infergo: bitbucket.org/dtolpin/infergo/infer Index | Files

package infer

import "bitbucket.org/dtolpin/infergo/infer"

Package infer contains inference algorithms: maximum likelihood estimation by gradient descent and approximation of the posterior by Markov Chain Monte Carlo methods (notably Hamiltonian Monte Carlo family of algorithms).

Index

Package Files

adapt.go gonum.go grad.go infer.go mcmc.go sgmcmc.go

func FuncGrad Uses

❖
func FuncGrad(m model.Model) (
    Func func(x []float64) float64,
    Grad func(grad, x []float64),
)

FuncGrad returns the function to minimize and the gradient, suitable as fields for gonum optimize.Problem, corresponding to maximization of the model's log-likelihood.

func Optimize Uses

❖
func Optimize(
    opt Grad,
    m model.Model, x []float64,
    niter, nplateau int,
    eps float64,
) (
    iter int,
    ll0, ll float64,
)

Optimize wraps a gradient-based optimizer into an optimization loop with early stopping if a plateau is reached.

type Adam Uses

❖
type Adam struct {
    Rate  float64 // learning rate
    Beta1 float64 // first momentum factor
    Beta2 float64 // second momentum factor
    Eps   float64 // stabilizer
    // contains filtered or unexported fields
}

Adam (https://arxiv.org/abs/1412.6980).

func (*Adam) Step Uses

❖
func (opt *Adam) Step(
    m model.Model,
    x []float64,
) (
    ll float64,
    grad []float64,
)

Step implements the Optimizer interface.

type DepthAdapter Uses

❖
type DepthAdapter struct {
    DualAveraging
    Depth   float64
    NAdpt   int
    MinGrad float64
}

Parameters of adaptation to the target depth

func (*DepthAdapter) Adapt Uses

❖
func (da *DepthAdapter) Adapt(
    nuts *NUTS,
    samples <-chan []float64,
    nIter int,
)

Adapt adapts NUTS sampler to the target depth. At most nIter iterations are run.

type DualAveraging Uses

❖
type DualAveraging struct {
    Rate float64
}

Parameters of dual averaging.

func (*DualAveraging) Step Uses

❖
func (da *DualAveraging) Step(t, x, gradSum float64) float64

Step implements Nesterov's primal-dual averaging, oversimplified. 

chi = -gradSum/math.Sqrt(t)
eta = Rate/t
x = eta*chi + (1-eta)*x

type Grad Uses

❖
type Grad interface {
    Step(m model.Model, x []float64) (ll float64, grad []float64)
}

Grad is the interface of gradient-based optimizers. Step makes a single step over parameters in the gradient direction.

type HMC Uses

❖
type HMC struct {
    Sampler
    // Parameters
    L   int     // number of leapfrog steps
    Eps float64 // leapfrog step size
}

Vanilla Hamiltonian Monte Carlo Sampler.

func (*HMC) Sample Uses

❖
func (hmc *HMC) Sample(
    m model.Model,
    x []float64,
    samples chan []float64,
)

type MCMC Uses

❖
type MCMC interface {
    Sample(
        m model.Model,
        x []float64,
        samples chan []float64,
    )
    Stop()
}

MCMC is the interface of MCMC samplers.

type Momentum Uses

❖
type Momentum struct {
    Rate  float64 //learning rate
    Decay float64 // rate decay
    Gamma float64 // gradient momentum factor
    // contains filtered or unexported fields
}

Gradient ascent with momentum (https://www.nature.com/articles/323533a0). If the momentum factor is not set, and thus 0, reduces to vanilla gradient ascent.

func (*Momentum) Step Uses

❖
func (opt *Momentum) Step(
    m model.Model,
    x []float64,
) (
    ll float64,
    grad []float64,
)

Step implements the Optimizer interface.

type NUTS Uses

❖
type NUTS struct {
    Sampler
    // Parameters
    Eps      float64 // step size
    Delta    float64 // lower bound on energy for doubling
    MaxDepth int     // maximum depth
    // Statistics
    // Depth belief is encoded as a vector of beta-bernoulli
    // distributions. If the depth is greater than the element's
    // index i, Depth[i][0] is incremented; for index depth,
    // Depth[depth][1] is incremented.
    Depth [][2]float64 // depth belief
    // contains filtered or unexported fields
}

No U-Turn Sampler (https://arxiv.org/abs/1111.4246).

func (*NUTS) MeanDepth Uses

❖
func (nuts *NUTS) MeanDepth() float64

MeanDepth returns the average observed depth.

func (*NUTS) Sample Uses

❖
func (nuts *NUTS) Sample(
    m model.Model,
    x []float64,
    samples chan []float64,
)

type Sampler Uses

❖
type Sampler struct {
    Stopped bool
    Samples chan []float64
    // Statistics
    NAcc, NRej int // the number of accepted and rejected samples
}

Sampler is the structure for embedding into concrete samplers.

func (*Sampler) Stop Uses

❖
func (s *Sampler) Stop()

Stop stops a sampler gracefully, using the samples channel for synchronization. Stop must be called before further calls to differentiated code. A part of the MCMC interface.

type SgHMC Uses

❖
type SgHMC struct {
    Sampler
    // Parameters
    L   int // number of steps
    // The parameterization follows Equation (15) in
    // https://arxiv.org/abs/1402.4102
    Eta   float64 // learning rate
    Alpha float64 // friction (1 - momentum)
    V     float64 // diffusion
}

Stochastic gradient Hamiltonian Monte Carlo

func (*SgHMC) Sample Uses

❖
func (sghmc *SgHMC) Sample(
    m model.Model,
    x []float64,
    samples chan []float64,
)

Package infer imports 7 packages (graph) and is imported by 2 packages. Updated 2021-01-26. Refresh now. Tools for package owners.