00001 """ This module implements the Lowess function for nonparametric regression. Functions: lowess Fit a smooth nonparametric regression curve to a scatterplot. For more information, see William S. Cleveland: "Robust locally weighted regression and smoothing scatterplots", Journal of the American Statistical Association, December 1979, volume 74, number 368, pp. 829-836. """ try: from Numeric import * from LinearAlgebra import solve_linear_equations except ImportError, x: raise ImportError, "This module requires Numeric (precursor to NumPy) with the LinearAlgebra and MLab libraries" try: from Bio.Cluster import median # The function median in Bio.Cluster is faster than the function median # in Numeric's MLab, as it does not require a full sort. except ImportError, x: # Use the median function in Numeric's MLab if Bio.Cluster is not available try: from MLab import median except ImportError, x: raise ImportError, "This module requires Numeric (precursor to NumPy) with the LinearAlgebra and MLab libraries" 00030 def lowess(x, y, f=2./3., iter=3): """lowess(x, y, f=2./3., iter=3) -> yest Lowess smoother: Robust locally weighted regression. The lowess function fits a nonparametric regression curve to a scatterplot. The arrays x and y contain an equal number of elements; each pair (x[i], y[i]) defines a data point in the scatterplot. The function returns the estimated (smooth) values of y. The smoothing span is given by f. A larger value for f will result in a smoother curve. The number of robustifying iterations is given by iter. The function will run faster with a smaller number of iterations.""" n = len(x) r = int(ceil(f*n)) h = [sort(abs(x-x[i]))[r] for i in range(n)] w = clip(abs(([x]-transpose([x]))/h),0.0,1.0) w = 1-w*w*w w = w*w*w yest = zeros(n,'d') delta = ones(n,'d') for iteration in range(iter): for i in range(n): weights = delta * w[:,i] b = array([sum(weights*y), sum(weights*y*x)]) A = array([[sum(weights), sum(weights*x)], [sum(weights*x), sum(weights*x*x)]]) beta = solve_linear_equations(A,b) yest[i] = beta[0] + beta[1]*x[i] residuals = y-yest s = median(abs(residuals)) delta = clip(residuals/(6*s),-1,1) delta = 1-delta*delta delta = delta*delta return yest

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