{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "X=np.array([[1,1],[1,2],[1,3],[1,4],[2,1],[2,2],[3,1],[4,1],[5,1],\n", " [5,2],[6,1],[6,2],[6,3],[6,4],[3,3],[3,4],[3,5],[4,3],[4,4],[4,5]])\n", "Y=np.array([1]*14+[-1]*6)\n", "T=np.array([[0.5,0.5],[1.5,1.5],[3.5,3.5],[4,5.5]])\n", "svc=SVC(kernel='poly',degree=2,gamma=1,coef0=0)\n", "svc.fit(X,Y)\n", "pre=svc.predict(T)\n", "print(Y)\n", "print (\"pre -->\",pre)\n", "print (\"n_support_ -->\",svc.n_support_)\n", "print (\"support_ -->\",svc.support_)\n", "print (\"support_vectors_ -->\",svc.support_vectors_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#https://stackoverflow.com/questions/43529388/speed-of-svm-kernels-linear-vs-rbf-vs-poly\n", "from datetime import datetime\n", "\n", "from sklearn.datasets import load_breast_cancer\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.svm import SVC\n", "\n", "data = load_breast_cancer()\n", "X = data.data\n", "y = data.target\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", "clf_rbf = SVC(kernel = 'rbf',gamma=0.02,C=1)\n", "clf_lin = SVC(kernel='linear',C=1.0,gamma=0.1)\n", "\n", "trarining_start_time = datetime.now()\n", "\n", "clf_lin.fit(X_train, y_train)\n", "\n", "training_stop_time = datetime.now()\n", "print (\"runing clf_lin time:\",(training_stop_time - trarining_start_time))\n", "\n", "#####################\n", "trarining_start_time = datetime.now()\n", "\n", "clf_rbf.fit(X_train, y_train)\n", "\n", "training_stop_time = datetime.now()\n", "print (\"runing clf_rbf time:\",(training_stop_time - trarining_start_time))\n", "\n", "#####################\n", "scaler = MinMaxScaler() # Default behavior is to scale to [0,1]\n", "X = scaler.fit_transform(X)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", "\n", "trarining_start_time = datetime.now()\n", "\n", "clf_lin.fit(X_train, y_train)\n", "\n", "training_stop_time = datetime.now()\n", "print (\"runing clf_lin time:\",(training_stop_time - trarining_start_time))\n", "\n", "#####################\n", "trarining_start_time = datetime.now()\n", "\n", "clf_rbf.fit(X_train, y_train)\n", "\n", "training_stop_time = datetime.now()\n", "print (\"runing clf_rbf time:\",(training_stop_time - trarining_start_time))\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd \n", "import seaborn as sns\n", "from IPython.display import display\n", "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "import sklearn\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "load data successful !!!!!\n" ] } ], "source": [ "try :\n", " data = pd.read_excel(\"train_features_9.xlsx\")\n", " \n", " print (\"load data successful !!!!!\")\n", "except :\n", " print (\"load data error !!!!!!!!!!\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'lelf_right_R', 'lelf_right_G', 'lelf_right_B', 'lelf_right_H',\n", " 'lelf_right_S', 'lelf_right_V', 'lelf_right_l', 'lelf_right_a',\n", " 'lelf_right_b', 'lelf_right_R_stddev', 'lelf_right_G_stddev',\n", " 'lelf_right_B_stddev', 'lelf_right_H_stddev', 'lelf_right_S_stddev',\n", " 'lelf_right_V_stddev', 'lelf_right_l_stddev', 'lelf_right_a_stddev',\n", " 'lelf_right_b_stddev', 'lelf_right_R_hist', 'lelf_right_G_hist',\n", " 'lelf_right_B_hist', 'lelf_right_H_hist', 'lelf_right_S_hist',\n", " 'lelf_right_V_hist', 'lelf_right_l_hist', 'lelf_right_a_hist',\n", " 'lelf_right_b_hist', 'lelf_right_R_max', 'lelf_right_G_max',\n", " 'lelf_right_B_max', 'lelf_right_H_max', 'lelf_right_S_max',\n", " 'lelf_right_V_max', 'lelf_right_l_max', 'lelf_right_a_max',\n", " 'lelf_right_b_max', 'lelf_right_R_min', 'lelf_right_G_min',\n", " 'lelf_right_B_min', 'lelf_right_H_min', 'lelf_right_S_min',\n", " 'lelf_right_V_min', 'lelf_right_l_min', 'lelf_right_a_min',\n", " 'lelf_right_b_min', 'lelf_right_gray_value', 'lelf_right_gray_stddev',\n", " 'lelf_right_gray_hist', 'lelf_right_gray_max', 'lelf_right_gray_min'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data.sort_values('index',ascending=False,inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "data.to_excel(\"train_features_9_sorted.xlsx\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " lelf_right_R lelf_right_G lelf_right_B lelf_right_H lelf_right_S \\\n", "count 14276.000000 14276.000000 14276.000000 14276.000000 14276.000000 \n", "mean 30.447254 72.962034 57.679742 -99.373284 -55.883861 \n", "std 4.945144 4.950271 