Asked  7 Months ago    Answers:  5   Viewed   46 times

That is, if I use the current time as an index into the array:

array[Date.getTime()] = value;

will the interpreter instantiate all the elements from 0 to now? Do different browsers do it differently?

I remember there used to be a bug in the AIX kernel, which would create pseudo-ttys on request, but if you did, say, "echo > /dev/pty10000000000" it would create /dev/pty0, /dev/pty1, .... and then fall over dead. It was fun at trade shows, but I don't want this to happen to my customers.



How exactly JavaScript arrays are implemented differs from browser to browser, but they generally fall back to a sparse implementation - most likely the same one used for property access of regular objects - if using an actual array would be inefficient.

You'll have to ask someone with more knowledge about specific implementations to answer what excatly triggers the shift from dense to sparse, but your example should be perfectly safe. If you want to get a dense array, you should call the constructor with an explicit length argument and hope you'll actually get one.

See this answer for a more detailed description by olliej.

Tuesday, June 1, 2021
answered 7 Months ago

Arrays are objects.

However, unlike regular objects, arrays have certain special features.

  1. Arrays have an additional object in their prototype chain - namely Array.prototype. This object contains so-called Array methods which can be called on array instances. (List of methods is here:

  2. Arrays have a length property (which is live, ergo, it auto-updates) (Read here:

  3. Arrays have a special algorithm regarding defining new properties (Read here: If you set a new property to an array and that property's name is a sting which can be coerced to an integer number (like '1', '2', '3', etc.) then the special algorithm applies (it is defined on p. 123 in the spec)

Other than these 3 things, arrays are just like regular objects.

Read about arrays in the spec:

Tuesday, June 1, 2021
answered 7 Months ago

I think I've recreated the csr row indexing with:

def extractor(indices, N):
   return sparse.csr_matrix((data,indices,indptr), shape=shape)

Testing on a csr I had hanging around:

In [185]: M
<30x40 sparse matrix of type '<class 'numpy.float64'>'
    with 76 stored elements in Compressed Sparse Row format>

In [186]: indices=np.r_[0:20]

In [187]: M[indices,:]
<20x40 sparse matrix of type '<class 'numpy.float64'>'
    with 57 stored elements in Compressed Sparse Row format>

In [188]: extractor(indices, M.shape[0])*M
<20x40 sparse matrix of type '<class 'numpy.float64'>'
    with 57 stored elements in Compressed Sparse Row format>

As with a number of other csr methods, it uses matrix multiplication to produce the final value. In this case with a sparse matrix with 1 in selected rows. Time is actually a bit better.

In [189]: timeit M[indices,:]
1000 loops, best of 3: 515 µs per loop
In [190]: timeit extractor(indices, M.shape[0])*M
1000 loops, best of 3: 399 µs per loop

In your case the extractor matrix is (len(training_indices),347) in shape, with only len(training_indices) values. So it is not big.

But if the matrix is so large (or at least the 2nd dimension so big) that it produces some error in the matrix multiplication routines, it could give rise to segmentation fault without python/numpy trapping it.

Does matrix.sum(axis=1) work. That too uses a matrix multiplication, though with a dense matrix of 1s. Or sparse.eye(347)*M, a similar size matrix multiplication?

Wednesday, June 9, 2021
answered 6 Months ago

In SpiderMonkey, arrays are implemented basically as C arrays of jsvals. These are referred to as "dense arrays". However, if you start doing un-array-like things to them -- like treating them like objects -- their implementation is changed to something which very much resembles objects.

Moral of the story: when you want an array, use an array. When you want an object, use an object.

Oh, a jsval is a sort of variadic type which can represent any possible JavaScript value in a 64 bit C type.

Monday, June 14, 2021
answered 6 Months ago

Using summary, here is an example:

mat <- Matrix(data = c(1, 0, 2, 0, 0, 3, 4, 0, 0), nrow = 3, ncol = 3,
              dimnames = list(Origin      = c("A", "B", "C"),
                              Destination = c("X", "Y", "Z")),
              sparse = TRUE)
# 3 x 3 sparse Matrix of class "dgCMatrix"
#    Destination
#     X Y Z
#   A 1 . 4
#   B . . .
#   C 2 3 .

summ <- summary(mat)
# 3 x 3 sparse Matrix of class "dgCMatrix", with 4 entries 
#   i j x
# 1 1 1 1
# 2 3 1 2
# 3 3 2 3
# 4 1 3 4

data.frame(Origin      = rownames(mat)[summ$i],
           Destination = colnames(mat)[summ$j],
           Weight      = summ$x)
#   Origin Destination Weight
# 1      A           X      1
# 2      C           X      2
# 3      C           Y      3
# 4      A           Z      4
Friday, June 18, 2021
answered 6 Months ago
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