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Get delta_blue benchmark working in Wren.

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Bob Nystrom
2014-02-10 07:56:11 -08:00
parent ec7e159017
commit 8ebbba2bc0
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-- Copyright 2008 the V8 project authors. All rights reserved.
-- Copyright 1996 John Maloney and Mario Wolczko.
-- This program 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 of the License, or
-- (at your option) any later version.
--
-- This program 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 this program; if not, write to the Free Software
-- Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
-- This implementation of the DeltaBlue benchmark is derived
-- from the Smalltalk implementation by John Maloney and Mario
-- Wolczko. Some parts have been translated directly, whereas
-- others have been modified more aggresively to make it feel
-- more like a JavaScript program.
--
-- A JavaScript 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.
--
-- From: https://github.com/mraleph/deltablue.lua
local planner
--- O b j e c t M o d e l ---
local function alert (...) print(...) end
local OrderedCollection = class()
function OrderedCollection:constructor()
self.elms = {}
end
function OrderedCollection:add(elm)
self.elms[#self.elms + 1] = elm
end
function OrderedCollection:at (index)
return self.elms[index]
end
function OrderedCollection:size ()
return #self.elms
end
function OrderedCollection:removeFirst ()
local e = self.elms[#self.elms]
self.elms[#self.elms] = nil
return e
end
function OrderedCollection:remove (elm)
local index = 0
local skipped = 0
for i = 1, #self.elms do
local value = self.elms[i]
if value ~= elm then
self.elms[index] = value
index = index + 1
else
skipped = skipped + 1
end
end
local l = #self.elms
for i = 1, skipped do self.elms[l - i + 1] = nil end
end
--
-- S t r e n g t h
--
--
-- 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 pointer comparison can be used for value comparison.
--
local Strength = class()
function Strength:constructor(strengthValue, name)
self.strengthValue = strengthValue
self.name = name
end
function Strength.stronger (s1, s2)
return s1.strengthValue < s2.strengthValue
end
function Strength.weaker (s1, s2)
return s1.strengthValue > s2.strengthValue
end
function Strength.weakestOf (s1, s2)
return Strength.weaker(s1, s2) and s1 or s2
end
function Strength.strongest (s1, s2)
return Strength.stronger(s1, s2) and s1 or s2
end
function Strength:nextWeaker ()
local v = self.strengthValue
if v == 0 then return Strength.WEAKEST
elseif v == 1 then return Strength.WEAK_DEFAULT
elseif v == 2 then return Strength.NORMAL
elseif v == 3 then return Strength.STRONG_DEFAULT
elseif v == 4 then return Strength.PREFERRED
elseif v == 5 then return Strength.REQUIRED
end
end
-- Strength constants.
Strength.REQUIRED = Strength.new(0, "required");
Strength.STONG_PREFERRED = Strength.new(1, "strongPreferred");
Strength.PREFERRED = Strength.new(2, "preferred");
Strength.STRONG_DEFAULT = Strength.new(3, "strongDefault");
Strength.NORMAL = Strength.new(4, "normal");
Strength.WEAK_DEFAULT = Strength.new(5, "weakDefault");
Strength.WEAKEST = Strength.new(6, "weakest");
--
-- C o n s t r a i n t
--
--
-- An abstract class representing a system-maintainable relationship
-- (or "constraint") between a set of variables. A constraint supplies
-- a strength instance variable; concrete subclasses provide a means
-- of storing the constrained variables and other information required
-- to represent a constraint.
--
local Constraint = class ()
function Constraint:constructor(strength)
self.strength = strength
end
--
-- Activate this constraint and attempt to satisfy it.
--
function Constraint:addConstraint ()
self:addToGraph()
planner:incrementalAdd(self)
end
--
-- 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.
--
function Constraint:satisfy (mark)
self:chooseMethod(mark)
if not self:isSatisfied() then
if self.strength == Strength.REQUIRED then
alert("Could not satisfy a required constraint!")
end
return nil
end
self:markInputs(mark)
local out = self:output()
local overridden = out.determinedBy
if overridden ~= nil then overridden:markUnsatisfied() end
out.determinedBy = self
if not planner:addPropagate(self, mark) then alert("Cycle encountered") end
out.mark = mark
return overridden
end
function Constraint:destroyConstraint ()
if self:isSatisfied()
then planner:incrementalRemove(self)
else self:removeFromGraph()
end
end
--
-- 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.
--
function Constraint:isInput ()
return false
end
--
-- U n a r y C o n s t r a i n t
--
--
-- Abstract superclass for constraints having a single possible output
-- variable.
--
local UnaryConstraint = class(Constraint)
function UnaryConstraint:constructor (v, strength)
UnaryConstraint.super.constructor(self, strength)
self.myOutput = v
self.satisfied = false
self:addConstraint()
end
--
-- Adds this constraint to the constraint graph
--
function UnaryConstraint:addToGraph ()
self.myOutput:addConstraint(self)
self.satisfied = false
end
--
-- Decides if this constraint can be satisfied and records that
-- decision.
--
function UnaryConstraint:chooseMethod (mark)
self.satisfied = (self.myOutput.mark ~= mark)
and Strength.stronger(self.strength, self.myOutput.walkStrength);
end
--
-- Returns true if this constraint is satisfied in the current solution.
--
function UnaryConstraint:isSatisfied ()
return self.satisfied;
end
function UnaryConstraint:markInputs (mark)
-- has no inputs
end
--
-- Returns the current output variable.
--
function UnaryConstraint:output ()
return self.myOutput
end
--
-- 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.
