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@adad

The sample of numpy.percentile and how to index the larger array than original array

Posted at 
print("====percentile====")
X = np.random.randn(1) # random 1D array
N = 10 # number of bootstrap samples
print(X.size)
idx = np.random.randint(0, X.size, (N, X.size))
print("====means====")
print(X)
print(idx)
print(X[idx])
print(X.shape, idx.shape, X[idx].shape)
means = X[idx].mean(axis=1)
print(means.shape)
confint = np.percentile(means, [2.5, 97.5])
print(confint)
  • e.g.
====percentile====
1
====means====
[ 0.41599196]
[[0]
 [0]
 [0]
 [0]
 [0]
 [0]
 [0]
 [0]
 [0]
 [0]]
[[ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]
 [ 0.41599196]]
((1,), (10, 1), (10, 1))
(10,)
[0.41599196248944836, 0.41599196248944836]

Refs.

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