2019-02-15 Octave控制语句及函数

>> v = zeros(10,1)
v =

   0
   0
   0
   0
   0
   0
   0
   0
   0
   0

>> for i=1:10,
>     v(i) = 2^i;
> end;

>> v
v =

      2
      4
      8
     16
     32
     64
    128
    256
    512
   1024

 

>> indices = 1:10
indices =

    1    2    3    4    5    6    7    8    9   10


>> for i=indices,
>     disp(i);
> end;
 1
 2
 3
 4
 5
 6
 7
 8
 9
 10

 

>> i = 1;
>> while i<=5,
>     v(i) = 100;
>     i++;
> end;

 

>> i=1;
>> while true,
>     v(i) = 999;
>     i++;
>     if i==6,
>         break;
>     end;
> end;

 

>> if v(1)==1,
>     disp('The value is one');
> elseif v(1)==2,
>     disp('The value is two');
> else
>     disp('The value is not one or two');
> end;

 

函数，需要先建一个.m文件：

function y = squareThisNumber(x)     %文件名和函数名一样
y = x^2;

>> squareThisNumber(5)     %调用函数
ans =  25

 

将代码文件所在路径添加到Octave系统搜索路径中：
这样即使你在别的路径下，也可以调用代码文件所在路径下的文件函数了。

>> addpath('C:\Users\mei\Workspaces\Octave_codes')

 

多返回值函数：

function [y1,y2] = squareAndCubeThisNumber(x)
y1 = x^2;
y2 = x^3;

>> [y1,y2] = squareAndCubeThisNumber(2)    %调用函数
y1 =  4
y2 =  8

 

 

应用举例：简单的训练数据集：

• 

>> X = [1 1; 1 2; 1 3]
X =
   1   1
   1   2
   1   3

>> y = [1; 2; 3]
y =
   1
   2
   3

编写代价函数 costFunctionJ.m：

function J = costFunctionJ(X, y, Theta)
% X is the "design matrix" containing our training examples.
% y is the class labels
% Theta is the Hypothesis's parameters is a vector.

m = size(X, 1)           % number of training examples
predictions = X*Theta;   % predictions of hypothesis on all m examples
sqrErrors = (predictions-y).^2;    % square errors
J = 1/(2*m) * sum(sqrErrors); 

假设我们已训练好了参数：
参数为0,1时：

>> Theta = [0; 1]    % h = 0 + 1*x
Theta =
   0
   1

>> j = costFunctionJ(X,y,Theta)
m =  3
j = 0     

%代价为0，说明该Theta参数用在假设函数上，假设函数和训练数据集拟合的很好

我们将参数设置为0,0，验证一下我们写的代价函数的正确性：

>> Theta = [0; 0]    % h = 0 + 0*x
Theta =
   0
   0

>> j = costFunctionJ(X,y,Theta)
m =  3
j =  2.3333      %参数都为0时误差这么大

>> sum((0-y).^2)/(2*3)    %假设函数为0时，代价就是这么算的
ans =  2.3333             %验证正确