Lab1 and preparation of Lab3
This commit is contained in:
Marek Moryl
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7fc7e1b83c
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79bca5203f
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w2/plantvillage dataset/
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l1/.ipynb_checkpoints/03_Regresja_liniowa-checkpoint.ipynb
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l1/.ipynb_checkpoints/03_Regresja_liniowa-checkpoint.ipynb
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"source": [
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"## Uczenie maszynowe – zastosowania\n",
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"### Zajęcia laboratoryjne\n",
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"# 3. Regresja liniowa – zadanie"
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]
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},
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"cell_type": "markdown",
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"## Zadanie 3"
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"cell_type": "markdown",
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"source": [
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"### Część podstawowa (4 punkty)\n",
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"\n",
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"Plik `fires_thefts.csv` zawiera rzeczywiste dane zebrane przez *U.S. Commission on Civil Rights*, przedstawiające liczbę pożarów w danej dzielnicy na tysiąc gospodarstw domowych (pierwsza kolumna) oraz liczbę włamań w tej samej dzielnicy na tysiąc mieszkańców (druga kolumna). \n",
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"\n",
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"Stwórz model (regresja liniowa) przewidujący liczbę włamań na podstawie liczby pożarów:\n",
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" * Oblicz parametry $\\theta$ krzywej regresyjnej za pomocą metody gradientu prostego (*gradient descent*). Możesz wybrać wersję iteracyjną lub macierzową algorytmu.\n",
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" * Wykorzystując uzyskaną krzywą regresyjną przepowiedz liczbę włamań na tysiąc mieszkańców dla dzielnicy, w której występuje średnio 50, 100, 200 pożarów na tysiąc gospodarstw domowych."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Część zaawansowana (2 punkty)\n",
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"\n",
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"Dla różnych wartości długości kroku $\\alpha \\in \\{ 0.001, 0.01 , 0.1 \\}$ stwórz wykres, który zilustruje progresję wartości $J(\\theta)$ dla pierwszych 200 króków algorytmu gradientu prostego:\n",
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" * Oś $x$ wykresu to kolejne kroki algorytmu – od 0 do 200.\n",
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" * Oś $y$ wykresu to wartosci $J(\\theta)$.\n",
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" * Wykres powinien skłądać się z trzech krzywych:\n",
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" 1. dla $\\alpha = 0.001$\n",
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" 2. dla $\\alpha = 0.01$\n",
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" 3. dla $\\alpha = 0.1$"
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l1/.ipynb_checkpoints/lab1-checkpoint.ipynb
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l1/.ipynb_checkpoints/lab1-checkpoint.ipynb
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"text": [
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"Predicted amount of thefts for 50 fires: 100.5454538681846\n",
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"Predicted amount of thefts for 100 fires: 195.86844057898603\n",
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"Predicted amount of thefts for 200 fires: 386.5144140005889\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"data = pd.read_csv('fires_thefts.csv', names = ['fires', 'thefts'])\n",
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"\n",
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"x = data[['fires']].to_numpy().flatten()\n",
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"y = data[['thefts']].to_numpy().flatten()\n",
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"\n",
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"def gradient_descent(h, cost_fun, theta, x, y, alpha, eps, max_steps = 1000000):\n",
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" current_cost = cost_fun(h, theta, x, y)\n",
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" log = [[current_cost, theta]]\n",
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" m = len(y)\n",
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" steps_counter = 0\n",
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" while True and steps_counter < max_steps:\n",
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" steps_counter += 1\n",
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" new_theta = [\n",
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" theta[0] - alpha/float(m) * sum(h(theta, x[i]) - y[i]\n",
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" for i in range(m)), \n",
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" theta[1] - alpha/float(m) * sum((h(theta, x[i]) - y[i]) * x[i]\n",
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" for i in range(m))]\n",
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" theta = new_theta\n",
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" prev_cost = current_cost\n",
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" current_cost = cost_fun(h, theta, x, y)\n",
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" if abs(prev_cost - current_cost) <= eps:\n",
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" break\n",
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" log.append([current_cost, theta])\n",
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" return theta, log\n",
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"\n",
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"def J(h, theta, x, y):\n",
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" m = len(y)\n",
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" return 1.0 / (2 * m) * sum((h(theta, x[i]) - y[i])**2 for i in range(m))\n",
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"\n",
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"def h(theta, x):\n",
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" return theta[0] + theta[1] * x\n",
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"\n",
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"def mse(expected, predicted):\n",
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" m = len(expected)\n",
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" if len(predicted) != m:\n",
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" raise Exception('Wektory mają różne długości!')\n",
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" return 1.0 / (2 * m) * sum((expected[i] - predicted[i])**2 for i in range(m))\n",
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"\n",
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"best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=0.001, eps=0.0000001, max_steps = 200)\n",
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"\n",
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"predicted_50 = h(best_theta, 50)\n",
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"predicted_100 = h(best_theta, 100)\n",
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"predicted_200 = h(best_theta, 200)\n",
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"print(f'Predicted amount of thefts for 50 fires: {predicted_50}')\n",
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"print(f'Predicted amount of thefts for 100 fires: {predicted_100}')\n",
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"print(f'Predicted amount of thefts for 200 fires: {predicted_200}')"
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]
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}
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],
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l1/.ipynb_checkpoints/lab2-checkpoint.ipynb
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l1/.ipynb_checkpoints/lab2-checkpoint.ipynb
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l1/02_Wczytywanie_i_prezentowanie_danych.ipynb
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l1/02_Wczytywanie_i_prezentowanie_danych.ipynb
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l1/03_Regresja_liniowa.ipynb
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l1/03_Regresja_liniowa.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"slide_type": "-"
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},
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"source": [
|
||||
"## Uczenie maszynowe – zastosowania\n",
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||||
"### Zajęcia laboratoryjne\n",
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"# 3. Regresja liniowa – zadanie"
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||||
]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zadanie 3"
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]
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},
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{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"### Część podstawowa (4 punkty)\n",
|
||||
"\n",
|
||||
"Plik `fires_thefts.csv` zawiera rzeczywiste dane zebrane przez *U.S. Commission on Civil Rights*, przedstawiające liczbę pożarów w danej dzielnicy na tysiąc gospodarstw domowych (pierwsza kolumna) oraz liczbę włamań w tej samej dzielnicy na tysiąc mieszkańców (druga kolumna). \n",
|
||||
"\n",
|
||||
"Stwórz model (regresja liniowa) przewidujący liczbę włamań na podstawie liczby pożarów:\n",
|
||||
" * Oblicz parametry $\\theta$ krzywej regresyjnej za pomocą metody gradientu prostego (*gradient descent*). Możesz wybrać wersję iteracyjną lub macierzową algorytmu.\n",
|
||||
" * Wykorzystując uzyskaną krzywą regresyjną przepowiedz liczbę włamań na tysiąc mieszkańców dla dzielnicy, w której występuje średnio 50, 100, 200 pożarów na tysiąc gospodarstw domowych."
