ML-DL-implementation 1.0.1

Package for ML and DL algorithms using nothing but numpy and matplotlib.

ML-DL-implementation

Machine Learning and Deep Learning library in python using numpy and matplotlib.

Why this repository?

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This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations for the sake of efficiency.

The goal of this repository is not to create the most efficient implementation but the most transparent one, so that anyone with little knowledge of the field can contribute and learn.

Installation

You can install the library by running the following command,

python3 setup.py install

For development purposes, you can use the option develop as shown below,

python3 setup.py develop

Testing

For testing your patch locally follow the steps given below,

1. Install pytest-cov. Skip this step if you are already having the package.
2. Run, python3 -m pytest --doctest-modules --cov=./ --cov-report=html. Look for, htmlcov/index.html and open it in your browser, which will show the coverage report. Try to ensure that the coverage is not decreasing by more than 1% for your patch.

Contributing to the repository

Follow the following steps to get started with contributing to the repository.

• Clone the project to you local environment. Use git clone https://github.com/RoboticsClubIITJ/ML-DL-implementation/ to get a local copy of the source code in your environment.

• Install dependencies: You can use pip to install the dependendies on your computer. To install use pip install -r requirements.txt

• Installation: use python setup.py develop if you want to setup for development or python setup.py install if you only want to try and test out the repository.

• Make changes, work on a existing issue or create one. Once assigned you can start working on the issue.

• While you are working please make sure you follow standard programming guidelines. When you send us a PR, your code will be checked for PEP8 formatting and soon some tests will be added so that your code does not break already existing code. Use tools like flake8 to check your code for correct formatting.

Algorithms Implemented

Activations	Location	Optimizers	Location	Models	Location	Backend	Location	Utils	Location
ACTIVATION FUNCTIONS		OPTIMIZERS		MODELS		BACKEND		PRE-PROCESSING METHODS
Sigmoid	activations.py	Gradient Descent	optimizers.py	Linear Regression	models.py	Autograd	autograd.py	Bell Curve	preprocessor_utils.py
Tanh	activations.py	Stochastic Gradient Descent	optimizers.py	Logistic Regression	models.py	Tensor	tensor.py	Standard_Scaler	preprocessor_utils.py
Softmax	activations.py	Mini Batch Gradient Descent	optimizers.py	Decision Tree Classifier	models.py	Functions	functional.py	MaxAbs_Scaler	preprocessor_utils.py
Softsign	activations.py	Momentum Gradient Descent	optimizers.py	KNN Classifier/Regessor	models.py			Z_Score_Normalization	preprocessor_utils.py
Relu	activations.py	Nesterov Accelerated Descent	optimizers.py	Naive Bayes	models.py			Mean_Normalization	preprocessor_utils.py
Leaky Relu	activations.py	Adagrad	optimizers.py	Gaussian Naive Bayes	models.py			Min Max Normalization	preprocessor_utils.py
Elu	activations.py	Adadelta	optimizers.py	Multinomial Naive Bayes	models.py			Feature Clipping	preprocessor_utils.py
Swish	activations.py	Adam	optimizers.py	Polynomial Regression	models.py
Unit Step	activations.py			Bernoulli Naive Bayes	models.py
				Random Forest Classifier	models.py
				K Means Clustering	models.py
				Divisive Clustering	models.py
				Agglomerative Clustering	models.py
				Bayes Optimization	models.py
				Numerical Outliers	models.py
				Principle Component Analysis	models.py
				Z_Score	models.py
				Sequential Neural Network	models.py

Loss Functions	Location	Regularizer	Location	Metrics	Location
LOSS FUNCTIONS		REGULARIZER		METRICS
Mean Squared Error	loss_func.py	L1_Regularizer	regularizer.py	Confusion Matrix	metrics.py
Logarithmic Error	loss_func.py	L2_Regularizer	regularizer.py	Precision	metrics.py
Absolute Error	loss_func.py			Accuracy	metrics.py
Cosine Similarity	loss_func.py			Recall	metrics.py
Log_cosh	loss_func.py			F1 Score	metrics.py
Huber	loss_func.py			F-B Theta	metrics.py
Mean Squared Log Error	loss_func.py			Specificity	metrics.py