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e780cda1d4
This fixes issue #4370 by restoring the correct error response.
247 lines
6.0 KiB
Go
247 lines
6.0 KiB
Go
// The MIT License (MIT)
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//
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// Copyright (C) 2016-2017 Vivint, Inc.
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//
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// Permission is hereby granted, free of charge, to any person obtaining a copy
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// of this software and associated documentation files (the "Software"), to deal
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// in the Software without restriction, including without limitation the rights
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// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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// copies of the Software, and to permit persons to whom the Software is
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// furnished to do so, subject to the following conditions:
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//
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// The above copyright notice and this permission notice shall be included in all
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// copies or substantial portions of the Software.
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//
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// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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// SOFTWARE.
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package infectious
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import (
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"errors"
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"sort"
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)
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// Decode will take a destination buffer (can be nil) and a list of shares
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// (pieces). It will return the data passed in to the corresponding Encode
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// call or return an error.
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//
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// It will first correct the shares using Correct, mutating and reordering the
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// passed-in shares arguments. Then it will rebuild the data using Rebuild.
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// Finally it will concatenate the data into the given output buffer dst if it
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// has capacity, growing it otherwise.
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//
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// If you already know your data does not contain errors, Rebuild will be
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// faster.
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//
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// If you only want to identify which pieces are bad, you may be interested in
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// Correct.
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//
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// If you don't want the data concatenated for you, you can use Correct and
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// then Rebuild individually.
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func (f *FEC) Decode(dst []byte, shares []Share) ([]byte, error) {
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err := f.Correct(shares)
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if err != nil {
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return nil, err
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}
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if len(shares) == 0 {
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return nil, errors.New("must specify at least one share")
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}
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piece_len := len(shares[0].Data)
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result_len := piece_len * f.k
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if cap(dst) < result_len {
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dst = make([]byte, result_len)
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} else {
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dst = dst[:result_len]
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}
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return dst, f.Rebuild(shares, func(s Share) {
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copy(dst[s.Number*piece_len:], s.Data)
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})
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}
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func (f *FEC) decode(shares []Share, output func(Share)) error {
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err := f.Correct(shares)
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if err != nil {
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return err
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}
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return f.Rebuild(shares, output)
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}
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// Correct implements the Berlekamp-Welch algorithm for correcting
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// errors in given FEC encoded data. It will correct the supplied shares,
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// mutating the underlying byte slices and reordering the shares
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func (fc *FEC) Correct(shares []Share) error {
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if len(shares) < fc.k {
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return errors.New("must specify at least the number of required shares")
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}
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sort.Sort(byNumber(shares))
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// fast path: check to see if there are no errors by evaluating it with
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// the syndrome matrix.
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synd, err := fc.syndromeMatrix(shares)
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if err != nil {
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return err
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}
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buf := make([]byte, len(shares[0].Data))
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for i := 0; i < synd.r; i++ {
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for j := range buf {
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buf[j] = 0
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}
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for j := 0; j < synd.c; j++ {
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addmul(buf, shares[j].Data, byte(synd.get(i, j)))
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}
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for j := range buf {
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if buf[j] == 0 {
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continue
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}
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data, err := fc.berlekampWelch(shares, j)
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if err != nil {
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return err
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}
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for _, share := range shares {
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share.Data[j] = data[share.Number]
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}
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}
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}
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return nil
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}
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func (fc *FEC) berlekampWelch(shares []Share, index int) ([]byte, error) {
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k := fc.k // required size
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r := len(shares) // required + redundancy size
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e := (r - k) / 2 // deg of E polynomial
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q := e + k // def of Q polynomial
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if e <= 0 {
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return nil, NotEnoughShares
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}
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const interp_base = gfVal(2)
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eval_point := func(num int) gfVal {
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if num == 0 {
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return 0
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}
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return interp_base.pow(num - 1)
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}
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dim := q + e
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// build the system of equations s * u = f
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s := matrixNew(dim, dim) // constraint matrix
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a := matrixNew(dim, dim) // augmented matrix
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f := make(gfVals, dim) // constant column vector
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u := make(gfVals, dim) // solution vector
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for i := 0; i < dim; i++ {
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x_i := eval_point(shares[i].Number)
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r_i := gfConst(shares[i].Data[index])
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f[i] = x_i.pow(e).mul(r_i)
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for j := 0; j < q; j++ {
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s.set(i, j, x_i.pow(j))
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if i == j {
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a.set(i, j, gfConst(1))
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}
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}
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for k := 0; k < e; k++ {
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j := k + q
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s.set(i, j, x_i.pow(k).mul(r_i))
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if i == j {
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a.set(i, j, gfConst(1))
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}
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}
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}
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// invert and put the result in a
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err := s.invertWith(a)
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if err != nil {
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return nil, err
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}
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// multiply the inverted matrix by the column vector
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for i := 0; i < dim; i++ {
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ri := a.indexRow(i)
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u[i] = ri.dot(f)
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}
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// reverse u for easier construction of the polynomials
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for i := 0; i < len(u)/2; i++ {
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o := len(u) - i - 1
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u[i], u[o] = u[o], u[i]
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}
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q_poly := gfPoly(u[e:])
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e_poly := append(gfPoly{gfConst(1)}, u[:e]...)
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p_poly, rem, err := q_poly.div(e_poly)
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if err != nil {
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return nil, err
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}
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if !rem.isZero() {
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return nil, TooManyErrors
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}
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out := make([]byte, fc.n)
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for i := range out {
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pt := gfConst(0)
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if i != 0 {
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pt = interp_base.pow(i - 1)
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}
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out[i] = byte(p_poly.eval(pt))
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}
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return out, nil
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}
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func (fc *FEC) syndromeMatrix(shares []Share) (gfMat, error) {
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// get a list of keepers
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keepers := make([]bool, fc.n)
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shareCount := 0
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for _, share := range shares {
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if !keepers[share.Number] {
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keepers[share.Number] = true
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shareCount++
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}
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}
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// create a vandermonde matrix but skip columns where we're missing the
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// share.
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out := matrixNew(fc.k, shareCount)
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for i := 0; i < fc.k; i++ {
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skipped := 0
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for j := 0; j < fc.n; j++ {
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if !keepers[j] {
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skipped++
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continue
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}
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out.set(i, j-skipped, gfConst(fc.vand_matrix[i*fc.n+j]))
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}
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}
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// standardize the output and convert into parity form
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err := out.standardize()
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if err != nil {
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return gfMat{}, err
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}
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return out.parity(), nil
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}
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