diff --git a/README.md b/README.md
index 31165d7f..92bdc294 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@
 ## 第四部分  其他工具
 
 * 13) 使用PCA来简化数据
-     *[利用PCA来简化数据](./docs/13.利用PCA来简化数据.md)
+    * [利用PCA来简化数据](./docs/13.利用PCA来简化数据.md)
 * 14) 使用SVD简化数据
 * 15) 大数据与MapReduce
 
diff --git a/docs/9.树回归.md b/docs/9.树回归.md
index bd68557c..f695bff5 100644
--- a/docs/9.树回归.md
+++ b/docs/9.树回归.md
@@ -8,6 +8,6 @@
     * 优点：可以对复杂和非线性的数据建模。
     * 缺点：结果不易理解。
     * 适用数据类型：数值型和标称型数据。
-    
-
-    
\ No newline at end of file
+* 那么问题来了，如何计算连续型数值的混乱度呢？
+    * `误差`：也就是计算平均差的总值(总方差=方差*样本数)
+    * 二元切分方式
diff --git a/src/python/01.NumPy.py b/src/python/01.NumPy.py
index 256b0b93..496dd0fe 100644
--- a/src/python/01.NumPy.py
+++ b/src/python/01.NumPy.py
@@ -20,10 +20,15 @@ randArray = random.rand(4, 4)
 
 # 转化关系， 数组转化为矩阵
 randMat = mat(randArray)
-# .I表示对矩阵求逆
+# .I表示对矩阵求逆(可以利用矩阵的初等变换
+#    # 意义：逆矩阵是一个判断相似性的工具。逆矩阵A与列向量p相乘后，将得到列向量q，q的第i个分量表示p与A的第i个列向量的相似度。
+#    # 参考案例链接：
+#    # https://www.zhihu.com/question/33258489
+#    # http://blog.csdn.net/vernice/article/details/48506027
+# .T表示对矩阵转置(行列颠倒)
 invRandMat = randMat.I
 # 输出结果
-print randArray, '\n', randMat, '\n', invRandMat
+print randArray, '\n---\n', randMat, '\n+++\n', invRandMat
 # 矩阵和逆矩阵 进行求积 (单位矩阵，对角线都为1嘛，理论上4*4的矩阵其他的都为0)
 myEye = randMat*invRandMat
 # 误差
diff --git a/src/python/03.DecisionTree/DTSklearn.py b/src/python/03.DecisionTree/DTSklearn.py
index 5155b214..a4890961 100644
--- a/src/python/03.DecisionTree/DTSklearn.py
+++ b/src/python/03.DecisionTree/DTSklearn.py
@@ -98,6 +98,7 @@ def show_pdf(clf):
     # from IPython.display import Image
     # Image(graph.create_png())
 
+
 if __name__ == '__main__':
     x, y = createDataSet()
 
diff --git a/src/python/03.DecisionTree/DecisionTreePlot.py b/src/python/03.DecisionTree/DecisionTreePlot.py
index cf11e382..737e4d31 100644
--- a/src/python/03.DecisionTree/DecisionTreePlot.py
+++ b/src/python/03.DecisionTree/DecisionTreePlot.py
@@ -77,9 +77,9 @@ def plotTree(myTree, parentPt, nodeTxt):
     plotTree.yOff = plotTree.yOff - 1.0/plotTree.totalD
     for key in secondDict.keys():
         # 判断该节点是否是Node节点
-        if type(secondDict[key]).__name__=='dict':
+        if type(secondDict[key]).__name__ == 'dict':
             # 如果是就递归调用[recursion]
-            plotTree(secondDict[key],cntrPt,str(key))
+            plotTree(secondDict[key], cntrPt, str(key))
         else:
             # 如果不是，就在原来节点一半的地方找到节点的坐标
             plotTree.xOff = plotTree.xOff + 1.0/plotTree.totalW
@@ -121,7 +121,7 @@ def createPlot(inTree):
 
 # 测试数据集
 def retrieveTree(i):
-    listOfTrees =[
+    listOfTrees = [
         {'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},
         {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}
     ]
diff --git a/src/python/09.RegTrees/TreeNode.py b/src/python/09.RegTrees/TreeNode.py
new file mode 100644
index 00000000..c6e76583
--- /dev/null
+++ b/src/python/09.RegTrees/TreeNode.py
@@ -0,0 +1,16 @@
+#!/usr/bin/python
+# coding:utf8
+
+'''
+Created on 2017-03-06
+Update on 2017-03-06
+@author: jiangzhonglian
+'''
+
+
+class treeNode():
+    def __init__(self, feat, val, right, left):
+        self.featureToSplitOn = feat
+        self.valueOfSplit = val
+        self.rightBranch = right
+        self.leftBranch = left
diff --git a/src/python/09.RegTrees/regTrees.py b/src/python/09.RegTrees/regTrees.py
index c1436e0e..bfe43a74 100644
--- a/src/python/09.RegTrees/regTrees.py
+++ b/src/python/09.RegTrees/regTrees.py
@@ -9,25 +9,136 @@ Tree-Based Regression Methods Source Code for Machine Learning in Action Ch. 9
 '''
 from numpy import *
 
-def loadDataSet(fileName):      #general function to parse tab -delimited floats
-    dataMat = []                #assume last column is target value
+
+# 默认解析的数据是用tab分隔，并且是数值类型
+# general function to parse tab -delimited floats
+def loadDataSet(fileName):
+    """loadDataSet(解析每一行，并转化为float类型)
+
+    Args:
+        fileName 文件名
+    Returns:
+        dataMat 每一行的数据集array类型
+    Raises:
+    """
+    # 假定最后一列是结果值
+    # assume last column is target value
+    dataMat = []
     fr = open(fileName)
     for line in fr.readlines():
         curLine = line.strip().split('\t')
-        fltLine = map(float,curLine) #map all elements to float()
+        # 将所有的元素转化为float类型
+        # map all elements to float()
+        fltLine = map(float, curLine)
         dataMat.append(fltLine)
     return dataMat
 
+
 def binSplitDataSet(dataSet, feature, value):
-    mat0 = dataSet[nonzero(dataSet[:,feature] > value)[0],:][0]
-    mat1 = dataSet[nonzero(dataSet[:,feature] <= value)[0],:][0]
-    return mat0,mat1
+    """binSplitDataSet(将数据集，按照feature列的value进行 二元切分)
 
-def regLeaf(dataSet):#returns the value used for each leaf
-    return mean(dataSet[:,-1])
+    Args:
+        fileName 文件名
+    Returns:
+        dataMat 每一行的数据集array类型
+    Raises:
+    """
+    # # 测试案例
+    # print 'dataSet[:, feature]=', dataSet[:, feature]
+    # print 'nonzero(dataSet[:, feature] > value)[0]=', nonzero(dataSet[:, feature] > value)[0]
+    # print 'nonzero(dataSet[:, feature] <= value)[0]=', nonzero(dataSet[:, feature] <= value)[0]
 
