diff --git a/docs/7.利用AdaBoost元算法提高分类.md b/docs/7.利用AdaBoost元算法提高分类.md
index a60e270a..191bc2da 100644
--- a/docs/7.利用AdaBoost元算法提高分类.md
+++ b/docs/7.利用AdaBoost元算法提高分类.md
@@ -24,3 +24,7 @@
     * 3. 由于boosting分类的结果是基于所有分类器的加权求和结果的，因此boosting与bagging不太一样。
     * 4. bagging中的分类器权重是相等的，而boosting中的分类器权重并不相等，每个权重代表的是其对应分类器在上一轮迭代中的成功度。
     * 目前boosting方法最流行的版本是： AdaBoost。
+* AdaBoost的一般流程
+    * 训练算法： 基于错误提升分类器的性能
+    * 基于单层决策树构建弱分类器
+        * 单层决策树(decision stump, 也称决策树桩)是一种简单的决策树。
\ No newline at end of file
diff --git a/src/python/07.AdaBoost/adaboost.py b/src/python/07.AdaBoost/adaboost.py
new file mode 100644
index 00000000..a387b4f2
--- /dev/null
+++ b/src/python/07.AdaBoost/adaboost.py
@@ -0,0 +1,204 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on Nov 28, 2010
+Adaboost is short for Adaptive Boosting
+@author: Peter/jiangzhonglian
+'''
+from numpy import *
+
+
+def loadSimpData():
+    """ 测试数据
+    Returns:
+        dataArr   feature对应的数据集
+        labelArr  feature对应的分类标签
+    """
+    dataArr = array([[1., 2.1], [2., 1.1], [1.3, 1.], [1., 1.], [2., 1.]])
+    labelArr = [1.0, 1.0, -1.0, -1.0, 1.0]
+    return dataArr, labelArr
+
+
+def stumpClassify(dataMat, dimen, threshVal, threshIneq):
+    """stumpClassify(将数据集，按照feature列的value进行 二元切分比较来赋值)
+
+    Args:
+        dataMat  Matrix数据集
+        dimen 特征列
+        threshVal 特征列要比较的值
+    Returns:
+        retArray 结果集
+    """
+    retArray = ones((shape(dataMat)[0], 1))
+    # dataMat[:, dimen] 表示数据集中第dimen列的所有值
+    # print '-----', threshIneq, dataMat[:, dimen], threshVal
+    if threshIneq == 'lt':
+        retArray[dataMat[:, dimen] <= threshVal] = -1.0
+    else:
+        retArray[dataMat[:, dimen] > threshVal] = -1.0
+    return retArray
+
+
+def buildStump(dataArr, labelArr, D):
+
+    # 转换数据
+    dataMat = mat(dataArr)
+    labelMat = mat(labelArr).T
+    # m行 n列
+    m, n = shape(dataMat)
+
+    # 初始化数据
+    numSteps = 10.0
+    bestStump = {}
+    bestClasEst = mat(zeros((m, 1)))
+    # 初始化的最小误差为无穷大
+    minError = inf
+
+    # 循环所有的feature列
+    for i in range(n):
+        rangeMin = dataMat[:, i].min()
+        rangeMax = dataMat[:, i].max()
+        # print 'rangeMin=%s, rangeMax=%s' % (rangeMin, rangeMax)
+        # 计算每一份的元素个数
+        stepSize = (rangeMax-rangeMin)/numSteps
+        # 分成-1～numSteps= 1+numSteps份, 加本身是需要+1的
+        for j in range(-1, int(numSteps)+1):
+            # go over less than and greater than
+            for inequal in ['lt', 'gt']:
+                # 如果是-1，那么得到rangeMin-stepSize; 如果是numSteps，那么得到rangeMax
+                threshVal = (rangeMin + float(j) * stepSize)
+                # 对单层决策树进行简单分类
+                predictedVals = stumpClassify(dataMat, i, threshVal, inequal)
+                # print predictedVals
+                errArr = mat(ones((m, 1)))
+                # 正确为0，错误为1
+                errArr[predictedVals == labelMat] = 0
+                # 计算 平均每个特征的概率0.2*错误概率的总和为多少，就知道错误率多高
+                # calc total error multiplied by D
+                weightedError = D.T*errArr
+                '''
+                dim       表示 feature列
+                threshVal 表示树的分界值
+                inequal   表示计算树左右颠倒的错误率的情况
+                weightedError 表示整体结果的错误率
+                '''
+                # print "split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError)
+                if weightedError < minError:
+                    minError = weightedError
+                    bestClasEst = predictedVals.copy()
+                    bestStump['dim'] = i
+                    bestStump['thresh'] = threshVal
+                    bestStump['ineq'] = inequal
+    return bestStump, minError, bestClasEst
+
+
+def adaBoostTrainDS(dataArr, labelArr, numIt=40):
+    weakClassArr = []
+    m = shape(dataArr)[0]
+    # 初始化 init D to all equal
+    D = mat(ones((m, 1))/m)
+    aggClassEst = mat(zeros((m, 1)))
+    for i in range(numIt):
+        # build Stump
+        bestStump, error, classEst = buildStump(dataArr, labelArr, D)
