I wonder if there is any way to make functions defined within the main function be local, in a similar way to local variables. For example, in this function that calculates the gradient of a scalar function,
grad(var,f) := block([aux],
aux : [gradient, DfDx[i]],
gradient : [],
DfDx[i] := diff(f(x_1,x_2,x_3),var[i],1),
for i in [1,2,3] do (
gradient : append(gradient, [DfDx[i]])
),
return(gradient)
)$
The variable gradient that has been defined inside the main function grad(var,f) has no effect outside the main function, as it is inside the aux list. However, I have observed that the function DfDx, despite being inside the aux list, does have an effect outside the main function.
Is there any way to make the sub-functions defined inside the main function to be local only, in a similar way to what can be made with local variables? (I know that one can kill them once they have been used, but perhaps there is a more elegant way)
To address the problem you are needing to solve here, another way to compute the gradient is to say
grad(var, e) := makelist(diff(e, var1), var1, var);
and then you can say for example
grad([x, y, z], sin(x)*y/z);
to get
cos(x) y sin(x) sin(x) y
[--------, ------, - --------]
z z 2
z
(There isn't a built-in gradient function; this is an oversight.)
About local functions, bear in mind that all function definitions are global. However you can approximate a local function definition via local, which saves and restores all properties of a symbol. Since the function definition is a property, local has the effect of temporarily wiping out an existing function definition and later restoring it. In between you can create a temporary function definition. E.g.
foo(x) := 2*x;
bar(y) := block(local(foo), foo(x) := x - 1, foo(y));
bar(100); /* output is 99 */
foo(100); /* output is 200 */
However, I don't this you need to use local -- just makelist plus diff is enough to compute the gradient.
There is more to say about Maxima's scope rules, named and unnamed functions, etc. I'll try to come back to this question tomorrow.
To compute the gradient, my advice is to call makelist and diff as shown in my first answer. Let me take this opportunity to address some related topics.
I'll paste the definition of grad shown in the problem statement and use that to make some comments.
grad(var,f) := block([aux],
aux : [gradient, DfDx[i]],
gradient : [],
DfDx[i] := diff(f(x_1,x_2,x_3),var[i],1),
for i in [1,2,3] do (
gradient : append(gradient, [DfDx[i]])
),
return(gradient)
)$
(1) Maxima works mostly with expressions as opposed to functions. That's not causing a problem here, I just want to make it clear. E.g. in general one has to say diff(f(x), x) when f is a function, instead of diff(f, x), likewise integrate(f(x), ...) instead of integrate(f, ...).
(2) When gradient and Dfdx are to be the local variables, you have to name them in the list of variables for block. E.g. block([gradient, Dfdx], ...) -- Maxima won't understand block([aux], aux: ...).
(3) Note that a function defined with square brackets instead of parentheses, e.g. f[x] := ... instead of f(x) := ..., is a so-called array function in Maxima. An array function is a memoizing function, i.e. if f[x] is called two or more times, the return value is only computed once, and then returned every time thereafter. Sometimes that's a useful optimization when the domain of the function comprises a finite set.
(4) Bear in mind that x_1, x_2, x_3, are distinct symbols, not related to each other, and not related to x[1], x[2], x[3], even if they are displayed the same. My advice is to work with subscripted symbols x[i] when i is a variable.
(5) About building up return values, try to arrange to compute the whole thing at one go, instead of growing the result incrementally. In this case, makelist is preferable to for plus append.
(6) The return function in Maxima acts differently than in other programming languages; it's a little hard to explain. A function returns the value of the last expression which was evaluated, so if gradient is that last expression, you can just write grad(var, f) := block(..., gradient).
Hope this helps, I know it's obscure and complex. The Maxima programming language was not designed before being implemented, and some of the decisions are clearly questionable at the long interval of more than 50 years (!) later. That's okay, they were figuring it out as they went along. There was not a body of established results which could provide a point of reference; the original authors were contributing to what's considered common knowledge today.
Related
I have the following Maxima code:
A[t] :=
if t=0 then A0
else (a+b)*A[t-1]+B[t-1]+c
;
B[t] :=
if t=0 then B0
else (a-b)*B[t-1]+c
;
a:0.1;
b:0.1;
c:1;
A0:100;
B0:0;
wxplot2d(A[t], [t, 0, 100]);
The only remotely weird thing I can think of is that recurion equation A depends on recurion equation B. I would think everything else is extremely basic.
But when I run it, I always get the following error repeated multiple times and no plot.
Maxima encountered a Lisp error:
Control stack exhausted (no more space for function call frames).
This is probably due to heavily nested or infinitely recursive function
calls, or a tail call that SBCL cannot or has not optimized away.
Even when I plot from time steps 0 to 1 with wxplot2d(A[t], [t, 0, 1]);, which by my count would only be two recursions and one external function reference, I still get the same error. Is there no way to have Maxima plot these equations?
I find that the following seems to work.
myvalues: makelist ([t, A[t]], t, 0, 100);
wxplot2d ([discrete, myvalues]);
Just to be clear, A[t] := ..., with square brackets, defines what is called an array function Maxima, which is a memoizing function (i.e. remembers previously calculated values). An ordinary, non-memoizing function is defined as A(t) := ..., with parentheses.
Given that A and B are defined only for nonnegative integers, it makes sense that they should be memoizing functions, so there's no need to change it.
The following fragment produces a compilation error:
arma::Mat<double> a(10,10,arma::fill::zeros);
arma::ucolvec w = whatever1;
whatever2 = a.rows(w).each_col() + another-col-vector;
The error is that arma::subview_elem2 has no member named each_col.
In a number of cases in Armadillo, the standard array functions are not always available on expressions or results of other function calls. Clearly the rows() function does not return a Mat object, but a subview_elem2 object, presumably for optimization. Another way to do this would be to declare all the array functions in interfaces/pure abstract classes that Mat and other internal classes, such as subviews, implement. It seems it should be possible to make all Armadillo array expressions interchangeable with array objects aside from write operations for expressions that only generate r-values.
So... I could wish for the following
a) An explanation of which methods are not available for which results.
b) Preferably, enabling all combinations of array methods that make sense.
Absent the above, how can accomplish the desired result, which is to evaluate the expression:
a.rows(w).each_col()
??
Some prior information about armadillo
The armadillo library uses templates heavily and most operations return expression templates. Only when you assign the result to a variable the actual computation is performed. This is why you should not store the result of some computation with armadillo using auto.
For instance, given some matrices A, B and C, something like
auto D = A * B + C;
will not perform the computation and only the expression template is stored in D. On the other hand, using
arma::mat D = A * B + C;
will force the computation to happen and the result is stored in D.
Solution to your problem
Particularly to your question, something like a.rows(w) returns an expression template of type subview_elem2 (this file is defined in the source code armadillo_bits/subview_elem2_bones.hpp). This "temporary type" does not have a .each_col method, which results in the error you got. One way around is to store the result of a.rows(w) in a variable, but since you are not interested in the variable you can use the .eval() method. The .eval() method forces the expression template to perform the actual computation up to that point and thus the subsequent call to .each_col will work. That is, replace
a.rows(w).each_col() + another-col-vector;
with
a.rows(w).eval().each_col() + another-col-vector;
I'd like the example computation expression and values below to return 6. For some the numbers aren't yielding like I'd expect. What's the step I'm missing to get my result? Thanks!
type AddBuilder() =
let mutable x = 0
member _.Yield i = x <- x + i
member _.Zero() = 0
member _.Return() = x
let add = AddBuilder()
(* Compiler tells me that each of the numbers in add don't do anything
and suggests putting '|> ignore' in front of each *)
let result = add { 1; 2; 3 }
(* Currently the result is 0 *)
printfn "%i should be 6" result
Note: This is just for creating my own computation expression to expand my learning. Seq.sum would be a better approach. I'm open to the idea that this example completely misses the value of computation expressions and is no good for learning.
There is a lot wrong here.
First, let's start with mere mechanics.
In order for the Yield method to be called, the code inside the curly braces must use the yield keyword:
let result = add { yield 1; yield 2; yield 3 }
But now the compiler will complain that you also need a Combine method. See, the semantics of yield is that each of them produces a finished computation, a resulting value. And therefore, if you want to have more than one, you need some way to "glue" them together. This is what the Combine method does.