--
function UnaryConstraint:recalculate ()
self.myOutput.walkStrength = self.strength
self.myOutput.stay = not self:isInput()
if self.myOutput.stay then
self:execute() -- Stay optimization
end
end
--
-- Records that this constraint is unsatisfied
--
function UnaryConstraint:markUnsatisfied ()
self.satisfied = false
end
function UnaryConstraint:inputsKnown ()
return true
end
function UnaryConstraint:removeFromGraph ()
if self.myOutput ~= nil then
self.myOutput:removeConstraint(self)
end
self.satisfied = false
end
--
-- S t a y C o n s t r a i n t
--
--
-- 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".
--
local StayConstraint = class(UnaryConstraint)
function StayConstraint:constructor(v, str)
StayConstraint.super.constructor(self, v, str)
end
function StayConstraint:execute ()
-- Stay constraints do nothing
end
--
-- E d i t C o n s t r a i n t
--
--
-- A unary input constraint used to mark a variable that the client
-- wishes to change.
--
local EditConstraint = class (UnaryConstraint)
function EditConstraint:constructor(v, str)
EditConstraint.super.constructor(self, v, str)
end
--
-- Edits indicate that a variable is to be changed by imperative code.
--
function EditConstraint:isInput ()
return true
end
function EditConstraint:execute ()
-- Edit constraints do nothing
end
--
-- B i n a r y C o n s t r a i n t
--
local Direction = {}
Direction.NONE = 0
Direction.FORWARD = 1
Direction.BACKWARD = -1
--
-- Abstract superclass for constraints having two possible output
-- variables.
--
local BinaryConstraint = class(Constraint)
function BinaryConstraint:constructor(var1, var2, strength)
BinaryConstraint.super.constructor(self, strength);
self.v1 = var1
self.v2 = var2
self.direction = Direction.NONE
self:addConstraint()
end
--
-- 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.
--
function BinaryConstraint:chooseMethod (mark)
if self.v1.mark == mark then
self.direction = (self.v2.mark ~= mark and Strength.stronger(self.strength, self.v2.walkStrength)) and Direction.FORWARD or Direction.NONE
end
if self.v2.mark == mark then
self.direction = (self.v1.mark ~= mark and Strength.stronger(self.strength, self.v1.walkStrength)) and Direction.BACKWARD or Direction.NONE
end
if Strength.weaker(self.v1.walkStrength, self.v2.walkStrength) then
self.direction = Strength.stronger(self.strength, self.v1.walkStrength) and Direction.BACKWARD or Direction.NONE
else
self.direction = Strength.stronger(self.strength, self.v2.walkStrength) and Direction.FORWARD or Direction.BACKWARD
end
end
--
-- Add this constraint to the constraint graph
--
function BinaryConstraint:addToGraph ()
self.v1:addConstraint(self)
self.v2:addConstraint(self)
self.direction = Direction.NONE
end
--
-- Answer true if this constraint is satisfied in the current solution.
--
function BinaryConstraint:isSatisfied ()
return self.direction ~= Direction.NONE
end
--
-- Mark the input variable with the given mark.
--
function BinaryConstraint:markInputs (mark)
self:input().mark = mark
end
--
-- Returns the current input variable
--
function BinaryConstraint:input ()
return (self.direction == Direction.FORWARD) and self.v1 or self.v2
end
--
-- Returns the current output variable
--
function BinaryConstraint:output ()
return (self.direction == Direction.FORWARD) and self.v2 or self.v1
end
--
-- 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.
--
function BinaryConstraint:recalculate ()
local ihn = self:input()
local out = self:output()
out.walkStrength = Strength.weakestOf(self.strength, ihn.walkStrength);
out.stay = ihn.stay
if out.stay then self:execute() end
end
--
-- Record the fact that self constraint is unsatisfied.
--
function BinaryConstraint:markUnsatisfied ()
self.direction = Direction.NONE
end
function BinaryConstraint:inputsKnown (mark)
local i = self:input()
return i.mark == mark or i.stay or i.determinedBy == nil
end
function BinaryConstraint:removeFromGraph ()
if (self.v1 ~= nil) then self.v1:removeConstraint(self) end
if (self.v2 ~= nil) then self.v2:removeConstraint(self) end
self.direction = Direction.NONE
end
--
-- S c a l e C o n s t r a i n t
--
--
-- 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.
--
local ScaleConstraint = class (BinaryConstraint)
function ScaleConstraint:constructor(src, scale, offset, dest, strength)
self.direction = Direction.NONE
self.scale = scale
self.offset = offset
ScaleConstraint.super.constructor(self, src, dest, strength)
end
--
-- Adds this constraint to the constraint graph.
--
function ScaleConstraint:addToGraph ()
ScaleConstraint.super.addToGraph(self)
self.scale:addConstraint(self)
self.offset:addConstraint(self)
end
function ScaleConstraint:removeFromGraph ()
ScaleConstraint.super.removeFromGraph(self)
if (self.scale ~= nil) then self.scale:removeConstraint(self) end
if (self.offset ~= nil) then self.offset:removeConstraint(self) end
end
function ScaleConstraint:markInputs (mark)
ScaleConstraint.super.markInputs(self, mark);
self.offset.mark = mark
self.scale.mark = mark
end
--
-- Enforce this constraint. Assume that it is satisfied.
--
function ScaleConstraint:execute ()
if self.direction == Direction.FORWARD then
self.v2.value = self.v1.value * self.scale.value + self.offset.value
else
self.v1.value = (self.v2.value - self.offset.value) / self.scale.value
end
end
--
-- 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.
--
function ScaleConstraint:recalculate ()
local ihn = self:input()
local out = self:output()
out.walkStrength = Strength.weakestOf(self.strength, ihn.walkStrength)
out.stay = ihn.stay and self.scale.stay and self.offset.stay
if out.stay then self:execute() end
end
--
-- E q u a l i t y C o n s t r a i n t
--
--
-- Constrains two variables to have the same value.
--
local EqualityConstraint = class (BinaryConstraint)
function EqualityConstraint:constructor(var1, var2, strength)
EqualityConstraint.super.constructor(self, var1, var2, strength)
end
--
-- Enforce this constraint. Assume that it is satisfied.
--
function EqualityConstraint:execute ()
self:output().value = self:input().value
end
--
-- V a r i a b l e
--
--
-- 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.
--
local Variable = class ()
function Variable:constructor(name, initialValue)
self.value = initialValue or 0
self.constraints = OrderedCollection.new()
self.determinedBy = nil
self.mark = 0
self.walkStrength = Strength.WEAKEST
self.stay = true
self.name = name
end
--
-- Add the given constraint to the set of all constraints that refer
-- this variable.
--
function Variable:addConstraint (c)
self.constraints:add(c)
end
--
-- Removes all traces of c from this variable.
--
function Variable:removeConstraint (c)
self.constraints:remove(c)
if self.determinedBy == c then
self.determinedBy = nil
end
end
--
-- P l a n n e r
--
--
-- The DeltaBlue planner
--
local Planner = class()
function Planner:constructor()
self.currentMark = 0
end
--
-- 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.
--
function Planner:incrementalAdd (c)
local mark = self:newMark()
local overridden = c:satisfy(mark)
while overridden ~= nil do
overridden = overridden:satisfy(mark)
end
end
--
-- 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.
--
function Planner:incrementalRemove (c)
local out = c:output()
c:markUnsatisfied()
c:removeFromGraph()
local unsatisfied = self:removePropagateFrom(out)
local strength = Strength.REQUIRED
repeat
for i = 1, unsatisfied:size() do
local u = unsatisfied:at(i)
if u.strength == strength then
self:incrementalAdd(u)
end
end
strength = strength:nextWeaker()
until strength == Strength.WEAKEST
end
--
-- Select a previously unused mark value.
--
function Planner:newMark ()
self.currentMark = self.currentMark + 1
return self.currentMark
end
--
-- 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.
--
local Plan -- FORWARD DECLARATION
function Planner:makePlan (sources)
local mark = self:newMark()
local plan = Plan.new()
local todo = sources
while todo:size() > 0 do
local c = todo:removeFirst()
if c:output().mark ~= mark and c:inputsKnown(mark) then
plan:addConstraint(c)
c:output().mark = mark
self:addConstraintsConsumingTo(c:output(), todo)
end
end
return plan
end
--
-- Extract a plan for resatisfying starting from the output of the
-- given constraints, usually a set of input constraints.
--
function Planner:extractPlanFromConstraints (constraints)
local sources = OrderedCollection.new()
for i = 1, constraints:size() do
local c = constraints:at(i)
if c:isInput() and c:isSatisfied() then
-- not in plan already and eligible for inclusion
sources:add(c)
end
end
return self:makePlan(sources)
end
--
-- 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.
--
function Planner:addPropagate (c, mark)
local todo = OrderedCollection.new()
todo:add(c)
while todo:size() > 0 do
local d = todo:removeFirst()
if d:output().mark == mark then
self:incrementalRemove(c)
return false
end
d:recalculate()
self:addConstraintsConsumingTo(d:output(), todo)
end
return true
end
--
-- 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.
--
function Planner:removePropagateFrom (out)
out.determinedBy = nil
out.walkStrength = Strength.WEAKEST
out.stay = true
local unsatisfied = OrderedCollection.new()
local todo = OrderedCollection.new()
todo:add(out)
while todo:size() > 0 do
local v = todo:removeFirst()
for i = 1, v.constraints:size() do
local c = v.constraints:at(i)
if not c:isSatisfied() then unsatisfied:add(c) end
end
local determining = v.determinedBy
for i = 1, v.constraints:size() do
local next = v.constraints:at(i);
if next ~= determining and next:isSatisfied() then
next:recalculate()
todo:add(next:output())
end
end
end
return unsatisfied
end
function Planner:addConstraintsConsumingTo (v, coll)
local determining = v.determinedBy
local cc = v.constraints
for i = 1, cc:size() do
local c = cc:at(i)
if c ~= determining and c:isSatisfied() then
coll:add(c)
end
end
end
--
-- P l a n
--
--
-- 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.
--
Plan = class()
function Plan:constructor()
self.v = OrderedCollection.new()
end
function Plan:addConstraint (c)
self.v:add(c)
end
function Plan:size ()
return self.v:size()
end
function Plan:constraintAt (index)
return self.v:at(index)
end
function Plan:execute ()
for i = 1, self:size() do
local c = self:constraintAt(i)
c:execute()
end
end
--
-- M a i n
--
--
-- 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.
--
local function chainTest(n)
planner = Planner.new()
local prev = nil
local first = nil
local last = nil
-- Build chain of n equality constraints
for i = 0, n do
local name = "v" .. i;
local v = Variable.new(name)
if prev ~= nil then EqualityConstraint.new(prev, v, Strength.REQUIRED) end
if i == 0 then first = v end
if i == n then last = v end
prev = v
end
StayConstraint.new(last, Strength.STRONG_DEFAULT)
local edit = EditConstraint.new(first, Strength.PREFERRED)
local edits = OrderedCollection.new()
edits:add(edit)
local plan = planner:extractPlanFromConstraints(edits)
for i = 0, 99 do
first.value = i
plan:execute()
if last.value ~= i then
alert("Chain test failed.")
end
end
end
local function change(v, newValue)
local edit = EditConstraint.new(v, Strength.PREFERRED)
local edits = OrderedCollection.new()
edits:add(edit)
local plan = planner:extractPlanFromConstraints(edits)
for i = 1, 10 do
v.value = newValue
plan:execute()
end
edit:destroyConstraint()
end
--
-- 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.
--
local function projectionTest(n)
planner = Planner.new();
local scale = Variable.new("scale", 10);
local offset = Variable.new("offset", 1000);
local src = nil
local dst = nil;
local dests = OrderedCollection.new();
for i = 0, n - 1 do
src = Variable.new("src" .. i, i);
dst = Variable.new("dst" .. i, i);
dests:add(dst);
StayConstraint.new(src, Strength.NORMAL);
ScaleConstraint.new(src, scale, offset, dst, Strength.REQUIRED);
end
change(src, 17)
if dst.value ~= 1170 then alert("Projection 1 failed") end
change(dst, 1050)
if src.value ~= 5 then alert("Projection 2 failed") end
change(scale, 5)
for i = 0, n - 2 do
if dests:at(i + 1).value ~= i * 5 + 1000 then
alert("Projection 3 failed")
end
end
change(offset, 2000)
for i = 0, n - 2 do
if dests:at(i + 1).value ~= i * 5 + 2000 then
alert("Projection 4 failed")
end
end
end
local function deltaBlue()
chainTest(100);
projectionTest(100);
end
DeltaBlue = BenchmarkSuite.new('DeltaBlue', 66118, {
Benchmark.new('DeltaBlue', deltaBlue)
})

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"""
deltablue.py
============
Ported for the PyPy project.
This implementation of the DeltaBlue benchmark was directly ported
from the `V8's source code`_, which was in turn derived
from the Smalltalk implementation by John Maloney and Mario
Wolczko. The original Javascript implementation was licensed under the GPL.
It's been updated in places to be more idiomatic to Python (for loops over
collections, a couple magic methods, ``OrderedCollection`` being a list & things
altering those collections changed to the builtin methods) but largely retains
the layout & logic from the original. (Ugh.)
.. _`V8's source code`: (http://code.google.com/p/v8/source/browse/branches/bleeding_edge/benchmarks/deltablue.js)
From: https://gist.github.com/toastdriven/6408132
"""
from __future__ import print_function
import time
__author__ = 'Daniel Lindsley'
__license__ = 'BSD'
# The JS variant implements "OrderedCollection", which basically completely
# overlaps with ``list``. So we'll cheat. :D
class OrderedCollection(list):
pass
class Strength(object):
REQUIRED = None
STRONG_PREFERRED = None
PREFERRED = None
STRONG_DEFAULT = None
NORMAL = None
WEAK_DEFAULT = None
WEAKEST = None
def __init__(self, strength, name):
super(Strength, self).__init__()
self.strength = strength
self.name = name
@classmethod
def stronger(cls, s1, s2):
return s1.strength < s2.strength
@classmethod
def weaker(cls, s1, s2):
return s1.strength > s2.strength
@classmethod
def weakest_of(cls, s1, s2):
if cls.weaker(s1, s2):
return s1
return s2
@classmethod
def strongest(cls, s1, s2):
if cls.stronger(s1, s2):
return s1
return s2
def next_weaker(self):
strengths = {
0: self.__class__.WEAKEST,
1: self.__class__.WEAK_DEFAULT,
2: self.__class__.NORMAL,
3: self.__class__.STRONG_DEFAULT,
4: self.__class__.PREFERRED,
# TODO: This looks like a bug in the original code. Shouldn't this be
# ``STRONG_PREFERRED? Keeping for porting sake...
5: self.__class__.REQUIRED,
}
return strengths[self.strength]
# This is a terrible pattern IMO, but true to the original JS implementation.
Strength.REQUIRED = Strength(0, "required")
Strength.STONG_PREFERRED = Strength(1, "strongPreferred")
Strength.PREFERRED = Strength(2, "preferred")
Strength.STRONG_DEFAULT = Strength(3, "strongDefault")
Strength.NORMAL = Strength(4, "normal")
Strength.WEAK_DEFAULT = Strength(5, "weakDefault")
Strength.WEAKEST = Strength(6, "weakest")
class Constraint(object):
def __init__(self, strength):
super(Constraint, self).__init__()
self.strength = strength
def add_constraint(self):
global planner
self.add_to_graph()
planner.incremental_add(self)
def satisfy(self, mark):
global planner
self.choose_method(mark)
if not self.is_satisfied():
if self.strength == Strength.REQUIRED:
print('Could not satisfy a required constraint!')
return None
self.mark_inputs(mark)
out = self.output()
overridden = out.determined_by
if overridden is not None:
overridden.mark_unsatisfied()
out.determined_by = self
if not planner.add_propagate(self, mark):
print('Cycle encountered')
out.mark = mark
return overridden
def destroy_constraint(self):
global planner
if self.is_satisfied():
planner.incremental_remove(self)
else:
self.remove_from_graph()
def is_input(self):
return False
class UrnaryConstraint(Constraint):
def __init__(self, v, strength):
super(UrnaryConstraint, self).__init__(strength)
self.my_output = v
self.satisfied = False
self.add_constraint()
def add_to_graph(self):
self.my_output.add_constraint(self)
self.satisfied = False
def choose_method(self, mark):
if self.my_output.mark != mark and \
Strength.stronger(self.strength, self.my_output.walk_strength):
self.satisfied = True
else:
self.satisfied = False
def is_satisfied(self):
return self.satisfied
def mark_inputs(self, mark):
# No-ops.
pass
def output(self):
# Ugh. Keeping it for consistency with the original. So much for
# "we're all adults here"...
return self.my_output
def recalculate(self):
self.my_output.walk_strength = self.strength
self.my_output.stay = not self.is_input()
if self.my_output.stay:
self.execute()
def mark_unsatisfied(self):
self.satisfied = False
def inputs_known(self, mark):
return True
def remove_from_graph(self):
if self.my_output is not None:
self.my_output.remove_constraint(self)
self.satisfied = False
class StayConstraint(UrnaryConstraint):
def __init__(self, v, string):
super(StayConstraint, self).__init__(v, string)
def execute(self):
# The methods, THEY DO NOTHING.
pass
class EditConstraint(UrnaryConstraint):
def __init__(self, v, string):
super(EditConstraint, self).__init__(v, string)
def is_input(self):
return True
def execute(self):
# This constraint also does nothing.
pass
class Direction(object):