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||||
]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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||||
"source": [
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"### Część zaawansowana (2 punkty)\n",
|
||||
"\n",
|
||||
"Dla różnych wartości długości kroku $\\alpha \\in \\{ 0.001, 0.01 , 0.1 \\}$ stwórz wykres, który zilustruje progresję wartości $J(\\theta)$ dla pierwszych 200 króków algorytmu gradientu prostego:\n",
|
||||
" * Oś $x$ wykresu to kolejne kroki algorytmu – od 0 do 200.\n",
|
||||
" * Oś $y$ wykresu to wartosci $J(\\theta)$.\n",
|
||||
" * Wykres powinien skłądać się z trzech krzywych:\n",
|
||||
" 1. dla $\\alpha = 0.001$\n",
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" 2. dla $\\alpha = 0.01$\n",
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" 3. dla $\\alpha = 0.1$"
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||||
]
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||||
}
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||||
],
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||||
"metadata": {
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"celltoolbar": "Slideshow",
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10
l1/data1.csv
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l1/data1.csv
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price,isNew,rooms,floor,location,sqrMetres
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476118.0,False,3,1,Centrum,78
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459531.0,False,3,2,Sołacz,62
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411557.0,False,3,0,Sołacz,15
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496416.0,False,4,0,Sołacz,14
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406032.0,False,3,0,Sołacz,15
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450026.0,False,3,1,Naramowice,80
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571229.15,False,2,4,Wilda,39
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325000.0,False,3,1,Grunwald,54
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268229.0,False,2,1,Grunwald,90
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l1/data1.tsv
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l1/data1.tsv
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price isNew rooms floor location sqrMetres
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476118.0 False 3 1 Centrum 78
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459531.0 False 3 2 Sołacz 62
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411557.0 False 3 0 Sołacz 15
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496416.0 False 4 0 Sołacz 14
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406032.0 False 3 0 Sołacz 15
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450026.0 False 3 1 Naramowice 80
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571229.15 False 2 4 Wilda 39
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325000.0 False 3 1 Grunwald 54
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268229.0 False 2 1 Grunwald 90
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l1/data2.csv
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l1/data2.csv
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1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
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1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
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1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
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1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
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1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
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1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
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1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
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1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
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1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
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1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
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1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
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1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