4.510348 15.952271 9.890148 \n", "min 21.000000 57.000000 43.000000 -214.000000 -81.000000 \n", "25% 26.000000 69.000000 54.000000 -114.000000 -64.000000 \n", "50% 29.000000 73.000000 57.000000 -94.000000 -58.000000 \n", "75% 35.000000 76.000000 61.000000 -85.000000 -49.000000 \n", "max 46.000000 99.000000 79.000000 -67.000000 -33.000000 \n", "\n", " lelf_right_V lelf_right_l lelf_right_a lelf_right_b \\\n", "count 14276.000000 14276.000000 14276.000000 14276.000000 \n", "mean 38.789087 59.999370 -19.272485 1.832656 \n", "std 6.213177 4.262055 1.135266 0.720246 \n", "min 26.000000 48.000000 -25.000000 -1.000000 \n", "25% 33.000000 57.000000 -20.000000 1.000000 \n", "50% 39.000000 59.000000 -19.000000 2.000000 \n", "75% 43.000000 64.000000 -18.000000 2.000000 \n", "max 60.000000 80.000000 -16.000000 4.000000 \n", "\n", " lelf_right_R_stddev ... lelf_right_S_min \\\n", "count 14276.000000 ... 14276.000000 \n", "mean -12.981648 ... -6.670076 \n", "std 2.047649 ... 7.051346 \n", "min -18.000000 ... -23.000000 \n", "25% -15.000000 ... -13.000000 \n", "50% -13.000000 ... -7.000000 \n", "75% -11.000000 ... 0.000000 \n", "max -8.000000 ... 10.000000 \n", "\n", " lelf_right_V_min lelf_right_l_min lelf_right_a_min lelf_right_b_min \\\n", "count 14276.000000 14276.000000 14276.000000 14276.000000 \n", "mean 64.054987 97.503012 -6.963785 3.183805 \n", "std 7.798639 6.538395 1.142899 0.974624 \n", "min 40.000000 74.000000 -12.000000 -2.000000 \n", "25% 58.000000 92.000000 -8.000000 3.000000 \n", "50% 63.000000 97.000000 -7.000000 3.000000 \n", "75% 70.000000 103.000000 -6.000000 4.000000 \n", "max 93.000000 127.000000 -2.000000 8.000000 \n", "\n", " lelf_right_gray_value lelf_right_gray_stddev lelf_right_gray_hist \\\n", "count 14276.000000 14276.000000 14276.000000 \n", "mean 58.462244 -21.538876 66.753362 \n", "std 4.732158 1.943031 15.616751 \n", "min 45.000000 -31.000000 14.000000 \n", "25% 55.000000 -23.000000 60.000000 \n", "50% 58.000000 -21.000000 66.000000 \n", "75% 62.000000 -20.000000 77.000000 \n", "max 81.000000 -16.000000 113.000000 \n", "\n", " lelf_right_gray_max lelf_right_gray_min \n", "count 14276.000000 14276.000000 \n", "mean 18.173928 94.753923 \n", "std 2.615314 7.306732 \n", "min 9.000000 68.000000 \n", "25% 16.000000 89.000000 \n", "50% 18.000000 96.000000 \n", "75% 20.000000 100.000000 \n", "max 28.000000 127.000000 \n", "\n", "[8 rows x 50 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_features = data_0\n", "train_features_9 = pd.DataFrame()\n", "train_features_9['lelf_right_R'] = train_features['left_block_R'] - train_features['right_block_R']\n", "train_features_9['lelf_right_G'] = train_features['left_block_G'] - train_features['right_block_G']\n", "train_features_9['lelf_right_B'] = train_features['left_block_B'] - train_features['right_block_B']\n", "\n", "train_features_9['lelf_right_H'] = train_features['left_block_H'] - train_features['right_block_H']\n", "train_features_9['lelf_right_S'] = train_features['left_block_S'] - train_features['right_block_S']\n", "train_features_9['lelf_right_V'] = train_features['left_block_V'] - train_features['right_block_V']\n", "\n", "train_features_9['lelf_right_l'] = train_features['left_block_l'] - train_features['right_block_l']\n", "train_features_9['lelf_right_a'] = train_features['left_block_a'] - train_features['right_block_a']\n", "train_features_9['lelf_right_b'] = train_features['left_block_b'] - train_features['right_block_b']\n", "\n", "train_features_9['lelf_right_R_stddev'] = train_features['left_block_R_stddev'] - train_features['right_block_R_stddev']\n", "train_features_9['lelf_right_G_stddev'] = train_features['left_block_G_stddev'] - train_features['right_block_G_stddev']\n", "train_features_9['lelf_right_B_stddev'] = train_features['left_block_B_stddev'] - train_features['right_block_B_stddev']\n", "\n", "train_features_9['lelf_right_H_stddev'] = train_features['left_block_H_stddev'] - train_features['right_block_H_stddev']\n", "train_features_9['lelf_right_S_stddev'] = train_features['left_block_S_stddev'] - train_features['right_block_S_stddev']\n", "train_features_9['lelf_right_V_stddev'] = train_features['left_block_V_stddev'] - train_features['right_block_V_stddev']\n", "\n", "train_features_9['lelf_right_l_stddev'] = train_features['left_block_l_stddev'] - train_features['right