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with the rec sum:
let rec sum a=if a==0 then 0 else a+sum(a-1)
if the compiler use the tail recursive optimization,it may create a variable "sum" to iteration（when I use the "ocamlc -dlambda",the recursive still there.when I use "ocamlc -dinstr" got the assemably code,I can't read it now）
but on the book《Design Concepts of programming languages》,page 287,it can change the function to this(the key line):n*(n+1)/2
"You should convince yourself that the least fixed point of this
function is the computation csum that returns a summation procedure that,returns n*(n+1)/2 if its argument is a nonnegative integer in"
I can't understand it,the prog not Gauss!I think it can't chang the "rec sum" to n*(n+1)/2 automatic!only man can do it,right?
So how this book write here means?Is anyone know?Thanks!
I believe your book is merely making a small point about equivalence of pure functions. Nevertheless, optimising away a loop that only contains affine operations is relatively easy.
Equivalence of pure functions
I haven't read that book, but from the paragraph you quote, I think the book merely makes a point about pure functions. Since sum is a pure function, i.e. a function without side-effect, then in a sense,
let rec sum n =
if n = 0 then 0
else n + sum (n - 1)
is equivalent to
let sum n =
n * (n + 1) / 2
But of course "equivalent" here ignores the time and space complexity, and unless the compiler has some sort of hardcoding for common functions to optimise, I'd be extremely surprised if it optimised sum like that.
Also note that the two above functions are only equivalent so far as they are only called on a nonnegative argument. The recursive version will loop infinitely (and provoke a stack overflow) if n is negative; the direct formula version will always return a result, although that result will be nonsensical if n is negative.
Optimising loops that only contain affine operations
Nevertheless, writing a compiler that would perform such optimisations is not complete science-fiction. At the end of this answer you will find links to two blogposts which you might be interested in. In this answer I will summarise how the method described in those blog posts can be applied to your problem.
First let's rewrite function sum as a loop in pseudo-code:
function sum(n):
s := 0
i := 1
repeat n:
s += i
i += 1
return s
This kind of rewriting is similar to what happens when sum is transformed into a tail-recursive function.
Now if you consider the vector v = [s, i, 1], then the affine operations s += i and i += 1 can be described as multiplying v by a matrix:
s += i
[[ 1, 0, 0 ], # matrix Msi
[ 1, 1, 0 ],
[ 0, 0, 1 ]]
i += 1
[[ 1, 0, 0 ], # matrix Mi1
[ 0, 1, 0 ],
[ 0, 1, 1 ]]
s += i, i += 1
[[ 1, 0, 0 ], # M = Msi * Mi1
[ 1, 1, 0 ],
[ 0, 1, 1 ]]
This affine operation is wrapped in a "repeat n" loop. So we have to multiply v by this matrix M, n times. But matrix multiplication is associative; so instead of doing n multiplications by matrix M, we can raise matrix M to its nth power, and then multiply v by the resulting matrix M**n.
As it turns out:
[[1, 0, 0], [[ 1, 0, 0],
[1, 1, 0], to the nth = [ n, 1, 0],
[0, 1, 1]] [n*(n - 1)/2, n, 1]]
which represents the affine operation:
s = s + n * i + n * (n - 1) / 2
i = i + n
Starting from s, i = 0, 1, this gives us s = n * (n+1) / 2 as expected.
More reading:
Using the Quick Raise of Matrices to a Power to Write a Very Fast Interpreter of a Simple Programming Language;
Automatic Algorithms Optimization via Fast Matrix Exponentiation.
Is it possible to make a delta operator like this in sympy? Im not really sure how to code it. Should be really eazy if there exists a method.
I don't know if SymPy exposes something that could be useful to you. If not, we can create something raw.
Note: the following approach requires a bit of knowledge in Object Oriented Programming and the way SymPy treats things. This is a 5 minutes attempt, and it is not meant to be used in production (as a matter of fact, no test has been done over this code). There are many things that may not work as expected. But, for your case, it might work :)
One possible way is to define a "gradient" class, like this:
class Grad(Expr):
def __mul__(self, other):
return other.diff(*self.args)
def _latex(self, printer):
# create a latex representation to be visualize in Jupyter Notebook
return r"\frac{\partial}{%s}" % " ".join([r"\partial %s" % latex(t) for t in self.args])
We can create a gradient of something with respect to x by writing gx = Grad(x). Once gx is multiplied with some other thing, it returns the partial derivative of that thing with respect to x.
