Implement golden tests for Poisson distribution

Signed-off-by: Jakub Sztandera <kubuxu@protocol.ai>

chain/types/electionproof.go

@@ -67,12 +67,13 @@ func init() {
 	expDenoCoef = parse(deno)
 }
 
-// expneg accepts x in Q.256 format, in range of 0 to 5 and computes e^-x,
-// output is in Q.256 format
+// expneg accepts x in Q.256 format and computes e^-x.
+// It is most precise within [0, 1.725) range, where error is less than 3.4e-30.
+// Over the [0, 5) range its error is less than 4.6e-15.
+// Output is in Q.256 format.
 func expneg(x *big.Int) *big.Int {
 	// exp is approximated by rational function
-	// polynomials of the rational function are evaluated using Horners method
-
+	// polynomials of the rational function are evaluated using Horner's method
 	num := polyval(expNumCoef, x)   // Q.256
 	deno := polyval(expDenoCoef, x) // Q.256
 
@@ -80,10 +81,11 @@ func expneg(x *big.Int) *big.Int {
 	return num.Div(num, deno)     // Q.512 / Q.256 => Q.256
 }
 
-// polyval evaluates a polynomial given by coefficients `p` in Q.256 format,
-// at point `x` in Q.256 format, output is in Q.256
-
+// polyval evaluates a polynomial given by coefficients `p` in Q.256 format
+// at point `x` in Q.256 format. Output is in Q.256.
+// Coefficients should be ordered from the highest order coefficient to the lowest.
 func polyval(p []*big.Int, x *big.Int) *big.Int {
+	// evaluation using Horner's method
 	res := new(big.Int).Set(p[0]) // Q.256
 	tmp := new(big.Int)           // big.Int.Mul doesn't like when input is reused as output
 	for _, c := range p[1:] {
@@ -108,11 +110,11 @@ var MaxWinCount = 3 * build.BlocksPerEpoch
 type poiss struct {
 	lam  *big.Int
 	pmf  *big.Int
-	tmp  *big.Int
 	icdf *big.Int
 
+	tmp *big.Int // temporary variable for optmization
+
 	k uint64
-	kBig *big.Int
 }
 
 // newPoiss starts poisson inverted CDF
@@ -120,12 +122,10 @@ type poiss struct {
 // returns (instance, `1-poisscdf(0, lambda)`)
 // CDF value returend is reused when calling `next`
 func newPoiss(lambda *big.Int) (*poiss, *big.Int) {
-	// e^-lambda
-	elam := expneg(lambda) // Q.256
 
 	// pmf(k) = (lambda^k)*(e^lambda) / k!
-	// k = 0 here so it similifies to just e^labda
-	pmf := new(big.Int).Set(elam) // Q.256
+	// k = 0 here, so it simplifies to just e^-lambda
+	pmf := expneg(lambda) // Q.256
 
 	// icdf(k) = 1 - ∑ᵏᵢ₌₀ pmf(i)
 	// icdf(0) = 1 - pmf(0)
@@ -134,34 +134,34 @@ func newPoiss(lambda *big.Int) (*poiss, *big.Int) {
 	icdf = icdf.Sub(icdf, pmf)       // Q.256
 
 	k := uint64(0)
-	kBig := new(big.Int).SetUint64(k) // Q.0
 
 	p := &poiss{
 		lam: lambda,
 		pmf: pmf,
 
-		tmp:  elam,
+		tmp:  new(big.Int),
 		icdf: icdf,
 
 		k: k,
-		kBig: kBig,
 	}
 
 	return p, icdf
 }
 
-// next computes next `k++, 1-poisscdf(k, lam)`
+// next computes `k++, 1-poisscdf(k, lam)`
 // return is in Q.256 format
 func (p *poiss) next() *big.Int {
-	// incrementally compute next pfm and icdf
+	// incrementally compute next pmf and icdf
+
 	// pmf(k) = (lambda^k)*(e^lambda) / k!
 	// so pmf(k) = pmf(k-1) * lambda / k
-
 	p.k++
-	p.kBig = p.kBig.SetUint64(p.k) // Q.0
+	p.tmp.SetUint64(p.k) // Q.0
 
 	// calculate pmf for k
-	p.pmf = p.pmf.Div(p.pmf, p.kBig)    // Q.256 / Q.0 => Q.256
+	p.pmf = p.pmf.Div(p.pmf, p.tmp) // Q.256 / Q.0 => Q.256
+	// we are using `tmp` as target for multiplication as using an input as output
+	// for Int.Mul causes allocations
 	p.tmp = p.tmp.Mul(p.pmf, p.lam)     // Q.256 * Q.256 => Q.512
 	p.pmf = p.pmf.Rsh(p.tmp, precision) // Q.512 >> 256 => Q.256
 
