{"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","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"import os\nimport numpy as np\nimport cv2\n\ndataset_train = \"/kaggle/input/2019-fall-pr-project/train/train/\"\ndata = []\nlabel = []\nraw_d = []\nfor d in os.listdir(dataset_train):\n  raw_d = cv2.imread(dataset_train + d )\n  raw_d = cv2.resize(raw_d, (32,32))\n  raw_d = raw_d.flatten()\n  data.append(raw_d)\n  if d.split('.')[0] == 'cat':\n    label.append('cat')\n  else:\n    label.append('dog')\n  if len(data)>=2000:break\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"trainD, testD, trainL, testL = train_test_split(data, label, test_size = 0.25, random_state = 42)\nfrom sklearn import svm\n\n\nclf = svm.SVC(gamma = 'scale' , kernel = 'rbf' ,random_state = 42)\nclf.fit(trainD, trainL)\n\ny = clf.predict(testD)\n\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndataset_train = \"/kaggle/input/2019-fall-pr-project/test1/test1/\"\n\n\ndata = []\nlabel = []\nraw_d = []\nfor d in os.listdir(dataset_test):\n  raw_d = cv2.imread(dataset_test + d )\n  raw_d = cv2.resize(raw_d, (32,32))\n  raw_d = raw_d.flatten()\n  data.append(raw_d)\n  if d.split('.')[0] == 'cat':\n    label.append('cat')\n  else:\n    label.append('dog')\n \n\n\n\n\n\ny_pred = clf.predict(data)\nprint(y_pred)\n\n\n\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"id = []\nfor i in range(1,5001):\n  id.append(i)\ny_pred=  list(y_pred)\ndic = {'id': id}\ndic2 = { 'label': y_pred}\nprint(dic)\nprint(id)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dic = {'id' : id, 'label':y_pred}\n\n# numpy 를 Pandas 이용하여 결과 파일로 저장\n\nresult = dic\nimport pandas as pd\n\n#print(result.shape)\ndf = pd.DataFrame(result)\ndf = df.replace('dog',1)\ndf = df.replace('cat',0)\nprint(df)\ndf.to_csv('results-yk-v2.csv',index=False, header=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.read_csv('results-yk-v2.csv')","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}