifier
cls = Classifier(classname="weka.classifiers.trees.J48")
cls.options = ["-C", "0.3"]
print(cls.options)
jvm.stop()
示例2: write_to_weka
# 需要导入模块: from weka.classifiers import Classifier [as 别名]
# 或者: from weka.classifiers.Classifier import options [as 别名]
Ypred = np.zeros(Y.shape, dtype='object')
print "Classification using K Nearest Neighbors"
for train_index, test_index in sss:
print "Iter", itr,
X_train, X_test = X[train_index], X[test_index]
X_test[:,-1] = classes[0] # make sure test classes is removed
y_test = Y[test_index]
write_to_weka('train.arff', 'training_data', data.columns, X_train, classes)
write_to_weka('test.arff', 'testing_data', data.columns, X_test, classes)
loader = Loader(classname="weka.core.converters.ArffLoader")
trdata = loader.load_file("train.arff")
trdata.class_is_last()
classifier = Classifier(classname="weka.classifiers.lazy.IBk")
classifier.options = ["-K", "10", "-W", "0", "-I", "-A",
"weka.core.neighboursearch.LinearNNSearch -A \"weka.core.ManhattanDistance -R first-last\""]
classifier.build_classifier(trdata)
tedata = loader.load_file("test.arff")
tedata.class_is_last()
for index, inst in enumerate(tedata):
result = classifier.classify_instance(inst)
Ypred[test_index[index]] = classes[int(result)]
accuracy = float(np.sum(y_test == Ypred[test_index])) / float(y_test.shape[0])
print " => Accuracy = ", accuracy
itr += 1
accuracy = float(np.sum(Y == Ypred)) / float(Y.shape[0])
print "Total accuracy = ", accuracy