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// Copyright 2011 Google Inc. All Rights Reserved.
// Copyright 1996 John Maloney and Mario Wolczko
//
// This file is part of GNU Smalltalk.
//
// GNU Smalltalk is free software; you can redistribute it and/or modify it
// under the terms of the GNU General Public License as published by the Free
// Software Foundation; either version 2, or (at your option) any later version.
//
// GNU Smalltalk is distributed in the hope that it will be useful, but WITHOUT
// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
// details.
//
// You should have received a copy of the GNU General Public License along with
// GNU Smalltalk; see the file COPYING. If not, write to the Free Software
// Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
//
// Translated first from Smalltalk to JavaScript, and finally to
// Dart by Google 2008-2010.
/// A Dart implementation of the DeltaBlue constraint-solving
/// algorithm, as described in:
///
/// "The DeltaBlue Algorithm: An Incremental Constraint Hierarchy Solver"
/// Bjorn N. Freeman-Benson and John Maloney
/// January 1990 Communications of the ACM,
/// also available as University of Washington TR 89-08-06.
///
/// Beware: this benchmark is written in a grotesque style where
/// the constraint model is built by side-effects from constructors.
/// I've kept it this way to avoid deviating too much from the original
/// implementation.
import 'package:benchmark_harness/benchmark_harness.dart';
/// algorithm, as described in:
///
/// "The DeltaBlue Algorithm: An Incremental Constraint Hierarchy Solver"
/// Bjorn N. Freeman-Benson and John Maloney
/// January 1990 Communications of the ACM,
/// also available as University of Washington TR 89-08-06.
///
/// Beware: this benchmark is written in a grotesque style where
/// the constraint model is built by side-effects from constructors.
/// I've kept it this way to avoid deviating too much from the original
/// implementation.
main() {
const DeltaBlue().report();
}
/// Benchmark class required to report results.
class DeltaBlue extends BenchmarkBase {
const DeltaBlue() : super("DeltaBlue");
void run() {
chainTest(100);
projectionTest(100);
}
}
/// Strengths are used to measure the relative importance of constraints.
/// New strengths may be inserted in the strength hierarchy without
/// disrupting current constraints. Strengths cannot be created outside
/// this class, so == can be used for value comparison.
class Strength {
final int value;
final String name;
const Strength(this.value, this.name);
Strength nextWeaker() => const <Strength>[
WEAKEST,
WEAK_DEFAULT,
NORMAL,
STRONG_DEFAULT,
PREFERRED,
STRONG_REFERRED
][value];
static bool stronger(Strength s1, Strength s2) {
return s1.value < s2.value;
}
static bool weaker(Strength s1, Strength s2) {
return s1.value > s2.value;
}
static Strength weakest(Strength s1, Strength s2) {
return weaker(s1, s2) ? s1 : s2;
}
static Strength strongest(Strength s1, Strength s2) {
return stronger(s1, s2) ? s1 : s2;
}
}
// Compile time computed constants.
const REQUIRED = Strength(0, "required");
const STRONG_REFERRED = Strength(1, "strongPreferred");
const PREFERRED = Strength(2, "preferred");
const STRONG_DEFAULT = Strength(3, "strongDefault");
const NORMAL = Strength(4, "normal");
const WEAK_DEFAULT = Strength(5, "weakDefault");
const WEAKEST = Strength(6, "weakest");
abstract class Constraint {
final Strength strength;
const Constraint(this.strength);
bool isSatisfied();
void markUnsatisfied();
void addToGraph();
void removeFromGraph();
void chooseMethod(int mark);
void markInputs(int mark);
bool inputsKnown(int mark);
Variable output();
void execute();
void recalculate();
/// Activate this constraint and attempt to satisfy it.
void addConstraint() {
addToGraph();
planner.incrementalAdd(this);
}
/// Attempt to find a way to enforce this constraint. If successful,
/// record the solution, perhaps modifying the current dataflow
/// graph. Answer the constraint that this constraint overrides, if
/// there is one, or nil, if there isn't.
/// Assume: I am not already satisfied.
