HOBIT 1.0.0

an installable package for Hybrid fitting of Sine and Cosine functions

HOBIT: Harmonic Oscillator hyBrid fIT

Efficient fit of sine/cosine functions using a hybrid method

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HOBIT is a Python library that combines Optuna's TPE algorithm with the flexibility of scikit-learn's LinearRegression to efficiently fit functions of the form

f(x) = y_0 + y_1 * Sin(omega * x + phi)
f(x) = y_0 + y_1 * Cos(omega * x + phi)

commonly used to describe harmonic oscillators.

Install

pip install HOBIT

Requirements

HOBIT requires Python >= 3.11 and will install the following dependencies:

• pandas >= 2.0.0
• numpy >= 1.26.0
• optuna >= 3.0.0
• scikit-learn >= 1.4.0

Get started

In the test_scripts/ folder you will find the following examples:

1. scipy_and_gradient_descent.py — fit of a cosine function using scipy's curve_fit and a walkthrough of gradient descent.
2. only_optuna.py — fit of a cosine function using Optuna's TPE sampler directly.
3. hybrid_method_cos.py — fit of a cosine function using HOBIT.
4. hybrid_method_sin.py — fit of a sine function using HOBIT.

Quick example

import numpy as np
from HOBIT import RegressionForTrigonometric

x = np.linspace(-5, 5, 100)
y = 10 + 5 * np.cos(3 * x + 2) + 1.5 * np.random.normal(size=100)

model = RegressionForTrigonometric()
model.fit_cos(x, y, n_trials=500)

print(model.best_parameters)
# {'omega': ..., 'phi': ..., 'intercept': ..., 'amplitude': ...}

y_pred = model.predict(x)