雅马哈nmax155新款什么时候上市
时间: 2023-09-27 13:06:21 浏览: 49
关于雅马哈NMAX 155新款的上市时间,目前还没有官方的宣布。根据一些媒体的报道和分析,新款NMAX 155可能会在2022年初或中期上市。不过具体时间还需要等待雅马哈官方的消息。新款NMAX 155预计会有一些升级和改进,例如更具有运动感的外观设计、更强大的引擎性能和更多的高科技配置。我们可以拭目以待,等待官方的消息和发布。
相关问题
for n = nmin:2:nmax
The syntax "for n = nmin:2:nmax" is a loop in MATLAB that iterates over the values of "n" starting from "nmin" and incrementing by 2 until it reaches or exceeds "nmax".
During each iteration of the loop, the value of "n" is updated and the code within the loop is executed. This loop is typically used when you want to perform the same set of operations on a range of values of "n".
Here's an example of how you can use this loop to print the values of "n" from 1 to 10, incrementing by 2:
```
nmin = 1;
nmax = 10;
for n = nmin:2:nmax
disp(n);
end
```
This will output:
```
1
3
5
7
9
```
I hope this helps! Let me know if you have any further questions.
function genWM(Ds,lambda,nmax)
This function generates the weight matrix for a given dataset using the Gaussian radial basis function (RBF) kernel.
Inputs:
- Ds: Dataset matrix of size (m x n), where m is the number of examples and n is the number of features.
- lambda: The width parameter of the RBF kernel.
- nmax: The maximum number of neurons to be used in the RBF network.
Output:
- WM: Weight matrix of size (m x nmax), where each row represents the weights for the corresponding example.
Here is the implementation of the function:
```
function WM = genWM(Ds,lambda,nmax)
% Calculate the pairwise distances between examples
D = pdist2(Ds,Ds);
% Sort the distances and select the nmax-1 nearest neighbors for each example
[~,idx] = sort(D);
idx = idx(2:nmax,:);
% Calculate the activation values for each neuron
phi = exp(-lambda*D(idx,:).^2);
% Normalize the activation values
phi = phi./sum(phi,1);
% Generate the weight matrix
WM = phi';
end
```
The function first calculates the pairwise distances between the examples in the dataset using the `pdist2` function. It then sorts the distances for each example in ascending order and selects the nmax-1 nearest neighbors for each example.
Next, it calculates the activation values for each neuron using the RBF kernel with the specified width parameter. The activation values are normalized by dividing by the sum of activation values for each example.
Finally, the function generates the weight matrix by transposing the normalized activation values. The weight matrix contains the weights for each example and each neuron.