# Hooray for things that ought to be structs!
NONE = 0
FORWARD = 1
BACKWARD = -1
class BinaryConstraint(Constraint):
def __init__(self, v1, v2, strength):
super(BinaryConstraint, self).__init__(strength)
self.v1 = v1
self.v2 = v2
self.direction = Direction.NONE
self.add_constraint()
def choose_method(self, mark):
if self.v1.mark == mark:
if self.v2.mark != mark and Strength.stronger(self.strength, self.v2.walk_strength):
self.direction = Direction.FORWARD
else:
self.direction = Direction.BACKWARD
if self.v2.mark == mark:
if self.v1.mark != mark and Strength.stronger(self.strength, self.v1.walk_strength):
self.direction = Direction.BACKWARD
else:
self.direction = Direction.NONE
if Strength.weaker(self.v1.walk_strength, self.v2.walk_strength):
if Strength.stronger(self.strength, self.v1.walk_strength):
self.direction = Direction.BACKWARD
else:
self.direction = Direction.NONE
else:
if Strength.stronger(self.strength, self.v2.walk_strength):
self.direction = Direction.FORWARD
else:
self.direction = Direction.BACKWARD
def add_to_graph(self):
self.v1.add_constraint(self)
self.v2.add_constraint(self)
self.direction = Direction.NONE
def is_satisfied(self):
return self.direction != Direction.NONE
def mark_inputs(self, mark):
self.input().mark = mark
def input(self):
if self.direction == Direction.FORWARD:
return self.v1
return self.v2
def output(self):
if self.direction == Direction.FORWARD:
return self.v2
return self.v1
def recalculate(self):
ihn = self.input()
out = self.output()
out.walk_strength = Strength.weakest_of(self.strength, ihn.walk_strength)
out.stay = ihn.stay
if out.stay:
self.execute()
def mark_unsatisfied(self):
self.direction = Direction.NONE
def inputs_known(self, mark):
i = self.input()
return i.mark == mark or i.stay or i.determined_by == None
def remove_from_graph(self):
if self.v1 is not None:
self.v1.remove_constraint(self)
if self.v2 is not None:
self.v2.remove_constraint(self)
self.direction = Direction.NONE
class ScaleConstraint(BinaryConstraint):
def __init__(self, src, scale, offset, dest, strength):
self.direction = Direction.NONE
self.scale = scale
self.offset = offset
super(ScaleConstraint, self).__init__(src, dest, strength)
def add_to_graph(self):
super(ScaleConstraint, self).add_to_graph()
self.scale.add_constraint(self)
self.offset.add_constraint(self)
def remove_from_graph(self):
super(ScaleConstraint, self).remove_from_graph()
if self.scale is not None:
self.scale.remove_constraint(self)
if self.offset is not None:
self.offset.remove_constraint(self)
def mark_inputs(self, mark):
super(ScaleConstraint, self).mark_inputs(mark)
self.scale.mark = mark
self.offset.mark = mark
def execute(self):
if self.direction == Direction.FORWARD:
self.v2.value = self.v1.value * self.scale.value + self.offset.value
else:
self.v1.value = (self.v2.value - self.offset.value) / self.scale.value
def recalculate(self):
ihn = self.input()
out = self.output()
out.walk_strength = Strength.weakest_of(self.strength, ihn.walk_strength)
out.stay = ihn.stay and self.scale.stay and self.offset.stay
if out.stay:
self.execute()
class EqualityConstraint(BinaryConstraint):
def execute(self):
self.output().value = self.input().value
class Variable(object):
def __init__(self, name, initial_value=0):
super(Variable, self).__init__()
self.name = name
self.value = initial_value
self.constraints = OrderedCollection()
self.determined_by = None
self.mark = 0
self.walk_strength = Strength.WEAKEST
self.stay = True
def __repr__(self):
# To make debugging this beast from pdb easier...
return '<Variable: %s - %s>' % (
self.name,
self.value
)
def add_constraint(self, constraint):
self.constraints.append(constraint)
def remove_constraint(self, constraint):
self.constraints.remove(constraint)
if self.determined_by == constraint:
self.determined_by = None
class Planner(object):
def __init__(self):
super(Planner, self).__init__()
self.current_mark = 0
def incremental_add(self, constraint):
mark = self.new_mark()
overridden = constraint.satisfy(mark)
while overridden is not None:
overridden = overridden.satisfy(mark)
def incremental_remove(self, constraint):
out = constraint.output()
constraint.mark_unsatisfied()
constraint.remove_from_graph()
unsatisfied = self.remove_propagate_from(out)
