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#!/usr/bin/env python
""" fitting funtions
module to fit x and y samples to a model
"""
from __future__ import division
from numpy import array
from cogent.maths.scipy_optimize import fmin
__author__ = "Antonio Gonzalez Pena"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Antonio Gonzalez Pena"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Antonio Gonzalez Pena"
__email__ = "antgonza@gmail.com"
__status__ = "Prototype"
def fit_function(x_vals, y_vals, func, n_params, iterations=2):
""" Fit any function to any array of values of x and y.
:Parameters:
x_vals : array
Values for x to fit the function func.
y_vals : array
Values for y to fit the function func.
func : callable ``f(x, a)``
Objective function (model) to be fitted to the data. This function
should return either an array for models that are not a constant,
i.e. f(x)=exp(a[0]+x*a[1]), or a single value for models that are a
cosntant, i.e. f(x)=a[0]
n_params : int
Number of parameters to fit in func
iterations : int
Number of iterations to fit func
:Returns: param_guess
param_guess : array
Values for each of the arguments to fit func to x_vals and y_vals
:Notes:
Fit a function to a given array of values x and y using simplex to
minimize the error.
"""
# internal function to minimize the error
def f2min(a):
#sum square deviation
return ((func(x_vals, a) - y_vals)**2).sum()
param_guess = array(range(n_params))
for i in range(iterations):
xopt = fmin(f2min, param_guess, disp=0)
param_guess = xopt
return xopt
#if __name__ == "__main__":
# main()
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