running-time | Sample Single Page Application using Laravel & Vue.js | State Container library

when you want to calculate the running time for merge sort, which is correct put the startTime before call the function of mergeSort or after call:

I needed to compute sums within a range on an array, so I came across Segment Tree and Fenwick Tree and I noticed that both of these trees query and update with the same asymptotic running time. I did a bit more research, and these 2 data structures seem to do everything at the same speed. Both have linear memory usage (Segment Tree uses twice as much).

Aside from constant factors in running-time/memory and implementations, is there any reason why I would choose one over the other?

I am looking for an objective answer, like some operation that is faster with one than the other, or maybe some restriction one has that the other does not.

I saw 2 other StackOverflow questions about this, but the answers just described both data structures rather than explaining when one might be better than the other.

Let's say we have two algorithms that solve the same problem.

Algorithm A has O(n) running-time complexity, and Algorithm B has O(n³) running-time complexity.

For an input of the size of 1000, A takes 1 second, and B takes 3 seconds.

Having said that, how long will Algorithms A and B take for input of size 3000?

My guess is:

• A will take 3 seconds, since it is linear (1 second for 1000 size).
• B will take 81 seconds, as it is 3 times the size of the original input, which takes 3 seconds (so, 3*3³)

Second question: If I were to have an algorithm with running-time complexity O(n), and I were to ram up the processor speed and memory size to twice of the original, will the running-time complexity for the algorithm still be the same in Big O, which is O(n)?

Please provide an explanation.

I'm trying to set up a grid with CSS and for some reason the elements that I defined to go into lower rows are getting merged into run above this way:

My code on the main element:

I have a python function foo with a while True loop inside. For background: It is expected do stream info from the web, do some writing and run indefinitely. The asserts test if the writing was done correctly.

Clearly I need it to stop sometime, in order to test.

What I did was to run via multirpocessing and introduce a timeout there, however when I see the test coverage, the function which ran through the multiprocessing, are not marked as covered.

Question 1: Why does pytest now work this way?

Question 2: How can I make this work?

I was thinking it's probably because I technically exit the loop, so maybe pytest does not mark this as tested....

I found a VBA code to test the run time of a code in the thread How do you test running time of VBA code? . I implemented and it worked. But every time I run a simple code, as below,it returns me a different result.

I searched and tested many codes, but didn't found what I was expecting.

Is there a way of test the code and return something like the number of clocks that the code demands? Something that every time I run with the code below, returns me the same value?

Looking at the fundamental structure of hash table. We know that it resizes WRT load factor or some other deterministic parameter. I get that if the resizing limit is reached within an insertion we need to create a bigger hash table and insert everything there. Here is the thing which I don't get.

Let's consider a hash table where each bucket contains an AVL - balanced BST. If my hash function returns the same index for every key then I would store everything in the same AVL tree. I know that this hash function would be a really bad function and would not be used but I'm doing a worst case scenario here. So after some time let's say that resizing factor has been reached. So in order to resize I created a new hash table and tried to insert every old elements in my previous table. Since the hash function mapped everything back into one AVL tree, I would need to insert all the N elements into the same AVL. N insertion on an AVL tree is N logN. So why is the worst case of insertion for hash tables considered O(N)?

Here is the proof of adding N elements into Avl three is N logN: Running time of adding N elements into an empty AVL tree

In Matlab:

How do I modify plot(x,y,'o'), where x=1:10 and y=ones(1,10), such that each point in the plot will have a random shape?

And how can I give it colors chosen from a scheme where the value at x=1 is the darkest blue, and x=10 is red (namely some sort of heat map)?

Can this be done without using loops? Perhaps I should replace "plot" with a different function for this purpose (like "scatter"? I don't know...)? The reason is that I am plotting this inside another loop, which is already very long, so I am interested in keeping the running-time short.

Thanks!

How to use the angular-dynamic-locale with webpack?

The angular-dynamic-locale always tries to load the angular-locale_en.js file from path http://localhost:8080/angular/i18n/angular-locale_de.js during the running-time, when the "tmhDynamicLocale.set('de');" is performed.

I'm using webpack therefore I define every dependencies either in the top of my app.js or in the top of my controllers. I tried to define this with require('angular-i18n/angular-locale_de') or with import, but unfortunatelly, I always get the following error messages: