vectorization

问题 I am struggling with the following code: import numpy as np e = np.linspace(0, 4, 10) def g(x): if x > 1: return x else: return 0 vg = np.vectorize(g) print(vg(e)) the result looks like this: [0 0 0 1 1 2 2 3 3 4] I also checked the dtype. It seems that the vectorize function is conveting the type to int64 from float64! 回答1: The documentation for np.vectorize explains: The data type of the output of vectorized is determined by calling the function with the first element of the input. This can

问题 I am struggling with the following code: import numpy as np e = np.linspace(0, 4, 10) def g(x): if x > 1: return x else: return 0 vg = np.vectorize(g) print(vg(e)) the result looks like this: [0 0 0 1 1 2 2 3 3 4] I also checked the dtype. It seems that the vectorize function is conveting the type to int64 from float64! 回答1: The documentation for np.vectorize explains: The data type of the output of vectorized is determined by calling the function with the first element of the input. This can

问题 I have sentences that I vectorize using sentence_vector() method of BiobertEmbedding python module (https://pypi.org/project/biobert-embedding/). For some group of sentences I have no problem but for some others I have the following error message : File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert_embedding/embedding.py", line 133, in sentence_vector encoded_layers = self.eval_fwdprop_biobert(tokenized_text) File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert

问题 I have sentences that I vectorize using sentence_vector() method of BiobertEmbedding python module (https://pypi.org/project/biobert-embedding/). For some group of sentences I have no problem but for some others I have the following error message : File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert_embedding/embedding.py", line 133, in sentence_vector encoded_layers = self.eval_fwdprop_biobert(tokenized_text) File "/home/nobunaga/.local/lib/python3.6/site-packages/biobert

问题 This is a follow up question to a question that I asked before (R apply multiple functions when large number of categories/types are present using case_when (R vectorization)). Unfortunately I have not been able to figure out the problem. I think I may have narrowed down the source of the problem an wanted to check if someone with a better understanding than me could help me figure out a solution. Suppose I have the following dataset: set.seed(100) City=c("City1","City2","City2","City1")

问题 This is a follow up question to a question that I asked before (R apply multiple functions when large number of categories/types are present using case_when (R vectorization)). Unfortunately I have not been able to figure out the problem. I think I may have narrowed down the source of the problem an wanted to check if someone with a better understanding than me could help me figure out a solution. Suppose I have the following dataset: set.seed(100) City=c("City1","City2","City2","City1")

问题 I'd like to know how to calculate the factorial of a matrix elementwise. For example, import numpy as np mat = np.array([[1,2,3],[2,3,4]]) np.the_function_i_want(mat) would give a matrix mat2 such that mat2[i,j] = mat[i,j]! . I've tried something like np.fromfunction(lambda i,j: np.math.factorial(mat[i,j])) but it passes the entire matrix as argument for np.math.factorial . I've also tried to use scipy.vectorize but for matrices larger than 10x10 I get an error. This is the code I wrote:

问题 Let X be a Bxn numpy matrix, i.e., import numpy as np B = 10 n = 2 X = np.random.random((B, n)) Now, I'm interested in computing the so-called kernel (or even similarity) matrix K , which is of shape BxB , and its {i,j} -th element is given as follows: K(i,j) = fun(x_i, x_j) where x_t denotes the t -th row of matrix X and fun is some function of x_i , x_j . For instance, this function could be the so-called RBF function, i.e., K(i,j) = exp(-|x_i - x_j|^2). For doing so, a naive way would be

问题 I have a N-dimensional array (Named A). For each row of the first axis of A, I want to obtain the coordinates of the maximum value along the other axes of A. Then I would return a 2-dimensional array with the coordinates of the maximum value for each row of the first axis of A. I already solved my problem using a loop, but I was wondering whether there is a more efficient way of doing this. My current solution (for an example array A) is as follows: import numpy as np A=np.reshape(np

问题 I have a N-dimensional array (Named A). For each row of the first axis of A, I want to obtain the coordinates of the maximum value along the other axes of A. Then I would return a 2-dimensional array with the coordinates of the maximum value for each row of the first axis of A. I already solved my problem using a loop, but I was wondering whether there is a more efficient way of doing this. My current solution (for an example array A) is as follows: import numpy as np A=np.reshape(np