python-expokar 0.1.3

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

• Linear Regression: Base, Ridge (L2) and Lasso (L1) implementations
• 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

Usage Examples

Linear Regression

from python_expokar.ml.linear_regression import Ridge, Lasso

# Ridge Regression
ridge_model = Ridge(alpha=1.0)  # L2 regularization strength
ridge_model.fit(X_train, y_train)
ridge_predictions = ridge_model.predict(X_test)

# Lasso Regression
lasso_model = Lasso(alpha=1.0)  # L1 regularization strength
lasso_model.fit(X_train, y_train)
lasso_predictions = lasso_model.predict(X_test)

Logistic Regression

from python_expokar.ml 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.ml 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.ml 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.