Cycling through multiple lists in shell for loop - shell

There is a way to do this in python using itertools.product, but, is there a way to do this in shell?
L1 = 'a b c'
L2 = '1 2'
L3 = 'x y'
for x, y, z in L1, L2, L3:
do
echo x,y,z
done
I would like the results to look like:
a1x
a1y
a2x
a2y
b1x
b1y
...

If you know what the lists are, you can use brace expansion:
$ printf '%s\n' {a,b,c}{1,2}{x,y}
a1x
a1y
a2x
a2y
b1x
b1y
b2x
b2y
c1x
c1y
c2x
c2y
If, however, the lists are in, say, three arrays, you have to do nested loops:
l1=(a b c); l2=(1 2); l3=(x y)
for i in "${l1[#]}"; do
for j in "${l2[#]}"; do
for k in "${l3[#]}"; do
echo "$i$j$k"
done
done
done

Related

How to perform several operations under the same condition?

I want to negate the chosen element of the matrix along with its adjacent elements.
My question is how do I make these multiple expressions happen without '&&'. I don't know the syntax very well.
I am getting-
Error: This expression has type unit but an expression was expected of type bool
let matrix2 =[|[|true;true;false;false|];
[|false;false;true;true|];
[|true;false;true;false|];
[|true;false;false;true|]|];;
let flip_matrix matrix a b=
let n=Array.length matrix in
for i=1 to n do
let n1=Array.length matrix in
for j=1 to n1 do
if i=a && j=b
then
matrix.(i).(j)<- not matrix.(i).(j)&&matrix.(i+1).(j+1)<- not matrix.(i+1).(j+1)&&matrix.(i-1).(j-1)<- not matrix.(i-1).(j-1)
done;
done;
matrix;;
flip_matrix matrix2 1 2;;
The sequencing operator ; is used to chain together several expressions,
<exp1>; <exp2>
means evaluate <exp1> first, then evaluate <exp2>, example:
print_endline "Hello!"; print_endline "World."
Note that ; works only for expressions that return a value of type unit, i.e., that are evaluated only for their side-effects and do not produce any useful values.
When you need to chain several expression that produce useful values, you need to bound those values to some variables, and have to use the let <v> = <exp1> in <exp2>. This expression will evaluate <exp1> and bound it to the variable <v> that becomes available for expression <exp2>, which is evaluated after that. Example,
let message = "hello", ^ ", world" in
print_endline message
As you can see, the <exp1>; <exp2> is just a short-hand notation for,
let () = <exp1> in <exp2>
Also, note that could be a let .. in .. expression itself, so that you can chain an arbitrary number of expressions in OCaml,
let x1 = f1 y1 in
let x2 = f2 y2 in
...
let xN = fN yN in
final_result
Now, we're ready for conditional expressions such as if. It would be natural to assume that
if x > 0 then print_endline "Hello"; print_endline "World"
Would print
Hello
World
If x is greater than zero. But that is wrong! As a I described recent in this answer, the if expression has higher precedence (priority) than ;, so in fact the OCaml parser splits this into two expressions:
(if x > 0 then print_endline "Hello"); print_endline "World"
So that at the end only one of the expressions is under condition. As always in such precedence problems the solution is to use parentheses (or begin/end, which is the same), e.g.,
if x > 0 then (print_endline "Hello"; print_endline "World")
You can also use the more generic let .. in .. if you would like, it works without any extra parentheses, e.g.,
if x > 0 then
let () = print_endline "Hello" in
print_endline "World"
albeit a little bit ugly :)
Mutating assignment of an array evaluates to the unit:
utop # let arr = Array.make 10 0;;
val arr : int array = [|0; 0; 0; 0; 0; 0; 0; 0; 0; 0|]
utop # arr.(0) <- 1;;
- : unit = ()
In this line:
matrix.(i).(j)<- not matrix.(i).(j)&&matrix.(i+1).(j+1)<- not matrix.(i+1).(j+1)&&matrix.(i-1).(j-1)<- not matrix.(i-1).(j-1)
You are using && to conjoin a boolean value with the result of evaluating matrix.(i+1).(j+1) <- ..., and that latter expression with be the unit. Of course && only works to conjoin too boolean values.
I think this should do it:
let matrix33 = [|[|true;true;false;false|];
[|false;false;true;true|];
[|true;false;true;false|];
[|true;false;false;true|]|];;
let flip_matrix matrix a b=
let n=Array.length matrix in
for i=1 to n do
let n1=Array.length matrix in
for j=1 to n1 do
if i=a && j=b
then begin
matrix.(i).(j)<- not matrix.(i).(j);
matrix.(i+1).(j)<- not matrix.(i+1).(j);
matrix.(i).(j+1)<- not matrix.(i).(j+1);
matrix.(i).(j-1)<- not matrix.(i).(j-1);
matrix.(i-1).(j)<- not matrix.(i-1).(j);
end;
done;
done;
matrix;;
flip_matrix matrix33 1 1 ;;```

Probability of a disjunction on N dependent events in Prolog

Does anybody know where to find a Prolog algorithm for computing the probability of a disjunction for N dependent events? For N = 2 i know that P(E1 OR E2) = P(E1) + P(E2) - P(E1) * P(E2), so one could do:
prob_disjunct(E1, E2, P):- P is E1 + E2 - E1 * E2
But how can this predicate be generalised to N events when the input is a list? Maybe there is a package which does this?
Kinds regards/JCR
The recursive formula from Robert Dodier's answer directly translates to
p_or([], 0).
p_or([P|Ps], Or) :-
p_or(Ps, Or1),
Or is P + Or1*(1-P).
Although this works fine, e.g.
?- p_or([0.5,0.3,0.7,0.1],P).
P = 0.9055
hardcore Prolog programmers can't help noticing that the definition isn't tail-recursive. This would really only be a problem when you are processing very long lists, but since the order of list elements doesn't matter, it is easy to turn things around. This is a standard technique, using an auxiliary predicate and an "accumulator pair" of arguments:
p_or(Ps, Or) :-
p_or(Ps, 0, Or).
p_or([], Or, Or).
p_or([P|Ps], Or0, Or) :-
Or1 is P + Or0*(1-P),
p_or(Ps, Or1, Or). % tail-recursive call
I don't know anything about Prolog, but anyway it's convenient to write the probability of a disjunction of a number of independent items p_m = Pr(S_1 or S_2 or S_3 or ... or S_m) recursively as
p_m = Pr(S_m) + p_{m - 1} (1 - P(S_m))
You can prove this by just peeling off the last item -- look at Pr((S_1 or ... or S_{m - 1}) or S_m) and just write that in terms of the usual formula, writing Pr(A or B) = Pr(A) + Pr(B) - Pr(A) Pr(B) = Pr(B) + Pr(A) (1 - Pr(B)), for A and B independent.
The formula above is item C.3.10 in my dissertation: http://riso.sourceforge.net/docs/dodier-dissertation.pdf It is a simple result, and I suppose it must be an exercise in some textbooks, although I don't remember seeing it.
For any event E I'll write E' for the complementary event (ie E' occurs iff E doesn't).
Then we have:
P(E') = 1 - P(E)
(A union B)' = A' inter B'
A and B are independent iff A' and B' are independent
so for independent E1..En
P( E1 union .. union En ) = 1 - P( E1' inter .. inter En')
= 1 - product{ i<=i<=n | 1 - P(E[i])}

What is an algorithm to find intersection of two linear equations?

