performance

问题 I have two list of names (strings) that look like this: executives = ['Brian Olsavsky', 'Some Guy', 'Some Lady'] analysts = ['Justin Post', 'Some Dude', 'Some Chick'] I need to find where those names occur in a list of strings that looks like this: str = ['Justin Post - Bank of America', "Great. Thank you for taking my question. I guess the big one is the deceleration in unit growth or online stores.", "I know it's a tough 3Q comp, but could you comment a little bit about that?", 'Brian

问题 Is there an implicit synchronization in the following GL code? glClear(GL_COLOR_BUFFER_BIT); glUseProgram(prg); glUniform1f(loc, a); glDrawArrays(GL_TRIANGLES, 0, N); glUniform1f(loc, b); // <-- Implicit synchronization?? glDrawArrays(GL_TRIANGLES, 0, N); swapBuffers(); 回答1: As always, OpenGL implementations can handle things the way they like, as long as it results in the correct behavior. So there's really no way to say how it has to work. That being said, updating uniforms is a common

问题 I am developing backend project using node.js and going to implement sorting products functionality. I researched some articles and there were several articles saying bubble sort is not efficient. Bubble sort was used in my previous projects and I was surprised why it is bad. Could anyone explain about why it is inefficient? If you can explain by c programming or assembler commands it would be much appreciated. 回答1: Bubble Sort has O(N^2) time complexity so it's garbage for large arrays

问题 I am developing backend project using node.js and going to implement sorting products functionality. I researched some articles and there were several articles saying bubble sort is not efficient. Bubble sort was used in my previous projects and I was surprised why it is bad. Could anyone explain about why it is inefficient? If you can explain by c programming or assembler commands it would be much appreciated. 回答1: Bubble Sort has O(N^2) time complexity so it's garbage for large arrays

问题 For example if i have: a=np.array([[1,1,4,1,4,3,1]]) We can see that we have the number 1 four times, the number 4 twice and 3 only ones. I want to have the following result: array(4,4,2,4,2,1,4) As you can see: each cell is replaced by the count of it's element. How can i do it in the best efficient way? 回答1: One vectorized approach with np.unique and np.searchsorted - # Get unique elements and their counts unq,counts = np.unique(a,return_counts=True) # Get the positions of unique elements

问题 For example if i have: a=np.array([[1,1,4,1,4,3,1]]) We can see that we have the number 1 four times, the number 4 twice and 3 only ones. I want to have the following result: array(4,4,2,4,2,1,4) As you can see: each cell is replaced by the count of it's element. How can i do it in the best efficient way? 回答1: One vectorized approach with np.unique and np.searchsorted - # Get unique elements and their counts unq,counts = np.unique(a,return_counts=True) # Get the positions of unique elements

问题 Sieve of Eratosthenes memory constraint issue Im currently trying to implement a version of the sieve of eratosthenes for a Kattis problem, however, I am running into some memory constraints that my implementation wont pass. Here is a link to the problem statement. In short the problem wants me to first return the amount of primes less or equal to n and then solve for a certain number of queries if a number i is a prime or not. There is a constraint of 50 MB memory usage as well as only using

问题 I have a question about how to improve the performance of a large horizontal linear layout. I am creating a table like view that can have anywhere from 50 to 2,500 entries. Each entry is a LinearLayout containing a TextView with some simple text. I have implemented the design by utilizing the LinearListView library. This library allows an ListAdapter to be bound to a LinearLayout to display a view in a horizontal or vertical orientation. The way I have implemented this currently is by

问题 I have a question about how to improve the performance of a large horizontal linear layout. I am creating a table like view that can have anywhere from 50 to 2,500 entries. Each entry is a LinearLayout containing a TextView with some simple text. I have implemented the design by utilizing the LinearListView library. This library allows an ListAdapter to be bound to a LinearLayout to display a view in a horizontal or vertical orientation. The way I have implemented this currently is by

问题 Given two numpy arrays of nx3 and mx3 , what is an efficient way to determine the row indices (counter) wherein the rows are common in the two arrays. For instance I have the following solution, which is significantly slow for not even much larger arrays def arrangment(arr1,arr2): hits = [] for i in range(arr2.shape[0]): current_row = np.repeat(arr2[i,:][None,:],arr1.shape[0],axis=0) x = current_row - arr1 for j in range(arr1.shape[0]): if np.isclose(x[j,0],0.0) and np.isclose(x[j,1],0.0) and