我可以将类型作为参数传递给此函数吗?

时间:2017-05-26 18:52:26

标签: types f# polymorphism parametric-polymorphism static-polymorphism

以下代码,大多是从

复制而来

http://accord-framework.net/docs/html/T_Accord_MachineLearning_VectorMachines_Learning_SequentialMinimalOptimization.htm

工作正常。

module SVMModule

open Accord.MachineLearning
open Accord.MachineLearning.VectorMachines
open Accord.MachineLearning.VectorMachines.Learning
open Accord.Statistics.Kernels
open Accord.Math.Optimization.Losses

// open MathNet.Numerics.LinearAlgebra.Matrix

let inputs = [| [| 0.; 0. |]; [| 0.; 1. |]; [| 1.; 0. |]; [| 1.; 1. |] |]
let xor = [| 0; 1; 1; 0 |]
/// Creates and trains a Support Vector Machine given inputs and outputs.
/// The kernel can be Linear, Gaussian, or Polynomial.
/// The default tolerance is 1e-2.
let train (C: float) (tol: float) (inputs: float [] []) =
    let learn = SequentialMinimalOptimization<Gaussian>()

    learn.UseComplexityHeuristic <- true
    learn.UseKernelEstimation <- true
    if C >= 0. then learn.Complexity <- C
    if tol > 0. then learn.Tolerance <- tol

    let svm = learn.Learn(inputs, xor)
    svm

let svm = train 0.5 1e-2 inputs
let prediction = svm.Decide inputs

printfn "SVM_0 Prediction: %A" prediction

我想实现train的多态版本,类似于

let train (kernel: string) (C: float) (tol: float) (inputs: float [] []) =
    let learn =
        if kernel = "Gaussian" then 
           SequentialMinimalOptimization<Gaussian>()
        else
           SequentialMinimalOptimization<Linear>()
    // More code

这不起作用,因为if表达式必须在其所有分支中返回相同类型的对象。

我想知道是否有办法将LinearGaussian作为类型传递给train(这些确实是类型),这样我就不必编写一个列车功能了每种类型(trainGaussiantrainLinear)。 Akso,即使我在编写这些单独的函数时遇到了麻烦,我想根据用户的选择在运行时调用它们也很困难,因为if语句的相同问题会使其丑陋的头脑。 / p>

我使用接口在F#中实现了多态性,但是我自己构建了类。这些类在Accord.NET中,即使它们从基类继承,我也无法处理类型问题并实现多态。

感谢您的任何建议。

1 个答案:

答案 0 :(得分:4)

简单地用类型参数Gaussian替换具体类型't应该是直截了当的(并且,可选地,将其作为显式类型参数添加到train)。在执行此操作时,我已经非常轻松地清理了现有代码:

let train<'t> (C: float) (tol: float) (inputs: float [] []) =
    let learn = SequentialMinimalOptimization<'t>(UseComplexityHeuristic = true, UseKernelEstimation = true)
    if C >= 0. then learn.Complexity <- C
    if tol > 0. then learn.Tolerance <- tol

    learn.Learn(inputs, xor)

然后在调用站点,需要通过某种方式让编译器知道要使用的类型,方法是明确地传递它:

let svm = train<Gaussian> 0.5 1e-2 inputs

或依靠类型推断来从程序的另一部分传递类型:

let svm:Gaussian = train 0.5 1e-2 inputs
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