_block_l_stddev']\n", "train_features_9['lelf_right_a_stddev'] = train_features['left_block_a_stddev'] - train_features['right_block_a_stddev']\n", "train_features_9['lelf_right_b_stddev'] = train_features['left_block_b_stddev'] - train_features['right_block_b_stddev']\n", "\n", "train_features_9['lelf_right_R_hist'] = train_features['left_block_R_hist'] - train_features['right_block_R_hist']\n", "train_features_9['lelf_right_G_hist'] = train_features['left_block_G_hist'] - train_features['right_block_G_hist']\n", "train_features_9['lelf_right_B_hist'] = train_features['left_block_B_hist'] - train_features['right_block_B_hist']\n", "\n", "train_features_9['lelf_right_H_hist'] = train_features['left_block_H_hist'] - train_features['right_block_H_hist']\n", "train_features_9['lelf_right_S_hist'] = train_features['left_block_S_hist'] - train_features['right_block_S_hist']\n", "train_features_9['lelf_right_V_hist'] = train_features['left_block_V_hist'] - train_features['right_block_V_hist']\n", "\n", "train_features_9['lelf_right_l_hist'] = train_features['left_block_l_hist'] - train_features['right_block_l_hist']\n", "train_features_9['lelf_right_a_hist'] = train_features['left_block_a_hist'] - train_features['right_block_a_hist']\n", "train_features_9['lelf_right_b_hist'] = train_features['left_block_b_hist'] - train_features['right_block_b_hist']\n", "\n", "train_features_9['lelf_right_R_max'] = train_features['left_block_R_max'] - train_features['right_block_R_max']\n", "train_features_9['lelf_right_G_max'] = train_features['left_block_G_max'] - train_features['right_block_G_max']\n", "train_features_9['lelf_right_B_max'] = train_features['left_block_B_max'] - train_features['right_block_B_max']\n", "\n", "train_features_9['lelf_right_H_max'] = train_features['left_block_H_max'] - train_features['right_block_H_max']\n", "train_features_9['lelf_right_S_max'] = train_features['left_block_S_max'] - train_features['right_block_S_max']\n", "train_features_9['lelf_right_V_max'] = train_features['left_block_V_max'] - train_features['right_block_V_max']\n", "\n", "train_features_9['lelf_right_l_max'] = train_features['left_block_l_max'] - train_features['right_block_l_max']\n", "train_features_9['lelf_right_a_max'] = train_features['left_block_a_max'] - train_features['right_block_a_max']\n", "train_features_9['lelf_right_b_max'] = train_features['left_block_b_max'] - train_features['right_block_b_max']\n", "\n", "train_features_9['lelf_right_R_min'] = train_features['left_block_R_min'] - train_features['right_block_R_min']\n", "train_features_9['lelf_right_G_min'] = train_features['left_block_G_min'] - train_features['right_block_G_min']\n", "train_features_9['lelf_right_B_min'] = train_features['left_block_B_min'] - train_features['right_block_B_min']\n", "\n", "train_features_9['lelf_right_H_min'] = train_features['left_block_H_min'] - train_features['right_block_H_min']\n", "train_features_9['lelf_right_S_min'] = train_features['left_block_S_min'] - train_features['right_block_S_min']\n", "train_features_9['lelf_right_V_min'] = train_features['left_block_V_min'] - train_features['right_block_V_min']\n", "\n", "train_features_9['lelf_right_l_min'] = train_features['left_block_l_min'] - train_features['right_block_l_min']\n", "train_features_9['lelf_right_a_min'] = train_features['left_block_a_min'] - train_features['right_block_a_min']\n", "train_features_9['lelf_right_b_min'] = train_features['left_block_b_min'] - train_features['right_block_b_min']\n", "\n", "\n", "train_features_9['lelf_right_gray_value'] = train_features['left_grayValue'] - train_features['right_grayValue']\n", "train_features_9['lelf_right_gray_stddev'] = train_features['left_grayStddevValue'] - train_features['right_grayStddevValue']\n", "train_features_9['lelf_right_gray_hist'] = train_features['left_grayHist'] - train_features['right_grayHist']\n", "train_features_9['lelf_right_gray_max'] = train_features['left_grayMax'] - train_features['right_grayMax']\n", "train_features_9['lelf_right_gray_min'] = train_features['left_grayMin'] - train_features['right_grayMin']\n", "#train_features_9['index'] = train_labels\n", "train_features_9.describe()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "train_features_9.to_csv(\"clf/50features_0.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }