API Reference

This section contains the complete API reference for JASMINE.

Core Modules

Package Overview

JASMINE is organized into several modules, each providing specific functionality:

  • jasmine.linear_model - Linear, ridge, lasso, and logistic regression models

  • jasmine.neighbors - Nearest-neighbor classifiers

  • jasmine.svm - Support Vector Machine classifiers

  • jasmine.preprocessing - Data preprocessing utilities

  • jasmine.datasets - Synthetic data generators

  • jasmine.metrics - Performance metrics

  • jasmine.model_selection - Data splitting utilities

Quick Reference

Most Common Classes

jasmine.linear_model.LinearRegression([...])

Linear regression trained with gradient descent.

jasmine.linear_model.LogisticRegression([...])

Binary logistic regression trained with gradient descent.

jasmine.linear_model.Ridge([alpha, ...])

Linear regression with L2 regularization.

jasmine.linear_model.Lasso([alpha, ...])

Linear regression with L1 regularization.

jasmine.neighbors.KNNClassifier(n_neighbors, ...)

K-Nearest Neighbors Classifier.

jasmine.svm.SVMClassifier(C, learning_rate, ...)

A linear Support Vector Machine (SVM) classifier.

jasmine.preprocessing.StandardScaler([epsilon])

StandardScaler standardizes features by removing the mean and scaling to unit variance.

Most Common Functions

jasmine.datasets.generate_regression([...])

Generate a random regression problem with JAX.

jasmine.datasets.generate_classification([...])

Generate a random n-class classification problem with.

jasmine.model_selection.train_test_split(X, y)

Split arrays into random train and test subsets.

jasmine.metrics.mean_squared_error(y_true, ...)

Mean Squared Error loss.

jasmine.metrics.accuracy_score(y_true, y_pred)

Compute the accuracy score between true and predicted values.