Principal Component Analysis(PCA) in C#之了解调用程序包

最新推荐文章于 2025-06-03 20:27:29 发布

前路漫漫其修远兮

最新推荐文章于 2025-06-03 20:27:29 发布

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本文链接：https://blog.youkuaiyun.com/weixin_42974146/article/details/90963923

本博客介绍如何在C#中使用Principal Component Analysis (PCA)进行数据降维，并利用Support Vector Machine (SVM)进行分类预测。通过具体实例展示了输入数据的处理，SVM模型的训练及预测过程，同时提供了可视化结果展示。

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Principal Component Analysis in C#之Geting Started
这篇博客在前面2篇的基础上做
1.添加链接描述
2.添加链接描述

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Accord.Controls;
using Accord.MachineLearning.VectorMachines.Learning;
using Accord.Math.Optimization.Losses;
using Accord.Statistics;
using Accord.Statistics.Kernels;

namespace ConsoleApp1
{
    class Program
    {
      //  [MTAThread]
        static void Main(string[] args)
        {
            double[][] inputs =
            {
                /* 1.*/ new double[] { 0, 0 },
                /* 2.*/ new double[] { 1, 0 }, 
                /* 3.*/ new double[] { 0, 1 }, 
                /* 4.*/ new double[] { 1, 1 },
            };
            int[] outputs =
           { 
                /* 1. 0 xor 0 = 0: */ 0,
                /* 2. 1 xor 0 = 1: */ 1,
                /* 3. 0 xor 1 = 1: */ 1,
                /* 4. 1 xor 1 = 0: */ 0,
            };
            var smo = new SequentialMinimalOptimization<Gaussian>()
            {
                Complexity = 100 // Create a hard-margin SVM 
            };
            // Use the algorithm to learn the svm
            var svm = smo.Learn(inputs, outputs);

            // Compute the machine's answers for the given inputs
            bool[] prediction = svm.Decide(inputs);
            // Compute the classification error between the expected 
            // values and the values actually predicted by the machine:
            double error = new AccuracyLoss(outputs).Loss(prediction);

            Console.WriteLine("Error: " + error);

            // Show results on screen 
            ScatterplotBox.Show("Training data", inputs, outputs);
            ScatterplotBox.Show("SVM results", inputs, prediction.ToZeroOne());

            Console.ReadKey();
        }
    }
}

在这里插入图片描述