package demo;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Set;
public class DicisionTree {
  public static void main(String[] args) throws Exception {
    System.out.print("腳本之家測(cè)試結(jié)果：");
    String[] attrNames = new String[] { "AGE", "INCOME", "STUDENT",
        "CREDIT_RATING" };
    // 讀取樣本集
    Map<Object, List<Sample>> samples = readSamples(attrNames);
    // 生成決策樹(shù)
    Object decisionTree = generateDecisionTree(samples, attrNames);
    // 輸出決策樹(shù)
    outputDecisionTree(decisionTree, 0, null);
  }
  /**
   * 讀取已分類(lèi)的樣本集，返回Map：分類(lèi) -> 屬于該分類(lèi)的樣本的列表
   */
  static Map<Object, List<Sample>> readSamples(String[] attrNames) {
    // 樣本屬性及其所屬分類(lèi)（數(shù)組中的最后一個(gè)元素為樣本所屬分類(lèi)）
    Object[][] rawData = new Object[][] {
        { "<30 ", "High ", "No ", "Fair   ", "0" },
        { "<30 ", "High ", "No ", "Excellent", "0" },
        { "30-40", "High ", "No ", "Fair   ", "1" },
        { ">40 ", "Medium", "No ", "Fair   ", "1" },
        { ">40 ", "Low  ", "Yes", "Fair   ", "1" },
        { ">40 ", "Low  ", "Yes", "Excellent", "0" },
        { "30-40", "Low  ", "Yes", "Excellent", "1" },
        { "<30 ", "Medium", "No ", "Fair   ", "0" },
        { "<30 ", "Low  ", "Yes", "Fair   ", "1" },
        { ">40 ", "Medium", "Yes", "Fair   ", "1" },
        { "<30 ", "Medium", "Yes", "Excellent", "1" },
        { "30-40", "Medium", "No ", "Excellent", "1" },
        { "30-40", "High ", "Yes", "Fair   ", "1" },
        { ">40 ", "Medium", "No ", "Excellent", "0" } };
    // 讀取樣本屬性及其所屬分類(lèi)，構(gòu)造表示樣本的Sample對(duì)象，并按分類(lèi)劃分樣本集
    Map<Object, List<Sample>> ret = new HashMap<Object, List<Sample>>();
    for (Object[] row : rawData) {
      Sample sample = new Sample();
      int i = 0;
      for (int n = row.length - 1; i < n; i++)
        sample.setAttribute(attrNames[i], row[i]);
      sample.setCategory(row[i]);
      List<Sample> samples = ret.get(row[i]);
      if (samples == null) {
        samples = new LinkedList<Sample>();
        ret.put(row[i], samples);
      }
      samples.add(sample);
    }
    return ret;
  }
  /**
   * 構(gòu)造決策樹(shù)
   */
  static Object generateDecisionTree(
      Map<Object, List<Sample>> categoryToSamples, String[] attrNames) {
    // 如果只有一個(gè)樣本，將該樣本所屬分類(lèi)作為新樣本的分類(lèi)
    if (categoryToSamples.size() == 1)
      return categoryToSamples.keySet().iterator().next();
    // 如果沒(méi)有供決策的屬性，則將樣本集中具有最多樣本的分類(lèi)作為新樣本的分類(lèi)，即投票選舉出分類(lèi)
    if (attrNames.length == 0) {
      int max = 0;
      Object maxCategory = null;
      for (Entry<Object, List<Sample>> entry : categoryToSamples
          .entrySet()) {
        int cur = entry.getValue().size();
        if (cur > max) {
          max = cur;
          maxCategory = entry.getKey();
        }
      }
      return maxCategory;
    }
    // 選取測(cè)試屬性
    Object[] rst = chooseBestTestAttribute(categoryToSamples, attrNames);
    // 決策樹(shù)根結(jié)點(diǎn)，分支屬性為選取的測(cè)試屬性
    Tree tree = new Tree(attrNames[(Integer) rst[0]]);
    // 已用過(guò)的測(cè)試屬性不應(yīng)再次被選為測(cè)試屬性
    String[] subA = new String[attrNames.length - 1];
    for (int i = 0, j = 0; i < attrNames.length; i++)
      if (i != (Integer) rst[0])
        subA[j++] = attrNames[i];
    // 根據(jù)分支屬性生成分支
    @SuppressWarnings("unchecked")
    Map<Object, Map<Object, List<Sample>>> splits =
    /* NEW LINE */(Map<Object, Map<Object, List<Sample>>>) rst[2];
    for (Entry<Object, Map<Object, List<Sample>>> entry : splits.entrySet()) {
      Object attrValue = entry.getKey();
      Map<Object, List<Sample>> split = entry.getValue();
      Object child = generateDecisionTree(split, subA);
      tree.setChild(attrValue, child);
    }
    return tree;
  }
  /**
   * 選取最優(yōu)測(cè)試屬性。最優(yōu)是指如果根據(jù)選取的測(cè)試屬性分支，則從各分支確定新樣本
   * 的分類(lèi)需要的信息量之和最小，這等價(jià)于確定新樣本的測(cè)試屬性獲得的信息增益最大
   * 返回?cái)?shù)組：選取的屬性下標(biāo)、信息量之和、Map(屬性值->(分類(lèi)->樣本列表))
   */
  static Object[] chooseBestTestAttribute(
      Map<Object, List<Sample>> categoryToSamples, String[] attrNames) {
    int minIndex = -1; // 最優(yōu)屬性下標(biāo)
    double minValue = Double.MAX_VALUE; // 最小信息量
    Map<Object, Map<Object, List<Sample>>> minSplits = null; // 最優(yōu)分支方案
