Generate the digital AWGN signal n[k] (sampled n(t)) by generating zero mean
Gaussian random variables independently (separately) for each k MATLAB function random.
Ex4-22 單射函數問題
« 問題描述：
設函數f將點集S = {0,1, , n -1}映射為f (S) = { f (i) | iÎ S} Í S 。單射函數問題要
從S中選取最大子集X Í S 使f (X )是單射函數。
例如，當n=7， f (S) = {1,0,0,2,2,3,6} Í S 時， X = {0,1,6} Í S 是所求的最大子集。
« 編程任務：
對于給定的點集S = {0,1, , n -1}上函數f，試用抽象數據類型隊列，設計一個O(n)時
間算法，計算f的最大單射子集。
« 數據輸入：
由文件input.txt 提供輸入數據。文件的第1 行有1 個正整數n，表示給定的點集
S = {0,1, , n -1}。第2 行是f (i)的值，0 £ i < n。
« 結果輸出:
程序運行結束時，將計算出的f的最大單射子集的大小輸出到output.txt中。
輸入文件示例 輸出文件示例
input.txt
7
1 0 0 2 2 3 6
output.txt
3
％ EM algorithm for k multidimensional Gaussian mixture estimation
％
％ Inputs:
％ X(n,d) - input data, n=number of observations, d=dimension of variable
％ k - maximum number of Gaussian components allowed
％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none)
％ maxiter - maximum number of iteration allowed ([] for none)
％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none)
％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none)
％
％ Ouputs:
％ W(1,k) - estimated weights of GM
％ M(d,k) - estimated mean vectors of GM
％ V(d,d,k) - estimated covariance matrices of GM
％ L - log likelihood of estimates
％
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
求標準偏差
> function c=myfunction(x)
> [m,n]=size(x)
> t=0
> for i=1:numel(x)
> t=t+x(i)*x(i)
> end
> c=sqrt(t/(m*n-1))
function c=myfunction(x)
[m,n]=size(x)
t=0
for i=1:m
for j=1:n
t=t+x(i,j)*x(i,j)
end
end
c=sqrt(t/(m*n-1
