Python:Fitting

This document contains examples of polynomial fitting, general linear regression, and nonlinear regression. In each section, there will be example code that may come in useful for later courses. The example code is based on the existence of a file in the same directory called Cantilever.dat that contains two columns of data - the first is an amount of mass (in kg) placed at the end of a beam and the second is a displacement, measured in inches, at the end of the beam. For EGR 103, this file is:

0.000000        0.005211
0.113510002     0.158707
0.227279999     0.31399
0.340790009     0.474619
0.455809998     0.636769
0.569320007     0.77989
0.683630005     0.936634
0.797140015     0.999986

Common Command Reference

All links below to NumPy v1.15 manual at NumPy v1.15 Manual; these commands show up in just about all the examples:

• numpy.ndarray.copy
• numpy.ndarray.min
• numpy.ndarray.max
• numpy.linspace
• numpy.loadtxt
• numpy.mean

In the scripts below, common code has a regular background while code that differs from script to script will be highlighted in yellow.

Common Code

The processes for loading and manipulating the original data, calculating the statistics, and creating the graph are the same across the examples. To simplify codes, those processes should be contained in a separate file called fitting_common.py as given below:

import numpy as np
import matplotlib.pyplot as plt

def get_data():    
    # Load data from Cantilever.dat
    beam_data = np.loadtxt('Cantilever.dat')
    # Copy data from each column into new variables
    mass = beam_data[:, 0].copy()
    disp = beam_data[:, 1].copy()
    # Convert mass to force
    force = mass * 9.81
    # Convert disp to meters
    disp = (disp * 2.54) / 100
    
    fmodel = np.linspace(np.min(force), np.max(force), 100)
    
    # Return values
    return force, disp, fmodel

def calc_stats(y, yhat, to_print=1):
    # Calculate statistics
    st = np.sum((y - np.mean(y))**2)
    sr = np.sum((y - yhat)**2)
    r2 = (st - sr) / st
    if to_print:
        print('st: {}\nsr: {}\nr2: {}'.format(st, sr, r2))
    return st, sr, r2

def make_plot(x, y, yhat, xmodel, ymodel, fig_num=1):
    plt.figure(fig_num).clf()
    fig, ax = plt.subplots(num=fig_num)
    ax.plot(x, y, 'ko', label='Data')
    ax.plot(x, yhat, 'ks', label='Estimates', mfc='none')
    ax.plot(xmodel, ymodel, 'k-', label='Model')
    ax.grid(True)
    ax.legend()
    fig.tight_layout()

Polynomial Fitting

Polynomial fits are those where the dependent data is related to some set of integer powers of the independent variable. MATLAB's built-in polyfit command can determine the coefficients of a polynomial fit.

Specific Command References

All links below to NumPy v1.15 manual at NumPy v1.15 Manual

• numpy.polyfit
• numpy.polyval

Example Code

In the example code below, n determines the order of the fit. Not much else would ever need to change.

 1 # %% Import modules
 2 import numpy as np
 3 from fitting_common import *
 4 
 5 # %% Load and manipulate data
 6 x, y, xmodel = get_data()
 7 
 8 # %% Perform calculations
 9 n = 1
10 p = np.polyfit(x, y, n)
11 print(p)
12 
13 
14 
15 
16 
17 
18 # %% Generate estimates and model
19 yhat = np.polyval(p, x)
20 ymodel = np.polyval(p, xmodel)
21 
22 # %% Calculate statistics
23 calc_stats(y, yhat, 1)
24 
25 # %% Generate plots
26 make_plot(x, y, yhat, xmodel, ymodel)

General Linear Regression

General linear regression involves finding some set of coefficients for fits that can be written as:

\( \hat{y}(x)=\sum_{j=1}^{M}c_j\phi_j(x) \)

where the \(c_j\) are the coefficients of the fit and the \(\phi_j\) are the specific functions of the independent variable that make up the fit.

Specific Command References

All links below to NumPy v1.15 manual at NumPy v1.15 Manual

• numpy.block
• numpy.linalg.lstsq
• numpy.reshape

Example Code

In the example code below, there is an example of a general linear fits of one variable. It is solving the same fit as given above, just in different way. Specifically it uses linear algebra to find the coefficients that minimize the sum of the squares of the estimate residuals for a general linear fit. In this code, variables ending in "v" explicitly need to be column vectors while variables ending in "e" can either be 1D arrays or 2D arrays (or lists).

Note - if you get an error about "TypeError: must be real number, not NoneType" then on line 27, replace None with -1.

