indexing

问题 Can anyone help me with finding an algorithm to change a position of a node in a linked list according to the input? Example: I have the linked list: 1->2->3->4->END and the user chose the second node and the index 4. The algorithm will give us the result: 1->3->4->2->END. Thanks for your help! 回答1: It is really easy to move nodes in a linked list because they are done for that... To change a node's position, just change it's next pointer to the next node address, and the previous node's next

问题 Greetings Stack Overflow denizens! I am having an issue trying to get a formula involving INDIRECT to behave itself in conjunction with INDEX. When i use specific row values, the array works fine, but when i try and use INDIRECT to get current row number i get a #VALUE! error. For example: I have this in cell R108, and it works as it should: =IF(OR(ISERROR(INDEX(MODEL,SMALL(IF(OWNERID=N108,ROW(OWNERID)-ROW(INDEX(OWNERID,1,1))+1),1))),N108=""),"",INDEX(MODEL,SMALL(IF(OWNERID=N108,ROW(OWNERID)

问题 I have a table in which I want to search by a prefix of the primary key. The primary key has values like 03.000221.1 , 03.000221.2 , 03.000221.3 , etc. and I want to retrieve all that begin with 03.000221. . My first thought was to filter with LIKE '03.000221.%' , thinking Postgres would be smart enough to look up 03.000221. in the index and perform a range scan from that point. But no, this performs a sequential scan. QUERY PLAN ---------------------------------------------------------------

问题 I took an excel sheet which has dates and some values and want to convert them to pandas dataframe and select only rows which are between certain dates. For some reason I cannot select a row by date index Raw Data in Excel file MCU Timestamp 50D 10P1 10P2 10P3 10P6 10P9 10P12 12-Feb-15 25.17 5.88 5.92 5.98 6.18 6.23 6.33 11-Feb-15 25.9 6.05 6.09 6.15 6.28 6.31 6.39 10-Feb-15 26.38 5.94 6.05 6.15 6.33 6.39 6.46 Code xls = pd.ExcelFile('e:/Data.xlsx') vols = xls.parse(asset.upper()+'VOL',header

问题 H ere is my code: An ArrayList of ArrayList that returns a float: public ArrayList walls=new ArrayList(); public void Start() { walls[0] = ReturnInArrayList(279,275,0,0,90); walls[1] = ReturnInArrayList(62,275,0,0,0); walls[2] = ReturnInArrayList(62,275,62,0,90); walls[3] = ReturnInArrayList(217,275,62,-62,0); walls[4] = ReturnInArrayList(62,275,279,0,90); walls[5] = ReturnInArrayList(41,275,279,0,0); walls[6] = ReturnInArrayList(279,275,320,0,9); walls[7] = ReturnInArrayList(320,275,0,-279,0

问题 H ere is my code: An ArrayList of ArrayList that returns a float: public ArrayList walls=new ArrayList(); public void Start() { walls[0] = ReturnInArrayList(279,275,0,0,90); walls[1] = ReturnInArrayList(62,275,0,0,0); walls[2] = ReturnInArrayList(62,275,62,0,90); walls[3] = ReturnInArrayList(217,275,62,-62,0); walls[4] = ReturnInArrayList(62,275,279,0,90); walls[5] = ReturnInArrayList(41,275,279,0,0); walls[6] = ReturnInArrayList(279,275,320,0,9); walls[7] = ReturnInArrayList(320,275,0,-279,0

问题 Lets say we have a vector in R : v <- (2, 3, 4, 5, 5, 5) We can easily find the max of the vector using max function : max(v) How can we find all the indexes where the max value is present. There is function which.max(v) which only returns the first index. Is there an easy way to get all the indexes having max values in R ? Its a dummy question, but just curious to know. 回答1: How about which(v == max(v)) ? 回答2: As @konvas sol gives indices, adding code snippet on how to retrieve elements.

问题 indexes = np.array([[0,1,3],[1,2,4 ]]) data = np.random.rand(2,5) Now, i would like an array of shape (2,3), where result[0] = data[0,indexes[0]] result[1] = data[1,indexes[1]] What would be the proper way to achieve this? A numpy way that yould generalize to bigger arrays (perhaps even higher dimensional). Please note the difference to questions like this, where the array of indexes contains tuples. This is not what I am asking. Edit A more general formulation of the question would be: data

问题 indexes = np.array([[0,1,3],[1,2,4 ]]) data = np.random.rand(2,5) Now, i would like an array of shape (2,3), where result[0] = data[0,indexes[0]] result[1] = data[1,indexes[1]] What would be the proper way to achieve this? A numpy way that yould generalize to bigger arrays (perhaps even higher dimensional). Please note the difference to questions like this, where the array of indexes contains tuples. This is not what I am asking. Edit A more general formulation of the question would be: data

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