Evaluating a compiled expression tree is sometimes slower - cil

I generate an expression tree containing simple math expressions. The expression types are limited to constants, variables, addition, subtraction, multiplication, division, negation, sqrt and a few trigonometric functions. Variables are like constants, but their values can change.
To evaluate the tree, I iterate from bottom to top and perform the operation indicated by the expression type. This involves switching on the expression type, for example:
for (int i = 0; i < count; ++i) {
ref Expr expr = ref expressions[i];
switch (expr.Type) {
case Op.Addition:
expr.Value = expressions[expr.Operand1].Value + expressions[expr.Operand2].Value;
break;
case ...
}
}
I also evaluate the derivatives using reverse automatic differentiation. This involves iterating the tree from top to bottom and computing the adjoint for each expression. There are simple rules for each expression type:
ref Expr root = ref expressions[expressions.Length - 1];
root.Adjoint = 1.0;
for (int i = expressions.Length - 1; i >= 0; --i) {
ref Expr expr = ref expressions[i];
switch (expr.Type) {
case Op.Addition:
ref Expr left = ref expressions[expr.Operand1];
ref Expr right = ref expressions[expr.Operand2];
left.Adjoint += expr.Adjoint;
right.Adjoint += expr.Adjoint;
break;
case ...
}
}
To avoid the branching, I thought that I'd compile this expression tree by generating IL code. To do this, I again iterate from bottom to top through the tree and emit instructions for calculating the expression values. Similarly, I iterate from top to bottom and emit instructions for calculating the adjoints. I then end up with two big functions that compute the values and adjoints for all expressions without any branching.
var method = new DynamicMethod("Evaluate", typeof(void), new Type[] { typeof(double[]) }, true);
var il = method.GetILGenerator();
for (int i = 0; i < expressions.Length; ++i) {
ref Expr expr = ref expressions[i];
switch (expr.Type) {
case Op.Addition:
il.Emit(OpCodes.Ldarg_0);
il.Emit(OpCodes.Ldc_I4, expr.Index);
il.Emit(OpCodes.Ldarg_0);
il.Emit(OpCodes.Ldc_I4, expr.Operand1);
il.Emit(OpCodes.Ldelem_R8);
il.Emit(OpCodes.Ldarg_0);
il.Emit(OpCodes.Ldc_I4, expr.Operand2);
il.Emit(OpCodes.Ldelem_R8);
il.Emit(OpCodes.Add);
il.Emit(OpCodes.Stelem_R8);
case ...
}
}
il.Emit(OpCodes.Ret);
evaluate = method.CreateDelegate<EvaluateFunction>();
For small expression trees, this proved to be quite effective. The time to evaluate the tree was quite significantly reduced. However, for larger expression trees the compiled evaluation function actually becomes slower.
Why could this be? Clearly, for large expression trees, the compiled function can contain a large number of instructions. Is this then simply due to the size of the instruction cache? There is no branching in the function, so I'd have thought that the instructions could be loaded very efficiently.

Related

Assignment destructuring and operator precedence

The documentation says that the comma operator has higher precedence than the assignment = operator, and this is specifically different than in Perl, so that we are allowed to remove parentheses in some contexts.
This allows us to do things like this:
my #array = 1, 2, 3;
What I don't understand is why when do something like this:
sub test() { return 1, 2 }
my ($a, $b);
$a, $b = test();
$b get assigned [1 2] while $a gets no value.
While I would assume that the following would be equivalent, because the comma operator is tighter than the assignment.
($a, $b) = test();
The semantics of Raku have a lot of subtlety and I guess I am thinking too much in terms of Perl.
Like raiph said in the comments, my original assumption that the comma operator has higher precedence than the assignment operator was false. And it was due to a problem in the rendering of the operator precedence table, which didn't presented operators in their precedence order.
This explains the actual behavior of Raku for my examples.
The = operator itself is always item assignment level, which is tighter than comma. However, it may apply a "sub-precedence", which is how it is compared to any further infixes in the expression that follow it. The term that was parsed prior to the = is considered, and:
In the case that the assignment is to a Scalar variable, then the = operator works just like any other item assignment precedence operator, which is tighter than the precedence of ,
In any other case, its precedence relative to following infixes is list prefix, which is looser than the precedence of ,
To consider some cases (first, where it doesn't impact anything):
$foo = 42; # $ sigil, so item assignment precedence after
#foo = 1; # # sigil, so list assignment precedence after
#foo[0] = 1; # postcircumfix (indexing operator) means list assignment after...
