I coded a function picircle() that estimates pi.
Now I would like to plot this function for N values.
function Plotpi()
p = 100 # precision of π
N = 5
for i in 1:N
picircle(p)
end
end
3.2238805970149254
3.044776119402985
3.1641791044776117
3.1243781094527363
3.084577114427861
Now I am not sure how to plot the function, I tried plot(PP()) but it didn't work
Here I defined picircle:
function picircle(n)
n = n
L = 2n+1
x = range(-1, 1, length=L)
y = rand(L)
center = (0,0)
radius = 1
n_in_circle = 0
for i in 1:L
if norm((x[i], y[i]) .- center) < radius
n_in_circle += 1
end
end
println(4 * n_in_circle / L)
end
Your problem is that your functions don't actually return anything:
julia> x = Plotpi()
3.263681592039801
3.0646766169154227
2.845771144278607
3.18407960199005
3.044776119402985
julia> x
julia> typeof(x)
Nothing
The numbers you see are just printed to the REPL, and print doesn't return any value:
julia> x = print(5)
5
julia> typeof(x)
Nothing
So you probably just want to change your function so that it returns what you want to plot:
julia> function picircle(n)
n = n
L = 2n+1
x = range(-1, 1, length=L)
y = rand(L)
center = (0,0)
radius = 1
n_in_circle = 0
for i in 1:L
if norm((x[i], y[i]) .- center) < radius
n_in_circle += 1
end
end
4 * n_in_circle / L
end
Then:
julia> x = picircle(100)
3.263681592039801
julia> x
3.263681592039801
So now the value of the function is actually returned (rather than just printed to the console). You don't really need a separate function if you just want to do this multiple times and plot the results, a comprehension will do. Here's an example comparing the variability of the estimate with 100 draws vs 50 draws:
julia> using Plots
julia> histogram([picircle(100) for _ ∈ 1:1_000], label = "100 draws", alpha = 0.5)
julia> histogram!([picircle(20) for _ ∈ 1:1_000], label = "20 draws", alpha = 0.5)
Given two vectors of candidates:
x = [1 3 5];
y = [1 2 3 4];
I want to find which candidates satisfy an equation or formula. This is what I want to do:
f = x + y - 6;
solve f;
And then, it spits out the solutions:
5 1
3 3
If it matters, I am actually using Octave, not MatLab because I don't have a Windows machine. I know that I can do this with a for loop:
for i=x
for j=y
if i+j-6==0
disp([i j]);
end
end
This is a trivial example. I am looking for a solution that will handle much larger examples.
Solving such equations per "brute force" is generally a bad idea but here you go:
x = [1 3 5];
y = [1 2 3 4];
## build grid (also works for n vars)
[xx, yy] = ndgrid (x, y);
## anonymous function
f = #(x,y) abs(x + y - 6) < 16*eps
## true?
t = f (xx, yy);
## build result
[xx(t) yy(t)]
I have to generate a dataset of N data points, which are defined as t_n=f(x_n)+e, where e is drawn from normal distribution and f(x) is a nonlinear function.
For example, i have a function f(x)=x²+2x+10, how can i fill a vector v, such:
x = 1:1:100;
v = create(f(x)+normrnd(0,1),x);
Thank you
There are many methods to do this. Here I show you how to do it with anonymous functions http://www.gnu.org/software/octave/doc/v4.0.1/Anonymous-Functions.html#Anonymous-Functions
f = #(x) polyval ([1 2 10], x)
x = 1:100;
v = f(x) + normrnd (0, 1, size (x));
Or without a function:
x = 1:100;
v = x.^2 + 2.*x + 10 + normrnd (0, 1, size (x));
I've adjusted x here so that the noise is visible:
x = linspace (-3, 3);
v = f(x) + normrnd (0, 1, size (x));
plot (x, v)
grid on
In Octave, I can do
octave:1> A = [1 2; 3 4]
A =
1 2
3 4
octave:2> A(A>1) -= 1
A =
1 1
2 3
but in Julia, the equivalent syntax does not work.
julia> A = [1 2; 3 4]
2x2 Array{Int64,2}:
1 2
3 4
julia> A[A>1] -= 1
ERROR: `isless` has no method matching isless(::Int64, ::Array{Int64,2})
in > at operators.jl:33
How do you conditionally assign values to certain array or matrix elements in Julia?
Your problem isn't with the assignment, per se, it's that A > 1 itself doesn't work. You can use the elementwise A .> 1 instead:
julia> A = [1 2; 3 4];
julia> A .> 1
2×2 BitArray{2}:
false true
true true
julia> A[A .> 1] .-= 1000;
julia> A
2×2 Array{Int64,2}:
1 -998
-997 -996
Update:
Note that in modern Julia (>= 0.7), we need to use . to say that we want to broadcast the action (here, subtracting by the scalar 1000) to match the size of the filtered target on the left. (At the time this question was originally asked, we needed the dot in A .> 1 but not in .-=.)
In Julia v1.0 you can use the replace! function instead of logical indexing, with considerable speedups:
julia> B = rand(0:20, 8, 2);
julia> #btime (A[A .> 10] .= 10) setup=(A=copy($B))
595.784 ns (11 allocations: 4.61 KiB)
julia> #btime replace!(x -> x>10 ? 10 : x, A) setup=(A=copy($B))
13.530 ns ns (0 allocations: 0 bytes)
For larger matrices, the difference hovers around 10x speedup.
The reason for the speedup is that the logical indexing solution relies on creating an intermediate array, while replace! avoids this.
A slightly terser way of writing it is
replace!(x -> min(x, 10), A)
There doesn't seem to be any speedup using min, though.
And here's another solution that is almost as fast:
A .= min.(A, 10)
and that also avoids allocations.
To make it work in Julia 1.0 one need to change = to .=. In other words:
julia> a = [1 2 3 4]
julia> a[a .> 1] .= 1
julia> a
1×4 Array{Int64,2}:
1 1 1 1
Otherwise you will get something like
ERROR: MethodError: no method matching setindex_shape_check(::Int64, ::Int64)
Assuming a 3 dimensional irregular matrix where y = 1.5(x) and z = .5(y).
Further assuming an object starts at 0,0,0 and must move positively in at least two dimensions, and must move in all three dimensions (x+1, y+1, z-1 is okay, x+1, y+1, z=z is not). It may move any number of "spaces", but must move the same number in all directions.
The object is allowed to wraparound (x(max +1) = x(0)).
Move said object from its starting position to (0, max(y), .5(max(z))) For z, round up for fractions (end point in 4, 6, 3 matrix becomes 0, 6, 2)
Input is an Integer (X).
Output is the list of moves you would make (extra credit for showing the number of spaces you moved)
Sample Input/Output:
X = 4
Y = 6 //(4 * 1.5)
Z = 3 // 6 / 2
0, 0, 0 //Start
2, 5, 2 // (+2, -2, +2)
1, 2, 2 // (+4, +4, +4)
3, 4, 0 // (+2, +2, -2)
1, 6, 2 // (-2, +2, +2)
3, 3, 3 // (-3, -3, -3)
1, 5, 1 // (-2, +2, -2)
0, 6, 2 // (-1, +1, -1)
7 Moves.
Lua, 68 Characters
The long version below always solves the problem with one move by searching for the first all positive move that will solve problem.
x=...
y,z=x*3/2,x*3/4
a,b,c=0,y,math.ceil(z/2)
x,y,z=x+1,y+1,z+1
for i=1,math.huge do
if (x*i)%y==b and (x*i)%z==c then x=x*i break end
end
print("0,0,0\n0,"..b..","..c.."//+"..x..",+"..x..",+"..x.."\n1 move.")
Output for x = 12:
0,0,0
0,18,5//+455,+455,+455
1 move.
Output for x = 1000:
0,0,0
0,1500,375//+557424868,+557424868,+557424868
1 move.
Seems like the search could be replaced with some simple algebraic equation. But why stop there? Rules are easier to bend in golfing then doing the actual work.
So, assuming that there is always a single 1 move answer, and that I do not have to disclose the "number of spaces you moved", here is the 68 character golfed answer:
x=...print("0,0,0\n0,"..(x*3/2)..","..math.ceil(x*3/8).."\n1 move.")
Mathematica - Not Golfed
Just to see if we can get the ball rolling
... and trying to understand the problem ....
f[x_] := (
(* Init code *)
xmax = x;
ymax = 3 Round[xmax]/2;
zmax = Round[ymax]/2;
xobj = 0;
yobj = ymax;
zobj = Ceiling[zmax/2];
p = Join[Permutations[{1, 1, -1}], {{1, 1, 1}}];
Print["X = ", xmax, "\nY = ", ymax, "\nZ = ", zmax];
(* Loop *)
i = 0;
pos = {0, 0, 0};
k = "Start";
While[
(npos= {Mod[pos[[1]], xmax+1], Mod[pos[[2]], ymax+1], Mod[pos[[3]], zmax+1]})
!= {xobj, yobj, zobj},
i++;
Print[npos, " // ", k];
pos= npos+ (k= RandomInteger[{1,xmax}] p[[RandomInteger[{1, Length[p]}]]]);
];
Print[npos, " // ", k];
Print[i, " Moves"];
);
Invoke with
f[4]
Sample Output
X = 4
Y = 6
Z = 3
{0,0,0} // Start
{3,4,3} // {3,-3,3}
{0,0,2} // {-3,3,3}
{2,3,1} // {-3,3,3}
{0,6,2} // {3,3,-3}
4 Moves
Not sure if I'm following the rules ...