+    # dataSet[:, feature] 取去每一行中，第1列的值(从0开始算)
+    # nonzero(dataSet[:, feature] > value)  返回结果为true行的index下标
+    mat0 = dataSet[nonzero(dataSet[:, feature] > value)[0], :]
+    mat1 = dataSet[nonzero(dataSet[:, feature] <= value)[0], :]
+    return mat0, mat1
+
+
+# 返回每一个叶子结点的均值
+# returns the value used for each leaf
+def regLeaf(dataSet):
+    return mean(dataSet[:, -1])
+
+
+# 计算总方差=方差*样本数
 def regErr(dataSet):
-    return var(dataSet[:,-1]) * shape(dataSet)[0]
+    # shape(dataSet)[0] 表示行数
+    return var(dataSet[:, -1]) * shape(dataSet)[0]
+
+
+# 1.用最佳方式切分数据集
+# 2.生成相应的叶节点
+def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)):
+    """chooseBestSplit(用最佳方式切分数据集 和 生成相应的叶节点)
+
+    Args:
+        dataSet 数据集
+        leafType 计算叶子节点的函数
+        errType 求总方差
+        ops [容许误差下降值，切分的最少样本数]
+    Returns:
+        bestIndex feature的index坐标
+        bestValue 切分的最优值
+    Raises:
+    """
+    tolS = ops[0]
+    tolN = ops[1]
+    # 如果结果集(最后一列为1个变量)，就返回推出
+    # .T 对数据集进行转置
+    # .tolist()[0] 转化为数组并取第0列
+    if len(set(dataSet[:, -1].T.tolist()[0])) == 1:
+        #  exit cond 1
+        return None, leafType(dataSet)
+    # 计算行列值
+    m, n = shape(dataSet)
+    print m, n
+    # 无分类误差的总方差和
+    # the choice of the best feature is driven by Reduction in RSS error from mean
+    S = errType(dataSet)
+    # inf 正无穷大
+    bestS, bestIndex, bestValue = inf, 0, 0
+    # 循环处理每一列对应的feature值
+    for featIndex in range(n-1):
+        for splitVal in set(dataSet[:, featIndex].T.tolist()[0]):
+            # 对该列进行分组，然后组内的成员的val值进行 二元切分
+            mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal)
+            # 判断二元切分的方式的元素数量是否符合预期
+            if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
+                continue
+            newS = errType(mat0) + errType(mat1)
+            # 如果二元切分，算出来的误差在可接受范围内，那么就记录切分点，并记录最小误差
+            if newS < bestS:
+                bestIndex = featIndex
+                bestValue = splitVal
+                bestS = newS
+    # 判断二元切分的方式的元素误差是否符合预期
+    # if the decrease (S-bestS) is less than a threshold don't do the split
+    if (S - bestS) < tolS:
+        return None, leafType(dataSet)
+    mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
+    # 对整体的成员进行判断，是否符合预期
+    if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):
+        return None, leafType(dataSet)
+    return bestIndex, bestValue
+
+
+# assume dataSet is NumPy Mat so we can array filtering
+def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1, 4)):
+    # 选择最好的切分方式： feature索引值，最优切分值
+    # choose the best split
+    feat, val = chooseBestSplit(dataSet, leafType, errType, ops)
+    # if the splitting hit a stop condition return val
+    if feat is None:
+        return val
+    retTree = {}
+    retTree['spInd'] = feat
+    retTree['spVal'] = val
+    lSet, rSet = binSplitDataSet(dataSet, feat, val)
+    retTree['right'] = createTree(lSet, leafType, errType, ops)
+    retTree['left'] = createTree(rSet, leafType, errType, ops)
+    return retTree
+
 
 def linearSolve(dataSet):   #helper function used in two places
     m,n = shape(dataSet)
@@ -49,43 +160,7 @@ def modelErr(dataSet):
     yHat = X * ws
     return sum(power(Y - yHat,2))
 
-def chooseBestSplit(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):
-    tolS = ops[0]; tolN = ops[1]
-    #if all the target variables are the same value: quit and return value
-    if len(set(dataSet[:,-1].T.tolist()[0])) == 1: #exit cond 1
-        return None, leafType(dataSet)
-    m,n = shape(dataSet)
-    #the choice of the best feature is driven by Reduction in RSS error from mean
-    S = errType(dataSet)
-    bestS = inf; bestIndex = 0; bestValue = 0
-    for featIndex in range(n-1):
-        for splitVal in set(dataSet[:,featIndex]):
-            mat0, mat1 = binSplitDataSet(dataSet, featIndex, splitVal)
-            if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN): continue
-            newS = errType(mat0) + errType(mat1)
-            if newS < bestS: 
-                bestIndex = featIndex
-                bestValue = splitVal
-                bestS = newS
-    #if the decrease (S-bestS) is less than a threshold don't do the split
-    if (S - bestS) < tolS: 
-        return None, leafType(dataSet) #exit cond 2
-    mat0, mat1 = binSplitDataSet(dataSet, bestIndex, bestValue)
-    if (shape(mat0)[0] < tolN) or (shape(mat1)[0] < tolN):  #exit cond 3
-        return None, leafType(dataSet)
-    return bestIndex,bestValue#returns the best feature to split on
-                              #and the value used for that split
 
