(n + 1)-dim布尔值，将带有均值数组的n-dim数组掩盖为所需的输出 [英] (n+1)-dim boolean masking a n-dim array with array of means as desired output

问题描述

我有一个带有值的2D数组

I have this 2D-array with values

values=np.random.rand(3,3)

和带有布尔蒙版的3D阵列

and a 3D-array with boolean masks

masks = np.random.rand(5,3,3)>0.5

我想要的输出是掩码值均值的数组.我可以这样:

My desired output is an array of the means of the masked values. I can do that with:

np.array([values[masks[i]].mean() for i in range(len(masks))])

有没有更有效的方法来实现这一目标?

Is there a more efficient way of achieving that ?

推荐答案

您可以将matrix-multplication与

You could use matrix-multplication with np.dot like so -

# Counts of valid mask elements for each element in output
counts = masks.sum(axis=(1,2))

# Use matrix multiplication to get sum of elementwise multiplications.
# Then, divide by counts for getting average/mean values as final output.
out = np.dot(masks.reshape(masks.shape[0],-1),values.ravel())/counts

也可以使用 np.tensordot 执行点积而无需重塑，就像这样-

One can also use np.tensordot to perform the dot-product without reshaping, like so -

out = np.tensordot(masks,values,axes=([1,2],[0,1]))/counts

---

对于涉及诸如min()& max()，您可以将values广播到具有与masks相同形状的3D阵列版本，并且将元素从values设置在True位置，否则设置为NaNs.然后，您可以使用类似 np.nanmin 和 np.nanmax 允许用户忽略NaNs执行此类操作，从而复制我们所需的行为.因此，我们将有-

---

For generic cases involving functions like min() & max(), you can broadcast values to a 3D array version of the same shape as masks and with elements set from values at True positions, otherwise set as NaNs. Then, you can use functions like np.nanmin and np.nanmax that allows users to perform such operations ignoring the NaNs, thus replicating our desired behavior. Thus, we would have -

# Masked array with values being put at True places of masks, otherwise NaNs
nan_masked_values = np.where(masks,values,np.nan)

# For performing .min() use np.nanmin
out_min = np.nanmin(nan_masked_values,axis=(1,2))

# For performing .max() use np.nanmax
out_max = np.nanmax(nan_masked_values,axis=(1,2))

因此，原始的.mean()计算可以使用

Thus, the original .mean() calculation could be performed with np.nanmean like so -

out_mean = np.nanmean(nan_masked_values,axis=(1,2))

这篇关于(n + 1)-dim布尔值，将带有均值数组的n-dim数组掩盖为所需的输出的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！