{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\n\nimport numpy as np\n\nimport cv2 \nimport os\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"\ndataset_train = \"../input/2019-fall-pr-project/train/train/\"\n\ndatas = []\nlabel = []\ncat_data = []\ndog_data = []\n\n#cat.255.jpg\n\nfor i in os.listdir(dataset_train):\n  \n  if 'cat' == i.split('.')[0]:\n    data=cv2.imread(dataset_train + i)\n    data= cv2.resize(data, (32,32))\n    cat_data.append(data.ravel())\n    label.append('0')\n    if len(cat_data) == 5000 : break\n\n  else:\n    data = cv2.imread(dataset_train + i)\n    data= cv2.resize(data, (32,32))\n    dog_data.append(data.ravel())\n    label.append('1')\n    if len(dog_data) == 5000 : break\n      \nfor c in cat_data:\n  datas.append(c)\nfor d in dog_data:\n  datas.append(d)\n\n#print(datas)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_d, test_d, train_l, test_l = train_test_split(datas, label, test_size = 0.75,random_state = 42)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.svm import LinearSVC,SVC\nfrom sklearn.svm import SVC\n\n\nmodel=SVC(kernel='linear',C=1E10)\nmodel.fit(train_d,train_l)\n ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndataset_test = \"../input/2019-fall-pr-project/test1/test1/\"\n\nresult=[]\nfor i in os.listdir(dataset_test):\n  data=cv2.imread(dataset_test+i)\n  data=cv2.resize(data,(32,32))\n  result.append(data.ravel())\n\n#print(result)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"yfit=model.predict(result)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# numpy 를 Pandas 이용하여 결과 파일로 저장\n\n\nimport pandas as pd\n\nID=[]\nfor i in range(1,5001):\n  ID.append(i)\n  \nid=pd.Series(ID)\nresult=pd.Series(yfit)\n\narray={}\narray['id']=ID\narray['label']=result\n\ndf = pd.DataFrame(array)\nprint(df)\n\nprint(result.shape)\ndf = df.replace('dog',1)\ndf = df.replace('cat',0)\n\ndf.to_csv('results-yk-v2.csv',index=False, header=True)","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}