    // 對(duì)每一個(gè)屬性，計(jì)算將其作為測(cè)試屬性的情況下在各分支確定新樣本的分類(lèi)需要的信息量之和，選取最小為最優(yōu)
    for (int attrIndex = 0; attrIndex < attrNames.length; attrIndex++) {
      int allCount = 0; // 統(tǒng)計(jì)樣本總數(shù)的計(jì)數(shù)器
      // 按當(dāng)前屬性構(gòu)建Map：屬性值->(分類(lèi)->樣本列表)
      Map<Object, Map<Object, List<Sample>>> curSplits =
      /* NEW LINE */new HashMap<Object, Map<Object, List<Sample>>>();
      for (Entry<Object, List<Sample>> entry : categoryToSamples
          .entrySet()) {
        Object category = entry.getKey();
        List<Sample> samples = entry.getValue();
        for (Sample sample : samples) {
          Object attrValue = sample
              .getAttribute(attrNames[attrIndex]);
          Map<Object, List<Sample>> split = curSplits.get(attrValue);
          if (split == null) {
            split = new HashMap<Object, List<Sample>>();
            curSplits.put(attrValue, split);
          }
          List<Sample> splitSamples = split.get(category);
          if (splitSamples == null) {
            splitSamples = new LinkedList<Sample>();
            split.put(category, splitSamples);
          }
          splitSamples.add(sample);
        }
        allCount += samples.size();
      }
      // 計(jì)算將當(dāng)前屬性作為測(cè)試屬性的情況下在各分支確定新樣本的分類(lèi)需要的信息量之和
      double curValue = 0.0; // 計(jì)數(shù)器：累加各分支
      for (Map<Object, List<Sample>> splits : curSplits.values()) {
        double perSplitCount = 0;
        for (List<Sample> list : splits.values())
          perSplitCount += list.size(); // 累計(jì)當(dāng)前分支樣本數(shù)
        double perSplitValue = 0.0; // 計(jì)數(shù)器：當(dāng)前分支
        for (List<Sample> list : splits.values()) {
          double p = list.size() / perSplitCount;
          perSplitValue -= p * (Math.log(p) / Math.log(2));
        }
        curValue += (perSplitCount / allCount) * perSplitValue;
      }
      // 選取最小為最優(yōu)
      if (minValue > curValue) {
        minIndex = attrIndex;
        minValue = curValue;
        minSplits = curSplits;
      }
    }
    return new Object[] { minIndex, minValue, minSplits };
  }
  /**
   * 將決策樹(shù)輸出到標(biāo)準(zhǔn)輸出
   */
  static void outputDecisionTree(Object obj, int level, Object from) {
    for (int i = 0; i < level; i++)
      System.out.print("|-----");
    if (from != null)
      System.out.printf("(%s):", from);
    if (obj instanceof Tree) {
      Tree tree = (Tree) obj;
      String attrName = tree.getAttribute();
      System.out.printf("[%s = ?]\n", attrName);
      for (Object attrValue : tree.getAttributeValues()) {
        Object child = tree.getChild(attrValue);
        outputDecisionTree(child, level + 1, attrName + " = "
            + attrValue);
      }
    } else {
      System.out.printf("[CATEGORY = %s]\n", obj);
    }
  }
  /**
   * 樣本，包含多個(gè)屬性和一個(gè)指明樣本所屬分類(lèi)的分類(lèi)值
   */
  static class Sample {
    private Map<String, Object> attributes = new HashMap<String, Object>();
    private Object category;
    public Object getAttribute(String name) {
      return attributes.get(name);
    }
    public void setAttribute(String name, Object value) {
      attributes.put(name, value);
    }
    public Object getCategory() {
      return category;
    }
    public void setCategory(Object category) {
      this.category = category;
    }
    public String toString() {
      return attributes.toString();
    }
  }
  /**
   * 決策樹(shù)（非葉結(jié)點(diǎn)），決策樹(shù)中的每個(gè)非葉結(jié)點(diǎn)都引導(dǎo)了一棵決策樹(shù)
   * 每個(gè)非葉結(jié)點(diǎn)包含一個(gè)分支屬性和多個(gè)分支，分支屬性的每個(gè)值對(duì)應(yīng)一個(gè)分支，該分支引導(dǎo)了一棵子決策樹(shù)
   */
  static class Tree {
    private String attribute;
    private Map<Object, Object> children = new HashMap<Object, Object>();
    public Tree(String attribute) {
      this.attribute = attribute;
    }
    public String getAttribute() {
      return attribute;
    }
    public Object getChild(Object attrValue) {
      return children.get(attrValue);
    }
    public void setChild(Object attrValue, Object child) {
      children.put(attrValue, child);
    }
    public Set<Object> getAttributeValues() {
      return children.keySet();
    }
  }
}