g=gama; %给γh赋值 %%%----------------------------------- i = 1; M = zeros(1, 991); while(i <= 991) % 逐一读取Z数据 z = Z_data2(1, i); % 给Z(f)赋值 F0 = z * exp(-2 * gama * x); % 被积函数F(f, x) h = int(F0, x, 1e6, 100e6); % 对f积分的F(x) result = vpa(h, 6); M(i) = [result]; % 结果写入M i = i + 1; end改正
时间: 2023-10-06 10:09:15 浏览: 93
根据你提供的代码,我发现有两个需要改正的地方:
1. 第一行的注释中给γh赋值,但实际上是给γ赋值,应该将注释改为“给γ赋值”。
2. 最后一行的M(i) = [result]应该改为M(i) = result,因为result已经是一个数值,不需要用方括号括起来。
改正后的代码如下:
```
gama = g; % 给γ赋值
%%%-----------------------------------
i = 1;
M = zeros(1, 991);
while(i <= 991) % 逐一读取Z数据
z = Z_data2(1, i); % 给Z(f)赋值
F0 = z * exp(-2 * gama * x); % 被积函数F(f, x)
h = int(F0, x, 1e6, 100e6); % 对f积分的F(x)
result = vpa(h, 6);
M(i) = result; % 结果写入M
i = i + 1;
end
```
相关问题
self.b = sigmoid(x @ self.v - self.gama)
As an AI language model, I don't have enough context to understand the full meaning of this code. However, I can provide some explanation of what the code might be doing.
It looks like this is a line of code in a Python class method. The "self" keyword refers to an instance of the class. The code appears to be calculating the output of a neural network node using the sigmoid function.
The "@" symbol is the matrix multiplication operator in Python. It multiplies the matrix "x" by the matrix "self.v". The result is then subtracted by the value of "self.gama" and passed through the sigmoid function.
Overall, this line of code is likely a part of a larger neural network implementation for performing some kind of classification or prediction task.
g=gama; %%%----------------------------------- i = 1; M = zeros(1, 991); while(i <= 991) % 逐一读取Z数据 z = Z_data2(1, i); % 给Z(f)赋值 F0 = z * exp(-2 * gama * x); % 被积函数F(f, x) h = int(F0, x, 1e6, 100e6); % 对f积分的F(x) result = vpa(h, 6); M(i) = result; % 结果写入M( i) = i + 1; end解决result跟M(i)元素数目不同的问题
问题出在最后一行代码,应该将 i = i + 1 放在 M(i) = result; 的前面,这样才能保证 M(i) 和 result 数组拥有相同数量的元素。修改后的代码如下:
```
g = gama;
i = 1;
M = zeros(1, 991);
while(i <= 991)
% 逐一读取Z数据
z = Z_data2(1, i);
% 给Z(f)赋值
F0 = z * exp(-2 * gama * x);
% 被积函数F(f, x)
h = int(F0, x, 1e6, 100e6);
% 对f积分的F(x)
result = vpa(h, 6);
% 结果写入M
M(i) = result;
i = i + 1;
end
```