numpy-broadcasting | 易学教程

How does numpy.newaxis work and when to use it?

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When I try numpy.newaxis the result gives me a 2-d plot frame with x-axis from 0 to 1. However, when I try using numpy.newaxis to slice a vector, vector[0:4,] [ 0.04965172 0.04979645 0.04994022 0.05008303] vector[:, np.newaxis][0:4,] [[ 0.04965172] [ 0.04979645] [ 0.04994022] [ 0.05008303]] Is it the same thing except that it changes a row vector to a column vector? Generally, what is the use of numpy.newaxis , and in which circumstances should we use it? Simply put, the newaxis is used to increase the dimension of the existing array by one more dimension , when used once . Thus, 1D array will

NumPy Broadcasting: Calculating sum of squared differences between two arrays

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问题 I have the following code. It is taking forever in Python. There must be a way to translate this calculation into a broadcast... def euclidean_square(a,b): squares = np.zeros((a.shape[0],b.shape[0])) for i in range(squares.shape[0]): for j in range(squares.shape[1]): diff = a[i,:] - b[j,:] sqr = diff**2.0 squares[i,j] = np.sum(sqr) return squares 回答1: 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

Numpy - create matrix with rows of vector

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问题 I have a vector [x,y,z,q] and I want to create a matrix: [[x,y,z,q], [x,y,z,q], [x,y,z,q], ... [x,y,z,q]] with m rows. I think this could be done in some smart way, using broadcasting, but I can only think of doing it with a for loop. 回答1: 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

How does numpy.newaxis work and when to use it?

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问题 When I try numpy.newaxis the result gives me a 2-d plot frame with x-axis from 0 to 1. However, when I try using numpy.newaxis to slice a vector, vector[0:4,] [ 0.04965172 0.04979645 0.04994022 0.05008303] vector[:, np.newaxis][0:4,] [[ 0.04965172] [ 0.04979645] [ 0.04994022] [ 0.05008303]] Is it the same thing except that it changes a row vector to a column vector? Generally, what is the use of numpy.newaxis, and in which circumstances should we use it? 回答1: Simply put, the newaxis is used