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1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
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1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
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1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
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1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
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1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
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1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
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1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
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1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
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1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
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1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
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1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
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1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
|
||||
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
|
||||
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
|
||||
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
|
||||
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
|
||||
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
|
||||
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
|
||||
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
|
||||
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
|
||||
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
|
||||
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
|
||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
|
||||
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
|
||||
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
|
||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
|
||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
|
||||
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
|
||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
|
||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
|
||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
||||
|
20
l1/data6.tsv
Normal file
20
l1/data6.tsv
Normal file
@ -0,0 +1,20 @@
|
||||
21.252 -555.640
|
||||
179.842 3840.141
|
||||
118.162 2274.989
|
||||
114.269 1146.575
|
||||
121.444 1840.589
|
||||
87.624 1663.894
|
||||
170.039 3504.537
|
||||
192.651 3708.239
|
||||
12.390 -358.240
|
||||
144.264 2444.162
|
||||
169.900 3348.941
|
||||
63.254 271.623
|
||||
72.439 900.423
|
||||
71.108 77.543
|
||||
179.476 3313.424
|
||||
169.084 2525.653
|
||||
99.073 734.413
|
||||
195.528 4067.410
|
||||
131.023 2182.147
|
||||
12.424 490.714
|
||||
|
42
l1/fires_thefts.csv
Normal file
42
l1/fires_thefts.csv
Normal file
@ -0,0 +1,42 @@
|
||||
6.2,29
|
||||
9.5,44
|
||||
10.5,36
|
||||
7.7,37
|
||||
8.6,53
|
||||
34.1,68
|
||||
11,75
|
||||
6.9,18
|
||||
7.3,31
|
||||
15.1,25
|
||||
29.1,34
|
||||
2.2,14
|
||||
5.7,11
|
||||
2,11
|
||||
2.5,22
|
||||
4,16
|
||||
5.4,27
|
||||
2.2,9
|
||||
7.2,29
|
||||
15.1,30
|
||||
16.5,40
|
||||
18.4,32
|
||||
36.2,41
|
||||
39.7,147
|
||||
18.5,22
|
||||
23.3,29
|
||||
12.2,46
|
||||
5.6,23
|
||||
21.8,4
|
||||
21.6,31
|
||||
9,39
|
||||
3.6,15
|
||||
5,32
|
||||
28.6,27
|
||||
17.4,32
|
||||
11.3,34
|
||||
3.4,17
|
||||
11.9,46
|
||||
10.5,42
|
||||
10.7,43
|
||||
10.8,34
|
||||
4.8,19
|
||||
|
169
l1/lab1.ipynb
Normal file
169
l1/lab1.ipynb
Normal file
File diff suppressed because one or more lines are too long
73
l1/lab1.py
Normal file
73
l1/lab1.py
Normal file
@ -0,0 +1,73 @@
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
|
||||
data = pd.read_csv('fires_thefts.csv', names = ['fires', 'thefts'])
|
||||
print(data)
|
||||
|
||||
x = data[['fires']].to_numpy().flatten()
|
||||
y = data[['thefts']].to_numpy().flatten()
|
||||
|
||||
def gradient_descent(h, cost_fun, theta, x, y, alpha, eps, max_steps = 1000000):
|
||||
current_cost = cost_fun(h, theta, x, y)
|
||||
log = [[current_cost, theta]]
|
||||
m = len(y)
|
||||
steps_counter = 0
|
||||
while True and steps_counter < max_steps:
|
||||
steps_counter += 1
|
||||
new_theta = [
|
||||
theta[0] - alpha/float(m) * sum(h(theta, x[i]) - y[i]
|
||||
for i in range(m)),
|
||||
theta[1] - alpha/float(m) * sum((h(theta, x[i]) - y[i]) * x[i]
|
||||
for i in range(m))]
|
||||
theta = new_theta
|
||||
prev_cost = current_cost
|
||||
current_cost = cost_fun(h, theta, x, y)
|
||||
if abs(prev_cost - current_cost) <= eps:
|
||||
break
|
||||
log.append([current_cost, theta])
|
||||
return theta, log
|
||||
|
||||
def J(h, theta, x, y):
|
||||
m = len(y)
|
||||
return 1.0 / (2 * m) * sum((h(theta, x[i]) - y[i])**2 for i in range(m))
|
||||
|
||||
def h(theta, x):
|
||||
return theta[0] + theta[1] * x
|
||||
|
||||
def mse(expected, predicted):
|
||||
m = len(expected)
|
||||
if len(predicted) != m:
|
||||
raise Exception('Wektory mają różne długości!')
|
||||
return 1.0 / (2 * m) * sum((expected[i] - predicted[i])**2 for i in range(m))
|
||||
|
||||
best_theta, log = gradient_descent(h, J, [0.0, 0.0], x, y, alpha=0.001, eps=0.0000001, max_steps = 1000)
|
||||
|
||||
predicted_50 = h(best_theta, 50)
|
||||
predicted_100 = h(best_theta, 100)
|
||||
predicted_200 = h(best_theta, 200)
|
||||
print(f'Predicted amount of thefts for 50 fires: {predicted_50}')
|
||||
print(f'Predicted amount of thefts for 100 fires: {predicted_100}')
|
||||
print(f'Predicted amount of thefts for 200 fires: {predicted_200}')
|
||||
|
||||
best_theta_01, log_01 = gradient_descent(h, J, [0.0, 0.0], x, y, alpha = 0.1, eps = 0.0000001, max_steps = 1000)
|
||||
best_theta_001, log_001 = gradient_descent(h, J, [0.0, 0.0], x, y, alpha = 0.01, eps = 0.0000001, max_steps = 1000)
|
||||
best_theta_0001, log_0001 = gradient_descent(h, J, [0.0, 0.0], x, y, alpha = 0.001, eps = 0.0000001, max_steps = 1000)
|
||||
|
||||
steps_range = np.arange(0, 200, 1)
|
||||
y_01, y_001, y_0001 = [], [], []
|
||||
for step in steps_range:
|
||||
y_01.append(log_01[step][0])
|
||||
y_001.append(log_001[step][0])
|
||||
y_0001.append(log_0001[step][0])
|
||||
|
||||
fig = plt.figure(figsize=(8, 7))
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(steps_range, y_01, label='alpha = 0.1')
|
||||
ax.plot(steps_range, y_001, label='alpha = 0.01')
|
||||
ax.plot(steps_range, y_0001, label='alpha = 0.001')
|
||||
ax.legend(loc='best')
|
||||
ax.set_xlabel('krok')
|
||||
ax.set_ylabel(r'$J(\theta)$')
|
||||
# plt.ylim([0, 800])
|
||||
plt.show()
|
||||
4939
l1/mieszkania4.tsv
Normal file
4939
l1/mieszkania4.tsv
Normal file
File diff suppressed because one or more lines are too long
7257
l1/mushrooms.tsv
Normal file
7257
l1/mushrooms.tsv
Normal file
File diff suppressed because it is too large
Load Diff
48815
w1/.ipynb_checkpoints/02_Regresja_liniowa-checkpoint.ipynb
Normal file
48815
w1/.ipynb_checkpoints/02_Regresja_liniowa-checkpoint.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
48815
w1/02_Regresja_liniowa.ipynb
Normal file
48815
w1/02_Regresja_liniowa.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
50
w1/data-metrics.tsv
Normal file
50
w1/data-metrics.tsv
Normal file
@ -0,0 +1,50 @@
|
||||
0 -0.9410633308036449 0.46518252113944425
|
||||
1 0.4700636553691919 -0.3970321538875541
|
||||
1 -0.01609299859794966 0.23161453968628254
|
||||
0 -0.9966154155058933 0.06419313152355421
|
||||
0 0.8000009607150127 0.44133107977776875
|
||||
0 0.389227379480078 -0.8415416694237676
|
||||
0 -0.7786281038890375 0.2833716839963434
|
||||
1 -0.10150562150521569 -0.02968754639839366
|
||||
1 -0.14995353486391494 0.30921523116923866
|
||||
0 0.3150219624148183 0.4186143523577863
|
||||
0 -0.5542734031872467 0.9291684810885719
|
||||
0 -0.44750469543445215 -0.8240387195698262
|
||||
0 -0.7875312310670415 0.27475695030524894
|
||||
0 0.20470154428730747 -0.8122722630746713
|
||||
0 0.07472783793361693 0.8936381678688297
|
||||
0 -0.6016285994197443 -0.9783927694535444
|
||||
0 0.4235345463350013 -0.23977977886239832
|
||||
0 0.256790496684171 -0.5587059709121811
|
||||
0 -0.2172656054288027 0.8015306542483966
|
||||
0 0.2009238354275602 0.9376873763906164
|
||||
0 -0.8760038215191506 0.015194717659306356
|
||||
0 -0.1512141038160364 -0.9575528046526418
|
||||
0 -0.6378974241766098 0.35900665963616696
|
||||
0 -0.6219617077011876 0.04019896541474166
|
||||
0 -0.2533778634666939 -0.8576798720089458
|
||||
0 -0.9398823073223508 0.806594859009744
|
||||
0 -0.24161324930138606 -0.6982896600554984
|
||||
0 -0.967724402993285 0.15651783268628372
|
||||
0 0.9587968810951801 -0.3382309645563397
|
||||
1 0.18040441263417084 -0.026706542719935777
|
||||
0 -0.2403226372749332 -0.2694487472698215
|
||||
0 -0.49494412803453747 -0.6833825934742561
|
||||
0 -0.32266963833818574 0.6299706350061482
