Okay, so the answer is that there is no way to do it without calculating the log function, but if you pre-calculate the logs, it's not so bad. My buffer is 4096 bytes in size, so there are between 1..2048 of each possible 2-byte value. So the logs of 1/2048...

encoding,decompression,entropy

Since you "seem to understand entropy", you can simply use the formula for entropy to do your homework. Here is a hint from Boltzmann's tombstone: ...

Missing Entropy Docker does not provide a virtual /dev/[u]random devices. If you haven't got enough entropy in the container, you haven't got enough entropy on the host. Check the contents of /proc/sys/kernel/random/entropy_avail, they should be pretty much the same on both the Docker host and container (if the number is...

You can't write individual bits to a file, the resolution is a single byte. If you want to write bits in sequence, you have to batch them up until you have a full byte, then write that. Psuedo-code (though C-like) for that would be along the lines of: currbyte =...

You can do the following import pandas as pd df = pd.read_csv('train_data.csv') grouped = df[['elevation','cover_type']].groupby(['elevation','cover_type'], as_index = False, sort = False)['cover_type'].count() ...

python,scipy,hierarchical-clustering,entropy

According to the paragraph on wikipedia that @cel pointed to, the Jaccard distance is a distance variant of Mutual Information. The module distance.pdist from scipy has support for computing the distance matrix using the Jaccard distance.

architecture,software-engineering,software-design,redundancy,entropy

What you are looking for is clone detection which is an established research area and there are a number of tools available to detect clones in you code. The central metric used to quantify the amount of redundancy in the code is called clone coverage. It measures the percentage of...

r,io,probability,sparse-matrix,entropy

Sample data Creating a data frame with only non-zero z values (suppose we can remove all of the zero lines from the flat file before importing data). N <- 50000 S <- N * 0.8 df_input <- data.frame( x = sample(1:N, S), y = sample(1:N, S), z = runif(S)) #...

Well, from what I can tell you have 27 characters total each with a pre-defined value and you want to simply sum them up? Let's start with variables: dim characters(1 to 27) as double dim x as integer 'for looping dim total as double 'The final value Define the values:...

matlab,image-processing,rgb,entropy,standard-deviation

After reading the paper, because you are dealing with colour images, you have three channels of information to access. This means that you could alter one of the channels for a colour image and it could still affect the information it's trying to portray. The author wasn't very clear on...

matlab,image-processing,entropy,information-theory

Yes, you can still use my post. Looking at your question above, the Kronecker Delta function is used such that for each i and j in your joint histogram, you want to search for all values where we encounter an intensity i in the image as well as the gradient...

performance,julia-lang,entropy

Here are a few ideas to speed up your function. If the range of all the columns is roughly the same, you can move the extrema computations outside the loop and reuse the same h array. hist2d creates a new array: you can use hist2d! to reuse the previous one....

You are most likely looking for Pseudo-Random Number Generators. They are initialized by a seed, thus taking in a finite amount of entropy. Good generators have a decent entropy coming out, supposing you judge it only from its output (thus you ignore the seed and/or the algorithm to generate the...

matlab,image-processing,entropy

Recall from definition that the entropy (Shannon) is defined as: In this case, b = 2. What you need to do is assuming we have a grayscale image, we need to find the probability distribution function of the image, then use the above definition to calculate our entropy. The entropy...

Ultimately I find no error in your code as it runs without error. The part I think you are missing is the calculation of the class frequencies and you will get your answer. Quickly running through the different objects you provide I suspect you are looking at buys. buys <-...

The documentation is incorrect, as confirmed by @user333700. Following advice on the matplotlib-users mailing list, I have submitted a pull request to fix the documentation....

matlab,vector,probability,entropy

You can generate a vector p of 3 real numbers between zero and one p = rand(1,3); then normalize p p = p / sum(p); Then p(1) + p(2) + p(3) is 1. EDIT: to respond to OP's comment N = 100; p = rand(N, 3); for k = 1:...

javascript,random,probability,entropy

You're up against something called the birthday problem: even though there are 366 possible birthdays, when you get only 26 people in a room, the chance that some pair will have the same birthday is better than 50-50. In general, collisions are likely when your numbers approach the square root...

I would suggest you create your own log2 function function res=mylog2(a) res=log2(a); res(isinf(res))=0; end This function, while breaking the log2 behaviour, can be used in your specific example because you are multiplying the result with the inside of the log, thus making it zero. It is not "mathematically correct", but...

Using numpy and the built-in collections module you can greatly simplify the code: import numpy as np import collections sample_ips = [ "131.084.001.031", "131.084.001.031", "131.284.001.031", "131.284.001.031", "131.284.001.000", ] C = collections.Counter(sample_ips) counts = np.array(C.values(),dtype=float) prob = counts/counts.sum() shannon_entropy = (-prob*np.log2(prob)).sum() print (shannon_entropy) ...