repeat

问题 I am looking to find and print all possible combinations of the set (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) of length 5. There should be 13 choose 5 combinations (6188) because order does NOT matter, and repetition is allowed. I found this code and was using it: from itertools import product for item in product([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], repeat=5): print(item) However, this is not printing all 6188 combinations. Trying to figure out how to tweak the code so it spits out

问题 I am looking to find and print all possible combinations of the set (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) of length 5. There should be 13 choose 5 combinations (6188) because order does NOT matter, and repetition is allowed. I found this code and was using it: from itertools import product for item in product([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], repeat=5): print(item) However, this is not printing all 6188 combinations. Trying to figure out how to tweak the code so it spits out

问题 I have a Power BI Matrix below. It has 2 measures - Main Measure (a simple sum) and a Variance Measure. It has a column group called Category, which has 2 values - Category A and Category B. The Variance Measure is the subtraction of the Main Measure values between Category A and Category B, for corresponding time periods. For example, Main Measure value of 2020 Q1 for Category B is subtracted from the Main Measure value of 2020 Q1 for Category A . The result is the Variance Measure for 2020

问题 I have a Power BI Matrix below. It has 2 measures - Main Measure (a simple sum) and a Variance Measure. It has a column group called Category, which has 2 values - Category A and Category B. The Variance Measure is the subtraction of the Main Measure values between Category A and Category B, for corresponding time periods. For example, Main Measure value of 2020 Q1 for Category B is subtracted from the Main Measure value of 2020 Q1 for Category A . The result is the Variance Measure for 2020

问题 I have a Power BI Matrix below. It has 2 measures - Main Measure (a simple sum) and a Variance Measure. It has a column group called Category, which has 2 values - Category A and Category B. The Variance Measure is the subtraction of the Main Measure values between Category A and Category B, for corresponding time periods. For example, Main Measure value of 2020 Q1 for Category B is subtracted from the Main Measure value of 2020 Q1 for Category A . The result is the Variance Measure for 2020

问题 This code does NOT work: def inc(x): return x + 1 def repeat(f, n): if n == 0: return lambda x: x else: return f( repeat(f, n - 1 ) ) inc_10 = repeat(inc, 10) #TypeError: unsupported operand type(s) for +: 'function' and 'int' ''' # Ideally print(inc_10(0)) # 10 ''' How can I write it in a more Pythonic way or in lambda calculus way ? 回答1: You still need to return a function, not the result of calling f , in the recursive case. # repeat :: (a -> a) -> Integer -> a -> a # repeat _ 0 = id #

问题 This code does NOT work: def inc(x): return x + 1 def repeat(f, n): if n == 0: return lambda x: x else: return f( repeat(f, n - 1 ) ) inc_10 = repeat(inc, 10) #TypeError: unsupported operand type(s) for +: 'function' and 'int' ''' # Ideally print(inc_10(0)) # 10 ''' How can I write it in a more Pythonic way or in lambda calculus way ? 回答1: You still need to return a function, not the result of calling f , in the recursive case. # repeat :: (a -> a) -> Integer -> a -> a # repeat _ 0 = id #

问题 I have two lists, x and y : >>> x = [2, 3, 4] >>> y = [1, 2, 3] I want to use these to create a new list. The new list will have each element in x repeated the number of times specified by the corresponding element in y . Hence, the desired output is >>> new_list [2, 3, 3, 4, 4, 4] The order of the elements in new_list doesn't matter to me. It's also not crucial that it be a list -- any sequence type is fine. What is the fastest, most efficient, most Pythonic way to achieve this? 回答1: You can

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding

问题 I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set. Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding