Revert "update vendor/"

This reverts commit 7ce522110e.

vendor/github.com/crillab/gophersat/bf/bf.go

@@ -8,7 +8,7 @@ import (
 	"strconv"
 	"strings"
 
-	"github.com/mudler/gophersat/solver"
+	"github.com/crillab/gophersat/solver"
 )
 
 // A Formula is any kind of boolean formula, not necessarily in CNF.
@@ -72,8 +72,8 @@ type trueConst struct{}
 // True is the constant denoting a tautology.
 var True Formula = trueConst{}
 
-func (t trueConst) nnf() Formula                    { return t }
-func (t trueConst) String() string                  { return "⊤" }
+func (t trueConst) nnf() Formula   { return t }
+func (t trueConst) String() string { return "⊤" }
 func (t trueConst) Eval(model map[string]bool) bool { return true }
 
 // The "false" constant.
@@ -82,8 +82,8 @@ type falseConst struct{}
 // False is the constant denoting a contradiction.
 var False Formula = falseConst{}
 
-func (f falseConst) nnf() Formula                    { return f }
-func (f falseConst) String() string                  { return "⊥" }
+func (f falseConst) nnf() Formula   { return f }
+func (f falseConst) String() string { return "⊥" }
 func (f falseConst) Eval(model map[string]bool) bool { return false }
 
 // Var generates a named boolean variable in a formula.

vendor/github.com/crillab/gophersat/solver/card.go

@@ -15,11 +15,10 @@ func AtLeast1(lits ...int) CardConstr {
 
 // AtMost1 returns a cardinality constraint stating that at most one of the given lits can be true.
 func AtMost1(lits ...int) CardConstr {
-	negated := make([]int, len(lits))
 	for i, lit := range lits {
-		negated[i] = -lit
+		lits[i] = -lit
 	}
-	return CardConstr{Lits: negated, AtLeast: len(lits) - 1}
+	return CardConstr{Lits: lits, AtLeast: len(lits) - 1}
 }
 
 // Exactly1 returns two cardinality constraints stating that exactly one of the given lits must be true.

vendor/github.com/crillab/gophersat/solver/parser.go

@@ -55,21 +55,17 @@ func ParseSlice(cnf [][]int) *Problem {
 			return &pb
 		}
 	}
-	pb.simplify2()
+	pb.simplify()
 	return &pb
 }
 
-func isSpace(b byte) bool {
-	return b == ' ' || b == '\t' || b == '\n' || b == '\r'
-}
-
 // readInt reads an int from r.
 // 'b' is the last read byte. It can be a space, a '-' or a digit.
 // The int can be negated.
 // All spaces before the int value are ignored.
 // Can return EOF.
 func readInt(b *byte, r *bufio.Reader) (res int, err error) {
-	for err == nil && isSpace(*b) {
+	for err == nil && (*b == ' ' || *b == '\t' || *b == '\n' || *b == '\r') {
 		*b, err = r.ReadByte()
 	}
 	if err == io.EOF {
@@ -92,7 +88,7 @@ func readInt(b *byte, r *bufio.Reader) (res int, err error) {
 		}
 		res = 10*res + int(*b-'0')
 		*b, err = r.ReadByte()
-		if isSpace(*b) {
+		if *b == ' ' || *b == '\t' || *b == '\n' || *b == '\r' {
 			break
 		}
 	}

vendor/github.com/crillab/gophersat/solver/parser_pb.go

@@ -65,14 +65,6 @@ func ParseCardConstrs(constrs []CardConstr) *Problem {
 	return &pb
 }
 
-func (pb *Problem) appendClause(constr PBConstr) {
-	lits := make([]Lit, len(constr.Lits))
-	for j, val := range constr.Lits {
-		lits[j] = IntToLit(int32(val))
-	}
-	pb.Clauses = append(pb.Clauses, NewPBClause(lits, constr.Weights, constr.AtLeast))
-}
-
 // ParsePBConstrs parses and returns a PB problem from PBConstr values.
 func ParsePBConstrs(constrs []PBConstr) *Problem {
 	var pb Problem
@@ -108,7 +100,11 @@ func ParsePBConstrs(constrs []PBConstr) *Problem {
 				}
 			}
 		} else {
-			pb.appendClause(constr)
+			lits := make([]Lit, len(constr.Lits))
+			for j, val := range constr.Lits {
+				lits[j] = IntToLit(int32(val))
+			}
+			pb.Clauses = append(pb.Clauses, NewPBClause(lits, constr.Weights, card))
 		}
 	}
 	pb.Model = make([]decLevel, pb.NbVars)

vendor/github.com/crillab/gophersat/solver/solver.go

@@ -87,16 +87,10 @@ func New(problem *Problem) *Solver {
 		return &Solver{status: Unsat}
 	}
 	nbVars := problem.NbVars
-
-	trailCap := nbVars
-	if len(problem.Units) > trailCap {
-		trailCap = len(problem.Units)
-	}
-
 	s := &Solver{
 		nbVars:     nbVars,
 		status:     problem.Status,
-		trail:      make([]Lit, len(problem.Units), trailCap),
+		trail:      make([]Lit, len(problem.Units), nbVars),
 		model:      problem.Model,
 		activity:   make([]float64, nbVars),
 		polarity:   make([]bool, nbVars),
@@ -349,7 +343,7 @@ func (s *Solver) propagateAndSearch(lit Lit, lvl decLevel) Status {
 				return Indet
 			}
 			if s.Stats.NbConflicts >= s.wl.idxReduce*s.wl.nbMax {
-				s.wl.idxReduce = s.Stats.NbConflicts/s.wl.nbMax + 1
+				s.wl.idxReduce = (s.Stats.NbConflicts / s.wl.nbMax) + 1
 				s.reduceLearned()
 				s.bumpNbMax()
 			}
@@ -744,7 +738,7 @@ func (s *Solver) Optimal(results chan Result, stop chan struct{}) (res Result) {
 		copy(s.lastModel, s.model) // Save this model: it might be the last one
 		cost = 0
 		for i, lit := range s.minLits {
-			if s.model[lit.Var()] > 0 == lit.IsPositive() {
+			if (s.model[lit.Var()] > 0) == lit.IsPositive() {
 				if s.minWeights == nil {
 					cost++
 				} else {
@@ -809,7 +803,7 @@ func (s *Solver) Minimize() int {
 		copy(s.lastModel, s.model) // Save this model: it might be the last one
 		cost = 0
 		for i, lit := range s.minLits {
-			if s.model[lit.Var()] > 0 == lit.IsPositive() {
+			if (s.model[lit.Var()] > 0) == lit.IsPositive() {
 				if s.minWeights == nil {
 					cost++
 				} else {

vendor/github.com/ecooper/qlearning/.gitignore

@@ -1,24 +0,0 @@
-# Compiled Object files, Static and Dynamic libs (Shared Objects)
-*.o
-*.a
-*.so
-
-# Folders
-_obj
-_test
-
-# Architecture specific extensions/prefixes
-*.[568vq]
-[568vq].out
-
-*.cgo1.go
-*.cgo2.c
-_cgo_defun.c
-_cgo_gotypes.go
-_cgo_export.*
-
-_testmain.go
-
-*.exe
-*.test
-*.prof

vendor/github.com/ecooper/qlearning/LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2016 Eric Cooper
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

