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lijie169
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jjdg: ½ð±Ò+1, ¸Ðл²ÎÓë 2012-06-16 21:52:29
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jjdg: ½ð±Ò+1, ¸Ðл²ÎÓë 2012-06-16 21:52:29
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help polyfit polyfit Fit polynomial to data. P = polyfit(X,Y,N) finds the coefficients of a polynomial P(X) of degree N that fits the data Y best in a least-squares sense. P is a row vector of length N+1 containing the polynomial coefficients in descending powers, P(1)*X^N + P(2)*X^(N-1) +...+ P(N)*X + P(N+1). [P,S] = polyfit(X,Y,N) returns the polynomial coefficients P and a structure S for use with POLYVAL to obtain error estimates for predictions. S contains fields for the triangular factor (R) from a QR decomposition of the Vandermonde matrix of X, the degrees of freedom (df), and the norm of the residuals (normr). If the data Y are random, an estimate of the covariance matrix of P is (Rinv*Rinv')*normr^2/df, where Rinv is the inverse of R. [P,S,MU] = polyfit(X,Y,N) finds the coefficients of a polynomial in XHAT = (X-MU(1))/MU(2) where MU(1) = MEAN(X) and MU(2) = STD(X). This centering and scaling transformation improves the numerical properties of both the polynomial and the fitting algorithm. Warning messages result if N is >= length(X), if X has repeated, or nearly repeated, points, or if X might need centering and scaling. |
2Â¥2012-06-14 07:47:01
Honbein
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3Â¥2012-06-16 10:29:06
sujie1988
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4Â¥2012-06-18 18:35:40













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