Constraint satisfy(int mark) {
chooseMethod(mark);
if (!isSatisfied()) {
if (strength == REQUIRED) {
print("Could not satisfy a required constraint!");
}
return null;
}
markInputs(mark);
Variable out = output();
Constraint overridden = out.determinedBy;
if (overridden != null) overridden.markUnsatisfied();
out.determinedBy = this;
if (!planner.addPropagate(this, mark)) print("Cycle encountered");
out.mark = mark;
return overridden;
}
void destroyConstraint() {
if (isSatisfied()) planner.incrementalRemove(this);
removeFromGraph();
}
/// Normal constraints are not input constraints. An input constraint
/// is one that depends on external state, such as the mouse, the
/// keybord, a clock, or some arbitraty piece of imperative code.
bool isInput() => false;
}
/// Abstract superclass for constraints having a single possible output variable.
abstract class UnaryConstraint extends Constraint {
final Variable myOutput;
bool satisfied = false;
UnaryConstraint(this.myOutput, Strength strength) : super(strength) {
addConstraint();
}
/// Adds this constraint to the constraint graph
void addToGraph() {
myOutput.addConstraint(this);
satisfied = false;
}
/// Decides if this constraint can be satisfied and records that decision.
void chooseMethod(int mark) {
satisfied = (myOutput.mark != mark) &&
Strength.stronger(strength, myOutput.walkStrength);
}
/// Returns true if this constraint is satisfied in the current solution.
bool isSatisfied() => satisfied;
void markInputs(int mark) {
// has no inputs.
}
/// Returns the current output variable.
Variable output() => myOutput;
/// Calculate the walkabout strength, the stay flag, and, if it is
/// 'stay', the value for the current output of this constraint. Assume
/// this constraint is satisfied.
void recalculate() {
myOutput.walkStrength = strength;
myOutput.stay = !isInput();
if (myOutput.stay) execute(); // Stay optimization.
}
/// Records that this constraint is unsatisfied.
void markUnsatisfied() {
satisfied = false;
}
bool inputsKnown(int mark) => true;
void removeFromGraph() {
if (myOutput != null) myOutput.removeConstraint(this);
satisfied = false;
}
}
/// Variables that should, with some level of preference, stay the same.
/// Planners may exploit the fact that instances, if satisfied, will not
/// change their output during plan execution. This is called "stay
/// optimization".
class StayConstraint extends UnaryConstraint {
StayConstraint(Variable v, Strength str) : super(v, str);
void execute() {
// Stay constraints do nothing.
}
}
/// A unary input constraint used to mark a variable that the client
/// wishes to change.
class EditConstraint extends UnaryConstraint {
EditConstraint(Variable v, Strength str) : super(v, str);
/// Edits indicate that a variable is to be changed by imperative code.
bool isInput() => true;
void execute() {
// Edit constraints do nothing.
}
}
// Directions.
const int NONE = 1;
const int FORWARD = 2;
const int BACKWARD = 0;
/// Abstract superclass for constraints having two possible output
/// variables.
abstract class BinaryConstraint extends Constraint {
Variable v1;
Variable v2;
int direction = NONE;
BinaryConstraint(this.v1, this.v2, Strength strength) : super(strength) {
addConstraint();
}
/// Decides if this constraint can be satisfied and which way it
/// should flow based on the relative strength of the variables related,
/// and record that decision.
void chooseMethod(int mark) {
if (v1.mark == mark) {
direction =
(v2.mark != mark && Strength.stronger(strength, v2.walkStrength))
? FORWARD
: NONE;
}
if (v2.mark == mark) {
direction =
(v1.mark != mark && Strength.stronger(strength, v1.walkStrength))
? BACKWARD
: NONE;
}
if (Strength.weaker(v1.walkStrength, v2.walkStrength)) {
direction =
Strength.stronger(strength, v1.walkStrength) ? BACKWARD : NONE;
} else {
direction =
Strength.stronger(strength, v2.walkStrength) ? FORWARD : BACKWARD;
}
}
/// Add this constraint to the constraint graph.
void addToGraph() {
v1.addConstraint(this);
v2.addConstraint(this);
direction = NONE;
}
/// Answer true if this constraint is satisfied in the current solution.
bool isSatisfied() => direction != NONE;
/// Mark the input variable with the given mark.
void markInputs(int mark) {
input().mark = mark;
}
/// Returns the current input variable
Variable input() => direction == FORWARD ? v1 : v2;
/// Returns the current output variable.
