mirror of
https://github.com/superseriousbusiness/gotosocial.git
synced 2024-11-28 06:36:39 +00:00
89 lines
3.4 KiB
Go
89 lines
3.4 KiB
Go
|
// Copyright 2017 Google Inc. All rights reserved.
|
||
|
|
//
|
||
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
// you may not use this file except in compliance with the License.
|
||
|
|
// You may obtain a copy of the License at
|
||
|
|
//
|
||
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
//
|
||
|
|
// Unless required by applicable law or agreed to in writing, software
|
||
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
// See the License for the specific language governing permissions and
|
||
|
|
// limitations under the License.
|
||
|
|
|
||
|
|
package s2
|
||
|
|
|
||
|
|
// nthDerivativeCoder provides Nth Derivative Coding.
|
||
|
|
// (In signal processing disciplines, this is known as N-th Delta Coding.)
|
||
|
|
//
|
||
|
|
// Good for varint coding integer sequences with polynomial trends.
|
||
|
|
//
|
||
|
|
// Instead of coding a sequence of values directly, code its nth-order discrete
|
||
|
|
// derivative. Overflow in integer addition and subtraction makes this a
|
||
|
|
// lossless transform.
|
||
|
|
//
|
||
|
|
// constant linear quadratic
|
||
|
|
// trend trend trend
|
||
|
|
// / \ / \ / \_
|
||
|
|
// input |0 0 0 0 1 2 3 4 9 16 25 36
|
||
|
|
// 0th derivative(identity) |0 0 0 0 1 2 3 4 9 16 25 36
|
||
|
|
// 1st derivative(delta coding) | 0 0 0 1 1 1 1 5 7 9 11
|
||
|
|
// 2nd derivative(linear prediction) | 0 0 1 0 0 0 4 2 2 2
|
||
|
|
// -------------------------------------
|
||
|
|
// 0 1 2 3 4 5 6 7 8 9 10 11
|
||
|
|
// n in sequence
|
||
|
|
//
|
||
|
|
// Higher-order codings can break even or be detrimental on other sequences.
|
||
|
|
//
|
||
|
|
// random oscillating
|
||
|
|
// / \ / \_
|
||
|
|
// input |5 9 6 1 8 8 2 -2 4 -4 6 -6
|
||
|
|
// 0th derivative(identity) |5 9 6 1 8 8 2 -2 4 -4 6 -6
|
||
|
|
// 1st derivative(delta coding) | 4 -3 -5 7 0 -6 -4 6 -8 10 -12
|
||
|
|
// 2nd derivative(linear prediction) | -7 -2 12 -7 -6 2 10 -14 18 -22
|
||
|
|
// ---------------------------------------
|
||
|
|
// 0 1 2 3 4 5 6 7 8 9 10 11
|
||
|
|
// n in sequence
|
||
|
|
//
|
||
|
|
// Note that the nth derivative isn't available until sequence item n. Earlier
|
||
|
|
// values are coded at lower order. For the above table, read 5 4 -7 -2 12 ...
|
||
|
|
type nthDerivativeCoder struct {
|
||
|
|
n, m int
|
||
|
|
memory [10]int32
|
||
|
|
}
|
||
|
|
|
||
|
|
// newNthDerivativeCoder returns a new coder, where n is the derivative order of the encoder (the N in NthDerivative).
|
||
|
|
// n must be within [0,10].
|
||
|
|
func newNthDerivativeCoder(n int) *nthDerivativeCoder {
|
||
|
|
c := &nthDerivativeCoder{n: n}
|
||
|
|
if n < 0 || n > len(c.memory) {
|
||
|
|
panic("unsupported n. Must be within [0,10].")
|
||
|
|
}
|
||
|
|
return c
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *nthDerivativeCoder) encode(k int32) int32 {
|
||
|
|
for i := 0; i < c.m; i++ {
|
||
|
|
delta := k - c.memory[i]
|
||
|
|
c.memory[i] = k
|
||
|
|
k = delta
|
||
|
|
}
|
||
|
|
if c.m < c.n {
|
||
|
|
c.memory[c.m] = k
|
||
|
|
c.m++
|
||
|
|
}
|
||
|
|
return k
|
||
|
|
}
|
||
|
|
|
||
|
|
func (c *nthDerivativeCoder) decode(k int32) int32 {
|
||
|
|
if c.m < c.n {
|
||
|
|
c.m++
|
||
|
|
}
|
||
|
|
for i := c.m - 1; i >= 0; i-- {
|
||
|
|
c.memory[i] += k
|
||
|
|
k = c.memory[i]
|
||
|
|
}
|
||
|
|
return k
|
||
|
|
}
|