-def createTree(dataSet, leafType=regLeaf, errType=regErr, ops=(1,4)):#assume dataSet is NumPy Mat so we can array filtering
-    feat, val = chooseBestSplit(dataSet, leafType, errType, ops)#choose the best split
-    if feat == None: return val #if the splitting hit a stop condition return val
-    retTree = {}
-    retTree['spInd'] = feat
-    retTree['spVal'] = val
-    lSet, rSet = binSplitDataSet(dataSet, feat, val)
-    retTree['left'] = createTree(lSet, leafType, errType, ops)
-    retTree['right'] = createTree(rSet, leafType, errType, ops)
-    return retTree  
 
 def isTree(obj):
     return (type(obj).__name__=='dict')
@@ -137,4 +212,21 @@ def createForeCast(tree, testData, modelEval=regTreeEval):
     yHat = mat(zeros((m,1)))
     for i in range(m):
         yHat[i,0] = treeForeCast(tree, mat(testData[i]), modelEval)
-    return yHat
\ No newline at end of file
+    return yHat
+
+if __name__ == "__main__":
+
+
+    # # 测试数据集
+    # testMat = mat(eye(4))
+    # print testMat
+    # print type(testMat)
+    # mat0, mat1 = binSplitDataSet(testMat, 1, 0.5)
+    # print mat0, '\n-----------\n', mat1
+
+    # 获取数据集
+    # myDat = loadDataSet('testData/RT_data1.txt')
+    myDat = loadDataSet('testData/RT_data2.txt')
+    myMat = mat(myDat)
+    myTree = createTree(myMat)
+    print myTree
diff --git a/testData/RT_data1.txt b/testData/RT_data1.txt
new file mode 100755
index 00000000..fa98fa58
--- /dev/null
+++ b/testData/RT_data1.txt
@@ -0,0 +1,200 @@
+0.036098	0.155096
+0.993349	1.077553
+0.530897	0.893462
+0.712386	0.564858
+0.343554	-0.371700
+0.098016	-0.332760
+0.691115	0.834391
+0.091358	0.099935
+0.727098	1.000567
+0.951949	0.945255
+0.768596	0.760219
+0.541314	0.893748
+0.146366	0.034283
+0.673195	0.915077
+0.183510	0.184843
+0.339563	0.206783
+0.517921	1.493586
+0.703755	1.101678
+0.008307	0.069976
+0.243909	-0.029467
+0.306964	-0.177321
+0.036492	0.408155
+0.295511	0.002882
+0.837522	1.229373
+0.202054	-0.087744
+0.919384	1.029889
+0.377201	-0.243550
+0.814825	1.095206
+0.611270	0.982036
+0.072243	-0.420983
+0.410230	0.331722
+0.869077	1.114825
+0.620599	1.334421
+0.101149	0.068834
+0.820802	1.325907
+0.520044	0.961983
+0.488130	-0.097791
+0.819823	0.835264
+0.975022	0.673579
+0.953112	1.064690
+0.475976	-0.163707
+0.273147	-0.455219
+0.804586	0.924033
+0.074795	-0.349692
+0.625336	0.623696
+0.656218	0.958506
+0.834078	1.010580
+0.781930	1.074488
+0.009849	0.056594
+0.302217	-0.148650
+0.678287	0.907727
+0.180506	0.103676
+0.193641	-0.327589
+0.343479	0.175264
+0.145809	0.136979
+0.996757	1.035533
+0.590210	1.336661
+0.238070	-0.358459
+0.561362	1.070529
+0.377597	0.088505
+0.099142	0.025280
+0.539558	1.053846
+0.790240	0.533214
+0.242204	0.209359
+0.152324	0.132858
+0.252649	-0.055613
+0.895930	1.077275
