The code is
syms x;
v = #(x) (4 - x^2)^(1/2) - (2 - x)^(1/2)*(x + 2)^(1/2);
ezplot(v(x),[-2,2]);
which produce a plot
Should not v(x)
be zero for every x in [-2,2]?
If the idea is to replace "small" values in the output of your equation with zero, then some sort of if condition on the output data is needed. Unfortunately, you can't add an if to the anonymous function but you can do one of two things: define a standard function (in a program file) or wrap your anonymous function in another. The first way is more straightforward:
function [y] = v_func(x)
y = (4 - x.^2).^(1/2) - (2 - x).^(1/2).*(x + 2).^(1/2);
y(abs(y)<1e-10)=0;
end
The above performs the same calculation as before with second line of code replacing all values that are less than some tolerance (1e-10) with zero. Note how the equation is slightly different than your original one. It has been "vectorized" (see the use of the periods) to allow for an input vector to be evaluated rather than having to loop over each element in the vector. Note also that when we pass it to ezplot we must prefix the function name with the ampersand:
ezplot(#v_func,[-2,2]);
The second way requires wrapping your first anonymous function v in a couple of others (see anonymous functions and conditions). We start with your original function that has been vectorized:
v = #(x) (4 - x.^2).^(1/2) - (2 - x).^(1/2).*(x + 2).^(1/2);
and a (condition) function that replaces all values less than some tolerance with zero:
cond = #(y)(y*(1-(abs(y)<1e-10)))
We then wrap the two together as
func = #(x)cond(v(x))
so that the condition function evaluates the output of v. Putting it together with ezplot gives:
ezplot(func,[-2,2]);
and all output, as shown in the plot, should be zero.
The figure title will not be what you want, so it can be replaced with some variation of:
plotTitle = func2str(v);
title(strrep(plotTitle(5:end),'.','')); % as I don't want '#(x)' or periods to
% appear in the title
I've ignored the use of sym as I don't have the Symbolic Toolbox.
Related
So i'm a little confounded by how to structure my problem.
So the assignment states as following:
Type a m-file numerical_derivative.m that performs numerical derivation. Use
it to calculate f'(-3) when f(x) = 3x^2 /(ln(1-x))
In the m-file you to use h = 10^-6 and have the following mainfunction:
function y = numericalderivative (f, x)
% Calculates the numerical value in the case of f in punk x.
% --- Input ---
% f: function handle f(x)
% x: the point where the derivative is calculated
% --- output ---
% y: the numerical derivative of f on the point x
If I want to save it as a file and run the program in matlab, does't it make it redundant to use handles then?
I won't give you the answer to your homework, but perhaps a simpler example would help.
Consider the following problem
Write a function named fdiff which takes the following two arguments:
A function f represented by a function handle which takes one argument,
and a point x which can be assumed to be in the domain of the f.
Write fdiff so that it returns the value f(x) - f(x-1)
Solution (would be in the file named fdiff.m)
function result = fdiff(f, x)
result = f(x) - f(x-1);
end
Example Use Cases
>> my_function1 = #(x) 3*x^2 /(log(1-x));
>> fdiff(my_function1, -3)
ans =
-10.3477
>> my_function2 = #(x) x^2;
>> fdiff(my_function2, 5)
ans =
9
What you've created with fdiff is a function which takes another function as an input. As you can see it doesn't just work for 3*x^2 /(log(1-x)) but any function you want to define.
The purpose of your assignment is to create something very similar, except instead of computing f(x) - f(x-1), you are asked write a function which approximates f'(x). Your use-case will be nearly identical to the first example except instead of fdiff your function will be named numericalderivative.
Note
In case it's not clear, the second example defines the my_function2 as x^2. The value returned by fdiff(my_function2, 5) is therefore 5^2 - 4^2 = 9.
When you make this as a function file and run this in MATLAB without any input arguments i.e., 'f' and 'x', it will give you the error: 'not enough input arguments'. In order to run the file you have to type something like numericalderivative (3x^2 /(ln(1-x)), 5), which gives the value of the numerical derivative at x = 5.
Functions and, in MATLAB function files are a simple implementation of the DRY programming method. You're being asked to create a function that takes a handle and an x file, then return the derivative of that function handle and that x value. The point of the function file is to be able to re-use your function with either multiple function handles or multiple x values. This is useful as it simply involves passing a function handle and a numeric value to a function.
In your case your script file or command window code would look something like:
func = #(x) (3*x^2)/log(1-x);
x = -3;
num_deriv = numericalderivative(func,x);
You should write the code to make the function numericalderivative work.
I have found the following Matlab code to simulate a Non-homogeneous Poisson Process
function x = nonhomopp(intens,T)
% example of generating a
% nonhomogeneousl poisson process on [0,T] with intensity function intens
x = 0:.1:T;
m = eval([intens 'x']);
m2 = max(m); % generate homogeneouos poisson process
u = rand(1,ceil(1.5*T*m2));
y = cumsum(-(1/m2)*log(u)); %points of homogeneous pp
y = y(y<T); n=length(y); % select those points less than T
m = eval([intens 'y']); % evaluates intensity function
y = y(rand(1,n)<m/m2); % filter out some points
hist(y,10)
% then run
% t = 7 + nonhomopp('100-10*',5)
I am new to Matlab and having trouble understanding how this works. I have read the Mathworks pages on all of these functions and am confused in four places:
1) Why is the function defined as x and then the intervals also called x? Like is this an abuse of notation?