vendor/github.com/ecooper/qlearning/README.md

@@ -1,72 +0,0 @@
-# qlearning
-
-The qlearning package provides a series of interfaces and utilities to implement
-the [Q-Learning](https://en.wikipedia.org/wiki/Q-learning) algorithm in
-Go.
-
-This project was largely inspired by [flappybird-qlearning-
-bot](https://github.com/chncyhn/flappybird-qlearning-bot).
-
-*Until a release is tagged, qlearning should be considered highly
-experimental and mostly a fun toy.*
-
-## Installation
-
-```shell
-$ go get https://github.com/ecooper/qlearning
-```
-
-## Quickstart
-
-qlearning provides example implementations in the [examples](examples/)
-directory of the project.
-
-[hangman.go](examples/hangman.go) provides a naive implementation of
-[Hangman](https://en.wikipedia.org/wiki/Hangman_(game)) for use with
-qlearning.
-
-```shell
-$ cd $GOPATH/src/github.com/ecooper/qlearning/examples
-$ go run hangman.go -h
-Usage of hangman:
-  -debug
-        Set debug
-  -games int
-        Play N games (default 5000000)
-  -progress int
-        Print progress messages every N games (default 1000)
-  -wordlist string
-        Path to a wordlist (default "./wordlist.txt")
-  -words int
-        Use N words from wordlist (default 10000)
-```
-
-By default, running [hangman.go](examples/hangman.go) will play millions
-of games against a 10,000-word corpus. That's a bit overkill for just
-trying out qlearning. You can run it against a smaller number of words
-for a few number of games using the `-games` and `-words` flags.
-
-```shell
-$ go run hangman.go -words 100 -progress 1000 -games 5000
-100 words loaded
-1000 games played: 92 WINS 908 LOSSES 9% WIN RATE
-2000 games played: 447 WINS 1553 LOSSES 36% WIN RATE
-3000 games played: 1064 WINS 1936 LOSSES 62% WIN RATE
-4000 games played: 1913 WINS 2087 LOSSES 85% WIN RATE
-5000 games played: 2845 WINS 2155 LOSSES 93% WIN RATE
-
-Agent performance: 5000 games played, 2845 WINS 2155 LOSSES 57% WIN RATE
-```
-
-"WIN RATE" per progress report is isolated within that cycle, a group of
-1000 games in this example. The win rate is meant to show the velocity
-of learning by the agent. If it is "learning", the win rate should be
-increasing until reaching convergence.
-
-As you can see, after 5000 games, the agent is able to "learn" and play
-hangman against a 100-word vocabulary.
-
-## Usage
-
-See [godocs](https://godoc.org/github.com/ecooper/qlearning) for the
-package documentation.

vendor/github.com/ecooper/qlearning/qlearning.go

@@ -1,167 +0,0 @@
-// Package qlearning is an experimental set of interfaces and helpers to
-// implement the Q-learning algorithm in Go.
-//
-// This is highly experimental and should be considered a toy.
-//
-// See https://github.com/ecooper/qlearning/tree/master/examples for
-// implementation examples.
-package qlearning
-
-import (
-	"fmt"
-	"math/rand"
-	"time"
-)
-
-// State is an interface wrapping the current state of the model.
-type State interface {
-
-	// String returns a string representation of the given state.
-	// Implementers should take care to insure that this is a consistent
-	// hash for a given state.
-	String() string
-
-	// Next provides a slice of possible Actions that could be applied to
-	// a state.
-	Next() []Action
-}
-
-// Action is an interface wrapping an action that can be applied to the
-// model's current state.
-//
-// BUG (ecooper): A state should apply an action, not the other way
-// around.
-type Action interface {
-	String() string
-	Apply(State) State
-}
-
-// Rewarder is an interface wrapping the ability to provide a reward
-// for the execution of an action in a given state.
-type Rewarder interface {
-	// Reward calculates the reward value for a given action in a given
-	// state.
-	Reward(action *StateAction) float32
-}
-
-// Agent is an interface for a model's agent and is able to learn
-// from actions and return the current Q-value of an action at a given state.
-type Agent interface {
-	// Learn updates the model for a given state and action, using the
-	// provided Rewarder implementation.
-	Learn(*StateAction, Rewarder)
-
-	// Value returns the current Q-value for a State and Action.
-	Value(State, Action) float32
-
-	// Return a string representation of the Agent.
-	String() string
-}
-
-// StateAction is a struct grouping an action to a given State. Additionally,
-// a Value can be associated to StateAction, which is typically the Q-value.
-type StateAction struct {
-	State  State
-	Action Action
-	Value  float32
-}
-
-// NewStateAction creates a new StateAction for a State and Action.
-func NewStateAction(state State, action Action, val float32) *StateAction {
-	return &StateAction{
-		State:  state,
-		Action: action,
-		Value:  val,
-	}
-}
-
-// Next uses an Agent and State to find the highest scored Action.
-//
-// In the case of Q-value ties for a set of actions, a random
-// value is selected.
-func Next(agent Agent, state State) *StateAction {
-	best := make([]*StateAction, 0)
-	bestVal := float32(0.0)
-
-	for _, action := range state.Next() {
-		val := agent.Value(state, action)
-
-		if bestVal == float32(0.0) {
-			best = append(best, NewStateAction(state, action, val))
-			bestVal = val
-		} else {
-			if val > bestVal {
-				best = []*StateAction{NewStateAction(state, action, val)}
-				bestVal = val
-			} else if val == bestVal {
-				best = append(best, NewStateAction(state, action, val))
-			}
-		}
-	}
-
-	return best[rand.Intn(len(best))]
-}
-
-// SimpleAgent is an Agent implementation that stores Q-values in a
-// map of maps.
-type SimpleAgent struct {
-	q  map[string]map[string]float32
-	lr float32
-	d  float32
-}
-
-// NewSimpleAgent creates a SimpleAgent with the provided learning rate
-// and discount factor.
-func NewSimpleAgent(lr, d float32) *SimpleAgent {
-	return &SimpleAgent{
-		q:  make(map[string]map[string]float32),
-		d:  d,
-		lr: lr,
-	}
-}
-
-// getActions returns the current Q-values for a given state.
-func (agent *SimpleAgent) getActions(state string) map[string]float32 {
-	if _, ok := agent.q[state]; !ok {
-		agent.q[state] = make(map[string]float32)
-	}
-
-	return agent.q[state]
-}
-
-// Learn updates the existing Q-value for the given State and Action
-// using the Rewarder.
-//
-// See https://en.wikipedia.org/wiki/Q-learning#Algorithm
-func (agent *SimpleAgent) Learn(action *StateAction, reward Rewarder) {
-	current := action.State.String()
-	next := action.Action.Apply(action.State).String()
-
-	actions := agent.getActions(current)
-
-	maxNextVal := float32(0.0)
-	for _, v := range agent.getActions(next) {
-		if v > maxNextVal {
-			maxNextVal = v
-		}
-	}
-
-	currentVal := actions[action.Action.String()]
-	actions[action.Action.String()] = currentVal + agent.lr*(reward.Reward(action)+agent.d*maxNextVal-currentVal)
-}
-
-// Value gets the current Q-value for a State and Action.
-func (agent *SimpleAgent) Value(state State, action Action) float32 {
-	return agent.getActions(state.String())[action.String()]
-}
-
-// String returns the current Q-value map as a printed string.
-//
-// BUG (ecooper): This is useless.
-func (agent *SimpleAgent) String() string {
-	return fmt.Sprintf("%v", agent.q)
-}
-
-func init() {
-	rand.Seed(time.Now().UTC().UnixNano())
-}