+0.133300	-0.223143
+0.559763	1.253151
+0.643665	1.024241
+0.877241	0.797005
+0.613765	1.621091
+0.645762	1.026886
+0.651376	1.315384
+0.697718	1.212434
+0.742527	1.087056
+0.901056	1.055900
+0.362314	-0.556464
+0.948268	0.631862
+0.000234	0.060903
+0.750078	0.906291
+0.325412	-0.219245
+0.726828	1.017112
+0.348013	0.048939
+0.458121	-0.061456
+0.280738	-0.228880
+0.567704	0.969058
+0.750918	0.748104
+0.575805	0.899090
+0.507940	1.107265
+0.071769	-0.110946
+0.553520	1.391273
+0.401152	-0.121640
+0.406649	-0.366317
+0.652121	1.004346
+0.347837	-0.153405
+0.081931	-0.269756
+0.821648	1.280895
+0.048014	0.064496
+0.130962	0.184241
+0.773422	1.125943
+0.789625	0.552614
+0.096994	0.227167
+0.625791	1.244731
+0.589575	1.185812
+0.323181	0.180811
+0.822443	1.086648
+0.360323	-0.204830
+0.950153	1.022906
+0.527505	0.879560
+0.860049	0.717490
+0.007044	0.094150
+0.438367	0.034014
+0.574573	1.066130
+0.536689	0.867284
+0.782167	0.886049
+0.989888	0.744207
+0.761474	1.058262
+0.985425	1.227946
+0.132543	-0.329372
+0.346986	-0.150389
+0.768784	0.899705
+0.848921	1.170959
+0.449280	0.069098
+0.066172	0.052439
+0.813719	0.706601
+0.661923	0.767040
+0.529491	1.022206
+0.846455	0.720030
+0.448656	0.026974
+0.795072	0.965721
+0.118156	-0.077409
+0.084248	-0.019547
+0.845815	0.952617
+0.576946	1.234129
+0.772083	1.299018
+0.696648	0.845423
+0.595012	1.213435
+0.648675	1.287407
+0.897094	1.240209
+0.552990	1.036158
+0.332982	0.210084
+0.065615	-0.306970
+0.278661	0.253628
+0.773168	1.140917
+0.203693	-0.064036
+0.355688	-0.119399
+0.988852	1.069062
+0.518735	1.037179
+0.514563	1.156648
+0.976414	0.862911
+0.919074	1.123413
+0.697777	0.827805
+0.928097	0.883225
+0.900272	0.996871
+0.344102	-0.061539
+0.148049	0.204298
+0.130052	-0.026167
+0.302001	0.317135
+0.337100	0.026332
+0.314924	-0.001952
+0.269681	-0.165971
+0.196005	-0.048847
+0.129061	0.305107
+0.936783	1.026258
+0.305540	-0.115991
+0.683921	1.414382
+0.622398	0.766330
+0.902532	0.861601
+0.712503	0.933490
+0.590062	0.705531
+0.723120	1.307248
+0.188218	0.113685
+0.643601	0.782552
+0.520207	1.209557
+0.233115	-0.348147
+0.465625	-0.152940
+0.884512	1.117833
+0.663200	0.701634
+0.268857	0.073447
+0.729234	0.931956
+0.429664	-0.188659
+0.737189	1.200781
+0.378595	-0.296094
+0.930173	1.035645
+0.774301	0.836763
+0.273940	-0.085713
+0.824442	1.082153
+0.626011	0.840544
+0.679390	1.307217
+0.578252	0.921885
+0.785541	1.165296
+0.597409	0.974770
+0.014083	-0.132525
+0.663870	1.187129
+0.552381	1.369630
+0.683886	0.999985
+0.210334	-0.006899
+0.604529	1.212685
+0.250744	0.046297
diff --git a/testData/RT_data2.txt b/testData/RT_data2.txt
new file mode 100755
index 00000000..bee167b9
--- /dev/null
+++ b/testData/RT_data2.txt
@@ -0,0 +1,200 @@
+1.000000	0.409175	1.883180
+1.000000	0.182603	0.063908
