From 7eeda4bdc5f89d9b8e46153859dc6ca1c8f614fe Mon Sep 17 00:00:00 2001 From: Pooriana Date: Tue, 7 Feb 2023 20:51:17 +0330 Subject: [PATCH 1/2] Tamrin team 8 - Amini - Tizkar - Elahi --- ...n_Data_Analysis.ipynb => HW_01_Team8.ipynb | 0 ...Library_Dataset.ipynb => HW_02_Team8.ipynb | 0 Graph_Analysis.ipynb => HW_03_Team8.ipynb | 0 HW_04_Team8.ipynb | 519 ++++++++++++++++++ ..._Mining_Apriori.ipynb => HW_05_Team8.ipynb | 0 5 files changed, 519 insertions(+) rename Exploration_Data_Analysis.ipynb => HW_01_Team8.ipynb (100%) rename Preparing_Library_Dataset.ipynb => HW_02_Team8.ipynb (100%) rename Graph_Analysis.ipynb => HW_03_Team8.ipynb (100%) create mode 100644 HW_04_Team8.ipynb rename Association_Rule_Mining_Apriori.ipynb => HW_05_Team8.ipynb (100%) diff --git a/Exploration_Data_Analysis.ipynb b/HW_01_Team8.ipynb similarity index 100% rename from Exploration_Data_Analysis.ipynb rename to HW_01_Team8.ipynb diff --git a/Preparing_Library_Dataset.ipynb b/HW_02_Team8.ipynb similarity index 100% rename from Preparing_Library_Dataset.ipynb rename to HW_02_Team8.ipynb diff --git a/Graph_Analysis.ipynb b/HW_03_Team8.ipynb similarity index 100% rename from Graph_Analysis.ipynb rename to HW_03_Team8.ipynb diff --git a/HW_04_Team8.ipynb b/HW_04_Team8.ipynb new file mode 100644 index 0000000..4928620 --- /dev/null +++ b/HW_04_Team8.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c4485712", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Loading libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "27e52151", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "libraries loaded.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from collections import Counter\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "sns.set_style('white')\n", + "\n", + "print('libraries loaded.')" + ] + }, + { + "cell_type": "markdown", + "id": "7ebfb208", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Loading dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "f951115b", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthiris_name
05.93.04.21.5Iris-versicolor
16.93.14.91.5Iris-versicolor
26.62.94.61.3Iris-versicolor
34.63.21.40.2Iris-setosa
46.02.24.01.0Iris-versicolor
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width iris_name\n", + "0 5.9 3.0 4.2 1.5 Iris-versicolor\n", + "1 6.9 3.1 4.9 1.5 Iris-versicolor\n", + "2 6.6 2.9 4.6 1.3 Iris-versicolor\n", + "3 4.6 3.2 1.4 0.2 Iris-setosa\n", + "4 6.0 2.2 4.0 1.0 Iris-versicolor" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_iris = pd.read_csv('Data/iris.txt', sep= ',', header= None) \n", + "\n", + "df_iris.columns= ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'iris_name']\n", + "\n", + "df_iris.head()" + ] + }, + { + "cell_type": "markdown", + "id": "35b796a1", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "# Finding max absolute value of first 2-dimension" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "9776c250", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sepal_lengthsepal_width
00.06-0.05
11.060.05
20.76-0.15
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40.16-0.85
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" + ], + "text/plain": [ + " sepal_length sepal_width\n", + "0 0.06 -0.05\n", + "1 1.06 0.05\n", + "2 0.76 -0.15\n", + "3 -1.24 0.15\n", + "4 0.16 -0.85" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "centred_sepal = pd.DataFrame()\n", + "\n", + "sl_mean, sw_mean = np.round(df_iris.loc[:, ['sepal_length', 'sepal_width']].mean(), 2)\n", + "\n", + "centred_sepal['sepal_length'] = df_iris['sepal_length'] - sl_mean\n", + "\n", + "centred_sepal['sepal_width'] = df_iris['sepal_width'] - sw_mean\n", + "\n", + "centred_sepal.head()" + ] + }, + { + "cell_type": "markdown", + "id": "6c93ceb3", + "metadata": { + "hidden": true + }, + "source": [ + "## Finding max value of each dimension" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "564d4c99", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sepal_length 2.06\n", + "sepal_width 1.35\n", + "dtype: float64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "centred_sepal.max()" + ] + }, + { + "cell_type": "markdown", + "id": "8033d8de", + "metadata": { + "hidden": true + }, + "source": [ + "## Find the max value index of the maximume value" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "4ae2582b", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sepal_length 70\n", + "sepal_width 22\n", + "dtype: int64" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "centred_sepal.idxmax()" + ] + }, + { + "cell_type": "markdown", + "id": "b58ff3dc", + "metadata": { + "hidden": true + }, + "source": [ + "## Caculate magnitude of the max_vector" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "7801c7e1", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.06, 0.75, 2.19)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l, w = np.round(centred_sepal.loc[centred_sepal.idxmax().max()], 2)\n", + "\n", + "magnitude = np.round(np.sqrt(l * l + w * w), 2)\n", + "\n", + "l, w, magnitude" + ] + }, + { + "cell_type": "markdown", + "id": "871e2131", + "metadata": { + "hidden": true + }, + "source": [ + "## Get the magnitude of the maximum vector straightforward " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ff8df487", + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2.19" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.round(centred_sepal.apply(np.linalg.norm, axis= 1).max(), 2)" + ] + }, + { + "cell_type": "markdown", + "id": "368dafdf", + "metadata": {}, + "source": [ + "# plotting centred_sepal" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "6bba21a2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figure, axes = plt.subplots() \n", + "\n", + "plt.scatter(centred_sepal['sepal_length'], centred_sepal['sepal_width'])\n", + "\n", + "plt.scatter(2.06, 0.75, marker= 'o', color= 'k')\n", + "\n", + "sphere = plt.Circle((0, 0), magnitude, color= 'k', fill= False)\n", + "\n", + "axes.set_aspect(1) \n", + "axes.add_artist(sphere)\n", + "\n", + "plt.axvline(x = 0, color = 'k')\n", + "plt.axhline(y = 0, color = 'k')\n", + "\n", + "plt.arrow(0, 0, 2, 0.75, color= 'k', width= .02, length_includes_head= True)\n", + "plt.text(1, 0.5, 'r', fontsize= 12)\n", + "\n", + "plt.xlim((-2.5, 2.5))\n", + "plt.ylim((-2.5, 2.5))\n", + "\n", + "plt.xlabel('Sepal Length')\n", + "plt.ylabel('Sepal Width')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf476d54", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Association_Rule_Mining_Apriori.ipynb b/HW_05_Team8.ipynb similarity index 100% rename from Association_Rule_Mining_Apriori.ipynb rename to HW_05_Team8.ipynb From 0ff07d6bf3917286c8c7ff52039104c83846e5eb Mon Sep 17 00:00:00 2001 From: Pooriana Date: Tue, 7 Feb 2023 21:41:54 +0330 Subject: [PATCH 2/2] Team 8 - Amini - Tizkar - Elahi --- HW_05_Team8.ipynb => Association_Rule_Mining_Apriori.team8.ipynb | 0 HW_03_Team8.ipynb => Exploration_Data_Analysis.team8.ipynb | 0 HW_01_Team8.ipynb => Graph_Analysis.team8.ipynb | 0 HW_04_Team8.ipynb => High_Dimentional_Data.team8.ipynb | 0 HW_02_Team8.ipynb => Preparing_Library_Dataset.team8.ipynb | 0 5 files changed, 0 insertions(+), 0 deletions(-) rename HW_05_Team8.ipynb => Association_Rule_Mining_Apriori.team8.ipynb (100%) rename HW_03_Team8.ipynb => Exploration_Data_Analysis.team8.ipynb (100%) rename HW_01_Team8.ipynb => Graph_Analysis.team8.ipynb (100%) rename HW_04_Team8.ipynb => High_Dimentional_Data.team8.ipynb (100%) rename HW_02_Team8.ipynb => Preparing_Library_Dataset.team8.ipynb (100%) diff --git a/HW_05_Team8.ipynb b/Association_Rule_Mining_Apriori.team8.ipynb similarity index 100% rename from HW_05_Team8.ipynb rename to Association_Rule_Mining_Apriori.team8.ipynb diff --git a/HW_03_Team8.ipynb b/Exploration_Data_Analysis.team8.ipynb similarity index 100% rename from HW_03_Team8.ipynb rename to Exploration_Data_Analysis.team8.ipynb diff --git a/HW_01_Team8.ipynb b/Graph_Analysis.team8.ipynb similarity index 100% rename from HW_01_Team8.ipynb rename to Graph_Analysis.team8.ipynb diff --git a/HW_04_Team8.ipynb b/High_Dimentional_Data.team8.ipynb similarity index 100% rename from HW_04_Team8.ipynb rename to High_Dimentional_Data.team8.ipynb diff --git a/HW_02_Team8.ipynb b/Preparing_Library_Dataset.team8.ipynb similarity index 100% rename from HW_02_Team8.ipynb rename to Preparing_Library_Dataset.team8.ipynb