Variable output() => direction == FORWARD ? v2 : v1;
/// Calculate the walkabout strength, the stay flag, and, if it is
/// 'stay', the value for the current output of this
/// constraint. Assume this constraint is satisfied.
void recalculate() {
Variable ihn = input(), out = output();
out.walkStrength = Strength.weakest(strength, ihn.walkStrength);
out.stay = ihn.stay;
if (out.stay) execute();
}
/// Record the fact that this constraint is unsatisfied.
void markUnsatisfied() {
direction = NONE;
}
bool inputsKnown(int mark) {
Variable i = input();
return i.mark == mark || i.stay || i.determinedBy == null;
}
void removeFromGraph() {
if (v1 != null) v1.removeConstraint(this);
if (v2 != null) v2.removeConstraint(this);
direction = NONE;
}
}
/// Relates two variables by the linear scaling relationship: "v2 =
/// (v1 * scale) + offset". Either v1 or v2 may be changed to maintain
/// this relationship but the scale factor and offset are considered
/// read-only.
class ScaleConstraint extends BinaryConstraint {
final Variable scale;
final Variable offset;
ScaleConstraint(
Variable src, this.scale, this.offset, Variable dest, Strength strength)
: super(src, dest, strength);
/// Adds this constraint to the constraint graph.
void addToGraph() {
super.addToGraph();
scale.addConstraint(this);
offset.addConstraint(this);
}
void removeFromGraph() {
super.removeFromGraph();
if (scale != null) scale.removeConstraint(this);
if (offset != null) offset.removeConstraint(this);
}
void markInputs(int mark) {
super.markInputs(mark);
scale.mark = offset.mark = mark;
}
/// Enforce this constraint. Assume that it is satisfied.
void execute() {
if (direction == FORWARD) {
v2.value = v1.value * scale.value + offset.value;
} else {
v1.value = (v2.value - offset.value) ~/ scale.value;
}
}
/// Calculate the walkabout strength, the stay flag, and, if it is
/// 'stay', the value for the current output of this constraint. Assume
/// this constraint is satisfied.
void recalculate() {
Variable ihn = input(), out = output();
out.walkStrength = Strength.weakest(strength, ihn.walkStrength);
out.stay = ihn.stay && scale.stay && offset.stay;
if (out.stay) execute();
}
}
/// Constrains two variables to have the same value.
class EqualityConstraint extends BinaryConstraint {
EqualityConstraint(Variable v1, Variable v2, Strength strength)
: super(v1, v2, strength);
/// Enforce this constraint. Assume that it is satisfied.
void execute() {
output().value = input().value;
}
}
/// A constrained variable. In addition to its value, it maintain the
/// structure of the constraint graph, the current dataflow graph, and
/// various parameters of interest to the DeltaBlue incremental
/// constraint solver.
class Variable {
List<Constraint> constraints = <Constraint>[];
Constraint determinedBy;
int mark = 0;
Strength walkStrength = WEAKEST;
bool stay = true;
int value;
final String name;
Variable(this.name, this.value);
/// Add the given constraint to the set of all constraints that refer
/// this variable.
void addConstraint(Constraint c) {
constraints.add(c);
}
/// Removes all traces of c from this variable.
void removeConstraint(Constraint c) {
constraints.removeWhere((e) => c == e);
if (determinedBy == c) determinedBy = null;
}
}
class Planner {
int currentMark = 0;
/// Attempt to satisfy the given constraint and, if successful,
/// incrementally update the dataflow graph. Details: If satifying
/// the constraint is successful, it may override a weaker constraint
/// on its output. The algorithm attempts to resatisfy that
/// constraint using some other method. This process is repeated
/// until either a) it reaches a variable that was not previously
/// determined by any constraint or b) it reaches a constraint that
/// is too weak to be satisfied using any of its methods. The
/// variables of constraints that have been processed are marked with
/// a unique mark value so that we know where we've been. This allows
/// the algorithm to avoid getting into an infinite loop even if the
/// constraint graph has an inadvertent cycle.
void incrementalAdd(Constraint c) {
int mark = newMark();
for (Constraint overridden = c.satisfy(mark);
overridden != null;
overridden = overridden.satisfy(mark)) {
// NOOP
}
}
/// Entry point for retracting a constraint. Remove the given
/// constraint and incrementally update the dataflow graph.
