把这段代码从python转换为matlab:

Python代码：

def fibonacci(n):
    if n <= 1:
        return n
    else:
        return (fibonacci(n-1) + fibonacci(n-2))
    
for i in range(10):
    print(fibonacci(i))

Matlab代码：

function f = fibonacci(n)
    if n <= 1
        f = n;
    else
        f = fibonacci(n-1) + fibonacci(n-2);
    end
end

for i = 0:9
    disp(fibonacci(i));
end

相关问题

把这段代码从python转换为matlab：“def grey_relation(X, Y, rho=0.5): X_norm = normalize(X) Y_norm = normalize(Y) n = X.shape[0] X_matrix = np.zeros((n, n)) for i in range(n): for j in range(n): X_matrix[i][j]=(np.min([X_norm[i],Y_norm[j]])+rho*np.max([X_norm[i], Y_norm[j]])) / (1+rho) X_avg = np.mean(X_matrix, axis=1) Y_avg = np.mean(X_matrix, axis=0) r_XY = np.mean(X_avg) return r_XY # 示例 A = np.array([1, 2, 3, 4, 5]) B = np.array([2, 4, 6, 8, 10]) r_AB = grey_relation(A, B) print(r_AB)”

function [r_XY] = grey_relation(X, Y, rho)
if nargin < 3
rho = 0.5;
end
X_norm = normalize(X);
Y_norm = normalize(Y);
n = size(X,1);
X_matrix = zeros(n);
for i = 1:n
for j = 1:n
X_matrix(i,j) = (min([X_norm(i),Y_norm(j)]) + rho*max([X_norm(i), Y_norm(j)])) / (1+rho);
end
end
X_avg = mean(X_matrix,2);
Y_avg = mean(X_matrix);
r_XY = mean(X_avg);
end

% 示例
A = [1, 2, 3, 4, 5];
B = [2, 4, 6, 8, 10];
r_AB = grey_relation(A, B);
fprintf('r_AB = %f\n', r_AB);

把这段代码从MATLAB转换成python:for i = 1:s I_total_filter(:,:,i)=medfilt2(I(:,:,i),[3,3]); end

我可以理解，您想将这段MATLAB代码转换为python。转换后的代码如下：

for i in range(s):
I_total_filter[:, :, i] = cv2.medianBlur(I[:, :, i], 3)

其中，cv2.medianBlur()是python中的函数，用于中值滤波。