Python:证明 NumPy 数组的合理性

Question

拜托，我对 Python 有点陌生，它一直很好，我可以评论说 python 非常性感，直到我需要移动一个 4x4 矩阵的内容，我想用它来构建一个游戏的 2048 游戏演示是 here我有这个功能

def cover_left(matrix):
        new=[[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]
        for i in range(4):
             count=0
             for j in range(4):
                if mat[i][j]!=0:
                    new[i][count]=mat[i][j]
                    count+=1
        return new

如果你这样调用它，这就是这个函数的作用

cover_left([
              [1,0,2,0], 
              [3,0,4,0], 
              [5,0,6,0], 
              [0,7,0,8]
          ])

它将覆盖左边的零并产生

[  [1, 2, 0, 0],
   [3, 4, 0, 0],
   [5, 6, 0, 0],
   [7, 8, 0, 0]]

我需要有人用 numpy 方法来帮助我，我相信这种方法会更快并且需要更少的代码(我在深度优先搜索算法中使用)，更重要的是cover_up、cover_down 和 cover_left 的实现。

`cover_up`
    [  [1, 7, 2, 8],
       [3, 0, 4, 0],
       [5, 0, 6, 0],
       [0, 0, 0, 0]]
`cover_down`
    [  [0, 0, 0, 0],
       [1, 0, 2, 0],
       [3, 0, 4, 0],
       [5, 7, 6, 8]]
`cover_right`
    [  [0, 0, 1, 2],
       [0, 0, 3, 4],
       [0, 0, 5, 6],
       [0, 0, 7, 8]]

Answer 1

这是受 this other post 启发的矢量化方法并概括为覆盖所有四个方向的非零 -

def justify(a, invalid_val=0, axis=1, side='left'):    
    """
    Justifies a 2D array

    Parameters
    ----------
    A : ndarray
        Input array to be justified
    axis : int
        Axis along which justification is to be made
    side : str
        Direction of justification. It could be 'left', 'right', 'up', 'down'
        It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0.

    """

    if invalid_val is np.nan:
        mask = ~np.isnan(a)
    else:
        mask = a!=invalid_val
    justified_mask = np.sort(mask,axis=axis)
    if (side=='up') | (side=='left'):
        justified_mask = np.flip(justified_mask,axis=axis)
    out = np.full(a.shape, invalid_val) 
    if axis==1:
        out[justified_mask] = a[mask]
    else:
        out.T[justified_mask.T] = a.T[mask.T]
    return out

样本运行-

In [473]: a # input array
Out[473]: 
array([[1, 0, 2, 0],
       [3, 0, 4, 0],
       [5, 0, 6, 0],
       [6, 7, 0, 8]])

In [474]: justify(a, axis=0, side='up')
Out[474]: 
array([[1, 7, 2, 8],
       [3, 0, 4, 0],
       [5, 0, 6, 0],
       [6, 0, 0, 0]])

In [475]: justify(a, axis=0, side='down')
Out[475]: 
array([[1, 0, 0, 0],
       [3, 0, 2, 0],
       [5, 0, 4, 0],
       [6, 7, 6, 8]])

In [476]: justify(a, axis=1, side='left')
Out[476]: 
array([[1, 2, 0, 0],
       [3, 4, 0, 0],
       [5, 6, 0, 0],
       [6, 7, 8, 0]])

In [477]: justify(a, axis=1, side='right')
Out[477]: 
array([[0, 0, 1, 2],
       [0, 0, 3, 4],
       [0, 0, 5, 6],
       [0, 6, 7, 8]])

通用案例(ndarray)

对于 ndarray，我们可以将其修改为 -

def justify_nd(a, invalid_val, axis, side):    
    """
    Justify ndarray for the valid elements (that are not invalid_val).

