If X and beta do not have the same shape as the second term in the rhs of your last line (i.e. nsample), then you will get this type of error. To add an array to a tuple of arrays, they all must be the same shape. I would recommend looking at the numpy broadcasting … Read more
Well, the meaning of trailing axes is explained on the linked documentation page. If you have two arrays with different dimensions number, say one 1x2x3 and other 2×3, then you compare only the trailing common dimensions, in this case 2×3. But if both your arrays are two-dimensional, then their corresponding sizes have to be either … Read more
Here’s another way to perform : (a-b)^2 = a^2 + b^2 – 2ab with np.einsum for the first two terms and dot-product for the third one – import numpy as np np.einsum(‘ij,ij->i’,A,A)[:,None] + np.einsum(‘ij,ij->i’,B,B) – 2*np.dot(A,B.T) Runtime test Approaches – def loopy_app(A,B): m,n = A.shape[0], B.shape[0] out = np.empty((m,n)) for i,a in enumerate(A): out[i] = … Read more
You can use np.einsum after calculating the differences in a broadcasted way, like so – ab = a[:,None,:] – b out = np.einsum(‘ijk,ijk->ij’,ab,ab) Or use scipy’s cdist with its optional metric argument set as ‘sqeuclidean’ to give us the squared euclidean distances as needed for our problem, like so – from scipy.spatial.distance import cdist out … Read more
You need to extend the dimensions of X with None/np.newaxis to form a 3D array and then do subtraction by w. This would bring in broadcasting into play for this 3D operation and result in an output with a shape of (5,n,3). The implementation would look like this – X[:,None] – w # or X[:,np.newaxis] … Read more
Here’s an approach using broadcasting – def create_ranges(start, stop, N, endpoint=True): if endpoint==1: divisor = N-1 else: divisor = N steps = (1.0/divisor) * (stop – start) return steps[:,None]*np.arange(N) + start[:,None] Sample run – In [22]: # Setup start, stop for each row and no. of elems in each row …: start = np.array([1,4,2]) …: … Read more
Simply put, numpy.newaxis is used to increase the dimension of the existing array by one more dimension, when used once. Thus, 1D array will become 2D array 2D array will become 3D array 3D array will become 4D array 4D array will become 5D array and so on.. Here is a visual illustration which depicts … Read more
Certainly possible with broadcasting after adding with m zeros along the columns, like so – np.zeros((m,1),dtype=vector.dtype) + vector Now, NumPy already has an in-built function np.tile for exactly that same task – np.tile(vector,(m,1)) Sample run – In [496]: vector Out[496]: array([4, 5, 8, 2]) In [497]: m = 5 In [498]: np.zeros((m,1),dtype=vector.dtype) + vector Out[498]: … Read more
Simply put, numpy.newaxis is used to increase the dimension of the existing array by one more dimension, when used once. Thus, 1D array will become 2D array 2D array will become 3D array 3D array will become 4D array 4D array will become 5D array and so on.. Here is a visual illustration which depicts … Read more
np.negative is silghtly faster than multiplying (as it is a ufunc) N = 5 arr = np.arange(N ** 3).reshape(N, N, N) %timeit arr.ravel()[1::2] *= -1 %timeit np.negative(arr.ravel()[1::2], out = arr.ravel()[1::2]) The slowest run took 8.74 times longer than the fastest. This could mean that an intermediate result is being cached. 100000 loops, best of 3: … Read more