strength = Strength.REQUIRED
# Do-while, the Python way.
repeat = True
while repeat:
for u in unsatisfied:
if u.strength == strength:
self.incremental_add(u)
strength = strength.next_weaker()
repeat = strength != Strength.WEAKEST
def new_mark(self):
self.current_mark += 1
return self.current_mark
def make_plan(self, sources):
mark = self.new_mark()
plan = Plan()
todo = sources
while len(todo):
c = todo.pop(0)
if c.output().mark != mark and c.inputs_known(mark):
plan.add_constraint(c)
c.output().mark = mark
self.add_constraints_consuming_to(c.output(), todo)
return plan
def extract_plan_from_constraints(self, constraints):
sources = OrderedCollection()
for c in constraints:
if c.is_input() and c.is_satisfied():
sources.append(c)
return self.make_plan(sources)
def add_propagate(self, c, mark):
todo = OrderedCollection()
todo.append(c)
while len(todo):
d = todo.pop(0)
if d.output().mark == mark:
self.incremental_remove(c)
return False
d.recalculate()
self.add_constraints_consuming_to(d.output(), todo)
return True
def remove_propagate_from(self, out):
out.determined_by = None
out.walk_strength = Strength.WEAKEST
out.stay = True
unsatisfied = OrderedCollection()
todo = OrderedCollection()
todo.append(out)
while len(todo):
v = todo.pop(0)
for c in v.constraints:
if not c.is_satisfied():
unsatisfied.append(c)
determining = v.determined_by
for c in v.constraints:
if c != determining and c.is_satisfied():
c.recalculate()
todo.append(c.output())
return unsatisfied
def add_constraints_consuming_to(self, v, coll):
determining = v.determined_by
cc = v.constraints
for c in cc:
if c != determining and c.is_satisfied():
# I guess we're just updating a reference (``coll``)? Seems
# inconsistent with the rest of the implementation, where they
# return the lists...
coll.append(c)
class Plan(object):
def __init__(self):
super(Plan, self).__init__()
self.v = OrderedCollection()
def add_constraint(self, c):
self.v.append(c)
def __len__(self):
return len(self.v)
def __getitem__(self, index):
return self.v[index]
def execute(self):
for c in self.v:
c.execute()
# Main
total = 0
def chain_test(n):
"""
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.
"""
global planner
global total
planner = Planner()
prev, first, last = None, None, None
# We need to go up to n inclusively.
for i in range(n + 1):
name = "v%s" % i
v = Variable(name)
if prev is not None:
EqualityConstraint(prev, v, Strength.REQUIRED)
if i == 0:
first = v
if i == n:
last = v
prev = v
StayConstraint(last, Strength.STRONG_DEFAULT)
edit = EditConstraint(first, Strength.PREFERRED)
edits = OrderedCollection()
edits.append(edit)
plan = planner.extract_plan_from_constraints(edits)
for i in range(100):
first.value = i
plan.execute()
total += last.value
if last.value != i:
print("Chain test failed.")
def projection_test(n):
"""
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.
"""
global planner
global total
planner = Planner()
scale = Variable("scale", 10)
offset = Variable("offset", 1000)
src, dest = None, None
dests = OrderedCollection()
for i in range(n):
src = Variable("src%s" % i, i)
dst = Variable("dst%s" % i, i)
dests.append(dst)
StayConstraint(src, Strength.NORMAL)
ScaleConstraint(src, scale, offset, dst, Strength.REQUIRED)
change(src, 17)
total += dst.value
if dst.value != 1170:
print("Projection 1 failed")
change(dst, 1050)
total += src.value
if src.value != 5:
print("Projection 2 failed")
change(scale, 5)
for i in range(n - 1):
total += dests[i].value
if dests[i].value != (i * 5 + 1000):
print("Projection 3 failed")
change(offset, 2000)
for i in range(n - 1):
total += dests[i].value
if dests[i].value != (i * 5 + 2000):
print("Projection 4 failed")
def change(v, new_value):
global planner
edit = EditConstraint(v, Strength.PREFERRED)
edits = OrderedCollection()
edits.append(edit)
plan = planner.extract_plan_from_constraints(edits)
for i in range(10):
v.value = new_value
plan.execute()
edit.destroy_constraint()
# HOORAY FOR GLOBALS... Oh wait.
# In spirit of the original, we'll keep it, but ugh.
planner = None
def delta_blue():
global total
start = time.clock()
for i in range(20):
chain_test(100)
projection_test(100)
print(total)
print("elapsed: " + str(time.clock() - start))
if __name__ == '__main__':
delta_blue()

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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.
//
// Translated to Wren by Bob Nystrom 2014.
// A Wren 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.
// TODO: Support forward declarations of globals.
var REQUIRED = null
var STRONG_REFERRED = null
var PREFERRED = null
var STRONG_DEFAULT = null
var NORMAL = null
var WEAK_DEFAULT = null
var WEAKEST = null
var ORDERED = null
// 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 {
new(value, name) {
_value = value
_name = name
}
value { return _value }
name { return _name }
nextWeaker { return ORDERED[_value] }
static stronger(s1, s2) { return s1.value < s2.value }
static weaker(s1, s2) { return s1.value > s2.value }
static weakest(s1, s2) {
// TODO: Ternary operator.
if (Strength.weaker(s1, s2)) return s1
return s2
}
static strongest(s1, s2) {
// TODO: Ternary operator.
if (Strength.stronger(s1, s2)) return s1
return s2
}
}
// Compile time computed constants.
REQUIRED = new Strength(0, "required")
STRONG_REFERRED = new Strength(1, "strongPreferred")
PREFERRED = new Strength(2, "preferred")
STRONG_DEFAULT = new Strength(3, "strongDefault")
NORMAL = new Strength(4, "normal")
WEAK_DEFAULT = new Strength(5, "weakDefault")
WEAKEST = new Strength(6, "weakest")
ORDERED = [
WEAKEST, WEAK_DEFAULT, NORMAL, STRONG_DEFAULT, PREFERRED, STRONG_REFERRED
]
// TODO: Forward declarations.
var planner
class Constraint {
new(strength) {
_strength = strength
}
strength { return _strength }
// Activate this constraint and attempt to satisfy it.
addConstraint {
this.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.
satisfy(mark) {
this.chooseMethod(mark)
if (!this.isSatisfied) {
if (_strength == REQUIRED) {
IO.print("Could not satisfy a required constraint!")