I'm struggling to figure out an algorithm to find the intersection of two linear equations like:
f(x)=2x+4
g(x)=x+2
I'd like to use the method where you set f (x)=g (x) and solve x, and I'd like to stay away from cross product.
Does anyone have any suggestion to how an algorithm like that would look like?
If your input lines are in slope-intercept form, an algorithm is an over-kill as there is a direct formula to calculate their point of intersection. It's given on a Wikipedia page and you can understand it as explained below.
Given the equations of the lines: The x and y coordinates of the
point of intersection of two non-vertical lines can easily be found
using the following substitutions and rearrangements.
Suppose that two lines have the equations y = ax + c and y = bx + d where a
and b are the slopes (gradients) of the lines and where c and d are
the y-intercepts of the lines. At the point where the two lines
intersect (if they do), both y coordinates will be the same, hence the
following equality:
ax + c = bx + d.
We can rearrange this expression in order to extract the
value of x,
ax - bx = d - c, and so,
x = (d-c)/(a-b).
To find the y coordinate, all we need to do is substitute the value of x into > either one of the two line equations. For example, into the first:
y=(a*(d-c)/(a-b))+c.
Hence, the Point of Intersection is {(d-c)/(a-b), (a*(d-c)/(a-b))+c}
Note: If a = b then the two lines are parallel. If c ≠ d as well, the lines
are different and there is no intersection, otherwise the two lines are
identical.
Given:
ax + b = cx + d
ax = cx + d - b
ax - cx = d - b
x(a - c) = d - b
Therefore, x = (d - b) / (a - c)
In your example, let a = 2, b = 4, c = 1 d = 2
x = (2 - 4) / (2 - 1)
x = -2 / 1
x = -2
General solution. Let
f(x) = a1x + b1 ....... g(x) = a2x + b2
Special cases:
a1 == a2 and b1 == b2 : lines coincide
a1 == a2 and b1 != b2 : lines are parallel, no intersection
General case: a1 != a2
X = (b2 - b1) / (a1 - a2) ....and... Y = (a1b2 - a2b1) / (a1 - a2)
I don't remember what cross products are in the context of equations.
One way to solve these is to set them equal to each other, solve for x, then use that value to solve for y:
2x + 4 = x + 2
2x + 2 = x
x = -2
y = f(x)
= g(x)
= x + 2
= -2 + 2
= 0
Solution: (-2, 0)

Question on "smart" replacing in mathematica

How do I tell mathematica to do this replacement smartly? (or how do I get smarter at telling mathematica to do what i want)
expr = b + c d + ec + 2 a;
expr /. a + b :> 1
Out = 2 a + b + c d + ec
I expect the answer to be a + cd + ec + 1. And before someone suggests, I don't want to do a :> 1 - b, because for aesthetic purposes, I'd like to have both a and b in my equation as long as the a+b = 1 simplification cannot be made.
In addition, how do I get it to replace all instances of 1-b, -b+1 or -1+b, b-1 with a or -a respectively and vice versa?
Here's an example for this part:
expr = b + c (1 - a) + (-1 + b)(a - 1) + (1 -a -b) d + 2 a
You can use a customised version of FullSimplify by supplying your own transformations to FullSimplify and let it figure out the details:
In[1]:= MySimplify[expr_,equivs_]:= FullSimplify[expr,
TransformationFunctions ->
Prepend[
Function[x,x-#]&/#Flatten#Map[{#,-#}&,equivs/.Equal->Subtract],
Automatic
]
]
In[2]:= MySimplify[2a+b+c*d+e*c, {a+b==1}]
Out[2]= a + c(d + e) + 1
equivs/.Equal->Subtract turns given equations into expressions equal to zero (e.g. a+b==1 -> a+b-1). Flatten#Map[{#,-#}&, ] then constructs also negated versions and flattens them into a single list. Function[x,x-#]& /# turns the zero expressions into functions, which subtract the zero expressions (the #) from what is later given to them (x) by FullSimplify.
It may be necessary to specify your own ComplexityFunction for FullSimplify, too, if your idea of simple differs from FullSimplify's default ComplexityFunction (which is roughly equivalent to LeafCount), e.g.:
MySimplify[expr_, equivs_] := FullSimplify[expr,
TransformationFunctions ->
Prepend[
Function[x,x-#]&/#Flatten#Map[{#,-#}&,equivs/.Equal->Subtract],
Automatic
],
ComplexityFunction -> (
1000 LeafCount[#] +
Composition[
Total,Flatten,Map[ArrayDepth[#]#&,#]&,CoefficientArrays
][#] &
)
]
In your example case, the default ComplexityFunction works fine, though.
For the first case, you might consider:
expr = b + c d + ec + 2 a
PolynomialReduce[expr, {a + b - 1}, {b, a}][[2]]
For the second case, consider:
expr = b + c (1 - a) + (-1 + b) (a - 1) + (1 - a - b) d + 2 a;
PolynomialReduce[expr, {x + b - 1}][[2]]
(% /. x -> 1 - b) == expr // Simplify
and:
PolynomialReduce[expr, {a + b - 1}][[2]]
Simplify[% == expr /. a -> 1 - b]

