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But using %lu solved the issue Import numpy as np np.random.seed(1007092020) a = np.random.randint(2,. Actually, rather than focusing on the problem and the line of codes, i want to know about the difference between %ul and %lu

Maybe i could figure out what's wrong The task asks me to generate a matrix with 50 columns and 50 rows with a random library of seed 1007092020 in the range [0,1] Searching doesn't give me something useful (except that they are different)

Any explanation or link/reference is appreciated.

Printf and %llu vs %lu on os x [duplicate] asked 12 years, 11 months ago modified 12 years, 11 months ago viewed 43k times Asked 11 years, 2 months ago modified 10 years ago viewed 27k times Import numpy as np from statsmodels.tsa.arima.model import arima items = np.log(og_items) items['count'] = items['count'].apply(lambda x 0 if math.isnan(x) or math.isinf(x) else x) model = arima(items, order=(14, 0, 7)) trained = model.fit() items is a dataframe containing a date index and a single column, count

I apply the lambda on the second line because some counts can be 0, resulting in. I get a 'lu decomposition' error where using sarimax in the statsmodels python package I’d suppose yes, since i can see no reason why not However, if yes, then this would remove the need for existence of these macroified specifiers like priu32, so i figure i’d better ask

The reason i’m asking it is that i’d like to create a format string for printf dynamically, and it’d be hard to allocate space for this format string if i don't know the size of.

A = p l u it is entirely expected that multiplying the p, l, and u matrices should produce something close to the array originally passed to scipy.linalg.lu You are not supposed to invert p. The printf function takes an argument type, such as %d or %i for a signed int However, i don't see anything for a long value.

I want to implement my own lu decomposition p,l,u = my_lu (a), so that given a matrix a, computes the lu decomposition with partial pivoting But i only know how to do it without pivoting.

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