+1.000000	0.663687	3.042257
+1.000000	0.517395	2.305004
+1.000000	0.013643	-0.067698
+1.000000	0.469643	1.662809
+1.000000	0.725426	3.275749
+1.000000	0.394350	1.118077
+1.000000	0.507760	2.095059
+1.000000	0.237395	1.181912
+1.000000	0.057534	0.221663
+1.000000	0.369820	0.938453
+1.000000	0.976819	4.149409
+1.000000	0.616051	3.105444
+1.000000	0.413700	1.896278
+1.000000	0.105279	-0.121345
+1.000000	0.670273	3.161652
+1.000000	0.952758	4.135358
+1.000000	0.272316	0.859063
+1.000000	0.303697	1.170272
+1.000000	0.486698	1.687960
+1.000000	0.511810	1.979745
+1.000000	0.195865	0.068690
+1.000000	0.986769	4.052137
+1.000000	0.785623	3.156316
+1.000000	0.797583	2.950630
+1.000000	0.081306	0.068935
+1.000000	0.659753	2.854020
+1.000000	0.375270	0.999743
+1.000000	0.819136	4.048082
+1.000000	0.142432	0.230923
+1.000000	0.215112	0.816693
+1.000000	0.041270	0.130713
+1.000000	0.044136	-0.537706
+1.000000	0.131337	-0.339109
+1.000000	0.463444	2.124538
+1.000000	0.671905	2.708292
+1.000000	0.946559	4.017390
+1.000000	0.904176	4.004021
+1.000000	0.306674	1.022555
+1.000000	0.819006	3.657442
+1.000000	0.845472	4.073619
+1.000000	0.156258	0.011994
+1.000000	0.857185	3.640429
+1.000000	0.400158	1.808497
+1.000000	0.375395	1.431404
+1.000000	0.885807	3.935544
+1.000000	0.239960	1.162152
+1.000000	0.148640	-0.227330
+1.000000	0.143143	-0.068728
+1.000000	0.321582	0.825051
+1.000000	0.509393	2.008645
+1.000000	0.355891	0.664566
+1.000000	0.938633	4.180202
+1.000000	0.348057	0.864845
+1.000000	0.438898	1.851174
+1.000000	0.781419	2.761993
+1.000000	0.911333	4.075914
+1.000000	0.032469	0.110229
+1.000000	0.499985	2.181987
+1.000000	0.771663	3.152528
+1.000000	0.670361	3.046564
+1.000000	0.176202	0.128954
+1.000000	0.392170	1.062726
+1.000000	0.911188	3.651742
+1.000000	0.872288	4.401950
+1.000000	0.733107	3.022888
+1.000000	0.610239	2.874917
+1.000000	0.732739	2.946801
+1.000000	0.714825	2.893644
+1.000000	0.076386	0.072131
+1.000000	0.559009	1.748275
+1.000000	0.427258	1.912047
+1.000000	0.841875	3.710686
+1.000000	0.558918	1.719148
+1.000000	0.533241	2.174090
+1.000000	0.956665	3.656357
+1.000000	0.620393	3.522504
+1.000000	0.566120	2.234126
+1.000000	0.523258	1.859772
+1.000000	0.476884	2.097017
+1.000000	0.176408	0.001794
+1.000000	0.303094	1.231928
+1.000000	0.609731	2.953862
+1.000000	0.017774	-0.116803
+1.000000	0.622616	2.638864
+1.000000	0.886539	3.943428
+1.000000	0.148654	-0.328513
+1.000000	0.104350	-0.099866
+1.000000	0.116868	-0.030836
+1.000000	0.516514	2.359786
+1.000000	0.664896	3.212581
+1.000000	0.004327	0.188975
+1.000000	0.425559	1.904109
+1.000000	0.743671	3.007114
+1.000000	0.935185	3.845834
+1.000000	0.697300	3.079411
+1.000000	0.444551	1.939739
+1.000000	0.683753	2.880078
+1.000000	0.755993	3.063577
+1.000000	0.902690	4.116296
