Remove backwards compatibility disclaimer from the README

README.md

@@ -3,54 +3,66 @@
 [![PyPI version](https://badge.fury.io/py/mlrose-ky.svg)](https://pypi.org/project/mlrose-ky/)
 [![Coverage badge](https://img.shields.io/badge/dynamic/json?color=brightgreen&label=coverage&query=%24.message&url=https%3A%2F%2Fraw.githubusercontent.com%2Fnkapila6%2Fmlrose-ky%2Fpython-coverage-comment-action-data%2Fendpoint.json)](https://htmlpreview.github.io/?https://github.com/nkapila6/mlrose-ky/blob/python-coverage-comment-action-data/htmlcov/index.html)
 
-mlrose-ky is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
-
-### Usage Warning (updated Aug. 22, 2024)
-
-This project is not fully backwards compatible with mlrose-hiive at this time. You should not expect existing mlrose-hiive example code to work right out of the box when using mlrose-ky. 
-
-Progress is currently being made ([issue #7](https://github.com/knakamura13/mlrose-ky/issues/7)) towards supporting backwards compatibility.
+mlrose-ky is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different
+optimization problems, over both discrete- and continuous-valued parameter spaces.
 
 ## Project Background
 
-mlrose-ky is a fork of the `mlrose-hiive` repository, which itself was a fork of the original `mlrose` repository. The original mlrose package was developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
+mlrose-ky is a fork of the `mlrose-hiive` repository, which itself was a fork of the original `mlrose` repository. The original mlrose
+package was developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
 
-This repository includes implementations of all randomized optimization algorithms taught in the course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
+This repository includes implementations of all randomized optimization algorithms taught in the course, as well as functionality to apply
+these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization
+problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has
+the flexibility to solve user-defined optimization problems.
 
 ## Main Features
 
 #### *Randomized Optimization Algorithms*
+
 - Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm, and (discrete) MIMIC;
 - Solve both maximization and minimization problems;
 - Define the algorithm's initial state or start from a random state;
-- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay, or exponential decay.
+- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay,
+  arithmetic decay, or exponential decay.
 
 #### *Problem Types*
+
 - Solve discrete-value (bit-string and integer-string), continuous-value, and tour optimization (travelling salesperson) problems;
 - Define your own fitness function for optimization or use a pre-defined function.
-- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens, and Max-K Color optimization problems.
+- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling
+  Salesperson, N-Queens, and Max-K Color optimization problems.
 
 #### *Machine Learning Weight Optimization*
-- Optimize the weights of neural networks, linear regression models, and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm, or gradient descent;
+
+- Optimize the weights of neural networks, linear regression models, and logistic regression models using randomized hill climbing,
+  simulated annealing, the genetic algorithm, or gradient descent;
 - Supports classification and regression neural networks.
 
 ## Project Improvements and Updates
 
-The `mlrose-ky` project is undergoing significant improvements to enhance code quality, documentation, and testing. Below is a list of tasks that have been completed or are in progress:
+The `mlrose-ky` project is undergoing significant improvements to enhance code quality, documentation, and testing. Below is a list of tasks
+that have been completed or are in progress:
+
+1. **Fix Python Warnings and Errors**: All Python warnings and errors have been addressed, except for a few unavoidable ones like "duplicate
+   code." ✅
 
-1. **Fix Python Warnings and Errors**: All Python warnings and errors have been addressed, except for a few unavoidable ones like "duplicate code." ✅
-
-2. **Add Python 3.10 Type Hints**: Type hints are being added to all function and method definitions, as well as method properties (e.g., `self.foo: str = 'bar'`), to improve code clarity and maintainability.
-
-3. **Enhance Documentation**: NumPy-style docstrings are being added to all functions and methods, with at least a one-line docstring at the top of every file summarizing its contents. This will make the codebase more understandable and easier to use for others.
-
-4. **Increase Test Coverage**: Tests are being added using Pytest, with a goal of achieving 100% code coverage to ensure the robustness of the codebase.
-
-5. **Resolve TODO/FIXME Comments**: A thorough search is being conducted for any TODO, FIXME, or similar comments, and their respective issues are being resolved.
+2. **Add Python 3.10 Type Hints**: Type hints are being added to all function and method definitions, as well as method properties (
+   e.g., `self.foo: str = 'bar'`), to improve code clarity and maintainability.
+
+3. **Enhance Documentation**: NumPy-style docstrings are being added to all functions and methods, with at least a one-line docstring at the
+   top of every file summarizing its contents. This will make the codebase more understandable and easier to use for others.
+
+4. **Increase Test Coverage**: Tests are being added using Pytest, with a goal of achieving 100% code coverage to ensure the robustness of
+   the codebase.
+
+5. **Resolve TODO/FIXME Comments**: A thorough search is being conducted for any TODO, FIXME, or similar comments, and their respective
+   issues are being resolved.
 
