我为下面的矩阵计算了braycurtis相异矩阵。行是社区，列是物种
[[ 0 0 0 0]
[ 13 110 0 0]
[ 6 3 0 0]
[ 0 5 0 0]
[ 0 128 0 0]
[ 0 0 0 0]
[ 11 76 11 0]
[ 8 29 3 0]
[ 0 58 5 0]
[ 1 3 0 0]
[ 4 11 1 0]
[ 3 38 0 0]
[ 9 35 8 7]
[ 0 0 0 0]
[ 0 0 0 0]
[ 63 576 11 9]
[ 24 99 0 0]
[ 1 29 5 0]
[ 0 0 0 0]
[ 0 0 0 0]
[ 0 0 0 0]
[ 0 0 0 0]
[ 2 0 0 0]
[ 1 4 0 0]
[ 0 0 0 0]
[ 8 20 0 0]
[ 3 21 13 6]
[ 1 4 0 0]
[ 0 0 0 0]
[ 0 0 0 0]
[ 0 4 0 0]]
我用过
dissimilarityMatrix=scipy.spatial.distance.pdist(CommGrouped, 'braycurtis')
并得到以下结果
[ 1. 1. 1. 1. nan 1. 1.
1. 1. 1. 1. 1. nan
nan 1. 1. 1. nan nan
nan nan 1. 1. nan 1. 1.
1. nan nan 1. 0.86363636 0.921875
0.12350598 1. 0.21266968 0.54601227 0.37634409 0.93700787
0.78417266 0.5 0.51648352 1. 1. 0.68542199
0.08943089 0.62025316 1. 1. 1. 1. 0.968
0.921875 1. 0.62913907 0.71084337 0.921875 1. 1.
0.93700787 0.57142857 0.95620438 1. 0.8317757 0.63265306
0.91666667 0.38461538 0.44 0.76 0.73529412 1. 1.
0.97305389 0.86363636 0.81818182 1. 1. 1. 1.
0.63636364 0.42857143 1. 0.51351351 0.76923077 0.42857143
1. 1. 0.53846154 0.92481203 1. 0.90291262
0.77777778 0.85294118 0.33333333 0.52380952 0.7826087 0.84375 1.
1. 0.98493976 0.921875 0.75 1. 1. 1.
1. 1. 0.2 1. 0.6969697 0.79166667
0.2 1. 1. 0.11111111 1. 0.32743363
0.6547619 0.39267016 0.95454545 0.84722222 0.55029586 0.62566845
1. 1. 0.6747141 0.21115538 0.64417178 1. 1.
1. 1. 1. 0.93984962 1. 0.74358974
0.75438596 0.93984962 1. 1. 0.93939394 1. 1.
1. 1. 1. 1. 1. nan
nan 1. 1. 1. nan nan
nan nan 1. 1. nan 1. 1.
1. nan nan 1. 0.42028986 0.2173913
0.92156863 0.71929825 0.41007194 0.33757962 1. 1.
0.74108322 0.21266968 0.47368421 1. 1. 1. 1.
0.96 0.90291262 1. 0.55555556 0.5035461 0.90291262
1. 1. 0.92156863 0.37864078 0.81818182 0.42857143
0.20987654 0.19191919 1. 1. 0.88555079 0.54601227
0.12 1. 1. 1. 1. 0.9047619
0.77777778 1. 0.17647059 0.34939759 0.77777778 1. 1.
0.81818182 0.91044776 0.69620253 0.26923077 0.3442623 1. 1.
0.82548476 0.37634409 0.30612245 1. 1. 1. 1.
1. 0.88235294 1. 0.56043956 0.50943396 0.88235294
1. 1. 0.88059701 0.6 0.82222222 0.87301587
1. 1. 0.98793363 0.93700787 0.79487179 1. 1.
1. 1. 0.66666667 0.11111111 1. 0.75
0.82978723 0.11111111 1. 1. 0.25 0.50877193
0.57333333 1. 1. 0.95259259 0.78417266 0.49019608
1. 1. 1. 1. 0.77777778 0.52380952
1. 0.31818182 0.49152542 0.52380952 1. 1. 0.6
0.24 1. 1. 0.88285714 0.5 0.21052632
1. 1. 1. 1. 0.90697674 0.7826087 1.
0.33333333 0.42857143 0.7826087 1. 1. 0.82222222
1. 1. 0.8356546 0.51648352 0.25531915 1. 1.
1. 1. 0.93442623 0.84375 1. 0.35632184
0.25490196 0.84375 1. 1. 0.87301587 nan
1. 1. 1. nan nan nan
nan 1. 1. nan 1. 1. 1.
nan nan 1. 1. 1. 1.
nan nan nan nan 1. 1.
nan 1. 1. 1. nan nan
1. 0.68542199 0.89913545 1. 1. 1. 1.
0.99394856 0.98493976 1. 0.91848617 0.88319088 0.98493976
1. 1. 0.98793363 0.62025316 1. 1. 1.
1. 0.968 0.921875 1. 0.62913907 0.71084337
0.921875 1. 1. 0.93700787 1. 1. 1.
1. 0.94594595 0.75 1. 0.33333333 0.30769231
0.75 1. 1. 0.79487179 nan nan
nan 1. 1. nan 1. 1. 1.
nan nan 1. nan nan 1. 1.
nan 1. 1. 1. nan nan
1. nan 1. 1. nan 1. 1.
1. nan nan 1. 1. 1.
nan 1. 1. 1. nan nan
1. 0.71428571 1. 0.86666667 0.91111111 0.71428571
1. 1. 1. 1. 0.6969697 0.79166667
0. 1. 1. 0.11111111 1. 1. 1.
nan nan 1. 0.35211268 0.6969697 1. 1.
0.75 0.79166667 1. 1. 0.82978723 1. 1.
0.11111111 nan 1. 1. ]
我无法弄清楚结果是否正确以及获得纳米的原因。
请帮忙！
答案 0 :(得分：0)
是的，输出正确。
很容易验证自己：documentation on pdist具有Bray-Curtis距离的实际公式：
d（u，v）=Σ i （u i - v i ）/Σ i （你 i + v i ）
因此，您可以自己计算所有距离（或者只需要检查一些距离）。
NaNs也很明显：它们是分子和分母都为0的结果，这对于输入中的一堆矢量组合会发生。