2) How does the square brackets affect eval,
eval([intens 'x'])
and why is x in single quotations?
3) Why do they use cumsum instead of sum?
4) The given intensity function is \lambda (t) = 100 - 10*(t-7) with 7 \leq t \leq 12 How does t = 7 + nonhomopp('100-10*',5) represent this?
Sorry if this is so much, thank you!
To answer 2). That's a unnecessary complicated piece of code. To understand it, evaluate only the squared brackets and it's content. It results in the string 100-10*x which is then evaluated. Here is a version without eval, using an anonymous function instead. This is how it should have been implemented.
function x = nonhomopp(intens,T)
% example of generating a
% nonhomogeneousl poisson process on [0,T] with intensity function intens
x = 0:.1:T;
m = intens(x);
m2 = max(m); % generate homogeneouos poisson process
u = rand(1,ceil(1.5*T*m2));
y = cumsum(-(1/m2)*log(u)); %points of homogeneous pp
y = y(y<T); n=length(y); % select those points less than T
m = intens(y); % evaluates intensity function
y = y(rand(1,n)<m/m2); % filter out some points
hist(y,10)
Which can be called like this
t = 7 + honhomopp(#(x)(100-10*x),5)
the function is not defined as x: x is just the output variable. In Matlab functions are declared as function [output variable(s)] = <function name>(input variables). If the function has only one output, the square brackets can be omitted (like in your case). The brackets around the input arguments are, as instead, mandatory, no matter how many input arguments there are. It is also good practice to end the body of a function with end, just like you do with loops and if/else.
eval works with a string as input and the square brackets apprently are concatenating the string 'intens' with the string 'x'. x is in quotes because, again, eval works with input in string format even if it's referring to variables.
cumsum and sum act differently. sum returns a scalar that is the sum of all the elements of the array whereas cumsum returns another array which contains the cumulative sum. If our array is [1:5], sum([1:5]) will return 15 because it's 1+2+3+4+5. As instead cumsum([1:5]) will return [1 3 6 10 15], where every element of the output array is the sum of the previous elements (itself included) from the input array.
what the command t = 7 + nonhomopp('100-10*',5) returns is simply the value of time t and not the value of lambda, indeed by looking at t the minimum value is 7 and the maximum value is 12. The Poisson distribution itself is returned via the histogram.
I have a problem with symbolic functions. I am creating function of my own whose first argument is a string. Then I am converting that string to symbolic function:
f = syms(func)
Lets say my string is sin(x). So now I want to calculate it using subs.
a = subs(f, 1)
The result is sin(1) instead of number.
For 0 it works and calculates correctly. What should I do to get the actual result, not only sin(1) or sin(2), etc.?
You can use also use eval() to evaluate the function that you get by subs() function
f=sin(x);
a=eval(subs(f,1));
disp(a);
a =
0.8415
syms x
f = sin(x) ;
then if you want to assign a value to x , e.g. pi/2 you can do the following:
subs(f,x,pi/2)
ans =
1
You can evaluate functions efficiently by using matlabFunction.
syms s t
x =[ 2 - 5*t - 2*s, 9*s + 12*t - 5, 7*s + 2*t - 1];
x=matlabFunction(x);
then you can type x in the command window and make sure that the following appears:
x
x =
#(s,t)[s.*-2.0-t.*5.0+2.0,s.*9.0+t.*1.2e1-5.0,s.*7.0+t.*2.0-1.0]
you can see that your function is now defined by s and t. You can call this function by writing x(1,2) where s=1 and t=1. It should generate a value for you.
Here are some things to consider: I don't know which is more accurate between this method and subs. The precision of different methods can vary. I don't know which would run faster if you were trying to generate enormous matrices. If you are not doing serious research or coding for speed then these things probably do not matter.
I want to minimize this function:
function [GCV2]=GCV(y,x,k)
[n, p]=size(x);
A=(x'*x+k*eye(p));
A=A\x';
A=x*A;
I_mat=eye(n);
num2=(I_mat-A);
num2=num2*y;
num2=norm(num2);
num2=num2^2;
num2=num2/n;
%(norm((I_mat-A)*y)^2)/n;
den2=(I_mat-A);
den2=trace(den2);
den2=den2/n;
den2=den2^2;
GCV2=num2/den2;
end
The x and y values are 13-by-4) and 13-by-1 arrays, respectively, and these values are already defined in the Matlab workspace. I want to optimize on the k value so that the function value GCV is minimized.
The parameter being optimized as well as the output are scalar so fminsearch should be appropriate.
But I can't get it to run?
I've tried several methods, the latest being:
k_min = fminsearch(#GCV,(x;y;0));
??? k_min = fminsearch(#GCV,(x;y;0));
|
Error: Unbalanced or unexpected parenthesis or bracket.
What am I doing wrong?
