{"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\n# for 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":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn import neighbors, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom keras.datasets import mnist\nimport numpy as np\n(x_train, y_train), (x_test, y_test) = mnist.load_data()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train.shape\nx_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(x_train[:10])):\n    print(y_train[i])\n    plt.imshow(x_train[i].reshape(28, 28))\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = x_train[:1000,:]\nX_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.reshape(len(X_train),28*28)\nX_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Y_train = y_train[:1000]\nY_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test = x_test.reshape(len(x_test),28*28)\nx_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(len(x_test[:10])):\n    print(y_test[i])\n    plt.imshow(x_test[i].reshape(28, 28))\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"clf = neighbors.KNeighborsClassifier(n_neighbors = 10, p = 1)  \n# KNN with K = 10 and p = 1, this is equivalent to using manhattan_distance (l1), \n#                   and euclidean_distance (l2) for p = 2. \n#                    For arbitrary p, minkowski_distance (l_p) is used.\nclf.fit(X_train, Y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = clf.predict(x_test)\naccuracy_score(y_test, y_pred)*100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# for i in range(len(X_train[:1000])):\n#     if Y_train[i] in [np.uint8(1),np.uint8(7)]:\n#         print(Y_train[i])\n#         plt.imshow(X_train[i].reshape(28, 28))\n#         plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample = x_test[-5:-1]\nfor i in range(len(sample)):\n    plt.imshow(sample[i].reshape(28, 28))\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"k = clf.predict(sample)\nprint(k)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample = x_test[-5:-1]\nfor i in range(len(sample)):\n    print(k[i])\n    plt.imshow(sample[i].reshape(28, 28))\n    plt.show()","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}