|
||||
0 -0.716450532167108 0.7792499086149187
|
||||
1 -0.5661825812948427 -0.3045016769669948
|
||||
0 -0.9014952263862088 0.19697267011506714
|
||||
1 0.3192734822128551 -0.3145295901019187
|
||||
1 -0.4386590899062277 0.6119229005694005
|
||||
0 -0.6306933372350818 0.4721301354446683
|
||||
0 0.3302936606411402 -0.3047093070118343
|
||||
1 -0.38049655790356285 -0.609474130471132
|
||||
1 0.32069301644263426 0.17266197471996692
|
||||
1 0.8349752241994568 0.4408717276862013
|
||||
0 -0.26741723386938343 -0.4919294757003996
|
||||
0 -0.7786699335922747 -0.47305795528791905
|
||||
0 0.723410510517891 -0.010095862311693793
|
||||
0 0.0902826080483603 -0.6805262097228113
|
||||
0 -0.9286972617786873 0.7200430642275493
|
||||
0 -0.0623197964184079 0.8187639325432745
|
||||
0 -0.20572090815735944 -0.6655000969777327
|
||||
|
97
w1/data01.csv
Normal file
97
w1/data01.csv
Normal file
@ -0,0 +1,97 @@
|
||||
6.1101,17.592
|
||||
5.5277,9.1302
|
||||
8.5186,13.662
|
||||
7.0032,11.854
|
||||
5.8598,6.8233
|
||||
8.3829,11.886
|
||||
7.4764,4.3483
|
||||
8.5781,12
|
||||
6.4862,6.5987
|
||||
5.0546,3.8166
|
||||
5.7107,3.2522
|
||||
14.164,15.505
|
||||
5.734,3.1551
|
||||
8.4084,7.2258
|
||||
5.6407,0.71618
|
||||
5.3794,3.5129
|
||||
6.3654,5.3048
|
||||
5.1301,0.56077
|
||||
6.4296,3.6518
|
||||
7.0708,5.3893
|
||||
6.1891,3.1386
|
||||
20.27,21.767
|
||||
5.4901,4.263
|
||||
6.3261,5.1875
|
||||
5.5649,3.0825
|
||||
18.945,22.638
|
||||
12.828,13.501
|
||||
10.957,7.0467
|
||||
13.176,14.692
|
||||
22.203,24.147
|
||||
5.2524,-1.22
|
||||
6.5894,5.9966
|
||||
9.2482,12.134
|
||||
5.8918,1.8495
|
||||
8.2111,6.5426
|
||||
7.9334,4.5623
|
||||
8.0959,4.1164
|
||||
5.6063,3.3928
|
||||
12.836,10.117
|
||||
6.3534,5.4974
|
||||
5.4069,0.55657
|
||||
6.8825,3.9115
|
||||
11.708,5.3854
|
||||
5.7737,2.4406
|
||||
7.8247,6.7318
|
||||
7.0931,1.0463
|
||||
5.0702,5.1337
|
||||
5.8014,1.844
|
||||
11.7,8.0043
|
||||
5.5416,1.0179
|
||||
7.5402,6.7504
|
||||
5.3077,1.8396
|
||||
7.4239,4.2885
|
||||
7.6031,4.9981
|
||||
6.3328,1.4233
|
||||
6.3589,-1.4211
|
||||
6.2742,2.4756
|
||||
5.6397,4.6042
|
||||
9.3102,3.9624
|
||||
9.4536,5.4141
|
||||
8.8254,5.1694
|
||||
5.1793,-0.74279
|
||||
21.279,17.929
|
||||
14.908,12.054
|
||||
18.959,17.054
|
||||
7.2182,4.8852
|
||||
8.2951,5.7442
|
||||
10.236,7.7754
|
||||
5.4994,1.0173
|
||||
20.341,20.992
|
||||
10.136,6.6799
|
||||
7.3345,4.0259
|
||||
6.0062,1.2784
|
||||
7.2259,3.3411
|
||||
5.0269,-2.6807
|
||||
6.5479,0.29678
|
||||
7.5386,3.8845
|
||||
5.0365,5.7014
|
||||
10.274,6.7526
|
||||
5.1077,2.0576
|
||||
5.7292,0.47953
|
||||
5.1884,0.20421
|
||||
6.3557,0.67861
|
||||
9.7687,7.5435
|
||||
6.5159,5.3436
|
||||
8.5172,4.2415
|
||||
9.1802,6.7981
|
||||
6.002,0.92695
|
||||
5.5204,0.152
|
||||
5.0594,2.8214
|
||||
5.7077,1.8451
|
||||
7.6366,4.2959
|
||||
5.8707,7.2029
|
||||
5.3054,1.9869
|
||||
8.2934,0.14454
|
||||
13.394,9.0551
|
||||
5.4369,0.61705
|
||||
|
17
w1/data01_test.csv
Normal file
17
w1/data01_test.csv
Normal file
@ -0,0 +1,17 @@
|
||||
5.7292,0.47953
|
||||
5.1884,0.20421
|
||||
6.3557,0.67861
|
||||
9.7687,7.5435
|
||||
6.5159,5.3436
|
||||
8.5172,4.2415
|
||||
9.1802,6.7981
|
||||
6.002,0.92695
|
||||
5.5204,0.152
|
||||
5.0594,2.8214
|
||||
5.7077,1.8451
|
||||
7.6366,4.2959
|
||||
5.8707,7.2029
|
||||
5.3054,1.9869
|
||||
8.2934,0.14454
|
||||
13.394,9.0551
|
||||
5.4369,0.61705
|
||||
|
80
w1/data01_train.csv
Normal file
80
w1/data01_train.csv
Normal file
@ -0,0 +1,80 @@
|
||||
6.1101,17.592
|
||||
5.5277,9.1302
|
||||
8.5186,13.662
|
||||
7.0032,11.854
|
||||
5.8598,6.8233
|
||||
8.3829,11.886
|
||||
7.4764,4.3483
|
||||
8.5781,12
|
||||
6.4862,6.5987
|
||||
5.0546,3.8166
|
||||
5.7107,3.2522
|
||||
14.164,15.505
|
||||
5.734,3.1551
|
||||
8.4084,7.2258
|
||||
5.6407,0.71618
|
||||
5.3794,3.5129
|
||||
6.3654,5.3048
|
||||
5.1301,0.56077
|
||||
6.4296,3.6518
|
||||
7.0708,5.3893
|
||||
6.1891,3.1386
|
||||
20.27,21.767
|
||||
5.4901,4.263
|
||||
6.3261,5.1875
|
||||
5.5649,3.0825
|
||||
18.945,22.638
|
||||
12.828,13.501
|
||||
10.957,7.0467
|
||||
13.176,14.692
|
||||
22.203,24.147
|
||||
5.2524,-1.22
|
||||
6.5894,5.9966
|
||||
9.2482,12.134
|
||||
5.8918,1.8495
|
||||
8.2111,6.5426
|
||||
7.9334,4.5623
|
||||
8.0959,4.1164
|
||||
5.6063,3.3928
|
||||
12.836,10.117
|
||||
6.3534,5.4974
|
||||
5.4069,0.55657
|
||||
6.8825,3.9115
|
||||
11.708,5.3854
|
||||
5.7737,2.4406
|
||||
7.8247,6.7318
|
||||
7.0931,1.0463
|
||||
5.0702,5.1337
|
||||
5.8014,1.844
|
||||
11.7,8.0043
|
||||
5.5416,1.0179
|
||||
7.5402,6.7504