}
return null
}
this.markInputs(mark)
var out = this.output
var overridden = out.determinedBy
if (overridden != null) overridden.markUnsatisfied
out.determinedBy = this
if (!planner.addPropagate(this, mark)) IO.print("Cycle encountered")
out.mark = mark
return overridden
}
destroyConstraint {
if (this.isSatisfied) planner.incrementalRemove(this)
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.
isInput { return false }
}
// Abstract superclass for constraints having a single possible output variable.
class UnaryConstraint is Constraint {
new(myOutput, strength) {
super(strength)
_satisfied = false
_myOutput = myOutput
this.addConstraint
}
// Adds this constraint to the constraint graph.
addToGraph {
_myOutput.addConstraint(this)
_satisfied = false;
}
// Decides if this constraint can be satisfied and records that decision.
chooseMethod(mark) {
_satisfied = (_myOutput.mark != mark) &&
Strength.stronger(this.strength, _myOutput.walkStrength)
}
// Returns true if this constraint is satisfied in the current solution.
isSatisfied { return _satisfied }
markInputs(mark) {
// has no inputs.
}
// Returns the current output variable.
output { return _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.
recalculate {
_myOutput.walkStrength = this.strength
_myOutput.stay = !this.isInput
if (_myOutput.stay) this.execute // Stay optimization.
}
// Records that this constraint is unsatisfied.
markUnsatisfied {
_satisfied = false
}
inputsKnown(mark) { return true }
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 is UnaryConstraint {
new(variable, strength) {
super(variable, strength)
}
execute {
// Stay constraints do nothing.
}
}
// A unary input constraint used to mark a variable that the client
// wishes to change.
class EditConstraint is UnaryConstraint {
EditConstraint(variable, strength) {
super(variable, strength)
}
// Edits indicate that a variable is to be changed by imperative code.
isInput { return true }
execute {
// Edit constraints do nothing.
}
}
// Directions.
var NONE = 1
var FORWARD = 2
var BACKWARD = 0
// Abstract superclass for constraints having two possible output
// variables.
class BinaryConstraint is Constraint {
new(v1, v2, strength) {
super(strength)
_v1 = v1
_v2 = v2
_direction = NONE
this.addConstraint
}
direction { return _direction }
v1 { return _v1 }
v2 { return _v2 }
// 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.
chooseMethod(mark) {
if (_v1.mark == mark) {
if (_v2.mark != mark &&
Strength.stronger(this.strength, _v2.walkStrength)) {
_direction = FORWARD
} else {
_direction = NONE
}
}
if (_v2.mark == mark) {
if (_v1.mark != mark &&
Strength.stronger(this.strength, _v1.walkStrength)) {
_direction = BACKWARD
} else {
_direction = NONE
}
}
if (Strength.weaker(_v1.walkStrength, _v2.walkStrength)) {
if (Strength.stronger(this.strength, _v1.walkStrength)) {
_direction = BACKWARD
} else {
_direction = NONE
}
} else {
if (Strength.stronger(this.strength, _v2.walkStrength)) {
_direction = FORWARD
} else {
_direction = BACKWARD
}
}
}
// Add this constraint to the constraint graph.
addToGraph {
_v1.addConstraint(this)
_v2.addConstraint(this)
_direction = NONE
}
// Answer true if this constraint is satisfied in the current solution.
isSatisfied { return _direction != NONE }
// Mark the input variable with the given mark.
markInputs(mark) {
this.input.mark = mark
}
// Returns the current input variable
input {
if (_direction == FORWARD) return _v1
return _v2
}
// Returns the current output variable.
output {
if (_direction == FORWARD) return _v2
return _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.
recalculate {
var ihn = this.input
var out = this.output
out.walkStrength = Strength.weakest(this.strength, ihn.walkStrength)
out.stay = ihn.stay
if (out.stay) this.execute
}
// Record the fact that this constraint is unsatisfied.
markUnsatisfied {
_direction = NONE
}
inputsKnown(mark) {
var i = this.input
return i.mark == mark || i.stay || i.determinedBy == null
}
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 is BinaryConstraint {
new(src, scale, offset, dest, strength) {
_scale = scale
_offset = offset
super(src, dest, strength)
}
// Adds this constraint to the constraint graph.
addToGraph {
super.addToGraph
_scale.addConstraint(this)
_offset.addConstraint(this)
}
removeFromGraph {
super.removeFromGraph
if (_scale != null) _scale.removeConstraint(this)
if (_offset != null) _offset.removeConstraint(this)
}
markInputs(mark) {
super.markInputs(mark)
_scale.mark = _offset.mark = mark
}
// Enforce this constraint. Assume that it is satisfied.
execute {
if (this.direction == FORWARD) {
this.v2.value = this.v1.value * _scale.value + _offset.value;
} else {
// TODO: Is this the same semantics as ~/?
this.v1.value = ((this.v2.value - _offset.value) / _scale.value).floor;
}
}
// 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.
recalculate {
var ihn = this.input
var out = this.output
out.walkStrength = Strength.weakest(this.strength, ihn.walkStrength)
out.stay = ihn.stay && _scale.stay && _offset.stay
if (out.stay) this.execute
}
}
// Constrains two variables to have the same value.
class EqualityConstraint is BinaryConstraint {
new(v1, v2, strength) {
super(v1, v2, strength)
}
// Enforce this constraint. Assume that it is satisfied.
execute {
this.output.value = this.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 {
new(name, value) {
_constraints = []
_determinedBy = null
_mark = 0
_walkStrength = WEAKEST
_stay = true
_name = name
_value = value
}
constraints { return _constraints }
determinedBy { return _determinedBy }
determinedBy = value { return _determinedBy = value }
mark { return _mark }
mark = value { return _mark = value }
walkStrength { return _walkStrength }
walkStrength = value { return _walkStrength = value }
stay { return _stay }
stay = value { return _stay = value }
value { return _value }
value = newValue { return _value = newValue }
// Add the given constraint to the set of all constraints that refer
// this variable.
