在嵌套函数中,我想调用内部函数

我有一个名为 IrisData 的类。我在其中定义了一个函数作为描述。

  • description 有多个我想要访问的子功能。

  • 我希望我的功能像

    1. 如果调用描述,它应该返回描述中定义的每个函数。代码行:打印(I.description())

    2. 当调用内部函数时,它应该只返回内部函数。代码行:打印(I.description.attribute())*

PFB 代码片段:

class IrisData:


    def urls(self):

        self.url='https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'

        return self.url

    def columns(self):

        self.column_name=['sepal length','sepal width','petal length','petal width','class']

        return self.column_name

    def description(self):

        def title():

            self.titles ='Title: Iris Plants Database'

            return self.titles

        def source():

            self.sources='''Sources:

     \t(a) Creator: R.A. Fisher

     \t(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)

     \t(c) Date: July, 1988'''

            return self.sources

        def info():

            self.descri='''Relevant Information:

     \t--- This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day.  (See Duda & Hart, for

 example.

     \t--- The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly

 separable from each other.

     \t--- Predicted attribute: class of iris plant.

     \t--- This is an exceedingly simple domain.

     \t--- This data differs from the data presented in Fishers article (identified by Steve Chadwick,  spchadwick@espeedaz.net )

     \tThe 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"

     \twhere the error is in the fourth feature.

     \tThe 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"

     \twhere the errors are in the second and third features. '''

            return self.descri

        def attribute():

            self.attri="""Attribute Information:



墨色风雨
浏览 193回答 2
2回答
打开App,查看更多内容
随时随地看视频慕课网APP

相关分类

Python