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					"contents": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: anchen\n# @Date:   2018-04-26 16:36:51\n# @Last Modified by:   anchen\n# @Last Modified time: 2018-05-24 18:02:54\nimport matplotlib.pyplot as plt#CANCEL\nimport numpy as np\nimport numpy.random as rnd\nimport pandas as pd\nimport  scipy as sp\n\n\ndef main():\n\n    # rankm,rankf=np.loadtxt('mao_fen.csv',delimiter=',',skiprows=1,usecols=(0,7),unpack=True)\n\n    # fig = plt.figure(figsize=(12, 6))\n    # ax = fig.add_subplot(111)\n    # ax.scatter(rankm,rankf,color='g',alpha=1)\n    # ax.set_xticks([0,10,20,30,40,50,60])\n    # ax.set_yticks([0,10,20,30,40,50,60])\n    # ax.axhline(30, color= 'r')\n    # ax.axvline(30, color= 'r')\n    # ax.axhline(0,color='gray',linestyle='--')\n    # ax.set_xlabel('maotuying')\n    # ax.set_ylabel('fengwo')\n    # ax.set_title('maotuying-fengwo')\n\n    # plt.show()\n    # \n    \n\n\n    # dates=pd.date_range('20130101',periods=6)\n    # df=pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D'])\n    # df.iloc[0,1]=np.nan\n    # df.iloc[1,2]=np.nan\n    # df.iloc[3,2]=np.nan\n    # print(df)\n    # print('------------------------------')\n    # c=df.dropna(axis=0,how='all')\n    # print(c)\n    # df1=pd.DataFrame(np.ones((3,4))*0,columns=['A','B','C','D'],index=[1,2,3]) \n    # df2=pd.DataFrame(np.ones((3,4))*1,columns=['B','C','D','E'],index=[2,3,4])\n    # df3=pd.DataFrame(np.ones((3,4))*1,columns=['B','C','D','E'],index=[2,3,4])\n    # s=pd.Series([1,2,3,np.nan,55,66])\n    # # dates=pd.date_range('20130101',periods=243)\n    # dd=pd.read_csv('000001.csv',encoding='utf-8')\n    # print(dd)\n    # \n\n\n    # dates=pd.date_range('20130101',periods=6) \n    # df=pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D'])\n    # print(df)\n    # print(df.loc['20130101',])\n\n    # A1,A2,A3,ALL,rank=np.loadtxt('pingjia.csv',delimiter=',',skiprows=1,usecols=(1,2,3,4,5),unpack=True)\n    # fig=plt.figure()\n    # ax1=fig.add_subplot(221)\n    # ax1.plot(rank,A1,c='r',alpha=0.5,marker='o')\n    # ax1.set_title('Rank-A1_Value')\n    # ax1.set_ylabel('A1_Value')\n    # ax2=fig.add_subplot(222)\n    # ax2.plot(rank,A2,c='r',alpha=0.5,marker='o')\n    # ax2.set_title('Rank-A2_Value')\n    # ax2.set_ylabel('A2_Value')\n    # ax3=fig.add_subplot(223)\n    # ax3.plot(rank,A3,c='r',alpha=0.5,marker='o')\n    # ax3.set_title('Rank-A3_Value')\n    # ax3.set_ylabel('A3_Value')\n    # ax4=fig.add_subplot(224)\n    # ax4.plot(rank,ALL,c='r',alpha=0.5,marker='o')\n    # ax4.set_title('Rank-All_Value')\n    # ax4.set_ylabel('All_Value')\n\n    # plt.show()\n    a = np.array([[1,1,1],[1,1,1],[3,3,3]])\n    b = np.array([\n        [1,2,3,4,6,3],\n        [1,2,3,2,6,3],\n        [6,6,6,3,6,3],\n        [7,7,7,4,9,3],\n        [6,6,6,3,6,3]])\n    c= np.array([0.1,0.86,0.28]).reshape((3,-1))\n    d= np.power(np.e,c)\n    # e=np.concatenate(\n    #         (np.array(b)[:2],np.array(b)[3:]),axis=0)\n    e=-1*np.log10(0.13)\n\n    \n    f=np.array([-0.14405829,0.00781588,-0.06042154,-0.0627726,-0.38443046,0.04139135\n,-0.0046923,0.2643729,0.10665923,0.11028627])\n\n    a=np.array([[1,2],[3,4]])\n    b=np.array([[1,2],[1,2]])\n    c=a.dot(b)\n    t=np.array([1,2,3,-1,0,-2,2,-5])\n    a=np.maximum(0,t)\n    \n    print(a)\n\n    \n\nif __name__ == '__main__':\n    main()\n\n\n\n# best_net = None # store the best model into this \n# from cs231n.classifiers.neural_net import TwoLayerNet\n\n# #################################################################################\n# # TODO: Tune hyperparameters using the validation set. Store your best trained  #\n# # model in best_net.                                                            #\n# #                                                                               #\n# # To help debug your network, it may help to use visualizations similar to the  #\n# # ones we used above; these visualizations will have significant qualitative    #\n# # differences from the ones we saw above for the poorly tuned network.          #\n# #                                                                               #\n# # Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n# # write code to sweep through possible combinations of hyperparameters          #\n# # automatically like we did on the previous exercises.                          #\n# #使用验证集调整超参数。\n# #################################################################################\n# results = {}\n# best_val = -1\n# best_net = None\n# learning_rates = [1e-2, 1e-3]\n# regularization_strengths = [0.4, 0.5, 0.6]\n# #按照前面几个作业的预测模式\n\n# for lr in learning_rates:   #遍历在当前学习率下的准确率情况\n#     for rs in regularization_strengths:\n#         net = TwoLayerNet(input_size, hidden_size, num_classes)\n#         loss_hist = net.train(X_train, y_train, X_val, y_val,num_iters=1000, batch_size=200,learning_rate=1e-4, learning_rate_decay=0.95,reg=0.25, verbose=True)#调用训练函数\n#         y_train_pred = net.predict(X_train)        #预测训练集的正确下标\n#         acc_tr = np.mean(y_train == y_train_pred)  #当预测值下标和训练集下标相等时，求下标均值\n        \n        \n#         y_val_pred = net.predict(X_val)        #预测  验证集正确下标\n#         acc_val = np.mean(y_val == y_val_pred) #当预测值下标和验证集下标相等时，求下标均值\n        \n#         results[(lr, rs)] = (acc_tr, acc_val) #把正确的训练集下标，验证集下标赋值给result \n#         if acc_val > best_val:                #当验证集下标大于当前最好验证集下标，把此时的值赋值给best_svm\n#             best_val = acc_val\n#             best_net = net\n    \n# # Print out results.\n# for lr, reg in sorted(results):\n#     train_accuracy, val_accuracy = results[(lr, reg)]\n#     print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n#                 lr, reg, train_accuracy, val_accuracy))\n    \n# print('best validation accuracy achieved during cross-validation: %f' % best_val)\n# #################################################################################\n# #                               END OF YOUR CODE                                #\n# #################################################################################",
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