1 files changed, 0 insertions, 245 deletions
diff --git a/common/probdist/weighted_dist.go b/common/probdist/weighted_dist.go
deleted file mode 100644
index 2822ce6..0000000
--- a/common/probdist/weighted_dist.go
+++ /dev/null
@@ -1,245 +0,0 @@
-/*
- * Copyright (c) 2014, Yawning Angel <yawning at torproject dot org>
- * All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- *  * Redistributions of source code must retain the above copyright notice,
- *    this list of conditions and the following disclaimer.
- *
- *  * Redistributions in binary form must reproduce the above copyright notice,
- *    this list of conditions and the following disclaimer in the documentation
- *    and/or other materials provided with the distribution.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- * POSSIBILITY OF SUCH DAMAGE.
- */
-
-// Package probdist implements a weighted probability distribution suitable for
-// protocol parameterization.  To allow for easy reproduction of a given
-// distribution, the drbg package is used as the random number source.
-package probdist
-
-import (
-	"bytes"
-	"container/list"
-	"fmt"
-	"math/rand"
-	"sync"
-
-	"github.com/OperatorFoundation/obfs4/common/csrand"
-	"github.com/OperatorFoundation/obfs4/common/drbg"
-)
-
-const (
-	minValues = 1
-	maxValues = 100
-)
-
-// WeightedDist is a weighted distribution.
-type WeightedDist struct {
-	sync.Mutex
-
-	minValue int
-	maxValue int
-	biased   bool
-	values   []int
-	weights  []float64
-
-	alias []int
-	prob  []float64
-}
-
-// New creates a weighted distribution of values ranging from min to max
-// based on a HashDrbg initialized with seed.  Optionally, bias the weight
-// generation to match the ScrambleSuit non-uniform distribution from
-// obfsproxy.
-func New(seed *drbg.Seed, min, max int, biased bool) (w *WeightedDist) {
-	w = &WeightedDist{minValue: min, maxValue: max, biased: biased}
-
-	if max <= min {
-		panic(fmt.Sprintf("wDist.Reset(): min >= max (%d, %d)", min, max))
-	}
-
-	w.Reset(seed)
-
-	return
-}
-
-// genValues creates a slice containing a random number of random values
-// that when scaled by adding minValue will fall into [min, max].
-func (w *WeightedDist) genValues(rng *rand.Rand) {
-	nValues := (w.maxValue + 1) - w.minValue
-	values := rng.Perm(nValues)
-	if nValues < minValues {
-		nValues = minValues
-	}
-	if nValues > maxValues {
-		nValues = maxValues
-	}
-	nValues = rng.Intn(nValues) + 1
-	w.values = values[:nValues]
-}
-
-// genBiasedWeights generates a non-uniform weight list, similar to the
-// ScrambleSuit prob_dist module.
-func (w *WeightedDist) genBiasedWeights(rng *rand.Rand) {
-	w.weights = make([]float64, len(w.values))
-
-	culmProb := 0.0
-	for i := range w.weights {
-		p := (1.0 - culmProb) * rng.Float64()
-		w.weights[i] = p
-		culmProb += p
-	}
-}
-
-// genUniformWeights generates a uniform weight list.
-func (w *WeightedDist) genUniformWeights(rng *rand.Rand) {
-	w.weights = make([]float64, len(w.values))
-	for i := range w.weights {
-		w.weights[i] = rng.Float64()
-	}
-}
-
-// genTables calculates the alias and prob tables used for Vose's Alias method.
-// Algorithm taken from http://www.keithschwarz.com/darts-dice-coins/
-func (w *WeightedDist) genTables() {
-	n := len(w.weights)
-	var sum float64
-	for _, weight := range w.weights {
-		sum += weight
-	}
-
-	// Create arrays $Alias$ and $Prob$, each of size $n$.
-	alias := make([]int, n)
-	prob := make([]float64, n)
-
-	// Create two worklists, $Small$ and $Large$.
