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  • python - What is the difference between flatten and ravel . . .
    Backcompat guarantees sometimes cause odd things like this to happen For example: the numpy developers recently (in 1 10) added a previously implicit guarantee that ravel would return a contiguous array (a property that is very important when writing C extensions), so now the API is a flatten() to get a copy for sure, a ravel() to avoid most copies but still guarantee that the array returned
  • python - Why do I need to use ravel() in this case? - Stack . . .
    I am really confused about why do I need to use ravel() before fitting the data to SGDRegressor This is the code: from sklearn linear_model import SGDRegressor sgd_reg = SGDRegressor(max_iter = 1000, tol = 1e-3, penalty
  • python - Flattening a list of NumPy arrays? - Stack Overflow
    As @aydow points out in the comments, using numpy ndarray ravel can be faster if one doesn't care about getting a copy or a view np array(list_of_arrays) ravel() Although, according to docs When a view is desired in as many
  • Ravel () on only two dimensions of a 3D numpy array
    I'm trying to reorganize it such that it is size (182, 39676) - e g , take each of the 182 slices of it and ravel() out each of those slices into one dimension, but still keep the slices separate I can think of a few ways of doing this with a loop, but it seems un-pythonic to make a loop in numpy
  • correct and efficient way to flatten array in numpy in python?
    You might need to check out numpy flatten and numpy ravel, both return a 1-d array from an n-d array Furthermore, if you're not going to modify the returned 1-d array, I suggest you use numpy ravel , since it doesn't make a copy of the array, but just return a view of the array, which is much faster than numpy flatten
  • python - Why is numpy. ravel() required in this code that . . .
    numpy: ravel_multi_index increment different results from iterating over indices loop 12 Differences between X ravel() and X reshape(s0*s1*s2) when number of axes known
  • A column-vector y was passed when a 1d array was expected
    As Python's ravel() may be a valid way to achieve the desired results in this particular case, I would, however, recommend using numpy squeeze() The problem here is, that if the shape of your y (numpy array) is e g (100, 2) , then y ravel() will concatenate the two variables on the second axis along the first axis, resulting in a shape like (200,)
  • numpy - Differences between X. ravel () and X. reshape (s0*s1 . . .
    A = X ravel() s0, s1, s2 = X shape B = X reshape(s0*s1*s2) C = X reshape(-1) # thanks to @hpaulj below I'm not asking if A and B and C are the same I'm wondering if the particular use of ravel and reshape in this situation are essentially the same, or if there are significant differences, advantages, or disadvantages to one or the other , provided that you know the number of axes of X ahead


















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