├── 2021autumn
├── ML012_terms_202102a.pdf
├── ML013_introclassreg_202102a.pdf
├── ML022_optimization_202105a______________.pdf
├── ML030_metric_202110a_____.pdf
├── ML040_control_202110a_______.pdf
├── ML051_linear_202115a______linreg.pdf
├── ML051_linear_202116a______logreg.pdf
├── ML052_SVM_202112a______.pdf
├── ML061_nonlinear_202113a_________.pdf
├── ML062_tree_202113a.pdf
├── ML081_complexity_202106a.pdf
├── MMO_lec3_kNN.ipynb
├── MMO_lec6_MS.ipynb
├── PZAD031_err_regression_202012n____.pdf
├── PZAD043_featureselection_202109___.pdf
├── PZAD051_ensemble_202102a____part1.pdf
├── PZAD052_rf_202101a_____+adaboost.pdf
├── PZAD053_gradboosting_202106n___.pdf
└── README.md
├── 2022-23new
├── ML015_python_202207a.pdf
├── ML093_USL_202202a_part_2.pdf
├── PZAD034_err_multirankcluster_202204____sm.pdf
└── README.md
├── 2022spring
├── DL_03archiall.pdf
├── DL_1NN_03_nn_202201a.pdf
├── DL_1NN_04learning_202201a___.pdf
├── ML082_bayes_202115n.pdf
├── ML091_cluster_202112n____.pdf
├── ML092_EM_202201a.pdf
├── ML093_USL_202201a___.pdf
├── ML094_anomaly_202201a.pdf
├── ML095_apriory_202204a.pdf
├── ML101_special_202202a___.pdf
├── ML105_range_202202a.pdf
├── PZAD071_RecSys_202201a____.pdf
└── README.md
└── README.md
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/2021autumn/ML022_optimization_202105a______________.pdf:
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/2021autumn/ML051_linear_202115a______linreg.pdf:
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/2021autumn/ML051_linear_202116a______logreg.pdf:
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/2021autumn/ML052_SVM_202112a______.pdf:
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/2021autumn/ML061_nonlinear_202113a_________.pdf:
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/2021autumn/ML062_tree_202113a.pdf:
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/2021autumn/ML081_complexity_202106a.pdf:
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/2021autumn/MMO_lec3_kNN.ipynb:
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1 | {
2 | "nbformat": 4,
3 | "nbformat_minor": 0,
4 | "metadata": {
5 | "colab": {
6 | "name": "MMO_lec3_kNN.ipynb",
7 | "provenance": [],
8 | "collapsed_sections": []
9 | },
10 | "kernelspec": {
11 | "name": "python3",
12 | "display_name": "Python 3"
13 | },
14 | "language_info": {
15 | "name": "python"
16 | }
17 | },
18 | "cells": [
19 | {
20 | "cell_type": "code",
21 | "metadata": {
22 | "id": "j1jkC_Kj9paP"
23 | },
24 | "source": [
25 | "import matplotlib.pyplot as plt\n",
26 | "import numpy as np\n",
27 | "import pandas as pd\n",
28 | "from mlxtend.plotting import plot_decision_regions"
29 | ],
30 | "execution_count": 42,
31 | "outputs": []
32 | },
33 | {
34 | "cell_type": "code",
35 | "metadata": {
36 | "colab": {
37 | "base_uri": "https://localhost:8080/",
38 | "height": 400
39 | },
40 | "id": "gfnr9BFR9xNw",
41 | "outputId": "65030583-2a5c-4e64-dd51-610dfafc68da"
42 | },
43 | "source": [
44 | "from sklearn.datasets import make_moons\n",
45 | "\n",
46 | "n_samples=1000\n",
47 | "\n",
48 | "X, y = make_moons(n_samples=n_samples, shuffle=False, noise=0.3, random_state=1)\n",
49 | "\n",
50 | "# прореживаем, чтобы был дисбаланс\n",
51 | "i = (y==0) | (np.random.rand(n_samples) > 0.75)\n",
52 | "X = X[i, :]\n",
53 | "y = y[i]\n",
54 | "\n",
55 | "plt.figure(figsize=(7, 3))\n",
56 | "plt.scatter(X[:,0], X[:,1], 40, y)\n",
57 | "pd.DataFrame(X[:5,:])"
58 | ],
59 | "execution_count": 65,
60 | "outputs": [
61 | {
62 | "output_type": "execute_result",
63 | "data": {
64 | "text/html": [
65 | "\n",
66 | "\n",
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86 | " \n",
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101 | " -0.677870 | \n",
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106 | " -0.209476 | \n",
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