$foo[0] = 1; # ...always, even in this case
If we have a single variable on the left and a list on the right, then:
#foo = 1, 2; # List assignment into #foo two values
$foo = 1, 2; # Assignment of 1 into $foo, 2 is dead code
These apply with the = initializer (following a my $var declaration). This means that:
loop (my $i = 0, my $j = $end; $i < $end; $i++, $j--) {
...
}
Will result in $i being assigned 0 and $j being assigned $end.
Effectively, the rule means we get to have parentheses-free initialization of array and hash variables, while still having lists of scalar initializations work out as in the loop case.
Turning to the examples in the question. First, this:
($a, $b) = test();
Parses a single term, then encounters the =. The precedence when comparing any following infixes would be list prefix (looser than ,). However, there are no more infixes here, so it doesn't really matter.
In this case:
sub test() { return 1, 2 }
my ($a, $b);
$a, $b = test();
The precedence parser sees the , infix, followed by the = infix. The = infix in itself is tighter than comma (item assignment precedence); the sub-precedence is only visible to infixes parsed after the = (and there are none here).
Note that were it not this way, and the precedence shift applied to the expression as a whole, then:
loop (my $i = 0, my #lagged = Nil, |#values; $i < #values; $i++) {
...
}
Would end up grouped not as (my $i = 0), (my #lagged = Nil, |#values), but rather (my $i = 0, my #lagged) = Nil, |#values, which is rather less useful.

Optimized recalculating all pairs shortest path when removing vertexes dynamically from an undirected graph

I use following dijkstra implementation to calculate all pairs shortest paths in an undirected graph. After calling calculateAllPaths(), dist[i][j] contains shortest path length between i and j (or Integer.MAX_VALUE if no such path available).
The problem is that some vertexes of my graph are removing dynamically and I should recalculate all paths from scratch to update dist matrix. I'm seeking for a solution to optimize update speed by avoiding unnecessary calculations when a vertex removes from my graph. I already search for solution and I now there is some algorithms such as LPA* to do this, but they seem very complicated and I guess a simpler solution may solve my problem.
public static void calculateAllPaths()
{
for(int j=graph.length/2+graph.length%2;j>=0;j--)
{
calculateAllPathsFromSource(j);
}
}
public static void calculateAllPathsFromSource(int s)
{
final boolean visited[] = new boolean[graph.length];
for (int i=0; i<dist.length; i++)
{
if(i == s)
{
continue;
}
//visit next node
int next = -1;
int minDist = Integer.MAX_VALUE;
for (int j=0; j<dist[s].length; j++)
{
if (!visited[j] && dist[s][j] < minDist)
{
next = j;
minDist = dist[s][j];
}
}
if(next == -1)
{
continue;
}
visited[next] = true;
for(int v=0;v<graph.length;v++)
{
if(v == next || graph[next][v] == -1)
{
continue;
}
int md = dist[s][next] + graph[next][v];
if(md < dist[s][v])
{
dist[s][v] = dist[v][s] = md;
}
}
}
}
If you know that vertices are only being removed dynamically, then instead of just storing the best path matrix dist[i][j], you could also store the permutation of each such path. Say, instead of dist[i][j] you make a custom class myBestPathInfo, and the array of an instance of this, say myBestPathInfo[i][j], contain members best distance as well as permutation of the best path. Preferably, the best path permutation is described as an ordered set of some vertex objects, where the latter are of reference type and unique for each vertex (however used in several myBestPathInfo instances). Such objects could include a boolean property isActive (true/false).
Whenever a vertex is removed, you traverse through the best path permutations for each vertex-vertex pair, to make sure no vertex has been deactivated. Finally, only for broken paths (deactivated vertices) do you re-run Dijkstra's algorithm.
Another solution would be to solve the shortest path for all pairs using linear programming (LP) techniques. A removed vertex can be easily implemented as an additional constraint in your program (e.g. flow in <=0 and and flow out of vertex <= 0*), after which the re-solving of the shortest path LP:s can use the previous optimal solution as a feasible basic feasible solution (BFS) in the dual LPs. This property holds since adding a constraint in the primal LP is equivalent to an additional variable in the dual; hence, previously optimal primal BFS will be feasible in dual after additional constraints. (on-the-fly starting on simplex solver for LPs).

