Likelihood

This release introduced some great new features. Most important among those are the return of the scipy.optimize.minimize wrappers Minimize and Maximize.
Furthermore, the Likelihood object allows fitting using the principle of maximum likelihood.
Lastly, submodels can now be evaluated more easely than ever before, allowing the following:

pop_1 = A_1 * exp(-(x - x0_1)**2/(2 * sig_1**2))
pop_2 = A_2 * exp(-(x - x0_2)**2/(2 * sig_2**2))
model = pop_1 + pop_2

fit = Fit(model, xdata, ydata)
fit_result = fit.execute()

y = model(x=xdata, **fit_result.params)
y_1 = pop_1(x=xdata, **fit_result.params)

symfit 0.2.2

Bugfix release.

Changelog

• fixed a fatal bug which appears in cases where the variance in parameters could not be calculated. This typically happens in models with a lot of parameters.
• bug when using parameter names with underscores fixed.
• more metadata in setup.py

symfit 0.2.1!

Stable release.
This has some minor changes under the hood as compared to previous versions, but no changes to the API.

symfit 0.1

First stable release of Symbolic Fitting!