+        # print "D:", D.T
+        # calc alpha, throw in max(error,eps) to account for error=0
+        alpha = float(0.5*log((1.0-error)/max(error, 1e-16)))
+        bestStump['alpha'] = alpha
+        # store Stump Params in Array
+        weakClassArr.append(bestStump)
+
+        # print "alpha=%s, classEst=%s, bestStump=%s, error=%s " % (alpha, classEst.T, bestStump, error)
+        # -1主要是下面求e的-alpha次方； 如果判断正确，乘积为1，否则为-1，这样就可以算出分类的情况了
+        expon = multiply(-1*alpha*mat(labelArr).T, classEst)
+        # print 'expon=', -1*alpha*mat(labelArr).T, classEst, expon
+        # 计算e的expon次方，然后计算得到一个综合的概率的值
+        # 结果发现： 正确的alpha的权重值变小了，错误的变大了。也就说D里面分类的权重值变了。（可以举例验证，假设：alpha=0.6，什么的）
+        D = multiply(D, exp(expon))
+        D = D/D.sum()
+        print "D: ", D.T
+        # 计算分类结果的值，在上一轮结果的基础上，进行加和操作
+        # calc training error of all classifiers, if this is 0 quit for loop early (use break)
+        aggClassEst += alpha*classEst
+        print "aggClassEst: ", aggClassEst.T
+        # sign 判断正为1， 0为0， 负为-1，通过最终加和的权重值，判断符号。
+        # 结果为：错误的样本标签集合，因为是 !=,那么结果就是0 正, 1 负
+        aggErrors = multiply(sign(aggClassEst) != mat(labelArr).T, ones((m, 1)))
+        errorRate = aggErrors.sum()/m
+        print "total error=%s " % (errorRate)
+        if errorRate == 0.0:
+            break
+    return weakClassArr, aggClassEst
+
+
+if __name__ == "__main__":
+    dataArr, labelArr = loadSimpData()
+    print '-----\n', dataArr, '\n', labelArr
+
+    # D表示最初，对1进行均分为5份，平均每一个初始的概率都为0.2
+    D = mat(ones((5, 1))/5)
+    # print '-----', D
+
+    # print buildStump(dataArr, labelArr, D)
+    weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, labelArr, 9)
+    print weakClassArr
+
+
+
+
+def loadDataSet(fileName):      #general function to parse tab -delimited floats
+    numFeat = len(open(fileName).readline().split('\t')) #get number of fields 
+    dataArr = []
+    labelArr = []
+    fr = open(fileName)
+    for line in fr.readlines():
+        lineArr = []
+        curLine = line.strip().split('\t')
+        for i in range(numFeat-1):
+            lineArr.append(float(curLine[i]))
+        dataArr.append(lineArr)
+        labelArr.append(float(curLine[-1]))
+    return dataArr, labelArr
+
+
+
+
+def adaClassify(datToClass,classifierArr):
+    dataMatrix = mat(datToClass)#do stuff similar to last aggClassEst in adaBoostTrainDS
+    m = shape(dataMatrix)[0]
+    aggClassEst = mat(zeros((m,1)))
+    for i in range(len(classifierArr)):
+        classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],\
+                                 classifierArr[i]['thresh'],\
+                                 classifierArr[i]['ineq'])#call stump classify
+        aggClassEst += classifierArr[i]['alpha']*classEst
+        print aggClassEst
+    return sign(aggClassEst)
+
+def plotROC(predStrengths, classLabels):
+    import matplotlib.pyplot as plt
+    cur = (1.0,1.0) #cursor
+    ySum = 0.0 #variable to calculate AUC
+    numPosClas = sum(array(classLabels)==1.0)
+    yStep = 1/float(numPosClas); xStep = 1/float(len(classLabels)-numPosClas)
+    sortedIndicies = predStrengths.argsort()#get sorted index, it's reverse
+    fig = plt.figure()
+    fig.clf()
+    ax = plt.subplot(111)
+    #loop through all the values, drawing a line segment at each point
+    for index in sortedIndicies.tolist()[0]:
+        if classLabels[index] == 1.0:
+            delX = 0; delY = yStep;
+        else:
+            delX = xStep; delY = 0;
+            ySum += cur[1]
+        #draw line from cur to (cur[0]-delX,cur[1]-delY)
+        ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b')
+        cur = (cur[0]-delX,cur[1]-delY)
+    ax.plot([0,1],[0,1],'b--')
+    plt.xlabel('False positive rate'); plt.ylabel('True positive rate')
+    plt.title('ROC curve for AdaBoost horse colic detection system')
+    ax.axis([0,1,0,1])
+    plt.show()
+    print "the Area Under the Curve is: ",ySum*xStep