antlr rule boolean parameter showing up in syntactic predicate code one level higher, causing compilation errors

I have a grammar that can parse expressions like 1+2-4 or 1+x-y, creating an appropriate structure on the fly which later, given a Map<String, Integer> with appropriate content, can be evaluated numerically (after parsing is complete, i.e. for x or y only known later).
Inside the grammar, there are also places where an expression that can be evaluated on the spot, i.e. does not contain variables, should occur. I figured I could parse these with the same logic, adding a boolean parameter variablesAllowed to the rule, like so:
grammar MiniExprParser;
INT : ('0'..'9')+;
ID : ('a'..'z'| 'A'..'Z')('a'..'z'| 'A'..'Z'| '0'..'9')*;
PLUS : '+';
MINUS : '-';
numexpr returns [Double val]:
expr[false] {$val = /* some evaluation code */ 0.;};
varexpr /*...*/:
expr[true] {/*...*/};
expr[boolean varsAllowed] /*...*/:
e=atomNode[varsAllowed] {/*...*/}
(PLUS e2=atomNode[varsAllowed] {/*...*/}
|MINUS e2=atomNode[varsAllowed] {/*...*/}
)* ;
atomNode[boolean varsAllowed] /*...*/:
(n=INT {/*...*/})
|{varsAllowed}?=> ID {/*...*/}
;
result:
(numexpr) => numexpr {System.out.println("Numeric result: " + $numexpr.val);}
|varexpr {System.out.println("Variable expression: " + $varexpr.text);};
However, the generated Java code does not compile. In the part apparently responsible for the final rule's syntactic predicate, varsAllowed occurs even although the variable is never defined at this level.
/* ... */
else if ( (LA3_0==ID) && ((varsAllowed))) {
int LA3_2 = input.LA(2);
if ( ((synpred1_MiniExprParser()&&(varsAllowed))) ) {
alt3=1;
}
else if ( ((varsAllowed)) ) {
alt3=2;
}
/* ... */
Am I using it wrong? (I am using Eclipse' AntlrIDE 2.1.2 with Antlr 3.5.2.)
This problem is part of the hoisting process the parser uses for prediction. I encountered the same problem and ended up with a member var (or static var for the C target) instead of a parameter.

Looping with iterator vs temp object gives different result graphically (Libgdx/Java)

I've got a particle "engine" whom I've implementing a Pool system to and I've tested two different ways of rendering every Particle in a list. Please note that the Pooling really doesn't have anything with the problem to do. I just followed a tutorial and tried to use the second method when I noticed that they behaved differently.
The first way:
for (int i = 0; i < particleList.size(); i++) {
Iterator<Particle> it = particleList.iterator();
while (it.hasNext()) {
Particle p = it.next();
if (p.isDead()){
it.remove();
}
p.render(batch, delta);
}
}
Which works just fine. My particles are sharp and they move with the correct speed.
The second way:
Particle p;
for (int i = 0; i < particleList.size(); i++) {
p = particleList.get(i);
p.render(batch, delta);
if (p.isDead()) {
particleList.remove(i);
bulletPool.free(p);
}
}
Which makes all my particles blurry and moving really slow!
The render method for my particles look like this:
public void render(SpriteBatch batch, float delta) {
sprite.setX(sprite.getX() + (dx * speed) * delta * Assets.FPS);
sprite.setY(sprite.getY() + (dy * speed) * delta * Assets.FPS);
ttl--;
sprite.setScale(sprite.getScaleX() - 0.002f);
if (ttl <= 0 || sprite.getScaleX() <= 0)
isDead = true;
sprite.draw(batch);
}
Why do the different rendering methods provide different results?
Thanks in advance
You are mutating (removing elements from) a list while iterating over it. This is a classic way to make a mess.
The Iterator must have code to handle the delete case correctly. But your index-based for loop does not. Specifically when you call particleList.remove(i) the i is now "out of sync" with the content of the list. Consider what happens when you remove the element at index 3: 'i' will increment to 4, but the old element 4 got shuffled down into index 3, so it will get skipped.
I assume you're avoiding the Iterator to avoid memory allocations. So, one way to side-step this issue is to reverse the loop (go from particleList.size() down to 0). Alternatively, you can only increment i for non-dead particles.

