distributional | Vectorised distributions for R | Development Tools library

unable to get a proper result. because string does not have any specific end. But every new string is starting with Author Name(s) following by year

This may be enough. I've written a regex that works on your whole sample,
however it is still subjective. Any add or subtract of name form or punctuation
will blow it out of the water.

It will call the __call__ method on the instance. See this demo:

The "dist" object should be an instance or class of scipy's statistical distributions. That is what is meant by:

dist : str or stats.distributions instance, optional

So a self-contained example would be:

I'd have a look at scikit-image. It's a great library for image processing of this kind. The first link is to the documentation, the second is to a specific page about adaptive histogram equalization that might be useful for you.

https://scikit-image.org/

http://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_equalize.html

As expected in Bayesian models, there are different ways to look at the extent of uncertainty. So, first, we no longer have a single parameter sigma; instead there are several standard deviation parameters in

text2vec handles everything automatically - it will make rows have unit L2 norm and then call dot product to calculate cosine similarity.

But if matrix already has rows with unit L2 norm then user can specify norm = "none" and sim2 will skip first normalization step (saves some computation).

I understand confusion - probably I need to remove norm option (it doesn't take much time to normalize matrix).

To directly answer your question, no, at least not in the automatic way I believe you mean by your comments.

There is no built-in mechanism to define multiple names for one option, and have Ruby automatically return the same value no matter which of those options you reference. By your comment, I assume this is the intended behavior, something akin to an alias keyword, and then having the ability to call either by old or new name.

Your only real option is to parse the options manually, which typically shouldn't add too much boring boiler-plate code:

I have changed the code to get the desired results. Replaced the index and bar width in below code snippet with custom positions in with a list. Here the list indicates the exact positions to plot the bar. I gave for first bar 0.1 and second bar at 0.5. Each bar width is 0.05 and there are 6 bars. So 0.1+ 6*0.05 + 0.1 as gap between two groups which gives me 0.5 as starting position for 2nd bar plot of skip-gram.

Before

If by test you mean a significance or hypothesis test, those tests are useless and you can ignore them.

The appropriate way to quantify uncertainty in language parsing or anything else is to express uncertainty as probability. In the context of language parsing, that means constructing a probability distribution over possible ways to parse a given sentence.

If you need to make decisions, you need to supply additional data which express preferences over outcomes (i.e. utility functions). Probability and utility are combined via the so-called expected utility hypothesis: the best action is the one which maximizes expected utility.

A useful introduction to these concepts, using example from many fields, is "Making Hard Decisions" by Robert Clemen. More specific to your problem, a web search for probabilistic language parsing turns up many hits.

You might get more interest in this question on stats.stackexchange.com. There might already answers to related questions there.