SymFit's little brother
Project description
SlimFit
SymFit's little brother
Documentation: https://jhsmit.github.io/slimfit/
This project is inspired by SymFit and is functional, to some degree, but in currently in BETA
- Free software: MIT license
Aims
- Inspiration for a potential SymFit 2.0
- Expectation-Maximization likelihood maximization
Quick Start
from sympy import symbols
from slimfit import Model, Fit, Parameter
import numpy as np
y, a, x, b = symbols('y a x b')
model = Model({y: a*x + b})
parameters = [
Parameter(a, guess=2.5),
Parameter(b, guess=1, lower_bound=0.)
]
xdata = np.linspace(0, 11, 25)
ydata = 0.5*xdata + 2.5
ydata += np.random.normal(0, scale= ydata / 10.0 + 0.2)
data = {'x': xdata, 'y': ydata}
fit = Fit(model, parameters, data)
result = fit.execute()
print(result.parameters)
>>> {'a': array(0.47572707), 'b': array(2.6199133)}
Installation
$ pip install slimfit
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
slimfit-0.1.4.tar.gz
(1.4 MB
view hashes)
Built Distribution
slimfit-0.1.4-py3-none-any.whl
(34.2 kB
view hashes)