修改逻辑回归梯度下降

src/python/05.Logistic/core/test_logRegression.py

@@ -3,14 +3,9 @@
 import os
 import sys
 sys.path.append("C:\Python27")
-
 from numpy import *
 
-
-
-import matplotlib.pyplot as plt
-
-from  logRegression import *
+from  logRegression01 import *
 """
 @version: 
 @author: yangjf
@@ -31,7 +26,7 @@ def loadData():
     # 截取字符串至项目名：Test\
     project_dir = project_dir[:project_dir.find("MachineLearning\\") + 15]
     print project_dir
-    fileIn = open("%s/testData/testSet.txt" % project_dir)
+    fileIn = open("%s/testData/Logistic_testdata.txt" % project_dir)
     for line in fileIn.readlines():
         lineArr = line.strip().split()
         train_x.append([1.0, float(lineArr[0]), float(lineArr[1])])

src/python/Logistic.py

@@ -117,7 +117,7 @@ def plotBestFit(dataArr, labelMat, weights):
 def main():
     project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
     # 1.收集并准备数据
-    dataMat, labelMat = loadDataSet("%s/testData/testSet.txt" % project_dir)
+    dataMat, labelMat = loadDataSet("%s/testData/Logistic_testdata.txt" % project_dir)
 
     # print dataMat, '---\n', labelMat
     # 2.训练模型，  f(x)=a1*x1+b2*x2+..+nn*xn中 (a1,b2, .., nn).T的矩阵值

src/python/apriori.py

@@ -73,7 +73,7 @@ def apriori(dataSet, minSupport = 0.5):
 def main():
     # project_dir = os.path.dirname(os.path.dirname(os.getcwd()))
     # 1.收集并准备数据
-    # dataMat, labelMat = loadDataSet("%s/resources/testSet.txt" % project_dir)
+    # dataMat, labelMat = loadDataSet("%s/resources/Logistic_testdata.txt" % project_dir)
 
 
     # 1. 加载数据