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But using %lu solved the issue That would save you one o(n^2) operation each time you want to use the factorization in another operation down the pipeline. 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 Then you obtain the low level lapack representations via lu_factor and then you use this representation in scipy.linalg.lu_solve function without explicitly obtaining the same lu factorization over and over again Searching doesn't give me something useful (except that they are different)

Any explanation or link/reference is appreciated.

What is the difference between %zu and %lu in string formatting in c %lu is used for unsigned long values and %zu is used for size_t values, but in practice, size_t is just an unsigned long. 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. Printf and %llu vs %lu on os x [duplicate] asked 12 years, 11 months ago modified 12 years, 10 months ago viewed 43k times Asked 11 years, 2 months ago modified 10 years ago viewed 27k times Conventional wisdom states that if you are solving ax = b several times with the same a and a different b, you should be using an lu factorization for lu

If i use p, l, u = scipy.linalg.lu(a) and.

When i print the number using the format specifier %llu, what is printed is %lu I also compare the value i get from atoll or strtoll with the expected value and it is smaller, which i guess shows that an overflow has occurred Why does an overflow occur if the number fits in a u64 variable The number for example is 946688831000.

I get a 'lu decomposition' error where using sarimax in the statsmodels python package 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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