The function match works on vectors: x <- sample(1:10) x # [1] 4 5 9 3 8 1 6 10 7 2 match(c(4,8),x) # [1] 1 5 match only returns the first encounter of a match, as you requested. It returns the position in the second argument of the values in the first argument. For … Read more
Problem Statement We have an array of values, vals and runlengths, runlens: vals = [1,3,2,5] runlens = [2,2,1,3] We are needed to repeat each element in vals times each corresponding element in runlens. Thus, the final output would be: output = [1,1,3,3,2,5,5,5] Prospective Approach One of the fastest tools with MATLAB is cumsum and is … Read more
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First of all, in your example you make tests on a “data.frame” which is not fair for colMeans, apply and “[.data.frame” since they have an overhead: system.time(as.matrix(m)) #called by `colMeans` and `apply` # user system elapsed # 1.03 0.00 1.05 system.time(for(i in 1:ncol(m)) m[, i]) #in the `for` loop # user system elapsed # 12.93 … Read more
AVX2 + BMI2. See my other answer for AVX512. (Update: saved a pdep in 64bit builds.) We can use AVX2 vpermps (_mm256_permutevar8x32_ps) (or the integer equivalent, vpermd) to do a lane-crossing variable-shuffle. We can generate masks on the fly, since BMI2 pext (Parallel Bits Extract) provides us with a bitwise version of the operation we … Read more
Approach #1 : Using broadcasting – def broadcasting_app(a, L, S ): # Window len = L, Stride len/stepsize = S nrows = ((a.size-L)//S)+1 return a[S*np.arange(nrows)[:,None] + np.arange(L)] Approach #2 : Using more efficient NumPy strides – def strided_app(a, L, S ): # Window len = L, Stride len/stepsize = S nrows = ((a.size-L)//S)+1 n = … Read more
Yes. (With exception) ifelse calculates both its yes value and its no value. Except in the case where the test condition is either all TRUE or all FALSE. We can see this by generating random numbers and observing how many numbers are actually generated. (by reverting the seed). # TEST CONDITION, ALL TRUE set.seed(1) dump … Read more
Straight from Wes McKinney’s Python for Data Analysis book, pg. 132 (I highly recommended this book): Another frequent operation is applying a function on 1D arrays to each column or row. DataFrame’s apply method does exactly this: In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list(‘bde’), index=[‘Utah’, ‘Ohio’, ‘Texas’, ‘Oregon’]) In [117]: frame Out[117]: b d e … Read more
TLDR; No, for loops are not blanket “bad”, at least, not always. It is probably more accurate to say that some vectorized operations are slower than iterating, versus saying that iteration is faster than some vectorized operations. Knowing when and why is key to getting the most performance out of your code. In a nutshell, … Read more
It works as though everything on the right hand side happens before anything on the left hand side, effectively doing the “snapshot” thing but with probably better memory efficiency. In reality, it may loop through or it may take a “snapshot”, but due to abstraction you don’t really need to know or care about that. … Read more