<Machine Learning in Action >之二 樸素貝葉斯 C#實現

標籤：

def trainNB0(trainMatrix,trainCategory):    numTrainDocs = len(trainMatrix)    numWords = len(trainMatrix[0])    pAbusive = sum(trainCategory)/float(numTrainDocs)    p0Num = ones(numWords); p1Num = ones(numWords)      #change to ones()     p0Denom = 2.0; p1Denom = 2.0                        #change to 2.0    for i in range(numTrainDocs):        if trainCategory[i] == 1:            p1Num += trainMatrix[i]            p1Denom += sum(trainMatrix[i])        else:            p0Num += trainMatrix[i]            p0Denom += sum(trainMatrix[i])    p1Vect = log(p1Num/p1Denom)          #change to log()    p0Vect = log(p0Num/p0Denom)          #change to log()    return p0Vect,p1Vect,pAbusive

def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):    p1 = sum(vec2Classify * p1Vec) + log(pClass1)    #element-wise mult    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)    if p1 > p0:        return 1    else:         return 0    

用C#隨便做個例子，實現文章類型的分類

1、建立詞向量：中超/亞冠/國足/足協/英超/西甲/歐冠/意甲/德甲/籃球/NBA/CBA/高爾夫/乒乓/排球/網球/羽毛球/跑步/賽車/棋牌/撞球/遊泳/馬術/拳擊/田徑/功夫/撲克/體育/球隊/球員/訓練/國家隊/聯賽/俱樂部/場地/翻盤/絕殺/熱身/隊友/冠軍/亞軍/季軍/犯規/賽季/加時/反超/半場/爭奪/戰術/陣容/比賽/德比/恢複/進球/失球/奧斯卡/娛樂/影迷/電影/電視/音樂/戲劇/視頻/演員/導演/明星/經紀人/歌手/連續劇/展映/粉絲/寫真/演技/作秀/節目/藝人/超模/女星/模特/男星/性感/主創/院線/影業/拍攝/編劇/情節/影像/劇情/主演/上映/票房/開機/集/表演/收視/預告片/主持人/艾美獎/角色/劇院/樂迷/影迷/演出/專輯/樂壇/劇場/文藝/芭蕾/戲曲/舞蹈/軍事/軍隊/軍機/炸彈/軍方/坦克/軍艦/炸死/軍演/戰備/部隊/軍區/國防/士兵/艦船/潛艇/飛機/直升機/艦隊/保衛/演習/武器/反擊/打擊/閱兵/對抗/防衛/海軍/空軍/陸軍/武裝/戰略/空襲/衝突/裝甲/步兵/作戰/飛彈/邊防/偵察/戰鬥機/雷達/轟炸/防禦/據點/火力/航空母艦/進攻/彈藥/軍營/包圍/攻佔/俘虜/參戰/戰友/戰鬥/入侵

2、搜狐上下載三類文章各10篇組成訓練樣本，計算出每篇文章的文檔矩陣，標註每篇文章的類別標籤

文檔矩陣：

000000000000000000100000000000000000001100010001001010000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000010000000000000100000000000000011110001010000000000000000011000000110000000000100000000000000000010000000000000000000000000000000000000000000000
000000000000000000000000000011000000000000000000000001001000001001000000001000000000000000000000010000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000001001000000000000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000010000000000000000000000000000010010000100000000000000010010000001000000000100000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000010000000010000010000010100000000111111111110000000100000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000110000000000000011010000001000010000000000001100001110000000000000000000000000000000000000000000000000000000000000000000000000000
000000000100000000000000000000000000000000000000000000001010000110000000000000000100000001101000000100000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000010000010000000000000001000000001100000100000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000001010000110000000000000000000001011000010000110000000000000000000000000000000000000000000000000000000000000000000
000000010000000000000000000011100000001000010110001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000
000000000000000000000000000000000000000000000000000000001001000100000000000000000000000010000100000100000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000011110000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000110000000111111111111100000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001100000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000100000000000000000000000000000000000
000000000000000000000000000000000000000000000001000000000001000000000000000000000000100000000000000000000000000100100000010010000000000000000100000000000100000000000010
000000000000000000000000000000100000000000000000000000000001000000000000000000000000000000000000000000000000000100010000010000000000000000000100000100000000000000000000
000000000000000000000000000000000000000000000000100000000000000000000000000000000000000000000000000000000000000000000000110010000000001001010000000010000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000000000000000000000010100000000000100000000010000000000000001000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000010000000000100000000100010000000000001000000000
000000010000000000000000000111001100000000010000001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000100000000000100000000000000100000000110000010000000000000
110000000000000000000000000100001000100000010000000001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000
110000000000000000000000000111001100100100010001111011000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000
000000000001000000000000000001000000101000100110001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000
000000000000010000000000000000000000001101000001001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000010000000000000000010000000000000010010001000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000001000000000000000111100000101000110100001000100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000001000000000
000000000000000000000000000100000000000110010100000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

