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Python 3.x Matlab的pdist2在python中的等价物如下_Python 3.x_Matlab_Euclidean Distance - Fatal编程技术网

Python 3.x Matlab的pdist2在python中的等价物如下

Python 3.x Matlab的pdist2在python中的等价物如下,python-3.x,matlab,euclidean-distance,Python 3.x,Matlab,Euclidean Distance,在计算大矩阵(90k x 4)的成对欧几里德距离时,有帖子显示了一个问题,因为它会导致超过内存限制 distance_matrix(MatA,MatA) 在Matlab中,pdist2命令有第五个参数。此参数限制返回距离矩阵的大小: pdist2(MatA,MatA,'euc','Smallest',minPnts) % minPnts is 4 为了绕过内存限制问题(Windows10上的Spyder),我求助于SplitMata循环 然而,这不仅非常缓慢,而且不确定作为替代方案是否正确

在计算大矩阵(90k x 4)的成对欧几里德距离时,有帖子显示了一个问题,因为它会导致超过内存限制

distance_matrix(MatA,MatA)
在Matlab中,pdist2命令有第五个参数。此参数限制返回距离矩阵的大小:

pdist2(MatA,MatA,'euc','Smallest',minPnts)  % minPnts is 4
为了绕过内存限制问题(Windows10上的Spyder),我求助于SplitMata循环

然而,这不仅非常缓慢,而且不确定作为替代方案是否正确

Kdlst = []
splits = np.array_split(MatA, 10)
for i in range(len(splits)):
    for j in range(i, len(splits)):
        tmpDist =  distance_matrix(splits[i], splits[j],p=2)
        Kd = np.sort(tmpDist,axis=0)
        Kd = Kd[:minPnts,:]
        Kd_ = np.sort(Kd.flatten())
        Kdlst.append(Kd_)
Kdf =  Kdlst[0]
for elem in Kdlst[1:]:
    Kdf = np.hstack((Kdf,elem))
显然,必须有更好的办法。Gpu实现是最佳的