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Add Testing and Documentation sections to README, add THIRD-PARTY file
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‎README.md

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--help, -h: Print this help message and exit.
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```
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## Testing
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Our test suite is divided into unit tests and "functional" tests. By "functional", we mean tests that verify the
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expected behavior of the algorithms defined in the package. For example, a functional test for the anomaly detection
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algorithm will first train a forest on a pre-defined distribution and then verify that the forest assigns a high
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anomaly score to anomalous points (where "anomalous" is with respect to the specified distribution). Functional tests
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are indicated both in the test class name (e.g., `RandomCutForestFunctionalTest`) and in a `@Tag` annotation on the
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test class.
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The full test suite including functional test currently takes over 10 minutes to complete. If you are contributing to
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this package, we recommend excluding the functional tests while actively developing, and only running the full test
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suite before creating a pull request. Functional tests can be excluded from Maven build targets by passing
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`-DexcludedGroups=functional` at the command line. For example:
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```text
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% mvn test -DexcludedGroups=functional
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```
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We currently have 90% line coverage with the full test suite, and 80% line coverage when running the unit tests only
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(i.e., when excluding functional tests). Our goal is to reach 100% unit test coverage, and we welcome (and encourage!)
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test contributions. After running tests with Maven, you can see the test coverage broken out by class by opening
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`target/site/jacoco/index.html` in a web browser.
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Our tests are implemented in [JUnit 5](https://junit.org/junit5/) with [Mockito](https://site.mockito.org/), [Powermock](https://github.com/powermock/powermock), and [Hamcrest](http://hamcrest.org/) for testing.
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Test dependencies will be downloaded automatically when invoking `mvn test` or `mvn package`.
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## Documentation
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* Guha, S., Mishra, N., Roy, G., & Schrijvers, O. (2016, June). Robust random cut forest based anomaly detection on streams. In *International conference on machine learning* (pp. 2712-2721).
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## Code of Conduct
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This project has adopted an [Open Source Code of Conduct](https://aws.github.io/code-of-conduct).

‎THIRD-PARTY

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** Gson 2.8; version 2.8.6 -- https://github.com/google/gson
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Copyright 2008 Google Inc.
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