From 7c2f1381d2fbda7813b98daa82e23662a123ec0c Mon Sep 17 00:00:00 2001 From: Vladimir Lekhterev Date: Wed, 12 Nov 2025 10:38:13 +0300 Subject: [PATCH] Fix task3 subdirectory name --- hometask/task1-3 /generator.ipynb | 101 ------------------------------ hometask/task1-3/generator.ipynb | 46 ++++---------- 2 files changed, 13 insertions(+), 134 deletions(-) delete mode 100644 hometask/task1-3 /generator.ipynb diff --git a/hometask/task1-3 /generator.ipynb b/hometask/task1-3 /generator.ipynb deleted file mode 100644 index 044f1f7..0000000 --- a/hometask/task1-3 /generator.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "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": "3.6.2" - }, - "colab": { - "name": "generator.ipynb", - "provenance": [] - } - }, - "cells": [ - { - "cell_type": "code", - "metadata": { - "id": "9CSy43_9KwdH", - "outputId": "9d83fbe5-c8ed-4cad-88ef-5cad0985638c", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "source": [ - "from zlib import crc32\n", - "import numpy as np\n", - "\n", - "types = ['regression', 'classification']\n", - "datasets = {'regression': [{'name': 'Servo Data Set',\n", - " 'url': 'https://archive.ics.uci.edu/ml/datasets/Servo'},\n", - " {'name': 'Forest Fires Data Set',\n", - " 'url': 'https://archive.ics.uci.edu/ml/datasets/Forest+Fires'},\n", - " {'name': 'Boston Housing Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston'},\n", - " {'name': 'Diabetes Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes'}],\n", - " 'classification': [{'name': 'Spambase Data Set',\n", - " 'url': 'https://archive.ics.uci.edu/ml/datasets/Spambase'},\n", - " {'name': 'Wine Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine'},\n", - " {'name': 'Breast Cancer Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer'},\n", - " {'name': 'MNIST',\n", - " 'url': 'http://yann.lecun.com/exdb/mnist/'}]}\n", - "methods = {'regression': ['kNN',\n", - " 'Метод опорных векторов', 'Надарая-Ватсона'],\n", - " 'classification': ['kNN',\n", - " 'Метод опорных векторов', 'Метод потенциальных функций']}\n", - "task = dict()\n", - "task['mail'] = input(prompt='Enter your mail: ')\n", - "task['id'] = crc32(b'2'+task['mail'].encode('utf-8'))\n", - "np.random.seed(task['id'])\n", - "task['type'] = np.random.choice(types)\n", - "task['dataset'] = np.random.choice(datasets[task['type']])\n", - "task['method'] = np.random.choice(\n", - " methods[task['type']], size=3, replace=False).tolist()\n", - "\n", - "\n", - "task" - ], - "execution_count": null, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter your mail: grabovoy.av@phystech.edu\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'mail': 'grabovoy.av@phystech.edu',\n", - " 'id': 1957435394,\n", - " 'type': np.str_('classification'),\n", - " 'dataset': {'name': 'Wine Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine'},\n", - " 'method': ['Метод опорных векторов', 'kNN', 'Метод потенциальных функций']}" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ] - } - ] -} \ No newline at end of file diff --git a/hometask/task1-3/generator.ipynb b/hometask/task1-3/generator.ipynb index b571917..044f1f7 100644 --- a/hometask/task1-3/generator.ipynb +++ b/hometask/task1-3/generator.ipynb @@ -21,26 +21,15 @@ }, "colab": { "name": "generator.ipynb", - "provenance": [], - "include_colab_link": true + "provenance": [] } }, "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, { "cell_type": "code", "metadata": { "id": "9CSy43_9KwdH", - "outputId": "e6aadc47-1358-473c-8ec0-3879d56bb73e", + "outputId": "9d83fbe5-c8ed-4cad-88ef-5cad0985638c", "colab": { "base_uri": "https://localhost:8080/" } @@ -66,18 +55,18 @@ " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer'},\n", " {'name': 'MNIST',\n", " 'url': 'http://yann.lecun.com/exdb/mnist/'}]}\n", - "methods = {'regression': ['Линейная регрессия',\n", - " 'Перцептрон'],\n", - " 'classification': ['Логистическая регрессия',\n", - " 'Перцептрон']}\n", + "methods = {'regression': ['kNN',\n", + " 'Метод опорных векторов', 'Надарая-Ватсона'],\n", + " 'classification': ['kNN',\n", + " 'Метод опорных векторов', 'Метод потенциальных функций']}\n", "task = dict()\n", "task['mail'] = input(prompt='Enter your mail: ')\n", - "task['id'] = crc32(task['mail'].encode('utf-8'))\n", + "task['id'] = crc32(b'2'+task['mail'].encode('utf-8'))\n", "np.random.seed(task['id'])\n", "task['type'] = np.random.choice(types)\n", "task['dataset'] = np.random.choice(datasets[task['type']])\n", "task['method'] = np.random.choice(\n", - " methods[task['type']], size=2, replace=False).tolist()\n", + " methods[task['type']], size=3, replace=False).tolist()\n", "\n", "\n", "task" @@ -96,26 +85,17 @@ "data": { "text/plain": [ "{'mail': 'grabovoy.av@phystech.edu',\n", - " 'id': 1191023426,\n", - " 'type': np.str_('regression'),\n", - " 'dataset': {'name': 'Diabetes Data Set',\n", - " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html#sklearn.datasets.load_diabetes'},\n", - " 'method': ['Перцептрон', 'Линейная регрессия']}" + " 'id': 1957435394,\n", + " 'type': np.str_('classification'),\n", + " 'dataset': {'name': 'Wine Data Set',\n", + " 'url': 'https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_wine.html#sklearn.datasets.load_wine'},\n", + " 'method': ['Метод опорных векторов', 'kNN', 'Метод потенциальных функций']}" ] }, "metadata": {}, "execution_count": 2 } ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "TCeJEv7tOCKl" - }, - "execution_count": null, - "outputs": [] } ] } \ No newline at end of file