Then you would define your symbols/functions and matrices like this:
from sympy import *
init_printing()
var("x, y")
N1, N2, N3 = [s(x, y) for s in symbols("N1:4", cls=Function)]
A = Matrix(3, 2, [Grad(x), 0, 0, Grad(y), Grad(x), Grad(y)])
B = Matrix(2, 6, [N1, 0, N2, 0, N3, 0, 0, N1, 0, N2, 0, N3])
display(A, B)
Finally, you multiply the matrices together to obtain the symbolic results:
A * B
Eventually, you might want to create a function:
def delta_operator(x, y, N1, N2, N3):
A = Matrix(3, 2, [Grad(x), 0, 0, Grad(y), Grad(x), Grad(y)])
B = Matrix(2, 6, [N1, 0, N2, 0, N3, 0, 0, N1, 0, N2, 0, N3])
return A * B
So, whenever you have to apply that operator, you just execute delta_operator(x, y, N1, N2, N3) to obtain a result similar to above.
I am trying to write a mutating function where the value passed as first argument mutates depending on the second one.
As an example, remove_when_zero_in_b below should return the values in vector a for those indexes where vector b is not 0.
"""filters 'a' when there is a zero in 'b'"""
function remove_when_zero_in_b!(a::AbstractVector, b::Vector{<:Real})
a = a[b .!= 0]
a
end
E.g.
x = [1.0, 2.0, 3.0, 4.0, 5.0]
y = [0, 1, 0, 2 , 0 ]
remove_when_zero_in_b!(x, y) # should mutate x
Then x should be:
println(x)
2-element Vector{Float64}:
2.0
4.0
However, the function above does not mutate x and remains as the initial vector with 5 elements.
What am I missing here? How would a function mutating x so I obtain the desired result look like?
a = a[b .!= 0] create a new copy of a, you can write,
function remove_when_zero_in_b!(a::AbstractVector, b::Vector{<:Real})
deleteat!(a, b .== 0)
a
end
I have a two-element vector whose elements can only be 0 or 1. For the sake of this example, suppose x = [0, 1]. Suppose also there are four objects y00, y01, y10, y11. My goal is to update the corresponding y (y01 in this example) according to the current value of x.
I am aware I can do this using a series of if statements:
if x == [0, 0]
y00 += 1
elseif x == [0, 1]
y01 += 1
elseif x == [1, 0]
y10 += 1
elseif x == [1, 1]
y11 += 1
end
However, I understand this can be done more succinctly using Julia's metaprogramming, although I'm unfamiliar with its usage and can't figure out how.
I want to be able to express something like y{x[1]}{x[2]} += 1 (which is obviously wrong); basically, be able to refer and modify the correct y according to the current value of x.
So far, I've been able to call the actual value of the correct y (but I can't summon the y object itself) with something like
eval(Symbol(string("y", x[1], x[2])))
I'm sorry if I did not use the appropriate lingo, but I hope I made myself clear.
There's a much more elegant way using StaticArrays. You can define a common type for your y values, which will behave like a matrix (which I assume the ys represent?), and defines a lot of things for you:
julia> mutable struct Thing2 <: FieldMatrix{2, 2, Float64}
y00::Float64
y01::Float64
y10::Float64
y11::Float64
end
julia> M = rand(Thing2)
2×2 Thing2 with indices SOneTo(2)×SOneTo(2):
0.695919 0.624941
0.404213 0.0317816
julia> M.y00 += 1
1.6959194941562996
julia> M[1, 2] += 1
1.6249412302897646
julia> M * [2, 3]
2-element SArray{Tuple{2},Float64,1,2} with indices SOneTo(2):
10.266662679181893
0.9037708026795666
(Side note: Julia indices begin at 1, so it might be more idiomatic to use one-based indices for y as well. Alternatively, can create array types with custom indexing, but that's more work, again.)
How about using x as linear indices into an array Y?
x = reshape(1:4, 2, 2)
Y = zeros(4);
Y[ x[1,2] ] += 1
Any time you find yourself naming variables with sequential numbers it's a HUGE RED FLAG that you should just use an array instead. No need to make it so complicated with a custom static array or linear indexing — you can just make y a plain old 2x2 array. The straight-forward transformation is:
y = zeros(2,2)
if x == [0, 0]
y[1,1] += 1
elseif x == [0, 1]
y[1,2] += 1
elseif x == [1, 0]
y[2,1] += 1
elseif x == [1, 1]
y[2,2] += 1
end
Now you can start seeing a pattern here and simplify this by using x as an index directly into y:
y[(x .+ 1)...] += 1
I'm doing two things there: I'm adding one to all the elements of x and then I'm splatting those elements into the indexing expression so they're treated as a two-dimensional lookup. From here, you could make this more Julian by just using one-based indices from the get-go and potentially making x a Tuple or CartesianIndex for improved performance.
I've gotten stuck getting my euler angles out my rotation matrix.
