本文整理匯總了Python中weka.filters.Filter.outputformat方法的典型用法代碼示例。如果您正苦於以下問題：Python Filter.outputformat方法的具體用法？Python Filter.outputformat怎麽用？Python Filter.outputformat使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類weka.filters.Filter的用法示例。
在下文中一共展示了Filter.outputformat方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: from weka.filters import Filter [as 別名]
# 或者: from weka.filters.Filter import outputformat [as 別名]
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(iris_file)
# remove class attribute
data.delete_last_attribute()
# build a clusterer and output model
helper.print_title("Training SimpleKMeans clusterer")
clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
clusterer.build_clusterer(data)
print(clusterer)
helper.print_info("Evaluating on data")
evaluation = ClusterEvaluation()
evaluation.set_model(clusterer)
evaluation.test_model(data)
print("# clusters: " + str(evaluation.num_clusters))
print("log likelihood: " + str(evaluation.log_likelihood))
print("cluster assignments:\n" + str(evaluation.cluster_assignments))
plc.plot_cluster_assignments(evaluation, data, inst_no=True)
# using a filtered clusterer
helper.print_title("Filtered clusterer")
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(iris_file)
clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans", options=["-N", "3"])
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
fclusterer = FilteredClusterer()
fclusterer.clusterer = clusterer
fclusterer.filter = remove
fclusterer.build_clusterer(data)
print(fclusterer)
# load a dataset incrementally and build clusterer incrementally
helper.print_title("Incremental clusterer")
loader = Loader("weka.core.converters.ArffLoader")
iris_inc = loader.load_file(iris_file, incremental=True)
clusterer = Clusterer("weka.clusterers.Cobweb")
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "last"])
remove.inputformat(iris_inc)
iris_filtered = remove.outputformat()
clusterer.build_clusterer(iris_filtered)
for inst in loader:
remove.input(inst)
inst_filtered = remove.output()
clusterer.update_clusterer(inst_filtered)
clusterer.update_finished()
print(clusterer.to_commandline())
print(clusterer)
print(clusterer.graph)
plg.plot_dot_graph(clusterer.graph)