wha*_*992 7 matlab for-loop octave derivative
输入:
Xf = 和保存点的 x 值的数组
Yf = 保存点方法的 y 值的数组 = 2 点前向差、2 点后向差、3 点中心差、5 点中心差
输出:
X= 包含有效 x 值的数组,其中选择的方法实际上可以使用(例如,您不能在Xf数组的上限使用前向差分方法,因为它后面没有值)
DF = 在这些点的导数
我需要为脚本提供一组点,然后使用 4 种不同的方法计算这些点的导数,而不使用像 diff. 我需要一些帮助来编写其中一个代码,然后我想我应该能够弄清楚如何完成其余的工作。
我的尝试:
[a, minidx] = min(Xf);
[b, maxidx] = max(Xf);
n = 10;
h = (b-a)/n;
f = (x .^3) .* e.^(-x) .* cos(x);
If method = "forward" #Input by user
X = [min(Xf), Xf(maxidx-1)];
for k = min(Xf):n # not sure if this is the right iteration range...
f(1) = f(x-2*h) + 8*f(x +h);
f(2) = 8*f(x-h) + f(x+2*h);
DF = (f1-f2)/(12*h);
endfor
endif
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小智 9
https://wiki.octave.org/Symbolic_package
% this is just a formula to start with,
% have fun and change it if you want to.
f = @(x) x.^2 + 3*x - 1 + 5*x.*sin(x);
% these next lines take the Anonymous function into a symbolic formula
pkg load symbolic
syms x;
ff = f(x);
% now calculate the derivative of the function
ffd = diff(ff, x)
% answer is ffd = (sym) 5*x*cos(x) + 2*x + 5*sin(x) + 3
...
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以下是 Matlab 如何计算导数的一些文档:
diff Difference and approximate derivative.
diff(X), for a vector X, is [X(2)-X(1) X(3)-X(2) ... X(n)-X(n-1)].
diff(X), for a matrix X, is the matrix of row differences,
[X(2:n,:) - X(1:n-1,:)].
diff(X), for an N-D array X, is the difference along the first
non-singleton dimension of X.
diff(X,N) is the N-th order difference along the first non-singleton
dimension (denote it by DIM). If N >= size(X,DIM), diff takes
successive differences along the next non-singleton dimension.
diff(X,N,DIM) is the Nth difference function along dimension DIM.
If N >= size(X,DIM), diff returns an empty array.
Examples:
h = .001; x = 0:h:pi;
diff(sin(x.^2))/h is an approximation to 2*cos(x.^2).*x
diff((1:10).^2) is 3:2:19
If X = [3 7 5
0 9 2]
then diff(X,1,1) is [-3 2 -3], diff(X,1,2) is [4 -2
9 -7],
diff(X,2,2) is the 2nd order difference along the dimension 2, and
diff(X,3,2) is the empty matrix.
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这是另一个例子:
xp= diff(xf);
yp= diff(yf);
% derivative:
dydx=yp./xp;
% also try:
dydx1=gradient(yf)./gradient(xf)
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