![]() linregress ( t, xn ) print ( 'Linear regression using stats.linregress' ) print ( 'parameters: a= %.2f b= %.2f \n regression: a= %.2f b= %.2f, std error= %.3f ' % ( a, b, a_s, b_s, stderr )) print ( ' \n ' ) # matplotlib ploting title ( 'Linear Regression Example' ) plot ( t, x, 'g.-' ) plot ( t, xn, 'k.' ) plot ( t, xr, 'r. From scipy import linspace, polyval, polyfit, sqrt, stats, randn from matplotlib.pyplot import plot, title, show, legend # Linear regression example # This is a very simple example of using two scipy tools # for linear regression, polyfit and stats.linregress # Sample data creation # number of points n = 50 t = linspace ( - 5, 5, n ) # parameters a = 0.8 b = - 4 x = polyval (, t ) # add some noise xn = x + randn ( n ) # Linear regressison -polyfit - polyfit can be used other orders polys ( ar, br ) = polyfit ( t, xn, 1 ) xr = polyval (, t ) # compute the mean square error err = sqrt ( sum (( xr - xn ) ** 2 ) / n ) print ( 'Linear regression using polyfit' ) print ( 'parameters: a= %.2f b= %.2f \n regression: a= %.2f b= %.2f, ms error= %.3f ' % ( a, b, ar, br, err )) print ( ' \n ' ) # Linear regression using stats.linregress ( a_s, b_s, r, tt, stderr ) = stats. from scipy import linspace, polyval, polyfit, sqrt, stats, randn from matplotlib.pyplot import plot, title, show, legend Linear regression example This is a very simple example of using two scipy tools for linear regression, polyfit and stats.linregress Sample data creation number of points n 50 t linspace(-5,5,n) parameters a.
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