{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import scale\n!pip install imutils\nimport imutils\nimport numpy as np\nimport cv2 \nimport os\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#os.listdir(\"/kaggle/input/2019-fall-pr-project/\")\n\ndataset_train = \"/kaggle/input/2019-fall-pr-project/train/train\"\nimgs=[]\nlabels=[]\n#import pdb;pdb.set_trace()\nfor i in os.listdir(dataset_train):\n \n  img=cv2.imread(os.path.join(dataset_train,i))\n  img.resize([32,32,3])\n  #img=np.transpose(img,(2,0,1))\n  img=img.reshape([3*32*32])\n\n  imgs.append(img) \n  label=i.split('.')[0]\n  if label=='cat':\n    label=0\n  elif label=='dog':\n    label=1\n  labels.append(label)\n  #import pdb;pdb.set_trace()\n  \nlabels=np.array(labels)\nimgs=np.array(imgs)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn import preprocessing\nnomal=preprocessing.MinMaxScaler()\n\nX_train, X_val, y_train, y_val=train_test_split(imgs,labels,test_size=0.9)\nX_train=scale(X_train)\nX_val=scale(X_val)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import classification_report\nparam_grid = {'C': [1e3,  1e4, 1e5],\n              'gamma': [ 0.0005,  0.005,  0.1], }\nclf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),\n                   param_grid, cv=5, iid=False,verbose=1)\nSVM=clf.fit(X_train, y_train)\n#clf = clf.fit(X_train, y_train)\nlabel=SVM.predict(X_val)\n\nprint(classification_report(y_val,label))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndataset_test = \"/kaggle/input/2019-fall-pr-project/test1/test1\"\n\n\n\nimgstest=[]\nlabelstest=[]\nids=[]\nfor i in os.listdir(dataset_test):\n  #import pdb;pdb.set_trace()\n  ids.append(int(i.split('.')[0]))\n  img=cv2.imread(os.path.join(dataset_test,i))\n  img.resize([32,32,3])\n  #img=np.transpose(img,(2,0,1))\n  img=img.reshape([3*32*32])\n\n  imgstest.append(img) \n\n\n\nimgstest=np.array(imgstest)\n\nlabelstest=np.array(labelstest)\n\nresult=SVM.predict(imgstest)\n\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}