vectorization

问题 Please correct me if there are somethings unclear in this question. I have two matrices pop , and ben of 3 dimensions. Call these dimensions as c,t,w . I want to repeat the exact same process I describe below for all of the c dimensions, without using a for loop as that is slow. For the discussion below, fix a value of the dimension c , to explain my thinking, later I will give a MWE. So when c is fixed I have a 2D matrix with dimension t,w . Now I repeat the entire process (coming below!)

问题 Please correct me if there are somethings unclear in this question. I have two matrices pop , and ben of 3 dimensions. Call these dimensions as c,t,w . I want to repeat the exact same process I describe below for all of the c dimensions, without using a for loop as that is slow. For the discussion below, fix a value of the dimension c , to explain my thinking, later I will give a MWE. So when c is fixed I have a 2D matrix with dimension t,w . Now I repeat the entire process (coming below!)

问题 I Want to write the code below as Pythonic way, applying mean over two axis. What the best way to do this? import numpy as np m = np.random.rand(30, 10, 10) m_mean = np.zeros((30, 1)) for j in range(30): m_mean[j, 0] = m[j, :, :].mean() 回答1: If you have a sufficiently recent NumPy, you can do m_mean = m.mean(axis=(1, 2)) I believe this was introduced in 1.7, though I'm not sure. The documentation was only updated to reflect this in 1.10, but it worked earlier than that. If your NumPy is too

问题 I am trying to get rid of an inefficient set of nested for loops in python. I have an array that I will call S(f k ,f q ) that needs to be mapped onto a different array that I will call Z(f i ,α j ). The arguments are all sampling frequencies. Both arrays have the same dimensions, which are user-selected. The mapping rule is fairly straightforward: f i = 0.5 · (f k - f q ) α j = f k + f q Currently I'm performing this via a series of nested for loops: import numpy as np nrows = 64 ncolumns =

问题 I can't seem to find an answer to my exact problem. Can anyone help? A simplified description of my dataframe ("df"): It has 2 columns: one is a bunch of text ("Notes"), and the other is a binary variable indicating if the resolution time was above average or not ("y"). I did bag-of-words on the text: from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer(lowercase=True, stop_words="english") matrix = vectorizer.fit_transform(df["Notes"]) My matrix is 6290 x

问题 I can't seem to find an answer to my exact problem. Can anyone help? A simplified description of my dataframe ("df"): It has 2 columns: one is a bunch of text ("Notes"), and the other is a binary variable indicating if the resolution time was above average or not ("y"). I did bag-of-words on the text: from sklearn.feature_extraction.text import CountVectorizer vectorizer = CountVectorizer(lowercase=True, stop_words="english") matrix = vectorizer.fit_transform(df["Notes"]) My matrix is 6290 x

问题 I am trying to vectorize the following operation: Place a smaller array into a bigger array, whereby the index changes as a function of another array for each row. Example data: array_large = [[0,0,0,0,0],[0,0,0,0,0],[0,0,0,0,0]] array_small = [[1,2],[3,4],[5,6]] array_index = [[1],[0],[2]] #*random index Desired output: array_combined = [[0,1,2,0,0],[3,4,0,0,0],[0,0,5,6,0]] So far I have been getting it to work with apply_along_axis - but I am wondering if there is a more efficient way of

问题 At the moment I have a quite long code with a for loop calculating the frequency of the various lengths at different maturities of a dataset, I would like to vectorize the code/find a more elegant solution, however so far I've not been able to work out how to do that. The frequency calculation is a relatively simple one: (count of occurances of a specific length at a certain maturity/total number of females or males)*100 Example data: Species Sex Maturity Length 1 HAK M 1 7 2 HAK M 2 24 3 HAK

问题 At the moment I have a quite long code with a for loop calculating the frequency of the various lengths at different maturities of a dataset, I would like to vectorize the code/find a more elegant solution, however so far I've not been able to work out how to do that. The frequency calculation is a relatively simple one: (count of occurances of a specific length at a certain maturity/total number of females or males)*100 Example data: Species Sex Maturity Length 1 HAK M 1 7 2 HAK M 2 24 3 HAK

问题 considering the following range based for loop in C++ 11 for ( T k : j ) { ... } there are g++ or clang++ optimization flags that can speed up the compiled code ? I'm not talking about any for cycle I'm only considering this new C++11 construct. 回答1: Optimizing loops is very rarely about optimizing the actual loop iteration code ( for ( T k : j ) in this case), but very much about optimizing what is IN the loop. Now, since this is ... in this case, it's impossible to say if, for example,