For an m x n matrix, what's the optimal fastest way to compute the mutual information for all pairs of columns n x n? Currently I'm using np. For a given matrix A e. As an aside, I've also looked for mapping functions on columns column-wise or row-wise operations on arrays, but haven't found a good general answer yet. Here is my full implementation, following the conventions in the Wiki page :. Whether there are efficient ways to map functions to operate on columns or rows of np.
Whether there are other optimal implementations for this specific calculation mutual information? In scipy 0. This is also often called the G or G 2 statistic.
The only difference between this and your implementation is that this implementation uses the natural logarithm instead of the base-2 logarithm so it is expressing the information in "nats" instead of "bits".
If you really prefer bits, just divide mi by log 2. If you have or can install sklearn i. Learn more. Optimal way to compute pairwise mutual information using numpy Ask Question. Asked 6 years, 10 months ago.
Active 8 months ago. Viewed 36k times. I'd also like to know: Whether there are efficient ways to map functions to operate on columns or rows of np. Make it so we can copy, paste and run. Will greatly help anyone trying to answer your question. Please read this sscce. My previous comment was inadvertently entered while I meant to respond to the suggestion.
Thanks for pointer to sscce. The problem with this is that it requires creating all of the intermediate calculation objects at once. With your approach above, you would have to figure out a way of creating a 4D histogramdd I don't see it working out with your huge dataset.The following are 27 code examples for showing how to use sklearn.
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Normalized Mutual Information NMI is an normalization of the Mutual Information MI score to scale the results between 0 no mutual information and 1 perfect correlation.
Series np. About Privacy Contact.Discovering directional relations via minimum predictive information regularization. Signal Processing toolkit includes ML models with visualization. Generative model based on Capsule and Mutual Information theories. PyTorch implementation of the estimator proposed in the paper "Estimating Differential Entropy under Gaussian Convolutions". It sorts two MSAs in a way that maximize or minimize their mutual information.
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It only takes a minute to sign up. I am trying to compute mutual information for 2 vectors. I made a general function that recognizes if the data is categorical or continuous. It's really difficult to find simple examples of this calculation and I have only found theoretical implementations e. How to calculate mutual information? I have counts data that have been normalized not integers anymore and I want to calculate the mutual information between 2 of the rows.
Why does it need to do this? Is my implementation correct? If not, why and how can it be fixed to accurately calculate mutual information?
Instead you have two one dimensional count vectors as arguments, that is you only know the marginal distributions. Computing the mutual information of two distributions does not make sense. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. How to correctly compute mutual information Python Example?
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You seem to be confusing values and counts of values. Why are you concatenating x and y? Your formula for continuous entropy is wrong, although off by an amount that will cancel out when doing mutual information. As for discretizing the data, if you're estimating the probs from empirical data, you need multiple instances of a value to calculate the prob for that value.
The probability of getting the exact same value twice from a continuous distribution is zero. Therefore, your calculated entropy will always be log size of sample. Does that mean you need to fit the count data to a distribution?
It only takes a minute to sign up. I am a bit confused. Can someone explain to me how to calculate mutual information between two terms based on a term-document matrix with binary term occurrence as weights? How about forming a joint probability table holding the normalized co-occurences in documents. Then you can obtain joint entropy and marginal entropies using the table. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered.
How to calculate mutual information? Ask Question. Asked 7 years, 9 months ago. Active 1 year, 6 months ago. Viewed 6k times. Siong Thye Goh 5, 3 3 gold badges 14 14 silver badges 24 24 bronze badges. X could be "Why" and Y could be "How".
Active Oldest Votes. Zoran Zoran 4 4 silver badges 11 11 bronze badges. Can't the mutual information be determined directly via the formula given by the OP since everything needed for "plugging in", viz. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. Upcoming Events.
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Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This provides Mutual Information mi functions in Python. Currently, this provides the mi between tensors as described by Kraskov et. The numpy array files should have same number of instances, i. Those instances can be a tensor.Testicular atrophy treatment ayurveda
Theoretical maximum Maximum mutual information can be understood to be of a variable with itself, i. I X,X. This is given by:. We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page.
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