whm1275的博客

robotium-solo-5.3.1 robotium-solo-5.3.1-javadoc 帮助文档 ExampleTestProject_Eclipse ExampleTestProject_v5.2.1

新建一个用户控件，以下陈述正确的是： 必须在第一行包含<%@Control %>指令。 必须在页面中包含<%@Control %>指令，出现在第几行没关系。 必须在第一行包含<%@ Register %>指令。 用户控件后缀名必须为 .ascx 必须在页面中包含<%@Page %>指令 A,D C,D B,D A,E 3

直接把NICTCASA.DLL 添加引用 把DATA文件放入bin Debug Data目录即可 namespace Test1 { public partial class Form1 : Form { NICTCLAS nictclas; public Form1() { InitializeComponent(); try { nictclas = new NICTCLAS(); } catch (Exception ex) { MessageBox.Show(ex.Message); } } private void button1_Click(object sender, EventArgs e) { if (radioButton1.Checked) nictclas.OperateType = eOperateType.OnlySegment; else if (radioButton2.Checked) nictclas.OperateType = eOperateType.FirstTag; else if (radioButton3.Checked) nictclas.OperateType = eOperateType.SecondTag; if (radioButton4.Checked) nictclas.OutputFormat = eOutputFormat.PKU; else if (radioButton5.Checked) nictclas.OutputFormat = eOutputFormat._973; else if (radioButton6.Checked) nictclas.OutputFormat = eOutputFormat.XML; DateTime start = DateTime.Now; string result = ""; nictclas.ParagraphProcessing(textBox1.Text,ref result); DateTime finish = DateTime.Now; TimeSpan t = (TimeSpan)(finish - start); textBox3.Text = t.TotalMilliseconds.ToString() + "ms"; textBox2.Text = result; } private void textBox1_TextChanged(object sender, EventArgs e) { } private void textBox2_TextChanged(object sender, EventArgs e) { } }

namespace Test.TFIDF { class IF_IDF { /// <summary> /// 获取拆分后的词组以及每个词的出现次数 /// </summary> /// <param name="text"></param> /// <returns></returns> public Dictionary<string, int> GetWordsFrequnce(string text) { Dictionary<string, int> dictionary = new Dictionary<string, int>(); Regex regex = new Regex(@"[\u4e00-\u9fa5]");//分拣出中文字符 MatchCollection results = regex.Matches(text); int temp; foreach (Match word in results) { if (dictionary.TryGetValue(word.Value, out temp)) { temp++; dictionary.Remove(word.Value); dictionary.Add(word.Value, temp); } else { dictionary.Add(word.Value, 1); } } return dictionary; } /// <summary> /// 文档中出现次数最多的词的出现次数 /// </summary> /// <param name="wordsfre">拆分后的词组字典</param> /// <returns></returns> public int MaxWordFrequence( Dictionary<string, int> wordsfre) { Dictionary<string, int>.ValueCollection values = wordsfre.Values; int maxfre = 0; foreach (int value in values) { if (maxfre < value) { maxfre = value; } } return maxfre; } /// <summary> /// 计算某词的IF，返回结果 /// </summary> /// <param name="wordFre"></param> /// <param name="maxFre"></param> /// <returns></returns>

namespace ServiceRanking { /// &lt;summary&gt; /// Summary description for TF_IDFLib. /// &lt;/summary&gt; public class TFIDFMeasure { private string[] _docs; private string[][] _ngramDoc; private int _numDocs=0; private int _numTerms=0; private ArrayList _terms; private int[][] _termFreq; private float[][] _termWeight; private int[] _maxTermFreq; private int[] _docFreq; public class TermVector { public static float ComputeCosineSimilarity(float[] vector1, float[] vector2) { if (vector1.Length != vector2.Length) throw new Exception(&quot;DIFER LENGTH&quot;); float denom=(VectorLength(vector1) * VectorLength(vector2)); if (denom == 0F) return 0F; else return (InnerProduct(vector1, vector2) / denom); } public static float InnerProduct(float[] vector1, float[] vector2) { if (vector1.Length != vector2.Length) throw new Exception(&quot;DIFFER LENGTH ARE NOT ALLOWED&quot;); float result=0F; for (int i=0; i &lt; vector1.Length; i++) result += vector1[i] * vector2[i]; return result; } public static float VectorLength(float[] vector) { float sum=0.0F; for (int i=0; i &lt; vector.Length; i++) sum=sum + (vector[i] * vector[i]); return (float)Math.Sqrt(sum); } } private IDictionary _wordsIndex=new Hashtable() ; public TFIDFMeasure(string[] documents) { _docs=documents; _numDocs=documents.Length ; MyInit(); } private void GeneratNgramText() { } private ArrayList GenerateTerms(string[] docs) { ArrayList uniques=new ArrayList() ; _ngramDoc=new string[_numDocs][] ; for (int i=0; i &lt; docs.Length ; i++) { Tokeniser tokenizer=new Tokeniser() ; string[] words=tokenizer.Partition(docs[i]); for (int j=0; j &lt; words.Length ; j++) if (!uniques.Contains(words[j]) ) uniques.Add(words[j]) ; } return uniques; } private static object