Since your computation builder doesn't actually produce any results, but instead mutates its internal variable, the ultimate result of the computation should be the value of that internal variable. So that's what Combine needs to return:
member _.Combine(a, b) = x
But now the compiler complains again: you need a Delay method. Delay is not strictly necessary, but it's required in order to mitigate performance pitfalls. When the computation consists of many "parts" (like in the case of multiple yields), it's often the case that some of them should be discarded. In these situation, it would be inefficient to evaluate all of them and then discard some. So the compiler inserts a call to Delay: it receives a function, which, when called, would evaluate a "part" of the computation, and Delay has the opportunity to put this function in some sort of deferred container, so that later Combine can decide which of those containers to discard and which to evaluate.
In your case, however, since the result of the computation doesn't matter (remember: you're not returning any results, you're just mutating the internal variable), Delay can just execute the function it receives to have it produce the side effects (which are - mutating the variable):
member _.Delay(f) = f ()
And now the computation finally compiles, and behold: its result is 6. This result comes from whatever Combine is returning. Try modifying it like this:
member _.Combine(a, b) = "foo"
Now suddenly the result of your computation becomes "foo".
And now, let's move on to semantics.
The above modifications will let your program compile and even produce expected result. However, I think you misunderstood the whole idea of the computation expressions in the first place.
The builder isn't supposed to have any internal state. Instead, its methods are supposed to manipulate complex values of some sort, some methods creating new values, some modifying existing ones. For example, the seq builder1 manipulates sequences. That's the type of values it handles. Different methods create new sequences (Yield) or transform them in some way (e.g. Combine), and the ultimate result is also a sequence.
In your case, it looks like the values that your builder needs to manipulate are numbers. And the ultimate result would also be a number.
So let's look at the methods' semantics.
The Yield method is supposed to create one of those values that you're manipulating. Since your values are numbers, that's what Yield should return:
member _.Yield x = x
The Combine method, as explained above, is supposed to combine two of such values that got created by different parts of the expression. In your case, since you want the ultimate result to be a sum, that's what Combine should do:
member _.Combine(a, b) = a + b
Finally, the Delay method should just execute the provided function. In your case, since your values are numbers, it doesn't make sense to discard any of them:
member _.Delay(f) = f()
And that's it! With these three methods, you can add numbers:
type AddBuilder() =
member _.Yield x = x
member _.Combine(a, b) = a + b
member _.Delay(f) = f ()
let add = AddBuilder()
let result = add { yield 1; yield 2; yield 3 }
I think numbers are not a very good example for learning about computation expressions, because numbers lack the inner structure that computation expressions are supposed to handle. Try instead creating a maybe builder to manipulate Option<'a> values.
Added bonus - there are already implementations you can find online and use for reference.
1 seq is not actually a computation expression. It predates computation expressions and is treated in a special way by the compiler. But good enough for examples and comparisons.
I am surprising to notice that it is somehow difficult to obtain a correct fit of interaction function from gam().
To be more specific, I want to estimate an additive function:
y=m_1(x)+m_2(z)+m_{12}(x,z)+u,
where m_1(x)=x^2, m_2(z)=z^2,m_{12}(x,z)=xz. The following code generate this model:
test1 <- function(x,z,sx=1,sz=1) {
#--m1(x) function
m.x<-x^2
m.x<-m.x-mean(m.x)
#--m2(z) function
m.z<-z^2
m.z<-m.z-mean(m.z)
#--m12(x,z) function
m.xz<-x*z
m.xz<-m.xz-mean(m.xz)
m<-m.x+m.z+m.xz
return(list(m=m,m.x=m.x,m.z=m.z,m.xz=m.xz))
}
n <- 1000
a=0
b=2
x <- runif(n,a,b)/20
z <- runif(n,a,b)
u <- rnorm(n,0,0.5)
model<-test1(x,z)
y <- model$m + u
So I use gam() by fitting the model as
b3 <- gam(y~ ti(x) + ti(z) + ti(x,z))
vis.gam(b3);title("tensor anova")
#---extracting basis matrix
B.f3<-model.matrix.gam(b3)
#---extracting series estimator
b3.hat<-b3$coefficients
Question: when I plot the estimated function by gam()above against its true function, I end up with
par(mfrow=c(1,3))
#---m1(x)
B.x<-B.f3[,c(2:5)]
b.x.hat<-b3.hat[c(2:5)]
plot(x,B.x%*%b.x.hat)
points(x,model$m.x,col='red')
legend('topleft',c('Estimate','True'),lty=c(1,1),col=c('black','red'))
#---m2(z)
B.z<-B.f3[,c(6:9)]
b.z.hat<-b3.hat[c(6:9)]
plot(z,B.z%*%b.z.hat)
points(z,model$m.z,col='red')
legend('topleft',c('Estimate','True'),lty=c(1,1),col=c('black','red'))
#---m12(x,z)
B.xz<-B.f3[,-c(1:9)]
b.xz.hat<-b3.hat[-c(1:9)]
plot(x,B.xz%*%b.xz.hat)
points(x,model$m.xz,col='red')
legend('topleft',c('Estimate','True'),lty=c(1,1),col=c('black','red'))
However, the function estimate of m_1(x) is largely different from x^2, and the interaction function estimate m_{12}(x,z) is also largely different from xz defined in test1 above. The results are the same if I use predict(b3).
