diff --git a/DecisionStump/.project b/DecisionStump/.project
new file mode 100644
index 0000000..58d61c6
--- /dev/null
+++ b/DecisionStump/.project
@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>DecisionStump</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>org.python.pydev.PyDevBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>org.python.pydev.pythonNature</nature>
+	</natures>
+</projectDescription>
diff --git a/DecisionStump/.pydevproject b/DecisionStump/.pydevproject
new file mode 100644
index 0000000..8b78a33
--- /dev/null
+++ b/DecisionStump/.pydevproject
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<?eclipse-pydev version="1.0"?>
+
+<pydev_project>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>
+<pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.7</pydev_property>
+<pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
+<path>/DecisionStump</path>
+</pydev_pathproperty>
+</pydev_project>
diff --git a/DecisionStump/src/Adaboosting.py b/DecisionStump/src/Adaboosting.py
new file mode 100644
index 0000000..d9da749
--- /dev/null
+++ b/DecisionStump/src/Adaboosting.py
@@ -0,0 +1,68 @@
+from numpy import *
+
+def loadSimpData():
+    datMat = matrix([[ 1. ,  2.1],
+        [ 2. ,  1.1],
+        [ 1.3,  1. ],
+        [ 1. ,  1. ],
+        [ 2. ,  1. ]])
+    classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
+    return datMat,classLabels
+
+def loadDataSet(fileName):      #general function to parse tab -delimited floats
+    numFeat = len(open(fileName).readline().split('\t')) #get number of fields 
+    dataMat = []; labelMat = []
+    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]))
+        dataMat.append(lineArr)
+        labelMat.append(float(curLine[-1]))
+    return dataMat,labelMat
+
+def stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data
+    retArray = ones((shape(dataMatrix)[0],1))
+    if threshIneq == 'lt':
+        retArray[dataMatrix[:,dimen] <= threshVal] = -1.0
+    else:
+        retArray[dataMatrix[:,dimen] > threshVal] = -1.0
+    
+    return retArray
+    
+
+def buildStump(dataArr,classLabels,D):
+    dataMatrix = mat(dataArr); labelMat = mat(classLabels).T
+    m,n = shape(dataMatrix)
+    numSteps = 10.0; bestStump = {}; bestClasEst = mat(zeros((m,1)))
+    minError = inf #init error sum, to +infinity
+    for i in range(n):#loop over all dimensions
+        rangeMin = dataMatrix[:,i].min(); rangeMax = dataMatrix[:,i].max();
+        
+        stepSize = (rangeMax-rangeMin)/numSteps
+        for j in range(-1,int(numSteps)+1):#loop over all range in current dimension
+            for inequal in ['lt', 'gt']: #go over less than and greater than
+                threshVal = (rangeMin + float(j) * stepSize)
+                
+                predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal)#call stump classify with i, j, lessThan
+                errArr = mat(ones((m,1)))
+                
+                
+                errArr[predictedVals == labelMat] = 0
+                
+                weightedError = D.T*errArr  #calc total error multiplied by D
+                #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
+
+if __name__=='__main__':
+    datMat,classLabels=loadSimpData()
+    d=mat(ones((5,1))/5)
+    bestStump,minError,bestClasEst=buildStump(datMat,classLabels,d)
+    print bestStump,minError,bestClasEst
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