对numpy.c_文档和示例代码感到困惑 [英] confused about numpy.c_ document and sample code

问题描述

我多次阅读有关numpy.c_的文档，但仍然感到困惑.据说-将切片对象沿第二个轴平移为串联".在以下文档中.谁能在下面的示例中阐明什么是切片对象，什么是第二轴?我看到它们都是一维的，并且混淆了第二轴的位置.

I read the document about numpy.c_ many times but still confused. It is said -- "Translates slice objects to concatenation along the second axis." in the following document. Could anyone clarify in the example below, what is slice objects, and what is 2nd axis? I see they are all one dimension and confused where the 2nd axis coming from.

在Windows上使用Python 2.7.

Using Python 2.7 on Windows.

http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.c_.html#numpy.c_

>>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
array([[1, 2, 3, 0, 0, 4, 5, 6]])

推荐答案

np.c_是进行数组连接的另一种方法

np.c_ is another way of doing array concatenate

In [701]: np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
Out[701]: array([[1, 2, 3, 0, 0, 4, 5, 6]])

In [702]: np.concatenate([np.array([[1,2,3]]), [[0]], [[0]], np.array([[4,5,6]])], 
     axis=1)
Out[702]: array([[1, 2, 3, 0, 0, 4, 5, 6]])

在两种情况下，输出形状均为(1,8)；串联在第1轴= 1上.

The output shape is (1,8) in both cases; the concatenation was on axis=1, the 2nd axis.

c_负责将0的尺寸扩展到np.array([[0]])，这是连接所需的2d(1,1).

c_ took care of expanding the dimensions of the 0 to np.array([[0]]), the 2d (1,1) needed to concatenate.

np.c_(和np.r_)实际上是具有__getitem__方法的类对象，因此它可以使用[]语法工作. numpy/lib/index_tricks.py源文件仅供参考.

np.c_ (and np.r_) is actually a class object with a __getitem__ method, so it works with the [] syntax. The numpy/lib/index_tricks.py source file is instructive reading.

请注意，row版本使用:slice语法，可生成一个1d(8，)数组(相同的数字，但在1d中)

Note that the row version works with the : slice syntax, producing a 1d (8,) array (same numbers, but in 1d)

In [706]: np.r_[1:4,0,0,4:7]
Out[706]: array([1, 2, 3, 0, 0, 4, 5, 6])
In [708]: np.concatenate((np.arange(4),[0],[0],np.arange(4,7)))
Out[708]: array([0, 1, 2, 3, 0, 0, 4, 5, 6])
In [710]: np.hstack((np.arange(4),0,0,np.arange(4,7)))
Out[710]: array([0, 1, 2, 3, 0, 0, 4, 5, 6])

np.c_是一种方便，但是您不是必须了解的内容.我认为能够直接与concatenate一起使用会更有用.它迫使您明确考虑输入的维度.

np.c_ is a convenience, but not something you are required to understand. I think being able to work with concatenate directly is more useful. It forces you to think explicitly about the dimensions of the inputs.

[[1,2,3]]实际上是一个列表-包含一个列表的列表. np.array([[1,2,3]])是形状为(1,3)的2d数组. np.arange(1,4)产生一个具有相同数字的(3，)数组. np.arange(1,4)[None,:]使其成为(1,3)数组.

[[1,2,3]] is actually a list - a list containing one list. np.array([[1,2,3]]) is a 2d array with shape (1,3). np.arange(1,4) produces a (3,) array with the same numbers. np.arange(1,4)[None,:] makes it a (1,3) array.

slice(1,4)是切片对象. np.r_和np.c_可以通过实际使用np.arange将切片对象转换为数组.

slice(1,4) is a slice object. np.r_ and np.c_ can turn a slice object into a array - by actually using np.arange.

In [713]: slice(1,4)
Out[713]: slice(1, 4, None)
In [714]: np.r_[slice(1,4)]
Out[714]: array([1, 2, 3])
In [715]: np.c_[slice(1,4)]   # (3,1) array
Out[715]: 
array([[1],
       [2],
       [3]])
In [716]: np.c_[1:4]   # equivalent with the : notation
Out[716]: 
array([[1],
       [2],
       [3]])

回到原始示例(可能不是最好的示例):

And to get back to the original example (which might not be the best):

In [722]: np.c_[[np.r_[1:4]],0,0,[np.r_[4:7]]]
Out[722]: array([[1, 2, 3, 0, 0, 4, 5, 6]])

==========

==========

In [731]: np.c_[np.ones((5,3)),np.random.randn(5,10)].shape
Out[731]: (5, 13)

对于np.c_两者的第一维都必须匹配.

For np.c_ the 1st dimension of both needs to match.

在learn示例中，n_samples是X(行)的第一个暗角，并且randn也需要具有这么多行.

In the learn example, n_samples is the 1st dim of X (rows), and the randn also needs to have that many rows.

n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]

np.concatenate([(X, randn(n_samples...)], axis=1)在这里应该也能正常工作.有点呆板，但功能上是相同的.

np.concatenate([(X, randn(n_samples...)], axis=1) should work just as well here. A little wordier, but functionally the same.

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