Sequence

问题 I was thinking about this the other day. Say you have the list of numbers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 19, 21, 22, 23, 999, 1000, 1001 is there any library or code snipped that will turn the following into something like: 1-13, 19, 21-23, 999-1001 In other words, reduce a full list of numbers to a bunch of ranges or so. I haven't been able to find anything. If no such thing exists, anyone have some ideas for an efficient implementation? 回答1: def get_groups(lst): slices = [i+1

问题 I have one row in Excel with a number pattern (each number is in a different cell) like this. 1 0 0 0 2 0 0 1 0 0 0 0 0 3 0 0 0 0 0 0 0 0 2 0 0 0 1 (My row has 300 cells). Definition: I call a sequence of zeros between non-zero values "0 sequence". I call the non-zero value which marks the start and end of a 0 sequence the "mark value" I want to calculate: Count for 0 sequence the number of 0. In this example above, the result is: 3, 2, 5, 8, 3 Determine the smallest 0 sequence. In the

问题 I am trying to solve a problem that requires writing a function called repeat_prod(n), that gets an integer n and returns the result of the following function: P = 1*1.2*1.4*.... (1+0.2 (n-1)) for exemple if n is 6: repeat_prod(6) ans = 9.6768 I tried the following: function P = repeat_prod(n) for 1:n-1 P = (1+0.2*(n-1)); end end But it does not run. How can I get the loop to work? 回答1: The logic within your function should be something like below function P = repeat_prod(n) P = 1; % initial

问题 I am trying to solve a problem that requires writing a function called repeat_prod(n), that gets an integer n and returns the result of the following function: P = 1*1.2*1.4*.... (1+0.2 (n-1)) for exemple if n is 6: repeat_prod(6) ans = 9.6768 I tried the following: function P = repeat_prod(n) for 1:n-1 P = (1+0.2*(n-1)); end end But it does not run. How can I get the loop to work? 回答1: The logic within your function should be something like below function P = repeat_prod(n) P = 1; % initial

问题 I am training a Tensorflow model with LSTMs for predictive maintenance. For each instance I create a matrix (50,4) where 50 is the length of the hisotry sequence, and 4 is the number of features for each records, so for training the model I use e.g. (55048, 50, 4) tensor and a (55048, 1) as labels. When I train on Jupyter on my computer it works (very slow, but it works), but on Colab I get this error: Training data shape is (55048, 50, 4) Labels shape is (55048, 1) WARNING:tensorflow:Layer

问题 I was hoping for an elegant or effective way to multiply sequences of integers (or floats). My first thought was to try (1, 2, 3) * (1, 2, 2) would result (1, 4, 6) , the products of the individual multiplications. Though python isn't preset to do that for sequences. Which is fine, I wouldn't really expect it to. So what's the pythonic way to multiply (or possibly other arithmetic operations as well) each item in two series with and to their respective indices? A second example (0.6, 3.5) *

问题 I was hoping for an elegant or effective way to multiply sequences of integers (or floats). My first thought was to try (1, 2, 3) * (1, 2, 2) would result (1, 4, 6) , the products of the individual multiplications. Though python isn't preset to do that for sequences. Which is fine, I wouldn't really expect it to. So what's the pythonic way to multiply (or possibly other arithmetic operations as well) each item in two series with and to their respective indices? A second example (0.6, 3.5) *

问题 here is what I am trying to achieve: I have an Oracle Sequence defined as follows: CREATE SEQUENCE "SEQ_ENDKUNDE" MINVALUE 1 MAXVALUE 9999999999999999999999999999 INCREMENT BY 1000 START WITH 1 CACHE 20 GLOBAL ; The Ids must be incremented by 1000 due to a complex replication infrastructure. The Hibernate part looks as follows: @Id @GeneratedValue( strategy = GenerationType.SEQUENCE, generator = "sequence-generator" ) @SequenceGenerator( name = "sequence-generator", sequenceName = "SEQ

问题 I'm using a random.seed() to try and keep the random.sample() the same as I sample more values from a list and at some point the numbers change.....where I thought the one purpose of the seed() function was to keep the numbers the same. Heres a test I did to prove it doesn't keep the same numbers. import random a=range(0,100) random.seed(1) a = random.sample(a,10) print a then change the sample much higher and the sequence will change(at least for me they always do): a = random.sample(a,40)

问题 I'm using a random.seed() to try and keep the random.sample() the same as I sample more values from a list and at some point the numbers change.....where I thought the one purpose of the seed() function was to keep the numbers the same. Heres a test I did to prove it doesn't keep the same numbers. import random a=range(0,100) random.seed(1) a = random.sample(a,10) print a then change the sample much higher and the sequence will change(at least for me they always do): a = random.sample(a,40)