addConstraint(constraint) {
_constraints.add(constraint)
}
// Removes all traces of c from this variable.
removeConstraint(constraint) {
// TODO: Better way to filter list.
var i = 0
while (i < _constraints.count) {
if (_constraints[i] == constraint) {
_constraints.removeAt(i)
} else {
i = i + 1
}
}
if (_determinedBy == constraint) _determinedBy = null
}
}
// 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 {
new {
_list = []
}
addConstraint(constraint) {
_list.add(constraint)
}
size { return _list.count }
execute {
for (constraint in _list) {
constraint.execute
}
}
}
class Planner {
new {
_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.
incrementalAdd(constraint) {
var mark = this.newMark
var overridden = constraint.satisfy(mark)
while (overridden != null) {
overridden = overridden.satisfy(mark)
}
}
// 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.
incrementalRemove(constraint) {
var out = constraint.output
constraint.markUnsatisfied
constraint.removeFromGraph
var unsatisfied = this.removePropagateFrom(out)
var strength = REQUIRED
while (true) {
for (i in 0...unsatisfied.count) {
var u = unsatisfied[i]
if (u.strength == strength) this.incrementalAdd(u)
}
strength = strength.nextWeaker
if (strength == WEAKEST) break
}
}
// Select a previously unused mark value.
newMark {
_currentMark = _currentMark + 1
return _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.
makePlan(sources) {
var mark = this.newMark
var plan = new Plan
var todo = sources
while (todo.count > 0) {
var constraint = todo.removeAt(-1)
if (constraint.output.mark != mark && constraint.inputsKnown(mark)) {
plan.addConstraint(constraint)
constraint.output.mark = mark
this.addConstraintsConsumingTo(constraint.output, todo)
}
}
return plan
}
// Extract a plan for resatisfying starting from the output of the
// given [constraints], usually a set of input constraints.
extractPlanFromConstraints(constraints) {
var sources = []
for (i in 0...constraints.count) {
var constraint = constraints[i]
// if not in plan already and eligible for inclusion.
if (constraint.isInput && constraint.isSatisfied) sources.add(constraint)
}
return this.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.
addPropagate(constraint, mark) {
var todo = [constraint]
while (todo.count > 0) {
var d = todo.removeAt(-1)
if (d.output.mark == mark) {
this.incrementalRemove(constraint)
return false
}
d.recalculate
this.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.
removePropagateFrom(out) {
out.determinedBy = null
out.walkStrength = WEAKEST
out.stay = true
var unsatisfied = []
var todo = [out]
while (todo.count > 0) {
var v = todo.removeAt(-1)
for (i in 0...v.constraints.count) {
var constraint = v.constraints[i]
if (!constraint.isSatisfied) unsatisfied.add(constraint)
}
var determining = v.determinedBy
for (i in 0...v.constraints.count) {
var next = v.constraints[i]
if (next != determining && next.isSatisfied) {
next.recalculate
todo.add(next.output)
}
}
}
return unsatisfied
}
addConstraintsConsumingTo(v, coll) {
var determining = v.determinedBy
for (i in 0...v.constraints.count) {
var constraint = v.constraints[i]
if (constraint != determining && constraint.isSatisfied) {
coll.add(constraint)
}
}
}
}
var total = 0
// 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.
var chainTest = fn(n) {
planner = new Planner
var prev = null
var first = null
var last = null
// Build chain of n equality constraints.
for (i in 0..n) {
var v = new Variable("v", 0)
if (prev != null) new EqualityConstraint(prev, v, REQUIRED)
if (i == 0) first = v
if (i == n) last = v
prev = v
}
new StayConstraint(last, STRONG_DEFAULT)
var edit = new EditConstraint(first, PREFERRED)
var plan = planner.extractPlanFromConstraints([edit])
for (i in 0...100) {
first.value = i
plan.execute
total = total + last.value
}
}
var change = fn(v, newValue) {
var edit = new EditConstraint(v, PREFERRED)
var plan = planner.extractPlanFromConstraints([edit])
for (i in 0...10) {
v.value = newValue
plan.execute
}
edit.destroyConstraint
}
// 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.
var projectionTest = fn(n) {
planner = new Planner
var scale = new Variable("scale", 10)
var offset = new Variable("offset", 1000)
var src = null
var dst = null
var dests = []
for (i in 0...n) {
src = new Variable("src", i)
dst = new Variable("dst", i)
dests.add(dst)
new StayConstraint(src, NORMAL)
new ScaleConstraint(src, scale, offset, dst, REQUIRED)
}
change.call(src, 17)
total = total + dst.value
if (dst.value != 1170) IO.print("Projection 1 failed")
change.call(dst, 1050)
total = total + src.value
if (src.value != 5) IO.print("Projection 2 failed")
change.call(scale, 5)
for (i in 0...n - 1) {
total = total + dests[i].value
if (dests[i].value != i * 5 + 1000) IO.print("Projection 3 failed")
}
change.call(offset, 2000)
for (i in 0...n - 1) {
total = total + dests[i].value
if (dests[i].value != i * 5 + 2000) IO.print("Projection 4 failed")
}
}
var start = IO.clock
for (i in 0...20) {
chainTest.call(100)
projectionTest.call(100)
}
IO.print(total)
IO.print("elapsed: " + (IO.clock - start).toString)

2
benchmark/run_bench
View File

@ -29,6 +29,8 @@ BENCHMARK("binary_trees", """stretch tree of depth 13 check: -1
32 trees of depth 12 check: -32
long lived tree of depth 12 check: -1""")
BENCHMARK("delta_blue", "7032700")
BENCHMARK("fib", r"""317811
317811
317811