import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import sklearn.neighbors as ne
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knn = ne.KNeighborsClassifier(n_neighbors = 5)
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data = pd.read_csv("Trans150000.csv")
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dataSort = data.sort_values(by='0')
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#data = pd.read_csv("Sample.csv")
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#dataSort = data.sort_values(by='X')
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testArray = np.array(dataSort)
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#创建训练集
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trainArray = np.array([[-565,3757],[-547,3755],[-541,3754],[39356,365],[39491,359],[40434,359],[40499,359],[-654,3237],[-613,3159],[-553,3112]])
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trainLabels = np.array([1,1,1,1,1,1,1,2,2,2])
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trainData = np.c_[trainArray,trainLabels]
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knn.fit(trainArray,trainLabels)
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ax = plt.figure()
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trainDataFrame = pd.DataFrame(trainData)
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for index in range(0,testArray.shape[0]-20,20):
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for i in range(index,index+19,1):
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offset = np.abs(testArray[i][1]-testArray[i+1][1])
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if(offset > 40):
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predict = knn.predict(testArray[i].reshape(1,2))
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trainArray = np.r_[trainArray,testArray[i].reshape(1,2),]
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trainLabels = np.r_[trainLabels,predict]
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predict = knn.predict(testArray[i+1].reshape(1,2))
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trainArray = np.r_[trainArray,testArray[i+1].reshape(1,2),]
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trainLabels = np.r_[trainLabels,predict]
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knn.fit(trainArray,trainLabels)
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trainData = np.c_[trainArray,trainLabels]
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trainDataFrame = pd.DataFrame(trainData)
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break
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plt.scatter(trainDataFrame[trainDataFrame[2]==1][0],trainDataFrame[trainDataFrame[2]==1][1],c='r')
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plt.scatter(trainDataFrame[trainDataFrame[2]==2][0],trainDataFrame[trainDataFrame[2]==2][1],c='b')
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plt.scatter(trainDataFrame[trainDataFrame[2]==3][0],trainDataFrame[trainDataFrame[2]==3][1],c='g')
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plt.show()
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pd.DataFrame(trainData).to_csv("D:\\Train150000.csv")
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"""
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dataTrain = pd.read_csv("D:\\Train90000.csv")
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trainArray = np.array(dataTrain)
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knn.fit(trainArray[:,1:3],trainArray[:,3])
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predict = knn.predict(testArray[:,0:3:2])
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result = np.c_[testArray,predict]
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resultDataFrame = pd.DataFrame(result)
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dataReAnalyze = resultDataFrame[((resultDataFrame[3]==2) | (resultDataFrame[3]==3)) & (((resultDataFrame[0] > 6500) & (resultDataFrame[0] < 8500 )) | ((resultDataFrame[0] > 58500) & (resultDataFrame[0] < 60500 ))) ]
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for index in range(0,dataReAnalyze.shape[0]-2,1):
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deltaX = dataReAnalyze.iloc[index+1][0]-dataReAnalyze.iloc[index][0]
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deltaY = np.abs(dataReAnalyze.iloc[index+1][2]-dataReAnalyze.iloc[index][2])
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if(deltaX < 1 and deltaY > 10):
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if(dataReAnalyze.iloc[index + 1][2] > dataReAnalyze.iloc[index][2]):
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resultDataFrame.loc[dataReAnalyze.index.tolist()[index],3] = 3
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resultDataFrame.loc[dataReAnalyze.index.tolist()[index + 1],3] = 2
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else:
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resultDataFrame.loc[dataReAnalyze.index.tolist()[index],3] = 2
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resultDataFrame.loc[dataReAnalyze.index.tolist()[index + 1],3] = 3
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level1 = resultDataFrame[resultDataFrame[4]==1]
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level2 = resultDataFrame[resultDataFrame[4]==2]
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level3 = resultDataFrame[resultDataFrame[4]==3]
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level1.to_csv("D:\\Level1Plane.csv",index=False)
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level2.to_csv("D:\\Level2Plane.csv",index=False)
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level3.to_csv("D:\\Level3Plane.csv",index=False)
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# 展示分层
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plt.scatter(level1[0],level1[2],c='r')
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plt.scatter(level2[0],level2[2],c='b')
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plt.scatter(level3[0],level3[2],c='g')
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plt.grid()
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plt.show()
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"""
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