|
||||
5.3077,1.8396
|
||||
7.4239,4.2885
|
||||
7.6031,4.9981
|
||||
6.3328,1.4233
|
||||
6.3589,-1.4211
|
||||
6.2742,2.4756
|
||||
5.6397,4.6042
|
||||
9.3102,3.9624
|
||||
9.4536,5.4141
|
||||
8.8254,5.1694
|
||||
5.1793,-0.74279
|
||||
21.279,17.929
|
||||
14.908,12.054
|
||||
18.959,17.054
|
||||
7.2182,4.8852
|
||||
8.2951,5.7442
|
||||
10.236,7.7754
|
||||
5.4994,1.0173
|
||||
20.341,20.992
|
||||
10.136,6.6799
|
||||
7.3345,4.0259
|
||||
6.0062,1.2784
|
||||
7.2259,3.3411
|
||||
5.0269,-2.6807
|
||||
6.5479,0.29678
|
||||
7.5386,3.8845
|
||||
5.0365,5.7014
|
||||
10.274,6.7526
|
||||
5.1077,2.0576
|
||||
|
1675
w1/data02.tsv
Normal file
1675
w1/data02.tsv
Normal file
File diff suppressed because it is too large
Load Diff
335
w1/data02_test.tsv
Normal file
335
w1/data02_test.tsv
Normal file
@ -0,0 +1,335 @@
|
||||
325000.0 True 2 0 Rataje 47
|
||||
285000.0 True 2 3 Grunwald 58
|
||||
360000.0 True 3 2 Rataje 69
|
||||
355000.0 True 2 2 Rataje 90
|
||||
362804.0 False 3 5 Jeżyce 39
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
324387.0 True 2 9 Grunwald 49
|
||||
333212.88 True 2 7 Grunwald 21
|
||||
579000.0 True 4 3 Naramowice 97
|
||||
215000.0 True 2 2 Grunwald 44
|
||||
267702.41 True 2 3 Grunwald 22
|
||||
245830.0 True 2 2 Grunwald 67
|
||||
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||||
365000.0 True 4 2 Łazarz 62
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
347090.0 True 3 1 Winogrady 57
|
||||
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|
||||
355000.0 True 3 3 Łazarz 62
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||||
330255.0 False 2 1 Stare 14
|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
290000.0 True 3 14 Nowe 20
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
319000.0 True 2 1 Centrum 52
|
||||
420000.0 True 2 1 Naramowice 50
|
||||
420000.0 True 2 3 Naramowice 50
|
||||
300000.0 True 3 2 Grunwald 49
|
||||
300000.0 True 3 2 Winogrady 48
|
||||
339000.0 True 2 2 Stare 53
|
||||
298000.0 True 3 1 Nowe 30
|
||||
250000.0 True 3 0 Stare 45
|
||||
475000.0 True 4 4 Naramowice 50
|
||||
329000.0 True 3 1 Jeżyce 112
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||||
299000.0 True 2 0 Piątkowo 20
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||||
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|
||||
370000.0 True 4 4 Piątkowo 60
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||||
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||||
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|
||||
390000.0 True 4 4 Piątkowo 74
|
||||
288000.0 True 3 4 Stare 30
|
||||
468000.0 True 2 3 Grunwald 60
|
||||
279000.0 True 3 1 Wilda 68
|
||||
250000.0 True 2 6 Winogrady 42
|
||||
350000.0 True 2 2 Jeżyce 46
|
||||
310000.0 True 2 1 Nowe 40
|
||||
289000.0 True 3 4 Winogrady 47
|
||||
405900.0 True 3 2 Stare 82
|
||||
359000.0 True 3 1 Wilda 20
|
||||
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|
||||
350000.0 True 2 2 Jeżyce 47
|
||||
239000.0 True 2 4 Grunwald 50
|
||||
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|
||||
362220.0 False 3 3 Starołęka 60
|
||||
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|
||||
320739.0 False 3 1 Starołęka 56
|
||||
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|
||||
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|
||||
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|
||||
285200.0 False 2 1 Winogrady 46
|
||||
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|
||||
309000.0 True 3 3 Wilda 80
|
||||
313000.0 True 2 1 Górczyn 48
|
||||
313000.0 True 2 1 Dębiec 48
|
||||
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|
||||
415000.0 True 2 5 Nowe 53
|
||||
325000.0 True 2 3 Stare 60
|
||||
599000.0 True 4 3 Stare 97
|
||||
319000.0 True 2 0 Grunwald 20
|
||||
299000.0 True 3 6 Stare 63
|
||||
299000.0 True 2 0 Grunwald 51
|
||||
339000.0 True 2 2 Centrum 53
|
||||
320000.0 True 3 4 Stare 65
|
||||
364000.0 True 3 1 Nowe 67
|
||||
209000.0 True 3 3 Grunwald 50
|
||||
|
1340
w1/data02_train.tsv
Normal file
1340
w1/data02_train.tsv
Normal file
File diff suppressed because it is too large
Load Diff
1675
w1/data_flats.tsv
Normal file
1675
w1/data_flats.tsv
Normal file
File diff suppressed because it is too large
Load Diff
4186
w1/data_flats_with_outliers.tsv
Normal file
4186
w1/data_flats_with_outliers.tsv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
w2/.DS_Store
vendored
Normal file
BIN
w2/.DS_Store
vendored
Normal file
Binary file not shown.
6364
w2/.ipynb_checkpoints/03_Regresja_logistyczna-checkpoint.ipynb
