rclone/vendor/github.com/vivint/infectious/berlekamp_welch.go
2020-05-12 15:56:50 +00:00

245 lines
6 KiB
Go

// The MIT License (MIT)
//
// Copyright (C) 2016-2017 Vivint, Inc.
//
// 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.
package infectious
import (
"sort"
)
// Decode will take a destination buffer (can be nil) and a list of shares
// (pieces). It will return the data passed in to the corresponding Encode
// call or return an error.
//
// It will first correct the shares using Correct, mutating and reordering the
// passed-in shares arguments. Then it will rebuild the data using Rebuild.
// Finally it will concatenate the data into the given output buffer dst if it
// has capacity, growing it otherwise.
//
// If you already know your data does not contain errors, Rebuild will be
// faster.
//
// If you only want to identify which pieces are bad, you may be interested in
// Correct.
//
// If you don't want the data concatenated for you, you can use Correct and
// then Rebuild individually.
func (f *FEC) Decode(dst []byte, shares []Share) ([]byte, error) {
err := f.Correct(shares)
if err != nil {
return nil, err
}
if len(shares) == 0 {
return nil, Error.New("must specify at least one share")
}
piece_len := len(shares[0].Data)
result_len := piece_len * f.k
if cap(dst) < result_len {
dst = make([]byte, result_len)
} else {
dst = dst[:result_len]
}
return dst, f.Rebuild(shares, func(s Share) {
copy(dst[s.Number*piece_len:], s.Data)
})
}
func (f *FEC) decode(shares []Share, output func(Share)) error {
err := f.Correct(shares)
if err != nil {
return err
}
return f.Rebuild(shares, output)
}
// Correct implements the Berlekamp-Welch algorithm for correcting
// errors in given FEC encoded data. It will correct the supplied shares,
// mutating the underlying byte slices and reordering the shares
func (fc *FEC) Correct(shares []Share) error {
if len(shares) < fc.k {
return Error.New("must specify at least the number of required shares")
}
sort.Sort(byNumber(shares))
// fast path: check to see if there are no errors by evaluating it with
// the syndrome matrix.
synd, err := fc.syndromeMatrix(shares)
if err != nil {
return err
}
buf := make([]byte, len(shares[0].Data))
for i := 0; i < synd.r; i++ {
for j := range buf {
buf[j] = 0
}
for j := 0; j < synd.c; j++ {
addmul(buf, shares[j].Data, byte(synd.get(i, j)))
}
for j := range buf {
if buf[j] == 0 {
continue
}
data, err := fc.berlekampWelch(shares, j)
if err != nil {
return err
}
for _, share := range shares {
share.Data[j] = data[share.Number]
}
}
}
return nil
}
func (fc *FEC) berlekampWelch(shares []Share, index int) ([]byte, error) {
k := fc.k // required size
r := len(shares) // required + redundancy size
e := (r - k) / 2 // deg of E polynomial
q := e + k // def of Q polynomial
if e <= 0 {
return nil, NotEnoughShares.New("")
}
const interp_base = gfVal(2)
eval_point := func(num int) gfVal {
if num == 0 {
return 0
}
return interp_base.pow(num - 1)
}
dim := q + e
// build the system of equations s * u = f
s := matrixNew(dim, dim) // constraint matrix
a := matrixNew(dim, dim) // augmented matrix
f := make(gfVals, dim) // constant column vector
u := make(gfVals, dim) // solution vector
for i := 0; i < dim; i++ {
x_i := eval_point(shares[i].Number)
r_i := gfConst(shares[i].Data[index])
f[i] = x_i.pow(e).mul(r_i)
for j := 0; j < q; j++ {
s.set(i, j, x_i.pow(j))
if i == j {
a.set(i, j, gfConst(1))
}
}
for k := 0; k < e; k++ {
j := k + q
s.set(i, j, x_i.pow(k).mul(r_i))
if i == j {
a.set(i, j, gfConst(1))
}
}
}
// invert and put the result in a
err := s.invertWith(a)
if err != nil {
return nil, err
}
// multiply the inverted matrix by the column vector
for i := 0; i < dim; i++ {
ri := a.indexRow(i)
u[i] = ri.dot(f)
}
// reverse u for easier construction of the polynomials
for i := 0; i < len(u)/2; i++ {
o := len(u) - i - 1
u[i], u[o] = u[o], u[i]
}
q_poly := gfPoly(u[e:])
e_poly := append(gfPoly{gfConst(1)}, u[:e]...)
p_poly, rem, err := q_poly.div(e_poly)
if err != nil {
return nil, err
}
if !rem.isZero() {
return nil, TooManyErrors.New("")
}
out := make([]byte, fc.n)
for i := range out {
pt := gfConst(0)
if i != 0 {
pt = interp_base.pow(i - 1)
}
out[i] = byte(p_poly.eval(pt))
}
return out, nil
}
func (fc *FEC) syndromeMatrix(shares []Share) (gfMat, error) {
// get a list of keepers
keepers := make([]bool, fc.n)
shareCount := 0
for _, share := range shares {
if !keepers[share.Number] {
keepers[share.Number] = true
shareCount++
}
}
// create a vandermonde matrix but skip columns where we're missing the
// share.
out := matrixNew(fc.k, shareCount)
for i := 0; i < fc.k; i++ {
skipped := 0
for j := 0; j < fc.n; j++ {
if !keepers[j] {
skipped++
continue
}
out.set(i, j-skipped, gfConst(fc.vand_matrix[i*fc.n+j]))
}
}
// standardize the output and convert into parity form
err := out.standardize()
if err != nil {
return gfMat{}, err
}
return out.parity(), nil
}