magnitude | A fast , efficient universal vector embedding utility | Search Engine library

You can try to perform complete clustering on dates by using hclust. The manhattan (i.e. absolute) distances are calculated between pairs of dates. The "complete" clustering method will ensure that every member of a cluster cut at h height will be distant at most h from the other members.

The inverse DFT in opencv will not scale the result by default, so you get your input times the length of the array. This is a common optimization, because the scaling is not always needed and the most efficient algorithms for the inverse DFT just use the forward DFT which does not produce the scaling. You can solve this by adding the cv::DFT_SCALE flag to your inverse DFT.

Some libraries scale both forward and backward transformation with 1/sqrt(N), so it is often useful to check the documentation (or write quick test code) when working with Fourier Transformations.

You can use gt package developed by RStudio team together with gtExtras (not yet on CRAN). Be careful to replace the commas that act as decimal separators.

I suspect it is the parser and how it feeds the matrices to the LAPACK library routines. E.g., in the matrix multiplication case of A'*B where A and B are matrices, the transpose operation isn't explicitly done. Rather, MATLAB calls the appropriate BLAS routine (e.g., DGEMM) with appropriate flags so that the equivalent operation is done, but may result in a different order of operations than if you had explicitly done the transpose first. I suspect this might be the case with your example, and that the transpose isn't explicitly done but flags are passed to the LAPACK library routines in the background to have a mathematically equivalent operation done but the actual order of operations is different resulting in a slightly different answer.

This is your problem:

Index Scan using i_pr_tax_bill_p_a_o_p_r_b_id on public.property_tax_bill_parsed_addresses (cost=0.11..4.12 rows=1 width=8) (actual time=0.002..0.002 rows=0 loops=1110860) ... Index Cond: (property_tax_bill_parsed_addresses.property_tax_bill_id = property_tax_bills.id) Filter: (property_tax_bill_parsed_addresses.parsed_address_id = 2)

Rows Removed by Filter: 1

It's doing 1110860 index scans and after successfully finding the data, removing most of it.

Add the parsed_address_id to this index, to avoid the filtering afterwards.

One potential solution is to specify the values manually for each scale, e.g.

There is no reason to avoid writing conds.R. This is very common and good practice in package development, especially as many of the checks you want to do will be applicable across many functions (like asserting the input is character, as you've done above. Here's a nice example from dplyr.

The difference is that if the generating function is already known in the benchmarked function, the generator is inlined and the involved Int-s are unboxed as well. If the generating function is the benchmark parameter, it cannot be inlined.

From the benchmarking perspective the second version is the correct one, since in normal usage we want the generating function to be inlined.

There are various definitions of order of magnitude, some of them are (assuming x is positive):

• floor(Int, log10(x))
• floor(Int, log10(2*x))
• floor(Int, log10(sqrt(10)*x))