Normal file
6364
w2/.ipynb_checkpoints/03_Regresja_logistyczna-checkpoint.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
1312
w2/.ipynb_checkpoints/04_Metody_ewaluacji-checkpoint.ipynb
Normal file
1312
w2/.ipynb_checkpoints/04_Metody_ewaluacji-checkpoint.ipynb
Normal file
File diff suppressed because one or more lines are too long
6364
w2/03_Regresja_logistyczna.ipynb
Normal file
6364
w2/03_Regresja_logistyczna.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
1312
w2/04_Metody_ewaluacji.ipynb
Normal file
1312
w2/04_Metody_ewaluacji.ipynb
Normal file
File diff suppressed because one or more lines are too long
50
w2/data-metrics.tsv
Normal file
50
w2/data-metrics.tsv
Normal file
@ -0,0 +1,50 @@
|
||||
0 -0.9410633308036449 0.46518252113944425
|
||||
1 0.4700636553691919 -0.3970321538875541
|
||||
1 -0.01609299859794966 0.23161453968628254
|
||||
0 -0.9966154155058933 0.06419313152355421
|
||||
0 0.8000009607150127 0.44133107977776875
|
||||
0 0.389227379480078 -0.8415416694237676
|
||||
0 -0.7786281038890375 0.2833716839963434
|
||||
1 -0.10150562150521569 -0.02968754639839366
|
||||
1 -0.14995353486391494 0.30921523116923866
|
||||
0 0.3150219624148183 0.4186143523577863
|
||||
0 -0.5542734031872467 0.9291684810885719
|
||||
0 -0.44750469543445215 -0.8240387195698262
|
||||
0 -0.7875312310670415 0.27475695030524894
|
||||
0 0.20470154428730747 -0.8122722630746713
|
||||
0 0.07472783793361693 0.8936381678688297
|
||||
0 -0.6016285994197443 -0.9783927694535444
|
||||
0 0.4235345463350013 -0.23977977886239832
|
||||
0 0.256790496684171 -0.5587059709121811
|
||||
0 -0.2172656054288027 0.8015306542483966
|
||||
0 0.2009238354275602 0.9376873763906164
|
||||
0 -0.8760038215191506 0.015194717659306356
|
||||
0 -0.1512141038160364 -0.9575528046526418
|
||||
0 -0.6378974241766098 0.35900665963616696
|
||||
0 -0.6219617077011876 0.04019896541474166
|
||||
0 -0.2533778634666939 -0.8576798720089458
|
||||
0 -0.9398823073223508 0.806594859009744
|
||||
0 -0.24161324930138606 -0.6982896600554984
|
||||
0 -0.967724402993285 0.15651783268628372
|
||||
0 0.9587968810951801 -0.3382309645563397
|
||||
1 0.18040441263417084 -0.026706542719935777
|
||||
0 -0.2403226372749332 -0.2694487472698215
|
||||
0 -0.49494412803453747 -0.6833825934742561
|
||||
0 -0.32266963833818574 0.6299706350061482
|
||||
0 -0.716450532167108 0.7792499086149187
|
||||
1 -0.5661825812948427 -0.3045016769669948
|
||||
0 -0.9014952263862088 0.19697267011506714
|
||||
1 0.3192734822128551 -0.3145295901019187
|
||||
1 -0.4386590899062277 0.6119229005694005
|
||||
0 -0.6306933372350818 0.4721301354446683
|
||||
0 0.3302936606411402 -0.3047093070118343
|
||||
1 -0.38049655790356285 -0.609474130471132
|
||||
1 0.32069301644263426 0.17266197471996692
|
||||
1 0.8349752241994568 0.4408717276862013
|
||||
0 -0.26741723386938343 -0.4919294757003996
|
||||
0 -0.7786699335922747 -0.47305795528791905
|
||||
0 0.723410510517891 -0.010095862311693793
|
||||
0 0.0902826080483603 -0.6805262097228113
|
||||
0 -0.9286972617786873 0.7200430642275493
|
||||
0 -0.0623197964184079 0.8187639325432745
|
||||
0 -0.20572090815735944 -0.6655000969777327
|
||||
|
4186
w2/data_flats_with_outliers.tsv
Normal file
4186
w2/data_flats_with_outliers.tsv
Normal file
File diff suppressed because it is too large
Load Diff
151
w2/iris.csv
Normal file
151
w2/iris.csv
Normal file
@ -0,0 +1,151 @@
|
||||
sl,sw,pl,pw,Gatunek
|
||||
5.2,3.4,1.4,0.2,Iris-setosa
|
||||
5.1,3.7,1.5,0.4,Iris-setosa
|
||||
6.7,3.1,5.6,2.4,Iris-virginica
|
||||
6.5,3.2,5.1,2.0,Iris-virginica
|
||||
4.9,2.5,4.5,1.7,Iris-virginica
|
||||
6.0,2.7,5.1,1.6,Iris-versicolor
|
||||
5.7,2.6,3.5,1.0,Iris-versicolor
|
||||
5.0,2.0,3.5,1.0,Iris-versicolor
|
||||
5.2,3.5,1.5,0.2,Iris-setosa
|
||||
4.8,3.0,1.4,0.1,Iris-setosa
|
||||
6.7,3.3,5.7,2.5,Iris-virginica
|
||||
6.1,3.0,4.9,1.8,Iris-virginica
|
||||
4.8,3.4,1.9,0.2,Iris-setosa
|
||||
5.8,2.8,5.1,2.4,Iris-virginica
|
||||
4.4,2.9,1.4,0.2,Iris-setosa
|
||||
7.2,3.0,5.8,1.6,Iris-virginica
|
||||
4.4,3.2,1.3,0.2,Iris-setosa
|
||||
5.0,3.5,1.3,0.3,Iris-setosa
|
||||
5.4,3.9,1.3,0.4,Iris-setosa
|
||||
7.7,2.8,6.7,2.0,Iris-virginica
|
||||
5.0,3.6,1.4,0.2,Iris-setosa
|
||||
6.2,2.8,4.8,1.8,Iris-virginica
|
||||
6.0,2.2,5.0,1.5,Iris-virginica
|
||||
7.4,2.8,6.1,1.9,Iris-virginica
|
||||
5.0,3.2,1.2,0.2,Iris-setosa
|
||||
6.7,3.1,4.4,1.4,Iris-versicolor
|
||||
6.7,3.1,4.7,1.5,Iris-versicolor
|
||||
5.6,2.7,4.2,1.3,Iris-versicolor
|
||||
5.6,2.5,3.9,1.1,Iris-versicolor
|
||||
6.3,3.3,4.7,1.6,Iris-versicolor
|
