optimization

问题 I have a trained keras model, and I am trying to run predictions with CPU only. I want this to be as quick as possible, so I thought I would use predict_generator with multiple workers. All of the data for my prediction tensor are loaded into memory beforehand. Just for reference, array is a list of tensors, with the first tensor having shape [nsamples, x, y, nchannels]. I made a thread-safe generator following the instructions here (I followed this when using fit_generator as well). class

问题 I'm trying to bring this constraint in my pyomo model [ 1 I define a set for indexing over time and I want to optimize the corresponding energy variable below model.grid_time = Set(initialize=range(0, 23))) model.charging_energy = Var(model.grid_time, initialize=0) My constraint definition looks like as follows: model.limits = ConstraintList() for t in model.grid_time: model.limits.add(sum(model.charging_energy[t] for t in model.grid >= energy_demand.at[t,"total_energy_demand"]) The problem

问题 How the dataset looks like: I'm trying to build an optimization tool for fantasy football, but I'm having difficulty forcing the model to use players from the same team. 9 players form a lineup, need to be under 50k and we are maximizing "proj" projected points self.salary_cap = 50000 self.Minsalary_cap = 0 self.header = ['QB', 'RB', 'RB','WR', 'WR', 'WR', 'TE','FLEX', 'Def'] #define the pulp object problem prob = pulp.LpProblem('NFL', pulp.LpMaximize) #define the player variabless players

问题 Problem I have 6 OnEdit Functions, which work as intended individually, but when together they don't work as intended. By this I mean some simply don't trigger. Properties of the Script They have different names - function onEdit(e) {code}, function onEdit1(e1) {code}, function onEdit2(e2) {code}, function onEdit3(e3) {code}, function onEdit4(e4) {code}, function onEdit5(e5) {code} They are all in the same .gs tab Some of them have the same variables. For example OnEdit has var range = e

问题 The target is to find all unique sums that can be created by adding one int of each array with the restriction that each integer value may only be used once within each final sum. e.g.: int[][] data = { [ 1, 2, 3], [ 1, 2, 3, 4], [ 4, 5, 6] } turns to // int[] path = [ int in data[0] , int in data[1], int in data[2] ] int[][] paths { [ 1, 2, 4 ] , [ 1, 2, 5 ] , [ 1, 2, 6 ] , [ 1, 3, 4 ] , [ 1, 3, 5 ] , [ 1, 3, 6 ] , [ 1, 4, 5 ] , [ 1, 4, 6 ] , [ 2, 1, 4 ] , [ 2, 1, 5 ] , [ 2, 1, 6 ] , [ 2, 3,

问题 I would appreciate any help, given. I have 7 separate arrays with approx. 90,000 numbers in each array (let's call them arrays1-arrays7). There are no duplicate numbers within each array itself. BUT, there can be duplicates between the arrays. for example, array2 has no duplicates but it is possible to have numbers in common with arrays3 and arrays4. The Problem: I am trying to identify all of the numbers that are duplicated 3 times once all 7 arrays are merged. I must do this calculation

问题 I've got some csv export from a Database that I want to organise, I've made a VBA macro in excel that takes around 40 min to finish and I would like to know how to optimise it (Mainly to learn). Imagine that you got different fruit shops that sells different fruits and the csv you get is like: Worksheets("Temp1")= Shop 1 ¦ Apple ¦ 10 Shop 1 ¦ Melon ¦ 20 Shop 2 ¦ Apple ¦ 30 Shop 3 ¦ Mango ¦ 40 Shop 1 ¦ Mango ¦ 50 I've already created a sheet like: Worksheets(NameOfWorkbook) = ¦Shop 1¦Shop 2

问题 I know that some functions like sin cos min max memcpy may be treated not as normal functions but may be replaced by built-in functions (which may be more optimal than merely inline function calls, when the replacement is (an) actual processor instruction(s), such as directly calling the FSIN instruction for standard sin function when compiled for an x86 with a floating point unit). The question I would like to use power of built-in functions (in C/C++ mostly in mingw/gcc maybe other compiler

问题 I know that some functions like sin cos min max memcpy may be treated not as normal functions but may be replaced by built-in functions (which may be more optimal than merely inline function calls, when the replacement is (an) actual processor instruction(s), such as directly calling the FSIN instruction for standard sin function when compiled for an x86 with a floating point unit). The question I would like to use power of built-in functions (in C/C++ mostly in mingw/gcc maybe other compiler

问题 This question already has answers here : Closed 9 years ago . Possible Duplicate: Is recursion ever faster than looping? I was first trained to program seriously in C, about 15 years ago. My employer wanted highly optimized code for computationally difficult tasks. I remember being advised more than once to rewrite recursions as loops, even at the expensive of readability, in order to avoid "recursion overhead." As I understood it then, recursion overhead was the extra effort required to push