Find the coefficients of the polynomial that fits the specified data in the least squares sense，,''' c = poly

Find the coefficients of the polynomial that fits the specified data in the least squares sense，,''' c = poly

''' c = polyFit(xData,yData,m).    Returns coefficients of the polynomial    p(x) = c[0] + c[1]x + c[2]x^2 +...+ c[m]x^m    that fits the specified data in the least    squares sense.    sigma = stdDev(c,xData,yData).    Computes the std. deviation between p(x)    and the data.'''    from numpy import zerosfrom math import sqrtfrom gaussPivot import *def polyFit(xData,yData,m):    a = zeros((m+1,m+1))    b = zeros(m+1)    s = zeros(2*m+1)    for i in range(len(xData)):        temp = yData[i]        for j in range(m+1):            b[j] = b[j] + temp            temp = temp*xData[i]        temp = 1.0        for j in range(2*m+1):            s[j] = s[j] + temp            temp = temp*xData[i]    for i in range(m+1):        for j in range(m+1):            a[i,j] = s[i+j]    return gaussPivot(a,b)def stdDev(c,xData,yData):    def evalPoly(c,x):        m = len(c) - 1        p = c[m]        for j in range(m):            p = p*x + c[m-j-1]        return p        n = len(xData) - 1    m = len(c) - 1    sigma = 0.0    for i in range(n+1):        p = evalPoly(c,xData[i])        sigma = sigma + (yData[i] - p)**2    sigma = sqrt(sigma/(n - m))    return sigma