Looks like you're learning about anonymous functions. fminsearch minimizes a single variable (which may be a vector). Your objective function must therefore have only one input. You have a function, GCV, that takes three inputs. Two are static and are defined in the workspace outside of the minimization, while k is the one to be minimized. To create a function with one input from GCV, you can use any anonymous function, taking care to specify which variables are parameters:
x = ...
y = ...
k0 = 0;
k_min = fminsearch(#(k)GCV(y,x,k),k0);
The second input to fminsearch is the starting parameter (i.e. k0), so specify a starting value of k. Then you can define an anonymous helper function and optimize on that:
>> % define x,y
>> GCVk = #(k) GCV(y,x,k);
>> k0 = 0;
>> k_min = fminsearch(GCVk,k0)
There is probably another way to do this, but this is one of the listed ways of passing additional parameters for the optimizer.
And since there are no bonus points for being first, how about humor. Let's have an example:
>> x=1; y=1;
>> GCVk = #(k) x+y+k; k0=0;
>> k_min = fminsearch(GCVk,k0)
Exiting: Maximum number of function evaluations has been exceeded
- increase MaxFunEvals option.
Current function value: -316912650057057490000000000.000000
k_min =
-3.1691e+26
Found it - the lowest number (minus 2) in the world! Profit?
I am new to Matlab. I was reading this code snippet, but in some parts (marked with asterisks) I don't understand what it means, so if anybody could help would be very much appreciated
function [A1nmb] = moran(initsize, popsize)
% MORAN generates a trajectory of a Moran type process
% which gives the number of genes of allelic type A1 in a population
% of haploid individuals that can exist in either type A1 or type A2.
% The population size is popsize and the initial number of type A1
% individuals os initsize.
% Inputs: initsize - initial number of A1 genes
% popsize - the total population size (preserved)
if (nargin==0)
initsize=10;
popsize=30;
end
A1nmb=zeros(1,popsize);
A1nmb(1)=initsize;
**lambda = inline('(x-1).*(1-(x-1)./N)', 'x', 'N');
mu = inline('(x-1).*(1-(x-1)./N)', 'x', 'N');**
x=initsize;
i=1;
while (x>1 & x<popsize+1)
if (lambda(x,popsize)/(lambda(x,popsize)+mu(x,popsize))>rand)
x=x+1;
A1nmb(i)=x;
else
x=x-1;
A1nmb(i)=x;
end;
i=i+1;
end;
nmbsteps=length(A1nmb);
***rate = lambda(A1nmb(1:nmbsteps-1),popsize) ...
+mu(A1nmb(1:nmbsteps-1),popsize);***
**jumptimes=cumsum(-log(rand(1,nmbsteps-1))./rate);**
jumptimes=[0 jumptimes];
stairs(jumptimes,A1nmb);
axis([0 jumptimes(nmbsteps) 0 popsize+1]);
The first line you marked
lambda = inline('(x-1).*(1-(x-1)./N)', 'x', 'N');
creates something called an inline function. It is equivalent to defining a mathematical function. Example:
y = inline('x^2')
would allow you to do
>> y(2)
4
This immediately explains the second line you marked.
rate = lambda(A1nmb(1:nmbsteps-1),popsize) ...
+mu(A1nmb(1:nmbsteps-1),popsize);
will compute the value of the function lambda(x,N) at x = A1nmb(1:nmbsteps-1) and N = popsize.
I will say immediately here that you should take a look at anonymous functions, a different format used to accomplish the same as inline. Only, anonymous functions are generally better supported, and usually a lot faster than inline functions.
Then, for the final line,
jumptimes = cumsum(-log(rand(1,nmbsteps-1))./rate);
is a nested command. rand will create a matrix containing pseudorandom numbers, log is the natural logarithm ("ln"), and cumsum creates a new matrix, where all the elements in the new matrix are the cumulative sum of the elements in the input matrix.
You will find the commands doc and help very useful. Try typing
doc cumsum
or
help inline
on the Matlab command prompt. Try that again with the commands forming the previous statement.
As a general word of advice: spend an insane lot of time reading through the documentation. Really, for each new command you encounter, read about it and play with it in a sandbox until you feel you understand it. Matlab only becomes powerful if you know all its commands, and there are a lot to get to know.
It defines an inline function object. For example this
lambda = inline('(x-1).*(1-(x-1)./N)', 'x', 'N')
defines lambda as a function with 2 variables. When you call lambda(A,n) Matlab simply expands the function you define in the first string. Thus lambda(A,n) using the variables you provide in the function call. lambda(A,n) would will evaluate to:
(A-1).*(1-(A-1)./n)
it just expands the function using the parameters you supply. Take a look at this link for more specific details http://www.mathworks.co.uk/help/techdoc/ref/inline.html
The cumsum function just returns the cumulative sum of a matrix along a particular dimension. Say we call cumsum on a vector X, then the value at element i in the result is equal to the sum of elements in X from index 1 to i. For example X = [1 2 1 3] we would get
AA = cumsum(X);
we would have
AA = [1 3 5 8]
See this link for more details and examples http://www.mathworks.co.uk/help/techdoc/ref/cumsum.html