@Jona It's a very useful little abstraction, I'm trying it out with some real data to see if there are any errors still.
I had to add a receive from the stored order object before the moses for [make(, this way when re-making the chain after it has been saved in a parent patch, the number of indices is checked against the correct order that was stored.

markov-poly4.zip

One annoying limitation of the way I've implemented state saving in these abstractions is that there is no way of keeping track of the order in which the saved parameters/arrays/texts are loaded. I have no idea how to do that reliably, otherwise it would be easier to just store and load the indices and memory texts first, then load the stored order and re-make the chain on loading.

@weightless Sorry for answering so late, i didn't find the time earlier.

I implemented a new system to generate the chains by simply iterating over all indices. Not sure if this is more or less efficient, but it is very simple.

Chains are build from left (oldest) to right (newest).

This system has the slight benefit that the order is not limited. An order higher than the length of the material is nonsensical and results in the original progression being played without jumps, but is not considered to be an error by this system.

I also incorporated the state saving into the object, so it doesn't rely on any other external, and made a new help file.

markov.zip

I used some code from weightless patch that uses code from markowGenerator. So i assume that the GPL still applies to this code. The authors can always reissue the code. @weightless, @Jona, would you agree to release the code used in this patch into the public domain?

I tested it and the process of creating the chains is now about 300 times faster

@ingox Great work, thanks for sharing. Great idea to include the state saving mechanism, in fact that subpatch can be taken as a nice starting point for state saving that requires a specific order of execution. As far as I'm concerned, of course you or anyone else can do whatever they want with the code I posted, which in turn was taken from somewhere else as you mentioned.

Out of curiosity, you say it's 300 times faster compared to which patch? Sounds like an amazing improvement.

@weightless This was stupid. I didn't specify the order. My patch is actually a bit slower...

@ingox Oh I see, well at this point it's not likely to get much simpler than this I reckon, and it's probably fast enough even with very large inputs.

@ingox @weightless I would agree to release the abstraction into the public domain (if I am in the position to do that). And I do not mind if the license is GPL or BDS ...

A slight improvement in speed: This version uses [array] to store the indices. To create the chains, the array is combined with the end of the array and chains are taken directly from this combined array. The downside is that the structure of the source material that is in the indices cannot be looked at like it was with [text], but for practical purposes, this should not be an issue. Also some other slight improvements.

markov.zip

This is now in the public domain. Thanks again for the great collaboration!

Edit:
Some more slight improvements. Also i added the clipping of the order again, as i think it contributes positively to the user experience. It is explained in the help file and there is a message when the chains are being created, so it should be reasonable.

This looks awesome. I don't get how the lists get encoded as one item (for chords). Can anyone please enligthen me?

@porres The lists are stored in a text, the markov chains are simple build upon the line numbers of the text, so over the indices.

yeah, now that I had a deeper look into it, I get it nice way to make multidimensional arrays

anyway, I was working on an object based on cyclone/anal + cyclone/prob for ELSE, but I'm gonna try and make something like this now! I just miss the possibility to set a probability transition matrix like in prob, but I may be able to work on this patch and get there.

great work and thanks for this

@porres Well, the [markov] object takes source material and generates something like an implicit probability transition matrix from that. If you already have a probability matrix, you only need a starting point and can play the markov chains immediately. A much more simpler abstraction can do that. Mixing the two approaches seems complicated, since [markov] follows a different philosophy, i.e. it allows adding more source material later in the process.

Interesting could be to have two separate abstractions: One to generate a probability matrix from source material and another one that plays markov chains from that. So you can have both approaches and combine them.

This would also be similar to the combination of [anal] and [prob], but as a generalized approach to have markov chains of arbitrary length.

The question is rather if it is a realistic scenario to have a complex probability matrix for markov chains of higher order. [markov] is built as a basic machine learning tool.

I have been working with the Markov object with great success, but my patch fails in loading the saved state.
I get this when I reload my patch

savestate
... couldn't create

Using PD 0.48.1.

Any ideas on how to debug the savestate function?

@MikkelM since [savestate] is a newer object, i would try to use the current PD version (it exists since 0.49 http://msp.ucsd.edu/Pd_documentation/x5.htm).

Ok! I am using PD on a Tiny Core Linux platform that runs 0.48.1 and I dont think I dare to touch it again soonish.

@ingox I see now that the approach of defining a probability matrix is hard with this abstraction. I guess we needed to generate a third [text define] which properly encodes a probability matrix (of any order). This could be generated from the input like the $0-markov text, but once we're editing and creating it as the source of the chain, we'd need to create $0-memory and $0-markov from it...

so yeah, seems like a lot of trouble.

Anyway, hope you don't mind, I'm including a variation of this abstraction in my ELSE library

@porres Sure thing, it is public domain.

And yes, this abstraction is basically creating a form of probability matrix out of the source material. If you already have the matrix you don't need most of the steps and can basically directly play the chains...

This abstraction is something like a very basic machine learning approach: Play some notes or read a midi file into it and get new stuff out of it that is computer generated, but based on human creativity.

@ingox said:

If you already have the matrix you don't need most of the steps and can basically directly play the chains...

the thing is that I was talking about another form of matrix like the one from [prob] which is kinda intuitive, unlike the one we have here

@porres Maybe you can post some sample data of your matrix?

just check cyclone/prob please, that's it