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中M(i)与result元素数目不一样怎么修改
时间: 2023-10-21 21:07:20 浏览: 20
在这段代码中,`M(i)`定义为1x991的矩阵,而`result`是一个标量。因此,在将`result`的值赋给`M(i)`之前,需要将其转换为一个1x1的矩阵,以匹配`M(i)`的形状,可以使用方括号将`result`括起来,如下所示:
```
M(i) = [result];
```
这样就可以将`result`存储在`M(i)`中,而不会改变`M(i)`的形状。
相关问题
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
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
```
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.