Mathematica: using simplify to do common sub-expression elimination and reduction in strength

So lately I have been toying around with how Mathematica's pattern matching and term rewriting might be put to good use in compiler optimizations...trying to highly optimize short blocks of code that are the inner parts of loops. Two common ways to reduce the amount of work it takes to evaluate an expression is to identify sub-expressions that occur more than once and store the result and then use the stored result at subsequent points to save work. Another approach is to use cheaper operations where possible. For instance, my understanding is that taking square roots take more clock cycles than additions and multiplications. To be clear, I am interested in the cost in terms of floating point operations that evaluating the expression would take, not how long it takes Mathematica to evaluate it.
My first thought was that I would tackle the problem developing using Mathematica's simplify function. It is possible to specify a complexity function that compares the relative simplicity of two expressions. I was going to create one using weights for the relevant arithmetic operations and add to this the LeafCount for the expression to account for the assignment operations that are required. That addresses the reduction in strength side, but it is the elimination of common subexpressions that has me tripped up.
I was thinking of adding common subexpression elimination to the possible transformation functions that simplify uses. But for a large expression there could be many possible subexpressions that could be replaced and it won't be possible to know what they are till you see the expression. I have written a function that gives the possible substitutions, but it seems like the transformation function you specify needs to just return a single possible transformation, at least from the examples in the documentation. Any thoughts on how one might get around this limitation? Does anyone have a better idea of how simplify uses transformation functions that might hint at a direction forward?
I imagine that behind the scenes that Simplify is doing some dynamic programming trying different simplifications on different parts of the expressions and returning the one with the lowest complexity score. Would I be better off trying to do this dynamic programming on my own using common algebraic simplifications such as factor and collect?
EDIT: I added the code that generates possible sub-expressions to remove
(*traverses entire expression tree storing each node*)
AllSubExpressions[x_, accum_] := Module[{result, i, len},
len = Length[x];
result = Append[accum, x];
If[LeafCount[x] > 1,
For[i = 1, i <= len, i++,
result = ToSubExpressions2[x[[i]], result];
];
];
Return[Sort[result, LeafCount[#1] > LeafCount[#2] &]]
]
CommonSubExpressions[statements_] := Module[{common, subexpressions},
subexpressions = AllSubExpressions[statements, {}];
(*get the unique set of sub expressions*)
common = DeleteDuplicates[subexpressions];
(*remove constants from the list*)
common = Select[common, LeafCount[#] > 1 &];
(*only keep subexpressions that occur more than once*)
common = Select[common, Count[subexpressions, #] > 1 &];
(*output the list of possible subexpressions to replace with the \
number of occurrences*)
Return[common];
]
Once a common sub-expression is chosen from the list returned by CommonSubExpressions the function that does the replacement is below.
eliminateCSE[statements_, expr_] := Module[{temp},
temp = Unique["r"];
Prepend[ReplaceAll[statements, expr -> temp], temp[expr]]
]