類別標籤向量：

122222222212333333333131111111

using System;using System.Text;using System.Windows.Forms;using System.IO;namespace NaiveBayes{    public partial class Form1 : Form    {        private string[] vocabArray;        private double[] p0Num, p1Num, p2Num;        public Form1()        {            InitializeComponent();            label2.Text = "體育1、娛樂2、軍事3\r\n每個類型10個訓練樣本\r\n文章全部出自搜狐新聞\r\n詞向量從各類文章中分詞獲得";            StreamReader sr = new StreamReader("vocabList.txt", Encoding.Default);            string line, all = "";            while ((line = sr.ReadLine()) != null)            {                all += line;            }            vocabArray = all.Split(new string[] { "/" }, StringSplitOptions.RemoveEmptyEntries);        }        private void Form1_Resize(object sender, EventArgs e)        {            this.Width = 800;            this.Height = 600;        }        private void button1_Click(object sender, EventArgs e)        {            //產生文檔矩陣和分類標籤向量            DirectoryInfo di = new DirectoryInfo("train");            FileInfo[] fi = di.GetFiles("*.txt");            string[] trainMatrix = new string[fi.Length];            p0Num = new double[vocabArray.Length];            p1Num = new double[vocabArray.Length];            p2Num = new double[vocabArray.Length];            double p0Denom = 2.0;            double p1Denom = 2.0;            double p2Denom = 2.0;            for (int i = 0; i < vocabArray.Length; i++)            {                p0Num[i] = p1Num[i] = p2Num[i] = 1.0;            }            string trainCategory = "";            int m = 0;            foreach (FileInfo i in fi)            {                StreamReader sr = new StreamReader(i.FullName, Encoding.Default);                string line, all = "";                while ((line = sr.ReadLine()) != null)                {                    all += line;                }                string strVec = "";                foreach (string j in vocabArray)                {                    if (all.Contains(j))                        strVec += "1";                    else                        strVec += "0";                }                trainMatrix[m] = strVec;                m++;                trainCategory += i.Name.Substring(i.Name.LastIndexOf("_") + 1, 1);            }            StreamWriter sw = new StreamWriter(".\\trainV\\trainMatrix.txt", true);            foreach (string i in trainMatrix)            {                sw.WriteLine(i);                sw.Flush();            }            sw.Close();            sw = new StreamWriter(".\\trainV\\trainCategory.txt", true);            sw.WriteLine(trainCategory);            sw.Close();            for (int i = 0; i < trainMatrix.Length; i++)            {                if (trainCategory.Substring(i, 1) == "1")                {                    double tmp = 0;                    for (int j = 0; j < vocabArray.Length; j++)                    {                        p0Num[j] += double.Parse(trainMatrix[i].Substring(j, 1));                        tmp += double.Parse(trainMatrix[i].Substring(j, 1));                    }                    p0Denom += tmp;                }                else if (trainCategory.Substring(i, 1) == "2")                {                    double tmp = 0;                    for (int j = 0; j < vocabArray.Length; j++)                    {                        p1Num[j] += double.Parse(trainMatrix[i].Substring(j, 1));                        tmp += double.Parse(trainMatrix[i].Substring(j, 1));                    }                    p1Denom += tmp;                }                else if (trainCategory.Substring(i, 1) == "3")                {                    double tmp = 0;                    for (int j = 0; j < vocabArray.Length; j++)                    {                        p2Num[j] += double.Parse(trainMatrix[i].Substring(j, 1));                        tmp += double.Parse(trainMatrix[i].Substring(j, 1));                    }                    p2Denom += tmp;                }                else                {                    //Undo                }            }            for (int j = 0; j < vocabArray.Length; j++)            {                p0Num[j] = Math.Log(p0Num[j] / p0Denom);                p1Num[j] = Math.Log(p1Num[j] / p1Denom);                p2Num[j] = Math.Log(p2Num[j] / p2Denom);            }            label4.Text = "處理樣本資料完成";        }        private void button2_Click(object sender, EventArgs e)        {            if (textBox1.Text.Trim() != "")            {                string strVec = "";                foreach (string i in vocabArray)                {                    if (textBox1.Text.Contains(i))                        strVec += "1";                    else                        strVec += "0";                }                double p0 = 0;                double p1 = 0;                double p2 = 0;                for (int j = 0; j < vocabArray.Length; j++)                {                    p0 += p0Num[j] * double.Parse(strVec.Substring(j, 1));                    p1 += p1Num[j] * double.Parse(strVec.Substring(j, 1));                    p2 += p2Num[j] * double.Parse(strVec.Substring(j, 1));                }                string catelog = "";                if (p0 > p1 && p0 > p2)                    catelog = "體育";                else if (p1 > p0 && p1 > p2)                    catelog = "娛樂";                else if (p2 > p0 && p2 > p1)                    catelog = "軍事";                else                    catelog = "無法判斷";                label3.Text = "體育：" + p0.ToString() + "\r\n娛樂：" + p1.ToString() + "\r\n軍事：" + p2.ToString();                label1.Text = "所屬類型是：" + catelog;            }        }    }}

<Machine Learning in Action >之二 樸素貝葉斯 C#實現