maimoons / 7641 Goto Github PK

Machine Learning

OMSCS 7641

Github Repo - https://github.com/Maimoons/7641/blob/master/README.md

Project 4: Reinforcement Learning The folder: Project 4 contains code for the forth assignment in the course. It implements 3 different RL algorithms on 2 different MDP:

• Policy Iteration- policy_iteration.py
• Value Iteration - value_iteration.py
• Q Learning - q_learn.py
• Experiments - experiments.py

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Project 3: Unsupervised Learning

The folder: Project 3 contains code for the third assignment in the course. It implements 2 different clustering problems and 4 dimensionality reduction problems:

• K Means- kmm.py
• Expectation Maximization - em.py
• PCA - pca.py
• IPA - ipa.py
• Gaussian Random Mixture - grp.py
• Random Forest - rf.py

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Project 2: Randomized Optimization

The folder: Project 2 contains code for the second assignment in the course. It implements 4 different problems:

• Neural Network- neural_network.py
• Knapsack - knapsack.py
• Traveling Salesman - tsp.py
• Continuous Peaks - continuos_peaks.py

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Project 1: Supervised Learning

The folder: Project 1 contains code for the first assignment in the course. It implements 5 classifiers:

• Decision Trees- decision_tree.py
• Boosted Decision Trees - boost.py
• Neural Network - neural_network.py
• SVM - svm.py
• KNN - knn.py

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Project 4 (the following on top of the dependencies from project 1)

• hiive.mdptoolbox
• mdptoolbox
• openaigym

Project 3 (the following on top of the dependencies from project 1)

• sklears.clusters
• sklearn.metrics
• sklearn.manifolds
• sklearn.mixture
• sklearn.decomposition

Project 2 (the following on top of the dependencies from project 1)

• MLrose --hiive

Project 1

• Numpy
• Pandas
• SkLearn
• Matplotlib

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Follow the commands to setup and run the experiments:

1. Clone the repo

   git clone https://github.com/Maimoons/7641

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The list of requirements for this repo is in requirements.txt as well as defined above so pip install each e.g

• pip

  pip install numpy

There are two problems used in this project.

• Grid problem
• Non grid problem

The configuration for both is defined in the experiments file -

Running the experiments

python experiments.py 0

The output graphs are produced in the images folder.

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There are two datasets used in this project.

• Dataset 0 - Titanic dataset from Kaggle
• Dataset 1 - Breast cancer dataset from sklearn

The dataset index needs to be passed in to run the experiments -

Running the experiments

python run_experiments.py 0

The output graphs are produced in the images folder.

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The dataset used for the Neural Network problem -

• Dataset 0 - Titanic dataset from Kaggle

Running the classifier

python neural_network.py

Running any of the second half of optimization problems

python tsp.py
python knapsack.py
python continuos_peaks.py

The output graphs are produced in the images folder.

To plot training and testing time bar graphs for Neural Network -

python base.py

There are two datasets used in this project.

• Dataset 0 - Titanic dataset from Kaggle
• Dataset 1 - Breast cancer dataset from sklearn

The dataset index needs to be passed in to whatever classifier is being run. For example -

Running the classifier

python decision_tree.py 0

The above calls both the training and testing and saves the trained model. The output graphs are produced in the images folder.

To plot training and testing time bar graphs - Running the classifier

python base.py

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Your Name - [email protected]

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• [https://github.com/othneildrew/Best-README-Template/blob/master/BLANK_README.md](Readme Template)

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