Comments (10)
Hi, just to be sure, you mean 4 outcomes per option (e.g., A0, A1, A2, A3), but still 2 options (A and B)? If so I might have some code that will allow for that, but I would have to track it down. Right now it would involve a number of changes to options.js, which at the moment is pretty limited to binary outcomes.
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Okay I was able to implement this pretty easily, thanks to a handy CSV library. I created the n_options
branch so that you can test it out before I merge it with the main branch.
https://github.com/dmarkant/SamplingParadigmDFE/tree/n_options
Take a look at the new CSV file (option_sets_4.csv
) to see how the options are input ("x" columns indicate outcomes, "p" columns indicate their respective probabilities). Let me know if you have problems getting it running.
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oops, just realized I should have named the branch n_outcomes
... still allows for only 2 options!
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I apologize for the delayed response. I have now downloaded the branch that you created, but had a question in regards to the folder titled " .ipynb_checkpoints" that folder is not showing in my download, but I noticed that the file in that folder is the same as the "option sets.ipynb" file. Should everything work without that folder.
Also, do I need to download additional software to support the ipynb file type?
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Currently downloading "Papa Parse" I am guessing that its needed
Is there anything else that I am missing?
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You can actually ignore the .ipynb files -- they are for a Jupyter notebook (http://jupyter.org) that I used to randomly generate the new option sets.
Also the PapaParse file is already in the js folder, so you shouldn't need to download it in order to run the experiment.
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Ah sorry about that, there was a typo that caused an error. It's working now for me, let me know if you still have trouble.
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