/// Details: Retracting the given constraint may allow some currently
/// unsatisfiable downstream constraint to be satisfied. We therefore collect
/// a list of unsatisfied downstream constraints and attempt to
/// satisfy each one in turn. This list is traversed by constraint
/// strength, strongest first, as a heuristic for avoiding
/// unnecessarily adding and then overriding weak constraints.
/// Assume: [c] is satisfied.
void incrementalRemove(Constraint c) {
Variable out = c.output();
c.markUnsatisfied();
c.removeFromGraph();
List<Constraint> unsatisfied = removePropagateFrom(out);
Strength strength = REQUIRED;
do {
for (int i = 0; i < unsatisfied.length; i++) {
Constraint u = unsatisfied[i];
if (u.strength == strength) incrementalAdd(u);
}
strength = strength.nextWeaker();
} while (strength != WEAKEST);
}
/// Select a previously unused mark value.
int newMark() => ++currentMark;
/// Extract a plan for resatisfaction starting from the given source
/// constraints, usually a set of input constraints. This method
/// assumes that stay optimization is desired; the plan will contain
/// only constraints whose output variables are not stay. Constraints
/// that do no computation, such as stay and edit constraints, are
/// not included in the plan.
/// Details: The outputs of a constraint are marked when it is added
/// to the plan under construction. A constraint may be appended to
/// the plan when all its input variables are known. A variable is
/// known if either a) the variable is marked (indicating that has
/// been computed by a constraint appearing earlier in the plan), b)
/// the variable is 'stay' (i.e. it is a constant at plan execution
/// time), or c) the variable is not determined by any
/// constraint. The last provision is for past states of history
/// variables, which are not stay but which are also not computed by
/// any constraint.
/// Assume: [sources] are all satisfied.
Plan makePlan(List<Constraint> sources) {
int mark = newMark();
Plan plan = Plan();
List<Constraint> todo = sources;
while (todo.isNotEmpty) {
Constraint c = todo.removeLast();
if (c.output().mark != mark && c.inputsKnown(mark)) {
plan.addConstraint(c);
c.output().mark = mark;
addConstraintsConsumingTo(c.output(), todo);
}
}
return plan;
}
/// Extract a plan for resatisfying starting from the output of the
/// given [constraints], usually a set of input constraints.
Plan extractPlanFromConstraints(List<Constraint> constraints) {
List<Constraint> sources = <Constraint>[];
for (int i = 0; i < constraints.length; i++) {
Constraint c = constraints[i];
// if not in plan already and eligible for inclusion.
if (c.isInput() && c.isSatisfied()) sources.add(c);
}
return makePlan(sources);
}
/// Recompute the walkabout strengths and stay flags of all variables
/// downstream of the given constraint and recompute the actual
/// values of all variables whose stay flag is true. If a cycle is
/// detected, remove the given constraint and answer
/// false. Otherwise, answer true.
/// Details: Cycles are detected when a marked variable is
/// encountered downstream of the given constraint. The sender is
/// assumed to have marked the inputs of the given constraint with
/// the given mark. Thus, encountering a marked node downstream of
/// the output constraint means that there is a path from the
/// constraint's output to one of its inputs.
bool addPropagate(Constraint c, int mark) {
List<Constraint> todo = <Constraint>[c];
while (todo.isNotEmpty) {
Constraint d = todo.removeLast();
if (d.output().mark == mark) {
incrementalRemove(c);
return false;
}
d.recalculate();
addConstraintsConsumingTo(d.output(), todo);
}
return true;
}
/// Update the walkabout strengths and stay flags of all variables
/// downstream of the given constraint. Answer a collection of
/// unsatisfied constraints sorted in order of decreasing strength.
