Comments (6)
I have been wanting to resume implementing them for a while here but haven't found the time. I got busy with work and have stalled on my re-reading of the book. I didn't realize it had been so long.
I'm fine with you implementing the rest and feel free to change anything I've created so far.
@ctjoreilly is the owner of this repo so he'd need to approve it, add you, and set you up with slack access
If you wanted to just PR the rest of the algorithms that's fine too. I am a bit rusty on some of the later algorithms but I can certainly review code.
This project is trying to maintain attribution so add your name to any existing @author
javadoc comments and make sure to add them for new code.
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Okay sure , I will send PRs and ask you as well as @ctjoreilly for review
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@anumehaagrawal @BusyByte I have a high-level question. Is it required to write all the algorithms from scratch in scala. We or User can just reuse/invoke the java implementation of the algo in Scala. Though any algo which needs a specific Scala implementation can be ported separately.
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@rishabhbhardwaj If all we did was call the Java version, then there would be no point to have the Scala version. That said, if there are cases where it would make sense to have a high-level part in Scala that calls low-level methods from the Java version, that would be ok.
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@norvig Totally agree with you to implement those cases in Scala. @anumehaagrawal @BusyByte It would be good if the community can come up with specific tasks in the issues of this repo. I would be happy to contribute.
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I have created milestones for each chapter and will be creating an issue for each figure to implement under the milestone.
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Related Issues (20)
- implement Figure 17.7 POLICY-ITERATION
- implement Figure 17.9 POMDP-VALUE-ITERATION
- implement Figure 18.4 DECISION-TREE-LEARNING
- implement Figure 18.7 CROSS-VALIDATION-WRAPPER
- implement Figure 18.10 DECISION-LIST-LEARNING
- implement Figure 18.23 BACK-PROP-LEARNING
- implement Figure 18.33 ADABOOST
- implemnt Figure 19.2 CURRENT-BEST-LEARNING
- implement Figure 19.3 VERSION-SPACE-LEARNING
- implement Figure 19.8 MINIMAL-CONSISTENT-DET
- implement Figure 19.12 FOIL
- implement Figure 21.2 PASSIVE-ADP-AGENT
- implement Figure 21.4 PASSIVE-TD-AGENT
- implement Figure 21.8 Q-LEARNING-AGENT
- implement Figure 22.1 HITS
- implement Figure 23.4 CYK-PARSE
- implement Figure 23.5
- implement Figure 25.9 MONTE-CARLO-LOCALIZATION
- implement Figure 29.1 POWERS-OF-2
- Measure agent performance HOT 1
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