Metadata-Version: 2.1
Name: brute-curvefit
Version: 0.0.4
Summary: Curve fitting using both brute force and scipy.optimize
Home-page: https://github.com/analkumar2/brute_force
Author: Anal Kumar
Author-email: analkumar2@gmail.com
License: UNKNOWN
Description: This is a simple script which tries to find the global minima using scipy.optimize.curve_fit as well as a parameter search over the parameter space.
        It first generates ntol random models, then selects ntol*returnnfactor best models and does scipy.optimize.curve_fit on all of them. It then returns the best model of them all.
        This script improves scipy.optimize.curve_fit in two ways - No need to give initial values and thus getting global minima instead of local minima. And second, it automatically normalize and standardizes the data.
        
        FUTURE:
        1. Parallelize
        2. Use Genetic algorithm instead of brute-force
        
        USAGE EXAMPLE:
        import brute_curvefit as bf
        import numpy as np
        import matplotlib.pyplot as plt
        def yourfunc(x, A,B,C):
        	return (x-A)/B +C
        
        params, error = bf.brute_then_scipy(func=yourfunc, x=np.arange(100), y=np.arange(100)**2, restrict=[[-1000,-1000,-1000],[1000,1000,1000]])
        plt.plot(np.arange(100), np.arange(100)**2) #plotting data
        plt.plot(np.arange(100), yourfunc(np.arange(100), *params)) #plotting the fitted function
        plt.show()
        #As simple as that
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