-	small := list.New()
-	large := list.New()
-
-	scaled := make([]float64, n)
-	for i, weight := range w.weights {
-		// Multiply each probability by $n$.
-		p_i := weight * float64(n) / sum
-		scaled[i] = p_i
-
-		// For each scaled probability $p_i$:
-		if scaled[i] < 1.0 {
-			// If $p_i < 1$, add $i$ to $Small$.
-			small.PushBack(i)
-		} else {
-			// Otherwise ($p_i \ge 1$), add $i$ to $Large$.
-			large.PushBack(i)
-		}
-	}
-
-	// While $Small$ and $Large$ are not empty: ($Large$ might be emptied first)
-	for small.Len() > 0 && large.Len() > 0 {
-		// Remove the first element from $Small$; call it $l$.
-		l := small.Remove(small.Front()).(int)
-		// Remove the first element from $Large$; call it $g$.
-		g := large.Remove(large.Front()).(int)
-
-		// Set $Prob[l] = p_l$.
-		prob[l] = scaled[l]
-		// Set $Alias[l] = g$.
-		alias[l] = g
-
-		// Set $p_g := (p_g + p_l) - 1$. (This is a more numerically stable option.)
-		scaled[g] = (scaled[g] + scaled[l]) - 1.0
-
-		if scaled[g] < 1.0 {
-			// If $p_g < 1$, add $g$ to $Small$.
-			small.PushBack(g)
-		} else {
-			// Otherwise ($p_g \ge 1$), add $g$ to $Large$.
-			large.PushBack(g)
-		}
-	}
-
-	// While $Large$ is not empty:
-	for large.Len() > 0 {
-		// Remove the first element from $Large$; call it $g$.
-		g := large.Remove(large.Front()).(int)
-		// Set $Prob[g] = 1$.
-		prob[g] = 1.0
-	}
-
-	// While $Small$ is not empty: This is only possible due to numerical instability.
-	for small.Len() > 0 {
-		// Remove the first element from $Small$; call it $l$.
-		l := small.Remove(small.Front()).(int)
-		// Set $Prob[l] = 1$.
-		prob[l] = 1.0
-	}
-
-	w.prob = prob
-	w.alias = alias
-}
-
-// Reset generates a new distribution with the same min/max based on a new
-// seed.
-func (w *WeightedDist) Reset(seed *drbg.Seed) {
-	// Initialize the deterministic random number generator.
-	drbg, _ := drbg.NewHashDrbg(seed)
-	rng := rand.New(drbg)
-
-	w.Lock()
-	defer w.Unlock()
-
-	w.genValues(rng)
-	if w.biased {
-		w.genBiasedWeights(rng)
-	} else {
-		w.genUniformWeights(rng)
-	}
-	w.genTables()
-}
-
-// Sample generates a random value according to the distribution.
-func (w *WeightedDist) Sample() int {
-	var idx int
-
-	w.Lock()
-	defer w.Unlock()
-
-	// Generate a fair die roll from an $n$-sided die; call the side $i$.
-	i := csrand.Intn(len(w.values))
-	// Flip a biased coin that comes up heads with probability $Prob[i]$.
-	if csrand.Float64() <= w.prob[i] {
-		// If the coin comes up "heads," return $i$.
-		idx = i
-	} else {
-		// Otherwise, return $Alias[i]$.
-		idx = w.alias[i]
-	}
-
-	return w.minValue + w.values[idx]
-}
-
-// String returns a dump of the distribution table.
-func (w *WeightedDist) String() string {
-	var buf bytes.Buffer
-
-	buf.WriteString("[ ")
-	for i, v := range w.values {
-		p := w.weights[i]
-		if p > 0.01 { // Squelch tiny probabilities.
-			buf.WriteString(fmt.Sprintf("%d: %f ", v, p))
-		}
-	}
-	buf.WriteString("]")
-	return buf.String()
-}