    Parameters
    ----------
    A : ndarray
        Input array to be justified
    invalid_val : scalar
        invalid value
    axis : int
        Axis along which justification is to be made
    side : str
        Direction of justification. Must be 'front' or 'end'.
        So, with 'front', valid elements are pushed to the front and
        with 'end' valid elements are pushed to the end along specified axis.
    """
    
    pushax = lambda a: np.moveaxis(a, axis, -1)
    if invalid_val is np.nan:
        mask = ~np.isnan(a)
    else:
        mask = a!=invalid_val
    justified_mask = np.sort(mask,axis=axis)
    
    if side=='front':
        justified_mask = np.flip(justified_mask,axis=axis)
            
    out = np.full(a.shape, invalid_val)
    if (axis==-1) or (axis==a.ndim-1):
        out[justified_mask] = a[mask]
    else:
        pushax(out)[pushax(justified_mask)] = pushax(a)[pushax(mask)]
    return out

样本运行-

输入数组:

In [87]: a
Out[87]: 
array([[[54, 57,  0, 77],
        [77,  0,  0, 31],
        [46,  0,  0, 98],
        [98, 22, 68, 75]],

       [[49,  0,  0, 98],
        [ 0, 47,  0, 87],
        [82, 19,  0, 90],
        [79, 89, 57, 74]],

       [[ 0,  0,  0,  0],
        [29,  0,  0, 49],
        [42, 75,  0, 67],
        [42, 41, 84, 33]],

       [[ 0,  0,  0, 38],
        [44, 10,  0,  0],
        [63,  0,  0,  0],
        [89, 14,  0,  0]]])

到 'front'，沿 axis =0 :

In [88]: justify_nd(a, invalid_val=0, axis=0, side='front')
Out[88]: 
array([[[54, 57,  0, 77],
        [77, 47,  0, 31],
        [46, 19,  0, 98],
        [98, 22, 68, 75]],

       [[49,  0,  0, 98],
        [29, 10,  0, 87],
        [82, 75,  0, 90],
        [79, 89, 57, 74]],

       [[ 0,  0,  0, 38],
        [44,  0,  0, 49],
        [42,  0,  0, 67],
        [42, 41, 84, 33]],

       [[ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [63,  0,  0,  0],
        [89, 14,  0,  0]]])

沿 axis=1 :

In [89]: justify_nd(a, invalid_val=0, axis=1, side='front')
Out[89]: 
array([[[54, 57, 68, 77],
        [77, 22,  0, 31],
        [46,  0,  0, 98],
        [98,  0,  0, 75]],

       [[49, 47, 57, 98],
        [82, 19,  0, 87],
        [79, 89,  0, 90],
        [ 0,  0,  0, 74]],

       [[29, 75, 84, 49],
        [42, 41,  0, 67],
        [42,  0,  0, 33],
        [ 0,  0,  0,  0]],

       [[44, 10,  0, 38],
        [63, 14,  0,  0],
        [89,  0,  0,  0],
        [ 0,  0,  0,  0]]])

沿 axis=2 :

In [90]: justify_nd(a, invalid_val=0, axis=2, side='front')
Out[90]: 
array([[[54, 57, 77,  0],
        [77, 31,  0,  0],
        [46, 98,  0,  0],
        [98, 22, 68, 75]],

       [[49, 98,  0,  0],
        [47, 87,  0,  0],
        [82, 19, 90,  0],
        [79, 89, 57, 74]],

       [[ 0,  0,  0,  0],
        [29, 49,  0,  0],
        [42, 75, 67,  0],
        [42, 41, 84, 33]],

       [[38,  0,  0,  0],
        [44, 10,  0,  0],
        [63,  0,  0,  0],
        [89, 14,  0,  0]]])

到'end':

In [94]: justify_nd(a, invalid_val=0, axis=2, side='end')
Out[94]: 
array([[[ 0, 54, 57, 77],
        [ 0,  0, 77, 31],
        [ 0,  0, 46, 98],
        [98, 22, 68, 75]],

       [[ 0,  0, 49, 98],
        [ 0,  0, 47, 87],
        [ 0, 82, 19, 90],
        [79, 89, 57, 74]],

       [[ 0,  0,  0,  0],
        [ 0,  0, 29, 49],
        [ 0, 42, 75, 67],
        [42, 41, 84, 33]],

       [[ 0,  0,  0, 38],
        [ 0,  0, 44, 10],
        [ 0,  0,  0, 63],
        [ 0,  0, 89, 14]]])