tensorfit 0.0.6

Python package for univariate curve fitting using TensorFlow.

TensorFit

TensorFit is an open source package for curve fitting. This package was designed with the intention of allowing students and researchers to quickly assess parametric functions for explaining experimental data and trends. The package currently only supports univariate functions, i.e. functions with a single independent variable.

Install

pip install tensorfit

Usage

Import packages as needed.

>>> import numpy as np
>>> from tensorfit import TensorFunction

Generate from fake experimental data for the purpose of demonstration.

>>> x = np.linspace(-1, 1, 100)
>>> y = 9.8 * x ** 2 + 6.1 * x + 0.87 * np.random.randn(*x.shape)

Create and initialize TensorFunction instance using a parametric model for your fitting function and a set of starting parameters.

>>> tfunc = TensorFunction()
>>> my_func = "self.a * self.X ** 2 + self.b * self.X + self.c"
>>> init_params = {"a": 0.1, "b": 0.1, "c": 0.1}
>>> tfunc.initialize(func=my_func, params=init_params)

After initialization, you can make a call to .fit() to fit your TensorFunction() to the experimental data.

>>> tfunc.fit(x, y, num_rounds=10000, early_stopping_rounds=10, verbose_eval=0)
Early stopping, best iteration is:
[Episode - 6046] mse: 0.81566346

Fitted parameters and a summary of your fit can then be looked at.

>>> tfunc.Params
{'a': 9.560993, 'b': 6.0437393, 'c': 0.11265278}
>>> tfunc.Summary
{'mse': 0.81566346, 'r2': 0.9623992666602135}

License

MIT License

This library uses:

• numpy, which is distributed under the BSD 3-Clause license.
• tensorflow, which is distributed under the Apache 2.0 license.