vendor/modules.txt

@@ -49,6 +49,9 @@ github.com/containerd/continuity/pathdriver
 github.com/containerd/continuity/syscallx
 # github.com/cpuguy83/go-md2man/v2 v2.0.0-20190314233015-f79a8a8ca69d
 github.com/cpuguy83/go-md2man/v2/md2man
+# github.com/crillab/gophersat v1.1.7
+github.com/crillab/gophersat/bf
+github.com/crillab/gophersat/solver
 # github.com/cyphar/filepath-securejoin v0.2.2
 github.com/cyphar/filepath-securejoin
 # github.com/docker/distribution v2.7.0+incompatible
@@ -90,8 +93,6 @@ github.com/docker/go-units
 github.com/docker/libnetwork/ipamutils
 # github.com/docker/libtrust v0.0.0-20160708172513-aabc10ec26b7
 github.com/docker/libtrust
-# github.com/ecooper/qlearning v0.0.0-20160612200101-3075011a69fd
-github.com/ecooper/qlearning
 # github.com/fatih/color v1.7.0
 github.com/fatih/color
 # github.com/fsnotify/fsnotify v1.4.7
@@ -156,9 +157,6 @@ github.com/mattn/go-isatty
 github.com/mitchellh/mapstructure
 # github.com/mudler/docker-companion v0.4.6-0.20191110154655-b8b364100616
 github.com/mudler/docker-companion/api
-# github.com/mudler/gophersat v1.1.5-0.20200211080010-645700b4b7c0
-github.com/mudler/gophersat/bf
-github.com/mudler/gophersat/solver
 # github.com/onsi/ginkgo v1.10.1
 github.com/onsi/ginkgo
 github.com/onsi/ginkgo/config
@@ -265,8 +263,8 @@ go.uber.org/multierr
 go.uber.org/tools/update-license
 # go.uber.org/zap v1.13.0
 go.uber.org/zap
-go.uber.org/zap/zapcore
 go.uber.org/zap/internal/bufferpool
+go.uber.org/zap/zapcore
 go.uber.org/zap/buffer
 go.uber.org/zap/internal/color
 go.uber.org/zap/internal/exit