shuffle

问题 For my experiment I have 20 categories which contain 9 pictures each. I want to show these pictures in a pseudo-random sequence where the only constraint to randomness is that one image may not be followed directly by one of the same category. So I need something similar to r = randi([1 20],1,180); just with an added constraint of two numbers not directly following each other. E.g. 14 8 15 15 7 16 6 4 1 8 is not legitimate, whereas 14 8 15 7 15 16 6 4 1 8 would be. An alternative way I was

问题 For my experiment I have 20 categories which contain 9 pictures each. I want to show these pictures in a pseudo-random sequence where the only constraint to randomness is that one image may not be followed directly by one of the same category. So I need something similar to r = randi([1 20],1,180); just with an added constraint of two numbers not directly following each other. E.g. 14 8 15 15 7 16 6 4 1 8 is not legitimate, whereas 14 8 15 7 15 16 6 4 1 8 would be. An alternative way I was

问题 Running jobs on a spark 2.3 cluster, I noted in the spark webUI that spill occurs for some tasks : I understand that on the reduce side, the reducer fetched the needed partitions (shuffle read), then performed the reduce computation using the execution memory of the executor. As there was not enough execution memory some data was spilled. My questions: Am I correct ? Where the data is spilled ? Spark webUI states some data is spilled to memory shuffle spilled (memory) , but nothing is spilled

问题 Here is the config of my model : "model": { "loss": "categorical_crossentropy", "optimizer": "adam", "layers": [ { "type": "lstm", "neurons": 180, "input_timesteps": 15, "input_dim": 103, "return_seq": true, "activation": "relu" }, { "type": "dropout", "rate": 0.1 }, { "type": "lstm", "neurons": 100, "activation": "relu", "return_seq": false }, { "type": "dropout", "rate": 0.1 }, { "type": "dense", "neurons": 30, "activation": "relu" }, { "type": "dense", "neurons": 3, "activation": "softmax"

问题 Simple question, how can I shuffle a list in PROLOG, so that A1 is my new list? shuffle([1,1,1,2,3,4],A1), I've tried a few predicates I found on the web but none of them seems to be working. Also found this but apparently it's not available anymore, according to SWI-PROLOG. 回答1: You can use random_permutation/2 . It is available in SWI-Prolog. ?- random_permutation([1,2,3],L). L = [1, 3, 2]. 来源： https://stackoverflow.com/questions/27431281/shuffling-a-list-in-prolog

问题 I was going thru bunch of mongo docs and can't find a possibility to shuffle or randomize result content is there any ? 回答1: Specifically for the aggregation framework itself there is not really any native way as there is no available operator as yet to do something like generate a random number. So whatever match you could possibly project a field to sort on would not be "truly random" for lack of a shifting seed value. The better approach is to "shuffle" the results as an array after the

问题 When I train the model using the .fit() layer there is the argument shuffle preset to True. Let's say that my dataset has 100 samples and that the batch size is 10. When I set shuffle = True then keras first randomly selects randomly the samples (now the 100 samples have a different order) and on the new order it will start creating the batches: batch 1: 1-10, batch 2: 11-20 etc. If I set shuffle = 'batch' how is it supposed to work in the background? Intuitively and using the previous

问题 I have a an array that is relatively sparse, and I would like to go through each row and shuffle only the non-zero elements. Example Input: [2,3,1,0] [0,0,2,1] Example Output: [2,1,3,0] [0,0,1,2] Note how the zeros have not changed position. To shuffle all elements in each row (including zeros) I can do this: for i in range(len(X)): np.random.shuffle(X[i, :]) What I tried to do then is this: for i in range(len(X)): np.random.shuffle(X[i, np.nonzero(X[i, :])]) But it has no effect. I've

问题 I have a an array that is relatively sparse, and I would like to go through each row and shuffle only the non-zero elements. Example Input: [2,3,1,0] [0,0,2,1] Example Output: [2,1,3,0] [0,0,1,2] Note how the zeros have not changed position. To shuffle all elements in each row (including zeros) I can do this: for i in range(len(X)): np.random.shuffle(X[i, :]) What I tried to do then is this: for i in range(len(X)): np.random.shuffle(X[i, np.nonzero(X[i, :])]) But it has no effect. I've

问题 I’m stuck here. Do I just keep making new strings and turn them to int or us there a faster better way? public void biggest(int a){ int random; String aS = String.valueOf(a); int ah=9; if (a<10) System.out.println(a); for(int i= 0;i<aS.length();i++){ String firstNum = aS.substring(i,i+1); for (int j = ah; j > Integer.parseInt(firstNum); j--){ System.out.println(ah); } } } ``` 回答1: There's no need to use conversion to String in this case, you can get the digits from the input number by getting