 1 # %% Import modules
 2 import numpy as np
 3 from fitting_common import *
 4 
 5 # %% Load and manipulate data
 6 x, y, xmodel = get_data()
 7 
 8 # %% Perform calculations
 9 def yfun(xe, coefs):
10     return coefs[0] * xe**1 + coefs[1] * xe**0
11 # Reshape data for block matrices
12 xv = np.reshape(x, (-1, 1))
13 yv = np.reshape(y, (-1, 1))
14 a_mat = np.block([[xv**1, xv**0]])
15 pvec = np.linalg.lstsq(a_mat, yv, rcond=None)[0]
16 print(pvec)
17 
18 # %% Generate estimates and model
19 yhat = yfun(x, pvec)
20 ymodel = yfun(xmodel, pvec)
21 
22 # %% Calculate statistics
23 calc_stats(y, yhat, 1)
24 
25 # %% Generate plots
26 make_plot(x, y, yhat, xmodel, ymodel)

Nonlinear Regression

Nonlinear regression is both more powerful and more sensitive than linear regression. For inherently nonlinear fits, it will also produce a better \(S_r\) value than linearization since the nonlinear regression process is minimizing the \(S_r\) of the actual data rather than that of the transformed values. The sensitivity comes into play as the optimization routine may find local minima versus global minima. A good starting guess will work wonders.

Specific Command References

The link below is to the SciPy v1.1.0 reference guide at SciPy

• scipy.optimize.curve_fit

Example Code

Note in the example code that the initial guess gives 0.6 for the slope and 0.1 for the intercept. While these numbers are quite far from the optimized values of 0.0034 for the slope and 0.00055 for the intercept, the optimization routine is still able to find the correct value. That is not always the case - try to find an initial guess close to the actual answer.

 1 # %% Import modules
 2 import scipy.optimize as opt
 3 from fitting_common import *
 4 
 5 # %% Load and manipulate data
 6 x, y, xmodel = get_data()
 7 
 8 # %% Perform calculations
 9 def yfun(x, *coefs):
10     return coefs[0] * x**1 + coefs[1] * x**0
11 
12 popt = opt.curve_fit(yfun, x, y, [0.6, 0.1])[0]
13 print(popt)
14 
15 
16 
17 
18 # %% Generate estimates and model
19 yhat = yfun(x, *popt)
20 ymodel = yfun(xmodel, *popt)
21 
22 # %% Calculate statistics
23 calc_stats(y, yhat, 1)
24 
25 # %% Generate plots
26 make_plot(x, y, yhat, xmodel, ymodel)

Example changes for different models

Polynomial

For the polynomial fitting model, really the only thing that would change would be the order of the fit and thus the value of \(n\) on line 23 of that code.

General Linear

For the general linear fit, the two places things will change will be in the function definition on line 10 and in the creation of the block matrix on line 14; the changes will mirror each other. For the straight line model - formally:

\( \hat{y}(x)=mx^1+bx^0 \)

the code is

 8 # %% Perform calculations
 9 def yfun(xe, coefs):
10     return coefs[0] * xe**1 + coefs[1] * xe**0
11 # Reshape data for block matrices
12 xv = np.reshape(x, (-1, 1))
13 yv = np.reshape(y, (-1, 1))
14 a_mat = np.block([[xv**1, xv**0]])
15 pvec = np.linalg.lstsq(a_mat, yv, rcond=None)[0]
16 print(pvec)

where pvec[0][0] will be the slope \(m\) and pvec[1][0] will be the intercept \(b\). If the model were changed to, say,

\( \hat{y}(x)=a\cos(x)+b\sin(x)+c \)

which, formally, is

\( \hat{y}(x)=a\cos(x)+b\sin(x)+cx^0 \)

then the code would change to:

 8 # %% Perform calculations
 9 def yfun(xe, coefs):
10     return coefs[0] * np.cos(xe) + coefs[1] * np.sin(xe) + coefs[2] * xe**0
11 # Reshape data for block matrices
12 xv = np.reshape(x, (-1, 1))
13 yv = np.reshape(y, (-1, 1))
14 a_mat = np.block([[np.cos(xv), np.sin(xv), xv**0]])
15 pvec = np.linalg.lstsq(a_mat, yv, rcond=None)[0]
16 print(pvec)

and pvec will be a 2D array with three rows.

Nonlinear

For the nonlinear fit, the two places things will change will be in the function definition on line 10 and in the initial parameter value guess on line 12; there need to be as many values in the initial parameter list as there are parameters. For the straight line model - formally:

\( \hat{y}(x)=mx^1+bx^0 \)

the code is

 8 # %% Perform calculations
 9 def yfun(x, *coefs):
10     return coefs[0] * x**1 + coefs[1] * x**0
11 
12 popt = opt.curve_fit(yfun, x, y, [0.6, 0.1])[0]
13 print(popt)

where popt[0] will be the slope \(m\) and popt[1] will be the intercept \(b\). If the model were changed to, say,

\( \hat{y}(x)=a\cos(bx+c)+d \)

which, formally, is

\( \hat{y}(x)=a\cos(bx+c)+dx^0 \)

then the code would change to:

 8 # %% Perform calculations
 9 def yfun(x, *coefs):
10     return coefs[0] * np.cos(coefs[1]*x + coefs[2]) + coefs[3] * x**0
11 
12 popt = opt.curve_fit(yfun, x, y, [.01, 1, np.pi/4, .0125])[0]
13 print(popt)

where [.01, 1, np.pi/4, .0125] represents initial guesses for the magnitude a, frequency b, phase c, and average value of the function. The popt variable would have 4 values.