+1.000000	0.094491	-0.240963
+1.000000	0.873831	4.066299
+1.000000	0.991810	4.011834
+1.000000	0.185611	0.077710
+1.000000	0.694551	3.103069
+1.000000	0.657275	2.811897
+1.000000	0.118746	-0.104630
+1.000000	0.084302	0.025216
+1.000000	0.945341	4.330063
+1.000000	0.785827	3.087091
+1.000000	0.530933	2.269988
+1.000000	0.879594	4.010701
+1.000000	0.652770	3.119542
+1.000000	0.879338	3.723411
+1.000000	0.764739	2.792078
+1.000000	0.504884	2.192787
+1.000000	0.554203	2.081305
+1.000000	0.493209	1.714463
+1.000000	0.363783	0.885854
+1.000000	0.316465	1.028187
+1.000000	0.580283	1.951497
+1.000000	0.542898	1.709427
+1.000000	0.112661	0.144068
+1.000000	0.816742	3.880240
+1.000000	0.234175	0.921876
+1.000000	0.402804	1.979316
+1.000000	0.709423	3.085768
+1.000000	0.867298	3.476122
+1.000000	0.993392	3.993679
+1.000000	0.711580	3.077880
+1.000000	0.133643	-0.105365
+1.000000	0.052031	-0.164703
+1.000000	0.366806	1.096814
+1.000000	0.697521	3.092879
+1.000000	0.787262	2.987926
+1.000000	0.476710	2.061264
+1.000000	0.721417	2.746854
+1.000000	0.230376	0.716710
+1.000000	0.104397	0.103831
+1.000000	0.197834	0.023776
+1.000000	0.129291	-0.033299
+1.000000	0.528528	1.942286
+1.000000	0.009493	-0.006338
+1.000000	0.998533	3.808753
+1.000000	0.363522	0.652799
+1.000000	0.901386	4.053747
+1.000000	0.832693	4.569290
+1.000000	0.119002	-0.032773
+1.000000	0.487638	2.066236
+1.000000	0.153667	0.222785
+1.000000	0.238619	1.089268
+1.000000	0.208197	1.487788
+1.000000	0.750921	2.852033
+1.000000	0.183403	0.024486
+1.000000	0.995608	3.737750
+1.000000	0.151311	0.045017
+1.000000	0.126804	0.001238
+1.000000	0.983153	3.892763
+1.000000	0.772495	2.819376
+1.000000	0.784133	2.830665
+1.000000	0.056934	0.234633
+1.000000	0.425584	1.810782
+1.000000	0.998709	4.237235
+1.000000	0.707815	3.034768
+1.000000	0.413816	1.742106
+1.000000	0.217152	1.169250
+1.000000	0.360503	0.831165
+1.000000	0.977989	3.729376
+1.000000	0.507953	1.823205
+1.000000	0.920771	4.021970
+1.000000	0.210542	1.262939
+1.000000	0.928611	4.159518
+1.000000	0.580373	2.039114
+1.000000	0.841390	4.101837
+1.000000	0.681530	2.778672
+1.000000	0.292795	1.228284
+1.000000	0.456918	1.736620
+1.000000	0.134128	-0.195046
+1.000000	0.016241	-0.063215
+1.000000	0.691214	3.305268
+1.000000	0.582002	2.063627
+1.000000	0.303102	0.898840
+1.000000	0.622598	2.701692
+1.000000	0.525024	1.992909
+1.000000	0.996775	3.811393
+1.000000	0.881025	4.353857
+1.000000	0.723457	2.635641
+1.000000	0.676346	2.856311
+1.000000	0.254625	1.352682
+1.000000	0.488632	2.336459
+1.000000	0.519875	2.111651
+1.000000	0.160176	0.121726
+1.000000	0.609483	3.264605
+1.000000	0.531881	2.103446
+1.000000	0.321632	0.896855
+1.000000	0.845148	4.220850
+1.000000	0.012003	-0.217283
+1.000000	0.018883	-0.300577
+1.000000	0.071476	0.006014