At the risk of this question getting long, I will put a little example code up. I thought a decent expression to try to optimize would be the classical Runge-Kutta method for solving differential equations.
Input:
nextY=statements[y + 1/6 h (f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] +
2 f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]] +
f[h + t,
y + h f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]])];
possibleTransformations=CommonSubExpressions[nextY]
transformed=eliminateCSE[nextY, First[possibleTransformations]]
Output:
{f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]],
y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]],
0.5 h f[0.5 h + t, y + 0.5 h f[t, n]],
f[0.5 h + t, y + 0.5 h f[t, n]], y + 0.5 h f[t, n], 0.5 h f[t, n],
0.5 h + t, f[t, n], 0.5 h}
statements[r1[f[0.5 h + t, y + 0.5 h f[0.5 h + t, y + 0.5 h f[t, n]]]],
y + 1/6 h (2 r1 + f[t, n] + 2 f[0.5 h + t, y + 0.5 h f[t, n]] +
f[h + t, h r1 + y])]
Finally, the code to judge the relative cost of different expressions is below. The weights are conceptual at this point as that is still an area I am researching.
Input:
cost[e_] :=
Total[MapThread[
Count[e, #1, Infinity, Heads -> True]*#2 &, {{Plus, Times, Sqrt,
f}, {1, 2, 5, 10}}]]
cost[transformed]
Output:
100
There are also some routines here implemented here by this author: http://stoney.sb.org/wordpress/2009/06/converting-symbolic-mathematica-expressions-to-c-code/
I packaged it into a *.M file and have fixed a bug (if the expression has no repeated subexpressions the it dies), and I am trying to find the author's contact info to see if I can upload his modified code to pastebin or wherever.
EDIT: I have received permission from the author to upload it and have pasted it here: http://pastebin.com/fjYiR0B3
To identify repeating subexpressions, you could use something like this
(*helper functions to add Dictionary-like functionality*)
index[downvalue_,
dict_] := (downvalue[[1]] /. HoldPattern[dict[x_]] -> x) //
ReleaseHold;
value[downvalue_] := downvalue[[-1]];
indices[dict_] :=
Map[#[[1]] /. {HoldPattern[dict[x_]] -> x} &, DownValues[dict]] //
ReleaseHold;
values[dict_] := Map[#[[-1]] &, DownValues[dict]];
items[dict_] := Map[{index[#, dict], value[#]} &, DownValues[dict]];
indexQ[dict_, index_] :=
If[MatchQ[dict[index], HoldPattern[dict[index]]], False, True];
(*count number of times each sub-expressions occurs *)
expr = Cos[x + Cos[Cos[x] + Sin[x]]] + Cos[Cos[x] + Sin[x]];
Map[(counts[#] = If[indexQ[counts, #], counts[#] + 1, 1]; #) &, expr,
Infinity];
items[counts] // Column
I tried to mimic the dictionary compression function appears on this blog: https://writings.stephenwolfram.com/2018/11/logic-explainability-and-the-future-of-understanding/
Here is what I made:
DictionaryCompress[expr_, count_, size_, func_] := Module[
{t, s, rule, rule1, rule2},
t = Tally#Level[expr, Depth[expr]];
s = Sort[
Select[{First##, Last##, Depth[First##]} & /#
t, (#[[2]] > count && #[[3]] > size) &], #1[[2]]*#1[[3]] < #2[[
2]]*#2[[2]] &];
rule = MapIndexed[First[#1] -> func ## #2 &, s];
rule = (# //. Cases[rule, Except[#]]) & /# rule;
rule1 = Select[rule, ! FreeQ[#, Plus] &];
rule2 = Complement[rule, rule1];
rule = rule1 //. (Reverse /# rule2);
rule = rule /. MapIndexed[ Last[#1] -> func ## #2 &, rule];
{
expr //. rule,
Reverse /# rule
}
];
poly = Sum[Subscript[c, k] x^k, {k, 0, 4}];
sol = Solve[poly == 0, x];
expr = x /. sol;
Column[{Column[
MapIndexed[
Style[TraditionalForm[Subscript[x, First[#2]] == #], 20] &, #[[
1]]], Spacings -> 1],
Column[Style[#, 20] & /# #[[2]], Spacings -> 1, Frame -> All]
}] &#DictionaryCompress[expr, 1, 1,
Framed[#, Background -> LightYellow] &]

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