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On geeksforgeeks in Selection sort algorithms it says that "It can cause poor branch prediction due to its high branch misprediction rate" and "Selection sort algorithm needs to iterate over the list multiple times, thus it can lead to an unbalanced branch" as disadvantage. What does it mean by branch or unbalanced branch or poor branch prediction?
I am expecting expaination for the concept of branch in any programming langauge.
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I am using jina's docarray to create a dataset for neural search solution, I need to perform some regex filters on datasets.
How I can run regex operations on docarray dataset and do jina have support for regex? Unable to find relevant resources for this.
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If i have 2 algorithms A and B
Where A uses O(n logn) time and O(1) extra space.
On the other hand B uses O(nk) time and O(k-1) extra space.
What you guys think which one would be a better choice? And why?
Better choice is the one which doesn't exceed memory budget and is measurably faster in production conditions. A reliable way to find that out is measuring.
If you're writing a library that will/may be used in many different environments, then a good choice is to implement both algorithms and let the user choose.
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I have a list of 90K Text Lines. I want to find near-duplicates from them and mark them as duplicates. How can I do this using Python?
You need to define what you mean by "near duplicate". If I were to guess, one possible definition of two lines of text being "near duplicates" would be that they have a low Levenshtein distance. One popular Python implementation seems to be this one, but I cannot vouch for it myself.
If that is an acceptable definition, then you can simply compute all pairwise Levenshtein distances between your text lines and mark those below a given threshold.
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I have a question about the Levenberg-Marquardt optimize method in Python:
Generally, the Lavenberg Maquardt is used for deterministic systems. Can I use it for stochastic model to estimate unknown parameters (inputs of my model).
Thanks
The requirement for the Levenberg Marquard algorithm is that you need to be able to calculate the jacoboan (derivative with respect to your parameter).
If this is the case for your problem then yes. I guess that it is not.
Perhaps the simplex algorithm is what you are looking for.
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I need to identify the songs by human hum. what are the best methodology and algorithm that i can use for achieve that. I search for code samples. But I couldn't find. Please help me....
You could begin a python program that uses tensorflow to deep-learn the correspondence between humming and songs - it should fall under the umbrella initiative by Google Brain called Magenta.
Of course for Deep-Learning you would need to have a large corpus of examples to learn from.