 6. **Optimize Code**: Vanilla Python loops are being optimized where possible by vectorizing them with NumPy to enhance performance.
 
-7. **Improve Code Quality**: Any other sub-optimal code, bugs, or code quality issues are being addressed to ensure a high standard of coding practices.
+7. **Improve Code Quality**: Any other sub-optimal code, bugs, or code quality issues are being addressed to ensure a high standard of
+   coding practices.
 
 8. **Clean Up Codebase**: All commented-out code is being removed to keep the codebase clean and maintainable.
 
@@ -74,47 +86,57 @@ import mlrose_ky
 
 The official mlrose-ky documentation can be found [here](https://nkapila6.github.io/mlrose-ky/).
 
-A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
+A Jupyter notebook containing the examples used in the documentation is also
+available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
 
 ## Licensing, Authors, Acknowledgements
 
 mlrose-ky was forked from the `mlrose-hiive` repository, which was a fork of the original `mlrose` repository.
 
-The original `mlrose` was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). 
+The original `mlrose` was written by Genevieve Hayes and is distributed under
+the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
 
 You can cite mlrose-ky in research publications and reports as follows:
-* Nakamura, K. (2024). ***mlrose-ky: Machine Learning, Randomized Optimization, and SEarch package for Python***. https://github.com/knakamura13/mlrose-ky/. Accessed: *day month year*.
+
+* Nakamura, K. (2024).
+  ***mlrose-ky: Machine Learning, Randomized Optimization, and SEarch package for Python***. https://github.com/knakamura13/mlrose-ky/.
+  Accessed: *day month year*.
 
 Please also keep the original authors' citations:
-* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
-* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
+
+* Rollings, A. (2020).***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix
+  ***. https://github.com/hiive/mlrose. Accessed: *day month year*.
+* Hayes, G. (2019).
+  ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day
+  month year*.
 
 Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
 
 BibTeX entry:
+
 ```bibtex
 @misc{Nakamura24,
- author = {Nakamura, K.},
- title  = {{mlrose-ky: Machine Learning, Randomized Optimization and SEarch package for Python}},
- year   = 2024,
- howpublished = {\url{https://github.com/knakamura13/mlrose-ky/}},
- note   = {Accessed: day month year}
+    author = {Nakamura, K.},
+    title = {{mlrose-ky: Machine Learning, Randomized Optimization and SEarch package for Python}},
+    year = 2024,
+    howpublished = {\url{https://github.com/knakamura13/mlrose-ky/}},
+    note = {Accessed: day month year}
 }
 
 @misc{Rollings20,
- author = {Rollings, A.},
- title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
- year 	= 2020,
- howpublished = {\url{https://github.com/hiive/mlrose/}},
- note 	= {Accessed: day month year}
+    author = {Rollings, A.},
+    title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
+    year = 2020,
+    howpublished = {\url{https://github.com/hiive/mlrose/}},
+    note = {Accessed: day month year}
 }
 
 @misc{Hayes19,
- author = {Hayes, G.},
- title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
- year 	= 2019,
- howpublished = {\url{https://github.com/gkhayes/mlrose/}},
- note 	= {Accessed: day month year}
+    author = {Hayes, G.},
+    title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
+    year = 2019,
+    howpublished = {\url{https://github.com/gkhayes/mlrose/}},
+    note = {Accessed: day month year}
 }
 ```