numpy.piecewise中的多个碎片

Question

我正在学习模糊系统课程,并在计算机上记笔记.这意味着我必须不时在我的计算机上绘制图形.由于这些图形定义得很好,我觉得用它们绘图numpy是一个好主意(我用LaTeX做笔记,我在python shell上很快,所以我想我可以侥幸逃脱).

模糊隶属函数的图形是高度分段的,例如:

为了绘制这个,我尝试了以下代码numpy.piecewise(这给了我一个神秘的错误):

In [295]: a = np.arange(0,5,1)

In [296]: condlist = [[b<=a<b+0.25, b+0.25<=a<b+0.75, b+0.75<=a<b+1] for b in range(3)]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-296-a951e2682357> in <module>()
----> 1 condlist = [[b<=a<b+0.25, b+0.25<=a<b+0.75, b+0.75<=a<b+1] for b in range(3)]

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

In [297]: funclist = list(itertools.chain([lambda x:-4*x+1, lambda x: 0, lambda x:4*x+1]*3))

In [298]: np.piecewise(a, condlist, funclist)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-298-41168765ae55> in <module>()
----> 1 np.piecewise(a, condlist, funclist)

/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/function_base.pyc in piecewise(x, condlist, funclist, *args, **kw)
    688     if (n != n2):
    689         raise ValueError(
--> 690                 "function list and condition list must be the same")
    691     zerod = False
    692     # This is a hack to work around problems with NumPy's

ValueError: function list and condition list must be the same

在这一点上,我对如何绘制这个函数感到很困惑.我真的不明白错误信息,这进一步阻碍了我调试它的努力.

最终,我希望绘制并将此函数导出到EPS文件中,所以我也很感激这些方面的任何帮助.

Answer 1

一般来说,当你只是编写代码时,numpy数组非常善于做出明智的事情,好像它们只是数字一样.链式比较是极少数例外情况之一.您看到的错误基本上是这样的(由piecewise内部和ipython错误格式化混淆了一点):

>>> a = np.array([1, 2, 3])
>>> 1.5 < a
array([False,  True,  True], dtype=bool)
>>> 
>>> 1.5 < a < 2.5
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
>>> 
>>> (1.5 < a) & (a < 2.5)
array([False,  True, False], dtype=bool)
>>> 

您也可以使用np.logical_and,但and在这里按位工作就好了.

就绘图而言,numpy本身并没有做任何事情.这是matplotlib的一个例子:

>>> import numpy as np
>>> def piecew(x):
...   conds = [x < 0, (x > 0) & (x < 1), (x > 1) & (x < 2), x > 2]
...   funcs = [lambda x: x+1, lambda x: 1, 
...            lambda x: -x + 2., lambda x: (x-2)**2]
...   return np.piecewise(x, conds, funcs)
>>>
>>> import matplotlib.pyplot as plt
>>> xx = np.linspace(-0.5, 3.1, 100)
>>> plt.plot(xx, piecew(xx))
>>> plt.show() # or plt.savefig('foo.eps')

请注意,这piecewise是一个反复无常的野兽.特别是,它需要它的x参数是一个数组,如果不是,它甚至不会尝试转换它(用说numpy:x需要是一个ndarray,而不是一个array_like):

>>> piecew(2.1)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 4, in piecew
  File "/home/br/.local/lib/python2.7/site-packages/numpy/lib/function_base.py", line 690, in piecewise
    "function list and condition list must be the same")
ValueError: function list and condition list must be the same
>>> 
>>> piecew(np.asarray([2.1]))
array([ 0.01])