@@ -171,11 +171,9 @@ func (p *poiss) next() *big.Int {
 	return p.icdf
 }
 
-// poissStep performs a step in evaluation of Poisson distribution
-// k should be incremented after each evaluation step
-// tmp is scratch space
-// ouput is (pmf, icdf)
-
+// ComputeWinCount uses VRFProof to compute number of wins
+// The algorithm is based on Algorand's Sortition with Binomial distribution
+// replaced by Poisson distribution.
 func (ep *ElectionProof) ComputeWinCount(power BigInt, totalPower BigInt) uint64 {
 	h := blake2b.Sum256(ep.VRFProof)
 

chain/types/electionproof_test.go

@@ -1,12 +1,52 @@
 package types
 
 import (
+	"bytes"
 	"fmt"
 	"math/big"
 	"os"
 	"testing"
+
+	"github.com/xorcare/golden"
 )
 
+func TestPoissonFunction(t *testing.T) {
+	tests := []struct {
+		lambdaBase  uint64
+		lambdaShift uint
+	}{
+		{10, 10},      // 0.0097
+		{209714, 20},  // 0.19999885
+		{1036915, 20}, // 0.9888792038
+		{1706, 10},    // 1.6660
+		{2, 0},        // 2
+		{5242879, 20}, //4.9999990
+		{5, 0},        // 5
+	}
+
+	for _, test := range tests {
+		t.Run(fmt.Sprintf("lam-%d-%d", test.lambdaBase, test.lambdaShift), func(t *testing.T) {
+			test := test
+
+			b := &bytes.Buffer{}
+			b.WriteString("icdf\n")
+
+			lam := new(big.Int).SetUint64(test.lambdaBase)
+			lam = lam.Lsh(lam, precision-test.lambdaShift)
+			p, icdf := newPoiss(lam)
+
+			b.WriteString(icdf.String())
+			b.WriteRune('\n')
+
+			for i := 0; i < 15; i++ {
+				b.WriteString(p.next().String())
+				b.WriteRune('\n')
+			}
+			golden.Assert(t, []byte(b.String()))
+		})
+	}
+}
+
 func q256ToF(x *big.Int) float64 {
 	deno := big.NewInt(1)
 	deno = deno.Lsh(deno, 256)

chain/types/testdata/TestPoissonFunction/lam-10-10.golden

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chain/types/testdata/TestPoissonFunction/lam-1036915-20.golden

@@ -0,0 +1,17 @@
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chain/types/testdata/TestPoissonFunction/lam-1706-10.golden

@@ -0,0 +1,17 @@
+icdf
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chain/types/testdata/TestPoissonFunction/lam-2-0.golden

@@ -0,0 +1,17 @@
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chain/types/testdata/TestPoissonFunction/lam-209714-20.golden

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chain/types/testdata/TestPoissonFunction/lam-5-0.golden

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chain/types/testdata/TestPoissonFunction/lam-5242879-20.golden

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go.mod

@@ -111,6 +111,7 @@ require (
 	github.com/whyrusleeping/cbor-gen v0.0.0-20200504204219-64967432584d
 	github.com/whyrusleeping/multiaddr-filter v0.0.0-20160516205228-e903e4adabd7
 	github.com/whyrusleeping/pubsub v0.0.0-20131020042734-02de8aa2db3d
+	github.com/xorcare/golden v0.6.1-0.20191112154924-b87f686d7542
 	go.opencensus.io v0.22.3
 	go.uber.org/dig v1.8.0 // indirect
 	go.uber.org/fx v1.9.0

go.sum

@@ -1373,6 +1373,8 @@ github.com/whyrusleeping/timecache v0.0.0-20160911033111-cfcb2f1abfee/go.mod h1:
 github.com/whyrusleeping/yamux v1.1.5/go.mod h1:E8LnQQ8HKx5KD29HZFUwM1PxCOdPRzGwur1mcYhXcD8=
 github.com/x-cray/logrus-prefixed-formatter v0.5.2/go.mod h1:2duySbKsL6M18s5GU7VPsoEPHyzalCE06qoARUCeBBE=
 github.com/xiang90/probing v0.0.0-20190116061207-43a291ad63a2/go.mod h1:UETIi67q53MR2AWcXfiuqkDkRtnGDLqkBTpCHuJHxtU=
+github.com/xorcare/golden v0.6.1-0.20191112154924-b87f686d7542 h1:oWgZJmC1DorFZDpfMfWg7xk29yEOZiXmo/wZl+utTI8=
+github.com/xorcare/golden v0.6.1-0.20191112154924-b87f686d7542/go.mod h1:7T39/ZMvaSEZlBPoYfVFmsBLmUl3uz9IuzWj/U6FtvQ=
 github.com/xordataexchange/crypt v0.0.3-0.20170626215501-b2862e3d0a77/go.mod h1:aYKd//L2LvnjZzWKhF00oedf4jCCReLcmhLdhm1A27Q=
 github.com/yuin/goldmark v1.1.25/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
 go.dedis.ch/fixbuf v1.0.3 h1:hGcV9Cd/znUxlusJ64eAlExS+5cJDIyTyEG+otu5wQs=