I really can't figure it out. Can anybody help me out by explaining why the results end up with this? Greatly appreciate it!
First, the problem of the above issue is not due to the package, of course. It is closely related to the identification conditions of the smooth functions. One common practice is to impose the assumptions that E(mj(.))=0 for all individual function j=1,...,d, and E(m_ij(x_i,x_j)|x_i)=E(m_ij(x_i,x_j)|x_j)=0 for i not equal to j. Those conditions require one to employ centered basis function in series estimator, which has been done already in GAM package. However, in my case above, function m(x,z)=x*z defined in test1 does not satisfy the above identification assumptions, since the integral of x*z with respect to either x or z is not zero when x and z have range from zero to two.
Furthermore, series estimator allows the individual and interaction function to be identified if one impose m(0)=0 or m(0,x_j)=m(x_i,0)=0. This can be readily achieved if we center the basis function around zero. I have tried both cases, and they work well whenever DGP satisfies the identification conditions.
I have a fairly large expression that involves a lot of subexpressions of the form (100*A^3 + 200*A^2 + 100*A)*x or (-A^2 - A)*y or (100*A^2 + 100*A)*z
I know, but I don't know how to tell Maxima this, that it in this case is valid to make the approximation A+1 ~ A, thereby effectively removing anything but the highest power of A in each coefficient.
I'm now looking for functions, tools, or methods that I can use to guide Maxima in dropping various terms that aren't important.
I have attempted with subst, but that requires me to specify each and every factor separately, because:
subst([A+1=B], (A+2)*(A+1)*2);
subst([A+1=B], (A+2)*(A*2+2));
(%o1) 2*(A+2)*B
(%o2) (A+2)*(2*A+2)
(that is, I need to add one expression for each slightly different variant)
I tried with ratsimp, but that's too eager to change every occurrence:
ratsubst(B, A+1, A*(A+1)*2);
ratsubst(B, A+1, A*(A*2+2));
(%o3) 2*B^2-2*B
(%o4) 2*B^2-2*B
which isn't actually simpler, as I would have preferred the answer to have been given as 2*B^2.
In another answer, (https://stackoverflow.com/a/22695050/5999883) the functions let and letsimp were suggested for the task of substituting values, but I fail to get them to really do anything:
x:(A+1)*A;
let ( A+1, B );
letsimp(x);
(x)A*(A+1)
(%o6) A+1 --\> B
(%o7) A^2+A
Again, I'd like to approximate this expression to A^2 (B^2, whatever it's called).
I understand that this is, in general, a hard problem (is e.g. A^2 + 10^8*A still okay to approximate as A^2?) but I think that what I'm looking for is a function or method of calculation that would be a little bit smarter than subst and can recognize that the same substitution could be done in the expression A^2+A as in the expression 100*A^2+100*A or -A^2-A instead of making me create a list of three (or twenty) individual substitutions when calling subst. The "nice" part of the full expression that I'm working on is that each of these A factors are of the form k*A^n*(A+1)^m for various small integers n, m, so I never actually end up with the degenerate case mentioned above.
(I was briefly thinking of re-expressing my expression as a polynomial in A, but this will not work as the only valid approximation of the expression (A^3+A^2+A)*x + y is A^3*x + y -- I know nothing about the relative sizes of x and y.