kron | Schedule jobs by programatically requesting webhooks | Version Control System library

In R you can do it this way:

This works, assuming you have no problem with the intermediate list (numpy needs the size before allocating the array):

According to the traceback T[k, k+1] doesn't work because T is a bsr format sparse matrix, which does not implement indexing. (coo is a more common format that doesn't have this either).

Looking at the sp.kron code, https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.kron.html

Here reshape and implicit expansion are used to compute the result:

Under "normal" circumstances, both approaches are equivalent.

In other words, whether you use a value through an explicit expression (eg, np.identity(3, dtype = np.double)/3.0) or through a variable-name that has been initialized with that expression (here, a), the outcome would "normally" be the same.

There are some not-so-normal circumstances, where they may produce different results. As far as I can see all these have to do with situations in which there are side-effects such that the outcome depends upon the order in which things happen. For example:

Consider a scenario where the initialization of the variable-name b involves a side-effect that affects the initialization of the variable-name a. And let's say your code depends on that side-effect. In this scenario, in the case of the fist approach (where you first initialize the variable-names and then use only those variables), your code would have to initialize b first, and a later -- the order of the initialization of the variable-names matters. In the second approach (where you would have explicit expressions rather than variable-names, participating in a larger expression), to achieve the same effect, you will have to pay attention to the order in which Python interpreter evaluates sub-expressions within an expression. If you don't, then the order of evaluation of sub-expressions may not produce the side-effect that your code needs, and you might end up getting a different result.

As for other programming languages the answer is a big yes, the two approaches can yield different results, in languages (such as Java), where the variable-names have associated data-types, which can cause some silent numerical conversions (such as truncations) to happen during variable-assignment.

The problem you're having is really simple. You are using at least O(n^12) memory. Since almost all of these values are 0, this is a huge waste. You should almost certainly be using Sparse arrays. This should bring your runtime to reasonable levels

it is Okay I got it already.thank you

As per Sameeresque's suggestion I tried using different backends for matplot lib. This was done by altering by import statements as follows.

Approach #1

Simplest one with np.triu_indices -