Lin Ma - 10 months ago 96

Python Question

Always confused how numpy reshape handle negative shape parameter, here is an example of code and output, could anyone explain what happens for reshape [-1, 1] here? Thanks.

Related document, using Python 2.7.

http://docs.scipy.org/doc/numpy/reference/generated/numpy.reshape.html

`import numpy as np`

from sklearn.preprocessing import LabelEncoder

from sklearn.preprocessing import OneHotEncoder

S = np.array(['box','apple','car'])

le = LabelEncoder()

S = le.fit_transform(S)

print(S)

ohe = OneHotEncoder()

one_hot = ohe.fit_transform(S.reshape(-1,1)).toarray()

print(one_hot)

[1 0 2]

[[ 0. 1. 0.]

[ 1. 0. 0.]

[ 0. 0. 1.]]

Answer

`-1`

is used to infer one missing length from the other. For example reshaping `(3,4,5)`

to `(-1,10)`

is equivalent to reshaping to `(6,10)`

because `6`

is the only length that makes sense form the other inputs.

Source (Stackoverflow)