15

请我有点新Python，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 2048 游戏演示在这里我有这个功能

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]
          ])

它将覆盖左侧的零并产生

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

请我需要有人帮助我以一种numpy我相信会更快并且需要更少代码的方式来帮助我（我在深度优先搜索算法中使用），更重要的是实现cover_up,cover_down和 cover_left.

`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]]
4

2 回答 2

36

这是一种矢量化方法，其灵感来自this other post并概括为涵盖non-zeros所有四个方向 -

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

样品运行 -

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

样品运行 -

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]]])
于 2017-06-15T05:25:31.693 回答
0

感谢这一切是我后来使用的

def justify(a, direction):
    mask = a>0
    justified_mask = numpy.sort(mask,0) if direction == 'up' or direction =='down' else numpy.sort(mask, 1)
    if direction == 'up':
        justified_mask = justified_mask[::-1]
    if direction =='left':
        justified_mask = justified_mask[:,::-1]
    if direction =='right':
        justified_mask = justified_mask[::-1, :]    
    out = numpy.zeros_like(a) 
    out.T[justified_mask.T] = a.T[mask.T]
    return out
于 2017-06-15T05:57:54.530 回答