My conventions are:
Left-handed (x right, z back, y up)
YZX
Left handed angle rotation
My rotation matrix is built up from Euler angles like (from my code):
var xRotationMatrix = $M([
[1, 0, 0, 0],
[0, cx, -sx, 0],
[0, sx, cx, 0],
[0, 0, 0, 1]
]);
var yRotationMatrix = $M([
[ cy, 0, sy, 0],
[ 0, 1, 0, 0],
[-sy, 0, cy, 0],
[ 0, 0, 0, 1]
]);
var zRotationMatrix = $M([
[cz, -sz, 0, 0],
[sz, cz, 0, 0],
[ 0, 0, 1, 0],
[ 0, 0, 0, 1]
]);
Which results in a final rotation matrix as:
R(YZX) = | cy.cz, -cy.sz.cx + sy.sx, cy.sz.sx + sy.cx, 0|
| sz, cz.cx, -cz.sx, 0|
|-sy.cz, sy.sz.cx + cy.sx, -sy.sz.sx + cy.cx, 0|
| 0, 0, 0, 1|
I'm calculating my euler angles back from this matrix using this code:
this.anglesFromMatrix = function(m) {
var y = 0, x = 0, z = 0;
if (m.e(2, 1) > 0.999) {
y = Math.atan2(m.e(1, 3), m.e(3, 3));
z = Math.PI / 2;
x = 0;
} else if (m.e(2, 1) < -0.999) {
y = Math.atan2(m.e(1, 3), m.e(3, 3));
z = -Math.PI / 2;
x = 0;
} else {
y = Math.atan2(-m.e(3, 1), -m.e(1, 1));
x = Math.atan2(-m.e(2, 3), m.e(2, 2));
z = Math.asin(m.e(2, 1));
}
return {theta: this.deg(x), phi: this.deg(y), psi: this.deg(z)};
};
I've done the maths backwards and forwards a few times, but I can't see what's wrong. Any help would hugely appreciated.
Your matrix and euler angles aren't consistent. It looks like you should be using
y = Math.atan2(-m.e(3, 1), m.e(1, 1));
instead of
y = Math.atan2(-m.e(3, 1), -m.e(1, 1));
for the general case (the else branch).
I said "looks like" because -- what language is this? I'm assuming you have the indexing correct for this language. Are you sure about atan2? There is no single convention for atan2. In some programming languages the sine term is the first argument, in others, the cosine term is the first argument.
The last and most important branch of the anglesFromMatrix function has a small sign error but otherwise works correctly. Use
y = Math.atan2(-m.e(3, 1), m.e(1, 1))
since only m.e(3, 1) of m.e(1, 1) = cy.cz and m.e(3, 1) = -sy.cz should be inverted. I haven't checked the other branches for errors.
Beware that since sz = m.e(2, 1) has two solutions, the angles (x, y, z) used to construct the matrix m might not be the same as the angles (rx, ry, rz) returned by anglesFromMatrix(m). Instead we can test that the matrix rm constructed from (rx, ry, rz) does indeed equal m.
I worked on this problem extensively to come up with the correct angles for a given matrix. The problem in the math comes from the inability to determine a precise value for the SIN since -SIN(x) = SIN(-x) and this will affect the other values of the matrix. The solution I came up with comes up with two equally valid solutions out of eight possible solutions. I used a standard Z . Y . X matrix form but it should be adaptable to any matrix. Start by findng the three angles from: X = atan(m32,m33): Y = -asin(m31) : Z = atan(m21,m11) : Then create angles X' = -sign(X)*PI+X : Y'= sign(Y)*PI-Y : Z = -sign(Z)*pi+Z . Using these angles create eight set of angle groups : XYZ : X'YZ : XYZ' : X'YZ' : X'Y'Z' : XY'Z' : X'Y'Z : XY'Z
Use these set to create the eight corresponding matrixes. Then do a sum of the difference between the unknown matrix and each matrix. This is a sum of each element of the unknown minus the same element of the test matrix. After doing this, two of the sums will be zero and those matrixes will represent the solution angles to the original matrix. This works for all possible angle combinations including 0's. As 0's are introduced, more of the eight test matrixes become valid. At 0,0,0 they all become idenity matrixes!
Hope this helps, it worked very well for my application.
Bruce
update
After finding problems with Y = -90 or 90 degrees in the solution above. I came up with this solution that seems to reproduce the matrix at all values!
X = if(or(m31=1,m31=-1),0,atan(m33+1e-24,m32))
Y = -asin(m31)
Z = if(or(m31=1,m31=-1),-atan2(m22,m12),atan2(m11+1e-24,m21))
I went the long way around to find this solution, but it wa very enlightening :o)
Hope this helps!
Bruce