python - 在python中将2d数组转换为3d数组

Question

很抱歉问这个问题，如果已经有人问过，但在我的例子中，我有一个大小为 3000000x50 的特殊矩阵，我想将它分成 300 个大小为 10000x50 的矩阵。我试过了，但没用

>>>import numpy as np
>>>data=np.random.randn(3000000,50)
>>>D=np.matrix.conjugate(data)
>>>ts=50
>>>ts=int(ts)       #number of time series that we have from our data
>>>lw=1e4
>>>lw=int(lw)    #length of each window 
>>>l=len(data)/lw   #l is number of windows
>>>l=np.floor(l)
>>>l=int(l)
#Dc is used to seperate each time series in l windows
>>>Dc=np.zeros((l,lw,ts))
>>>for i in range(l):
    Dc[i][0:lw-1][0:ts-1]=D[(lw)*(i):(lw*(i+1))-1][0:ts-1]

Answer 1

您正在寻找np.vsplit (将数组垂直(按行)拆分为多个子数组)-

np.vsplit(data,300)

sample 运行-

In [56]: data
Out[56]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [57]: np.vsplit(data,3)
Out[57]: 
[array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]]),
 array([[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]]),
 array([[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])]

---

根据您打算如何使用输出，您可以将 2D 输入数组重新整形为沿第一个轴长度为 300 的 3D 数组，这在性能和内存方面。内存方面，它必须是免费的，因为 reshaping 只创建 numpy 数组的 View 。实现将是 -

data.reshape(300,-1,data.shape[1])

sample 运行-

In [68]: data
Out[68]: 
array([[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
       [ 0.40789713,  0.36018843,  0.41731607,  0.17348898],
       [ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
       [ 0.52407036,  0.89913995,  0.59097535,  0.38376443],
       [ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
       [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]])

In [69]: data.reshape(3,-1,data.shape[1])
Out[69]: 
array([[[ 0.46677419,  0.07402051,  0.87270029,  0.12481164],
        [ 0.40789713,  0.36018843,  0.41731607,  0.17348898]],

       [[ 0.4701256 ,  0.10056201,  0.31289602,  0.18681709],
        [ 0.52407036,  0.89913995,  0.59097535,  0.38376443]],

       [[ 0.06734662,  0.24470334,  0.09523911,  0.35680219],
        [ 0.91178257,  0.58710922,  0.75099017,  0.24929987]]])

这里有一些运行时测试，用于检查比较实际拆分与 reshape 的性能 -

In [72]: data = np.random.rand(6000,40)

In [73]: %timeit np.vsplit(data,300)
100 loops, best of 3: 7.05 ms per loop

In [74]: %timeit data.reshape(300,-1,data.shape[1])
1000000 loops, best of 3: 1.08 µs per loop