numpy.mean(X, axis=0) when 2d array, else do nothing

user_3696204 0
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I have a function that, among other things, calculates the mean of rows a ndarray (2d or 1d). This is by way of ndarray.mean(axis=0)

For the 1d array, I'd like it to just return itself since there is only 1 "row", instead of averaging the elements and returning a scalar.

Is there a pythonic way to do this other than just checking the ndim attribute before taking the average?

def d_Error(X, y, weights, bias):
    y_hat = probability(X, weights, bias)
    dE_matrix =  (X.T * (y - y_hat)).T  # each row is the gradient at that sample
    dEdw = np.mean(dE_matrix, axis=0) # get average gradient
    dEdb = (y - y_hat).mean() # gives scalar
    dEdz = np.append(dEdw, dEdb)
    return dEdz
linear-algebra numpy python
2 Answers

Use np.atleast_2d -

np.atleast_2d(ar).mean(axis=0)

For 2D, np.atleast_2d doesn't change anything. For 1D, let's look at a sample case -

In [125]: a1D = np.arange(4).astype(float)

In [126]: a1D
Out[126]: array([0., 1., 2., 3.])

In [127]: np.atleast_2d(a1D).mean(axis=0)
Out[127]: array([0., 1., 2., 3.])

Another with reshaping -

ar.reshape(-1,ar.shape[-1]).mean(0)
user_32938810

A less elegant solution, but more a "trick" is passing tuple of indices to the axis=… parameter. If that is empty, it returns the original array. So you can pass it a range of indices:

dEdw = dE_matrix.mean(axis=tuple(range(dE_matrix.ndim-1)))

This thus will result in a singleton tuple (0,) for a 2d array, and an empty tuple () for the 1d array.

user_675790
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