python-expokar 0.1.2

A collection of machine learning algorithm implementations with ready-to-use experiments

Python Expokar

A comprehensive collection of machine learning algorithm implementations including Logistic Regression, Support Vector Machines (SVM), Neural Networks, Principal Component Analysis (PCA), and more.

Installation

pip install python-expokar

Features

• Logistic Regression: Implementation with various regularization options
• Support Vector Machines: Implementation with multiple kernel options
• Neural Networks: McCulloch-Pitts neuron and Hebbian learning implementations
• Principal Component Analysis (PCA): Dimensionality reduction implementation
• Ridge and Lasso Regression: Regularized linear regression implementations

Usage Examples

Logistic Regression

from python_expokar.logistic_regression import CustomLogisticRegression

# Create and train the model
model = CustomLogisticRegression(learning_rate=0.01, n_iterations=1000)
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Support Vector Machine

from python_expokar.svm import SVM

# Train SVM with different kernels
svm_model = SVM(kernel='rbf', gamma='scale', C=1.0)
svm_model.fit(X_train, y_train)

Neural Networks

from python_expokar.neural_networks import HebbianNeuron

# Create and train a Hebbian neuron
neuron = HebbianNeuron(input_size=2, learning_rate=0.1)
neuron.train_hebbian(X_train, y_train)

Requirements

• Python >= 3.6
• NumPy >= 1.19.0
• scikit-learn >= 0.24.0
• matplotlib >= 3.3.0
• pandas >= 1.2.0
• seaborn >= 0.11.0

License

This project is licensed under the MIT License - see the LICENSE file for details.