||||
5.1,3.4,1.5,0.2,Iris-setosa
|
||||
6.0,2.9,4.5,1.5,Iris-versicolor
|
||||
5.3,3.7,1.5,0.2,Iris-setosa
|
||||
5.6,2.9,3.6,1.3,Iris-versicolor
|
||||
5.5,2.5,4.0,1.3,Iris-versicolor
|
||||
5.5,2.4,3.7,1.0,Iris-versicolor
|
||||
4.4,3.0,1.3,0.2,Iris-setosa
|
||||
6.6,3.0,4.4,1.4,Iris-versicolor
|
||||
7.9,3.8,6.4,2.0,Iris-virginica
|
||||
5.7,2.8,4.1,1.3,Iris-versicolor
|
||||
5.8,2.7,4.1,1.0,Iris-versicolor
|
||||
6.5,2.8,4.6,1.5,Iris-versicolor
|
||||
6.1,2.8,4.7,1.2,Iris-versicolor
|
||||
5.1,3.8,1.9,0.4,Iris-setosa
|
||||
5.0,3.4,1.6,0.4,Iris-setosa
|
||||
5.5,2.6,4.4,1.2,Iris-versicolor
|
||||
5.0,3.4,1.5,0.2,Iris-setosa
|
||||
6.8,2.8,4.8,1.4,Iris-versicolor
|
||||
6.9,3.1,4.9,1.5,Iris-versicolor
|
||||
6.1,2.9,4.7,1.4,Iris-versicolor
|
||||
5.1,3.8,1.6,0.2,Iris-setosa
|
||||
6.4,3.2,5.3,2.3,Iris-virginica
|
||||
6.4,2.7,5.3,1.9,Iris-virginica
|
||||
5.7,2.8,4.5,1.3,Iris-versicolor
|
||||
5.8,2.6,4.0,1.2,Iris-versicolor
|
||||
4.7,3.2,1.6,0.2,Iris-setosa
|
||||
5.1,3.3,1.7,0.5,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
6.3,3.4,5.6,2.4,Iris-virginica
|
||||
5.1,3.8,1.5,0.3,Iris-setosa
|
||||
7.0,3.2,4.7,1.4,Iris-versicolor
|
||||
5.4,3.0,4.5,1.5,Iris-versicolor
|
||||
6.0,2.2,4.0,1.0,Iris-versicolor
|
||||
6.0,3.0,4.8,1.8,Iris-virginica
|
||||
6.2,2.9,4.3,1.3,Iris-versicolor
|
||||
5.6,3.0,4.1,1.3,Iris-versicolor
|
||||
4.9,3.0,1.4,0.2,Iris-setosa
|
||||
5.0,2.3,3.3,1.0,Iris-versicolor
|
||||
6.3,2.5,5.0,1.9,Iris-virginica
|
||||
4.8,3.4,1.6,0.2,Iris-setosa
|
||||
5.9,3.0,4.2,1.5,Iris-versicolor
|
||||
4.6,3.6,1.0,0.2,Iris-setosa
|
||||
5.0,3.5,1.6,0.6,Iris-setosa
|
||||
5.7,4.4,1.5,0.4,Iris-setosa
|
||||
5.0,3.0,1.6,0.2,Iris-setosa
|
||||
5.6,3.0,4.5,1.5,Iris-versicolor
|
||||
6.3,2.8,5.1,1.5,Iris-virginica
|
||||
5.2,2.7,3.9,1.4,Iris-versicolor
|
||||
5.9,3.2,4.8,1.8,Iris-versicolor
|
||||
7.7,3.0,6.1,2.3,Iris-virginica
|
||||
6.2,3.4,5.4,2.3,Iris-virginica
|
||||
6.4,2.9,4.3,1.3,Iris-versicolor
|
||||
6.5,3.0,5.5,1.8,Iris-virginica
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.9,3.2,5.7,2.3,Iris-virginica
|
||||
6.4,2.8,5.6,2.2,Iris-virginica
|
||||
4.7,3.2,1.3,0.2,Iris-setosa
|
||||
5.5,2.4,3.8,1.1,Iris-versicolor
|
||||
5.4,3.4,1.5,0.4,Iris-setosa
|
||||
7.2,3.6,6.1,2.5,Iris-virginica
|
||||
6.7,2.5,5.8,1.8,Iris-virginica
|
||||
6.1,3.0,4.6,1.4,Iris-versicolor
|
||||
6.0,3.4,4.5,1.6,Iris-versicolor
|
||||
6.3,2.7,4.9,1.8,Iris-virginica
|
||||
6.9,3.1,5.1,2.3,Iris-virginica
|
||||
5.5,3.5,1.3,0.2,Iris-setosa
|
||||
6.7,3.0,5.2,2.3,Iris-virginica
|
||||
4.6,3.1,1.5,0.2,Iris-setosa
|
||||
5.8,2.7,5.1,1.9,Iris-virginica
|
||||
6.4,3.1,5.5,1.8,Iris-virginica
|
||||
7.3,2.9,6.3,1.8,Iris-virginica
|
||||
4.8,3.0,1.4,0.3,Iris-setosa
|
||||
7.1,3.0,5.9,2.1,Iris-virginica
|
||||
5.9,3.0,5.1,1.8,Iris-virginica
|
||||
6.1,2.6,5.6,1.4,Iris-virginica
|
||||
5.4,3.9,1.7,0.4,Iris-setosa
|
||||
6.4,3.2,4.5,1.5,Iris-versicolor
|
||||
5.1,2.5,3.0,1.1,Iris-versicolor
|
||||
6.3,2.9,5.6,1.8,Iris-virginica
|
||||
7.2,3.2,6.0,1.8,Iris-virginica
|
||||
5.4,3.4,1.7,0.2,Iris-setosa
|
||||
4.6,3.2,1.4,0.2,Iris-setosa
|
||||
6.1,2.8,4.0,1.3,Iris-versicolor
|
||||
7.7,3.8,6.7,2.2,Iris-virginica
|
||||
5.7,2.9,4.2,1.3,Iris-versicolor
|
||||
5.1,3.5,1.4,0.2,Iris-setosa
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
6.5,3.0,5.2,2.0,Iris-virginica
|
||||
4.9,3.1,1.5,0.1,Iris-setosa
|
||||
6.3,2.3,4.4,1.3,Iris-versicolor
|
||||
6.2,2.2,4.5,1.5,Iris-versicolor
|
||||
5.7,3.8,1.7,0.3,Iris-setosa
|
||||
6.4,2.8,5.6,2.1,Iris-virginica
|
||||
4.9,2.4,3.3,1.0,Iris-versicolor
|
||||
5.7,2.5,5.0,2.0,Iris-virginica
|
||||
5.5,4.2,1.4,0.2,Iris-setosa
|
||||
6.7,3.0,5.0,1.7,Iris-versicolor
|
||||
5.0,3.3,1.4,0.2,Iris-setosa
|
||||
6.3,2.5,4.9,1.5,Iris-versicolor
|
||||
5.4,3.7,1.5,0.2,Iris-setosa
|
||||
7.7,2.6,6.9,2.3,Iris-virginica
|
||||
5.7,3.0,4.2,1.2,Iris-versicolor
|
||||
7.6,3.0,6.6,2.1,Iris-virginica
|
||||
4.8,3.1,1.6,0.2,Iris-setosa
|
||||
5.6,2.8,4.9,2.0,Iris-virginica
|
||||
4.5,2.3,1.3,0.3,Iris-setosa
|
||||
6.8,3.2,5.9,2.3,Iris-virginica
|
||||
6.3,3.3,6.0,2.5,Iris-virginica
|
||||
4.6,3.4,1.4,0.3,Iris-setosa
|
||||
5.8,2.7,3.9,1.2,Iris-versicolor
|
||||
5.5,2.3,4.0,1.3,Iris-versicolor
|
||||
5.2,4.1,1.5,0.1,Iris-setosa
|
||||
6.6,2.9,4.6,1.3,Iris-versicolor
|
||||
4.3,3.0,1.1,0.1,Iris-setosa
|
||||
6.8,3.0,5.5,2.1,Iris-virginica
|
||||
5.8,4.0,1.2,0.2,Iris-setosa
|
||||
5.1,3.5,1.4,0.3,Iris-setosa
|
||||
6.5,3.0,5.8,2.2,Iris-virginica
|
||||
6.9,3.1,5.4,2.1,Iris-virginica
|
||||
6.7,3.3,5.7,2.1,Iris-virginica
|
||||
|
Loading…
Reference in New Issue
Block a user