List<Constraint> removePropagateFrom(Variable out) {
out.determinedBy = null;
out.walkStrength = WEAKEST;
out.stay = true;
List<Constraint> unsatisfied = <Constraint>[];
List<Variable> todo = <Variable>[out];
while (todo.isNotEmpty) {
Variable v = todo.removeLast();
for (int i = 0; i < v.constraints.length; i++) {
Constraint c = v.constraints[i];
if (!c.isSatisfied()) unsatisfied.add(c);
}
Constraint determining = v.determinedBy;
for (int i = 0; i < v.constraints.length; i++) {
Constraint next = v.constraints[i];
if (next != determining && next.isSatisfied()) {
next.recalculate();
todo.add(next.output());
}
}
}
return unsatisfied;
}
void addConstraintsConsumingTo(Variable v, List<Constraint> coll) {
Constraint determining = v.determinedBy;
for (int i = 0; i < v.constraints.length; i++) {
Constraint c = v.constraints[i];
if (c != determining && c.isSatisfied()) coll.add(c);
}
}
}
/// A Plan is an ordered list of constraints to be executed in sequence
/// to resatisfy all currently satisfiable constraints in the face of
/// one or more changing inputs.
class Plan {
List<Constraint> list = <Constraint>[];
void addConstraint(Constraint c) {
list.add(c);
}
int size() => list.length;
void execute() {
for (int i = 0; i < list.length; i++) {
list[i].execute();
}
}
}
/// This is the standard DeltaBlue benchmark. A long chain of equality
/// constraints is constructed with a stay constraint on one end. An
/// edit constraint is then added to the opposite end and the time is
/// measured for adding and removing this constraint, and extracting
/// and executing a constraint satisfaction plan. There are two cases.
/// In case 1, the added constraint is stronger than the stay
/// constraint and values must propagate down the entire length of the
/// chain. In case 2, the added constraint is weaker than the stay
/// constraint so it cannot be accomodated. The cost in this case is,
/// of course, very low. Typical situations lie somewhere between these
/// two extremes.
void chainTest(int n) {
planner = Planner();
Variable prev, first, last;
// Build chain of n equality constraints.
for (int i = 0; i <= n; i++) {
Variable v = Variable("v", 0);
if (prev != null) EqualityConstraint(prev, v, REQUIRED);
if (i == 0) first = v;
if (i == n) last = v;
prev = v;
}
StayConstraint(last, STRONG_DEFAULT);
EditConstraint edit = EditConstraint(first, PREFERRED);
Plan plan = planner.extractPlanFromConstraints(<Constraint>[edit]);
for (int i = 0; i < 100; i++) {
first.value = i;
plan.execute();
if (last.value != i) {
print("Chain test failed.\n{last.value)\n{i}");
}
}
}
/// This test constructs a two sets of variables related to each
/// other by a simple linear transformation (scale and offset). The
/// time is measured to change a variable on either side of the
/// mapping and to change the scale and offset factors.
void projectionTest(int n) {
planner = Planner();
Variable scale = Variable("scale", 10);
Variable offset = Variable("offset", 1000);
Variable src, dst;
List<Variable> dests = <Variable>[];
for (int i = 0; i < n; i++) {
src = Variable("src", i);
dst = Variable("dst", i);
dests.add(dst);
StayConstraint(src, NORMAL);
ScaleConstraint(src, scale, offset, dst, REQUIRED);
}
change(src, 17);
if (dst.value != 1170) print("Projection 1 failed");
change(dst, 1050);
if (src.value != 5) print("Projection 2 failed");
change(scale, 5);
for (int i = 0; i < n - 1; i++) {
if (dests[i].value != i * 5 + 1000) print("Projection 3 failed");
}
change(offset, 2000);
for (int i = 0; i < n - 1; i++) {
if (dests[i].value != i * 5 + 2000) print("Projection 4 failed");
}
}
void change(Variable v, int newValue) {
EditConstraint edit = EditConstraint(v, PREFERRED);
Plan plan = planner.extractPlanFromConstraints(<EditConstraint>[edit]);
for (int i = 0; i < 10; i++) {
v.value = newValue;
plan.execute();
}
edit.destroyConstraint();
}
Planner planner;
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