x f(x)
=======
1.0 0.0
2.0 0.693
3.0 1.099
程式
using Printf
function lagrange(x::Array{Float64,1},f::Array{Float64,1},xa::Float64)
#
# implements the interpolation algorithm of Newton
#
# ON ENTRY :
# x abscisses, given as a column vector;
# f ordinates, given as a column vector;
# xa point where to evaluate the interpolating
# polynomial through (x[i],f[i]).
#
# ON RETURN :
# d divided differences, computed from and f;
# p value of the interpolating polynomial at xa.
#
# EXAMPLE :
n = length(x)
tmp2=0.0
for k=1:n
tmp1=1.0
for i=1:n
if (i != k)
tmp1 *= (xa-x[i]) / (x[k]-x[i])
end
end
tmp2=tmp2+tmp1*f[k]
end
return tmp2
end
x = [1.0 , 2.0 , 3.0 ]
f = [0.0 , 0.693 , 1.099]
xa = 1.5
result1 = lagrange(x,f,xa)
println("Lagrange 內插法理則 ")
println("x= " , x)
println("f(x)= " , f)
s = @sprintf("Pn(x)=%0.5f" , result1 )
println(s)
s = @sprintf("f(xa)=%0.5f" , log(xa) )
println(s)
s = @sprintf("誤差 =%0.5f" , abs(log(xa)-result1) )
println(s)
輸出畫面
Lagrange 內插法理則
x= [1.0, 2.0, 3.0]
f(x)= [0.0, 0.693, 1.099]
Pn(x)=0.38237
f(xa)=0.40547
誤差 =0.02309
沒有留言:
張貼留言