Expression Evaluation and Tree Walking using polymorphism? (ala Steve Yegge)

This morning, I was reading Steve Yegge's: When Polymorphism Fails, when I came across a question that a co-worker of his used to ask potential employees when they came for their interview at Amazon.
As an example of polymorphism in
action, let's look at the classic
"eval" interview question, which (as
far as I know) was brought to Amazon
by Ron Braunstein. The question is
quite a rich one, as it manages to
probe a wide variety of important
skills: OOP design, recursion, binary
trees, polymorphism and runtime
typing, general coding skills, and (if
you want to make it extra hard)
parsing theory.
At some point, the candidate hopefully
realizes that you can represent an
arithmetic expression as a binary
tree, assuming you're only using
binary operators such as "+", "-",
"*", "/". The leaf nodes are all
numbers, and the internal nodes are
all operators. Evaluating the
expression means walking the tree. If
the candidate doesn't realize this,
you can gently lead them to it, or if
necessary, just tell them.
Even if you tell them, it's still an
interesting problem.
The first half of the question, which
some people (whose names I will
protect to my dying breath, but their
initials are Willie Lewis) feel is a
Job Requirement If You Want To Call
Yourself A Developer And Work At
Amazon, is actually kinda hard. The
question is: how do you go from an
arithmetic expression (e.g. in a
string) such as "2 + (2)" to an
expression tree. We may have an ADJ
challenge on this question at some
point.
The second half is: let's say this is
a 2-person project, and your partner,
who we'll call "Willie", is
responsible for transforming the
string expression into a tree. You get
the easy part: you need to decide what
classes Willie is to construct the
tree with. You can do it in any
language, but make sure you pick one,
or Willie will hand you assembly
language. If he's feeling ornery, it
will be for a processor that is no
longer manufactured in production.
You'd be amazed at how many candidates
boff this one.
I won't give away the answer, but a
Standard Bad Solution involves the use
of a switch or case statment (or just
good old-fashioned cascaded-ifs). A
Slightly Better Solution involves
using a table of function pointers,
and the Probably Best Solution
involves using polymorphism. I
encourage you to work through it
sometime. Fun stuff!
So, let's try to tackle the problem all three ways. How do you go from an arithmetic expression (e.g. in a string) such as "2 + (2)" to an expression tree using cascaded-if's, a table of function pointers, and/or polymorphism?
Feel free to tackle one, two, or all three.
[update: title modified to better match what most of the answers have been.]
Polymorphic Tree Walking, Python version
#!/usr/bin/python
class Node:
"""base class, you should not process one of these"""
def process(self):
raise('you should not be processing a node')
class BinaryNode(Node):
"""base class for binary nodes"""
def __init__(self, _left, _right):
self.left = _left
self.right = _right
def process(self):
raise('you should not be processing a binarynode')
class Plus(BinaryNode):
def process(self):
return self.left.process() + self.right.process()
class Minus(BinaryNode):
def process(self):
return self.left.process() - self.right.process()
class Mul(BinaryNode):
def process(self):
return self.left.process() * self.right.process()
class Div(BinaryNode):
def process(self):
return self.left.process() / self.right.process()
class Num(Node):
def __init__(self, _value):
self.value = _value
def process(self):
return self.value
def demo(n):
print n.process()
demo(Num(2)) # 2
demo(Plus(Num(2),Num(5))) # 2 + 3
demo(Plus(Mul(Num(2),Num(3)),Div(Num(10),Num(5)))) # (2 * 3) + (10 / 2)
The tests are just building up the binary trees by using constructors.
program structure:
abstract base class: Node
all Nodes inherit from this class
abstract base class: BinaryNode
all binary operators inherit from this class
process method does the work of evaluting the expression and returning the result
binary operator classes: Plus,Minus,Mul,Div
two child nodes, one each for left side and right side subexpressions
number class: Num
holds a leaf-node numeric value, e.g. 17 or 42
The problem, I think, is that we need to parse perentheses, and yet they are not a binary operator? Should we take (2) as a single token, that evaluates to 2?
The parens don't need to show up in the expression tree, but they do affect its shape. E.g., the tree for (1+2)+3 is different from 1+(2+3):
+
/ \
+ 3
/ \
1 2
versus
+
/ \
1 +
/ \
2 3
The parentheses are a "hint" to the parser (e.g., per superjoe30, to "recursively descend")
This gets into parsing/compiler theory, which is kind of a rabbit hole... The Dragon Book is the standard text for compiler construction, and takes this to extremes. In this particular case, you want to construct a context-free grammar for basic arithmetic, then use that grammar to parse out an abstract syntax tree. You can then iterate over the tree, reducing it from the bottom up (it's at this point you'd apply the polymorphism/function pointers/switch statement to reduce the tree).
I've found these notes to be incredibly helpful in compiler and parsing theory.
Representing the Nodes
If we want to include parentheses, we need 5 kinds of nodes:
the binary nodes: Add Minus Mul Divthese have two children, a left and right side
+
/ \
node node
a node to hold a value: Valno children nodes, just a numeric value
a node to keep track of the parens: Parena single child node for the subexpression
( )
|
node
For a polymorphic solution, we need to have this kind of class relationship:
Node
BinaryNode : inherit from Node
Plus : inherit from Binary Node
Minus : inherit from Binary Node
Mul : inherit from Binary Node
Div : inherit from Binary Node
Value : inherit from Node
Paren : inherit from node
There is a virtual function for all nodes called eval(). If you call that function, it will return the value of that subexpression.
String Tokenizer + LL(1) Parser will give you an expression tree... the polymorphism way might involve an abstract Arithmetic class with an "evaluate(a,b)" function, which is overridden for each of the operators involved (Addition, Subtraction etc) to return the appropriate value, and the tree contains Integers and Arithmetic operators, which can be evaluated by a post(?)-order traversal of the tree.
I won't give away the answer, but a
Standard Bad Solution involves the use
of a switch or case statment (or just
good old-fashioned cascaded-ifs). A
Slightly Better Solution involves
using a table of function pointers,
and the Probably Best Solution
involves using polymorphism.
The last twenty years of evolution in interpreters can be seen as going the other way - polymorphism (eg naive Smalltalk metacircular interpreters) to function pointers (naive lisp implementations, threaded code, C++) to switch (naive byte code interpreters), and then onwards to JITs and so on - which either require very big classes, or (in singly polymorphic languages) double-dispatch, which reduces the polymorphism to a type-case, and you're back at stage one. What definition of 'best' is in use here?
For simple stuff a polymorphic solution is OK - here's one I made earlier, but either stack and bytecode/switch or exploiting the runtime's compiler is usually better if you're, say, plotting a function with a few thousand data points.
Hm... I don't think you can write a top-down parser for this without backtracking, so it has to be some sort of a shift-reduce parser. LR(1) or even LALR will of course work just fine with the following (ad-hoc) language definition:
Start -> E1
E1 -> E1+E1 | E1-E1
E1 -> E2*E2 | E2/E2 | E2
E2 -> number | (E1)
Separating it out into E1 and E2 is necessary to maintain the precedence of * and / over + and -.
But this is how I would do it if I had to write the parser by hand:
Two stacks, one storing nodes of the tree as operands and one storing operators
Read the input left to right, make leaf nodes of the numbers and push them into the operand stack.
If you have >= 2 operands on the stack, pop 2, combine them with the topmost operator in the operator stack and push this structure back to the operand tree, unless
The next operator has higher precedence that the one currently on top of the stack.
This leaves us the problem of handling brackets. One elegant (I thought) solution is to store the precedence of each operator as a number in a variable. So initially,
int plus, minus = 1;
int mul, div = 2;
Now every time you see a a left bracket increment all these variables by 2, and every time you see a right bracket, decrement all the variables by 2.
This will ensure that the + in 3*(4+5) has higher precedence than the *, and 3*4 will not be pushed onto the stack. Instead it will wait for 5, push 4+5, then push 3*(4+5).
Re: Justin
I think the tree would look something like this:
+
/ \
2 ( )
|
2
Basically, you'd have an "eval" node, that just evaluates the tree below it. That would then be optimized out to just being:
+
/ \
2 2
In this case the parens aren't required and don't add anything. They don't add anything logically, so they'd just go away.
I think the question is about how to write a parser, not the evaluator. Or rather, how to create the expression tree from a string.
Case statements that return a base class don't exactly count.
The basic structure of a "polymorphic" solution (which is another way of saying, I don't care what you build this with, I just want to extend it with rewriting the least amount of code possible) is deserializing an object hierarchy from a stream with a (dynamic) set of known types.
The crux of the implementation of the polymorphic solution is to have a way to create an expression object from a pattern matcher, likely recursive. I.e., map a BNF or similar syntax to an object factory.
Or maybe this is the real question:
how can you represent (2) as a BST?
That is the part that is tripping me
up.
Recursion.
#Justin:
Look at my note on representing the nodes. If you use that scheme, then
2 + (2)
can be represented as
.
/ \
2 ( )
|
2
should use a functional language imo. Trees are harder to represent and manipulate in OO languages.
As people have been mentioning previously, when you use expression trees parens are not necessary. The order of operations becomes trivial and obvious when you're looking at an expression tree. The parens are hints to the parser.
While the accepted answer is the solution to one half of the problem, the other half - actually parsing the expression - is still unsolved. Typically, these sorts of problems can be solved using a recursive descent parser. Writing such a parser is often a fun exercise, but most modern tools for language parsing will abstract that away for you.
The parser is also significantly harder if you allow floating point numbers in your string. I had to create a DFA to accept floating point numbers in C -- it was a very painstaking and detailed task. Remember, valid floating points include: 10, 10., 10.123, 9.876e-5, 1.0f, .025, etc. I assume some dispensation from this (in favor of simplicty and brevity) was made in the interview.
I've written such a parser with some basic techniques like
Infix -> RPN and
Shunting Yard and
Tree Traversals.
Here is the implementation I've came up with.
It's written in C++ and compiles on both Linux and Windows.
Any suggestions/questions are welcomed.
So, let's try to tackle the problem all three ways. How do you go from an arithmetic expression (e.g. in a string) such as "2 + (2)" to an expression tree using cascaded-if's, a table of function pointers, and/or polymorphism?
This is interesting,but I don't think this belongs to the realm of object-oriented programming...I think it has more to do with parsing techniques.
I've kind of chucked this c# console app together as a bit of a proof of concept. Have a feeling it could be a lot better (that switch statement in GetNode is kind of clunky (it's there coz I hit a blank trying to map a class name to an operator)). Any suggestions on how it could be improved very welcome.
using System;
class Program
{
static void Main(string[] args)
{
string expression = "(((3.5 * 4.5) / (1 + 2)) + 5)";
Console.WriteLine(string.Format("{0} = {1}", expression, new Expression.ExpressionTree(expression).Value));
Console.WriteLine("\nShow's over folks, press a key to exit");
Console.ReadKey(false);
}
}
namespace Expression
{
// -------------------------------------------------------
abstract class NodeBase
{
public abstract double Value { get; }
}
// -------------------------------------------------------
class ValueNode : NodeBase
{
public ValueNode(double value)
{
_double = value;
}
private double _double;
public override double Value
{
get
{
return _double;
}
}
}
// -------------------------------------------------------
abstract class ExpressionNodeBase : NodeBase
{
protected NodeBase GetNode(string expression)
{
// Remove parenthesis
expression = RemoveParenthesis(expression);
// Is expression just a number?
double value = 0;
if (double.TryParse(expression, out value))
{
return new ValueNode(value);
}
else
{
int pos = ParseExpression(expression);
if (pos > 0)
{
string leftExpression = expression.Substring(0, pos - 1).Trim();
string rightExpression = expression.Substring(pos).Trim();
switch (expression.Substring(pos - 1, 1))
{
case "+":
return new Add(leftExpression, rightExpression);
case "-":
return new Subtract(leftExpression, rightExpression);
case "*":
return new Multiply(leftExpression, rightExpression);
case "/":
return new Divide(leftExpression, rightExpression);
default:
throw new Exception("Unknown operator");
}
}
else
{
throw new Exception("Unable to parse expression");
}
}
}
private string RemoveParenthesis(string expression)
{
if (expression.Contains("("))
{
expression = expression.Trim();
int level = 0;
int pos = 0;
foreach (char token in expression.ToCharArray())
{
pos++;
switch (token)
{
case '(':
level++;
break;
case ')':
level--;
break;
}
if (level == 0)
{
break;
}
}
if (level == 0 && pos == expression.Length)
{
expression = expression.Substring(1, expression.Length - 2);
expression = RemoveParenthesis(expression);
}
}
return expression;
}
private int ParseExpression(string expression)
{
int winningLevel = 0;
byte winningTokenWeight = 0;
int winningPos = 0;
int level = 0;
int pos = 0;
foreach (char token in expression.ToCharArray())
{
pos++;
switch (token)
{
case '(':
level++;
break;
case ')':
level--;
break;
}
if (level <= winningLevel)
{
if (OperatorWeight(token) > winningTokenWeight)
{
winningLevel = level;
winningTokenWeight = OperatorWeight(token);
winningPos = pos;
}
}
}
return winningPos;
}
private byte OperatorWeight(char value)
{
switch (value)
{
case '+':
case '-':
return 3;
case '*':
return 2;
case '/':
return 1;
default:
return 0;
}
}
}
// -------------------------------------------------------
class ExpressionTree : ExpressionNodeBase
{
protected NodeBase _rootNode;
public ExpressionTree(string expression)
{
_rootNode = GetNode(expression);
}
public override double Value
{
get
{
return _rootNode.Value;
}
}
}
// -------------------------------------------------------
abstract class OperatorNodeBase : ExpressionNodeBase
{
protected NodeBase _leftNode;
protected NodeBase _rightNode;
public OperatorNodeBase(string leftExpression, string rightExpression)
{
_leftNode = GetNode(leftExpression);
_rightNode = GetNode(rightExpression);
}
}
// -------------------------------------------------------
class Add : OperatorNodeBase
{
public Add(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value + _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Subtract : OperatorNodeBase
{
public Subtract(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value - _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Divide : OperatorNodeBase
{
public Divide(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value / _rightNode.Value;
}
}
}
// -------------------------------------------------------
class Multiply : OperatorNodeBase
{
public Multiply(string leftExpression, string rightExpression)
: base(leftExpression, rightExpression)
{
}
public override double Value
{
get
{
return _leftNode.Value * _rightNode.Value;
}
}
}
}
Ok, here is my naive implementation. Sorry, I did not feel to use objects for that one but it is easy to convert. I feel a bit like evil Willy (from Steve's story).
#!/usr/bin/env python
#tree structure [left argument, operator, right argument, priority level]
tree_root = [None, None, None, None]
#count of parethesis nesting
parenthesis_level = 0
#current node with empty right argument
current_node = tree_root
#indices in tree_root nodes Left, Operator, Right, PRiority
L, O, R, PR = 0, 1, 2, 3
#functions that realise operators
def sum(a, b):
return a + b
def diff(a, b):
return a - b
def mul(a, b):
return a * b
def div(a, b):
return a / b
#tree evaluator
def process_node(n):
try:
len(n)
except TypeError:
return n
left = process_node(n[L])
right = process_node(n[R])
return n[O](left, right)
#mapping operators to relevant functions
o2f = {'+': sum, '-': diff, '*': mul, '/': div, '(': None, ')': None}
#converts token to a node in tree
def convert_token(t):
global current_node, tree_root, parenthesis_level
if t == '(':
parenthesis_level += 2
return
if t == ')':
parenthesis_level -= 2
return
try: #assumption that we have just an integer
l = int(t)
except (ValueError, TypeError):
pass #if not, no problem
else:
if tree_root[L] is None: #if it is first number, put it on the left of root node
tree_root[L] = l
else: #put on the right of current_node
current_node[R] = l
return
priority = (1 if t in '+-' else 2) + parenthesis_level
#if tree_root does not have operator put it there
if tree_root[O] is None and t in o2f:
tree_root[O] = o2f[t]
tree_root[PR] = priority
return
#if new node has less or equals priority, put it on the top of tree
if tree_root[PR] >= priority:
temp = [tree_root, o2f[t], None, priority]
tree_root = current_node = temp
return
#starting from root search for a place with higher priority in hierarchy
current_node = tree_root
while type(current_node[R]) != type(1) and priority > current_node[R][PR]:
current_node = current_node[R]
#insert new node
temp = [current_node[R], o2f[t], None, priority]
current_node[R] = temp
current_node = temp
def parse(e):
token = ''
for c in e:
if c <= '9' and c >='0':
token += c
continue
if c == ' ':
if token != '':
convert_token(token)
token = ''
continue
if c in o2f:
if token != '':
convert_token(token)
convert_token(c)
token = ''
continue
print "Unrecognized character:", c
if token != '':
convert_token(token)
def main():
parse('(((3 * 4) / (1 + 2)) + 5)')
print tree_root
print process_node(tree_root)
if __name__ == '__main__':
main()