[英]How to compare two arrays of arrays 'array-elementwise' in Numpy?
[英]How to compare 3 numpy arrays elementwise and get the results as the array with maximum values?
numpy數組包含如下所示的預測概率:
predict_prob1 =([[0.95602106, 0.04397894],
[0.93332366, 0.06667634],
[0.97311459, 0.02688541],
[0.97323962, 0.02676038]])
predict_prob2 =([[0.70425144, 0.29574856],
[0.69751251, 0.30248749],
[0.7072872 , 0.2927128 ],
[0.68683139, 0.31316861]])
predict_prob3 =([[0.56551921, 0.43448079],
[0.93321106, 0.06678894],
[0.92345399, 0.07654601],
[0.88396842, 0.11603158]])
我想將這三個numpy.ndarray逐元素進行比較,並找出哪個數組具有最大的概率。 三個數組的長度相同。 我試圖實現這種不正確的方法。
for i in range(len(predict_prob1)):
if(predict_prob1[i] > predict_prob2[i])
c = predict_prob1[i]
else
c = predict_prob2[i]
if(c > predict_prob3[i])
result = c
else
result = array[i]
請幫忙!!
您可以使用np.maximum.reduce
:
np.maximum.reduce([A, B, C])
其中A
, B
, C
是numpy.ndarray
對於您的示例,結果為:
[[0.95602106 0.43448079]
[0.93332366 0.30248749]
[0.97311459 0.2927128 ]
[0.97323962 0.31316861]]
對我來說,還不清楚您要問的是什么—
如果您想要的結果是一個4x2數組,該數組索引三個數組中哪個數組在i,j
位置具有最大值i,j
那么您想使用np.argmax
>>> import numpy as np
>>> predict_prob1 =([[0.95602106, 0.04397894],
[0.93332366, 0.06667634],
[0.97311459, 0.02688541],
[0.97323962, 0.02676038]])
>>> predict_prob2 =([[0.70425144, 0.29574856],
[0.69751251, 0.30248749],
[0.7072872 , 0.2927128 ],
[0.68683139, 0.31316861]])
>>> predict_prob3 =([[0.56551921, 0.43448079],
[0.93321106, 0.06678894],
[0.92345399, 0.07654601],
[0.88396842, 0.11603158]])
>>> np.argmax((predict_prob1,predict_prob2,predict_prob3), 0)
array([[0, 2],
[0, 1],
[0, 1],
[0, 1]])
>>>
附錄
閱讀了OP的評論后,我在回答中添加了以下內容
>>> names = np.array(['predict_prob%d'%(i+1) for i in range(3)])
>>> names[np.argmax((predict_prob1,predict_prob2,predict_prob3),0)]
array([['predict_prob1', 'predict_prob3'],
['predict_prob1', 'predict_prob2'],
['predict_prob1', 'predict_prob2'],
['predict_prob1', 'predict_prob2']], dtype='<U13')
>>>
假設您要為每一行分配類別0概率最高的數組索引:
which = 0
np.stack([predict_prob1, predict_prob2, predict_prob3], axis=2)[:, which, :].argmax(axis=1)
輸出:
array([0, 0, 0, 0])
對於第1類:
array([2, 1, 1, 1])
您可以使用操作數>和<產生數組的布爾掩碼的事實。
import numpy as np
predict_prob1 =np.array([[0.95602106, 0.04397894],
[0.93332366, 0.06667634],
[0.97311459, 0.02688541],
[0.97323962, 0.02676038]])
predict_prob2 =np.array([[0.70425144, 0.29574856],
[0.69751251, 0.30248749],
[0.7072872 , 0.2927128 ],
[0.68683139, 0.31316861]])
predict_prob3 =np.array([[0.56551921, 0.43448079],
[0.93321106, 0.06678894],
[0.92345399, 0.07654601],
[0.88396842, 0.11603158]])
predict_prob = (predict_prob1>predict_prob2)*predict_prob1 + (predict_prob1<predict_prob2)*predict_prob2
predict_prob = (predict_prob>predict_prob3)*predict_prob + (predict_prob<predict_prob3)*predict_prob3
print(predict_prob)
結果是:
[[0.95602106 0.43448079]
[0.93332366 0.30248749]
[0.97311459 0.2927128 ]
[0.97323962 0.31316861]]
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