Materiały na podstawie ubiegłorocznych
This commit is contained in:
Paweł Skórzewski
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lab/2001_Python.ipynb
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lab/2001_Python.ipynb
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lab/2002_Wczytywanie_i_prezentowanie_danych.ipynb
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lab/2002_Wczytywanie_i_prezentowanie_danych.ipynb
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lab/2003_Regresja_liniowa.ipynb
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lab/2003_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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"metadata": {
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"## Uczenie maszynowe 2019/2020 – laboratoria\n",
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"### 23/24 marca 2020\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": [
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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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]
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}
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],
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"metadata": {
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"celltoolbar": "Slideshow",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"theme": "amu"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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lab/2007_scikit-learn.ipynb
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lab/2007_scikit-learn.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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"metadata": {
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"slideshow": {
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"slide_type": "-"
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}
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},
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"source": [
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"## Uczenie maszynowe 2019/2020 – laboratoria\n",
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"### 27/28 kwietnia 2020\n",
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"# 7. Korzystanie z gotowych implementacji algorytmów na przykładzie pakietu *scikit-learn*"
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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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"[Scikit-learn](https://scikit-learn.org) jest otwartoźródłową biblioteką programistyczną dla języka Python wspomagającą uczenie maszynowe. Zawiera implementacje wielu algorytmów uczenia maszynowego."
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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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"Poniżej przykład, jak stworzyć klasyfikator regresji liniowej wielu zmiennych z użyciem `scikit-learn`.\n",
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"\n",
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"Na podobnej zasadzie można korzystać z innych modeli dostępnych w bibliotece."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"#! /usr/bin/env python3\n",
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"# -*- coding: utf-8 -*-\n",
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"\n",
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"# Regresja liniowa wielu zmiennych\n",
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"\n",
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"import csv\n",
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"import numpy\n",
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"import pandas\n",
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"import sys\n",
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"\n",
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"from sklearn import linear_model # Model regresji liniowej z biblioteki scikit-learn\n",
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"\n",
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"\n",
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"FEATURES = [\n",
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" 'Powierzchnia w m2',\n",
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" 'Liczba pokoi',\n",
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" 'Liczba pięter w budynku',\n",
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" 'Piętro',\n",
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" 'Rok budowy',\n",
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"]\n",
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"\n",
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"\n",
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"def preprocess(data):\n",
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" \"\"\"Wstępne przetworzenie danych\"\"\"\n",
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" data = data.replace({'parter': 0, 'poddasze': 0}, regex=True)\n",
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" data = data.applymap(numpy.nan_to_num) # Zamienia \"NaN\" na liczby\n",
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" return data\n",
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"\n",
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"# Nazwy plików\n",
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"input_filename = 'flats-test.tsv'\n",
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"output_filename = 'flats-predicted.tsv'\n",
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"trainset_filename = 'flats-train.tsv'\n",
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"\n",
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"# Wczytanie danych uczących\n",
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"data = pandas.read_csv(trainset_filename, header=0, sep='\\t')\n",
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"columns = data.columns[1:] # wszystkie kolumny oprócz pierwszej (\"cena\")\n",
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"data = data[FEATURES + ['cena']] # wybór cech\n",
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"data = preprocess(data) # wstępne przetworzenie danych\n",
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"y = pandas.DataFrame(data['cena'])\n",
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"x = pandas.DataFrame(data[FEATURES])\n",
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"model = linear_model.LinearRegression() # definicja modelu\n",
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"model.fit(x, y) # dopasowanie modelu\n",
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"\n",
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"# Wczytanie danych testowych\n",
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"data = pandas.read_csv(input_filename, header=None, sep='\\t', names=columns)\n",
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"x = pandas.DataFrame(data[FEATURES]) # wybór cech\n",
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"x = preprocess(x) # wstępne przetworzenie danych\n",
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"y = model.predict(x) # przewidywania modelu\n",
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"\n",
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"# Zapis wyników do pliku\n",
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"pandas.DataFrame(y).to_csv(output_filename, index=None, header=None, sep='\\t')"
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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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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"livereveal": {
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"start_slideshow_at": "selected",
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"theme": "amu"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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lab/2010_Sieci_neuronowe.ipynb
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lab/2010_Sieci_neuronowe.ipynb
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lab/2011_Wielowarstwowe_sieci_neuronowe.ipynb
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lab/2011_Wielowarstwowe_sieci_neuronowe.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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"metadata": {},
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"source": [
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"## Wielowarstwowe sieci neuronowe\n",
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"\n",
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"[Keras](https://keras.io/) to zaawansowany pakiet do tworzenia sieci neuronowych w języku Python.\n",
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"Na komputerach wydziałowych powinien być zainstalowany.\n",
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"Na własnych komputerach pod Linuxem można go zainstalować poleceniem: `sudo pip install keras`.\n",
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"\n",
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"**Uwaga:** pierwsze uruchomienie zazwyczaj trwa jakiś czas, ponieważ pod spodem model kompiluje się jako aplikacja w C++. \n",
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"\n",
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"#### 1. Iris dataset\n",
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"\n",
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"Korzystając z [oficjalnej dokumentacji](http://keras.io) oraz materiałów szkoleniowych znalezionych w internecie (np. [machinelearningmastery.com](http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/)), zbuduj (co najmniej) dwuwarstwową sieć neuronową do klasyfkacji _Iris dataset_. Opisz stworzony model: architekturę sieci, jej rozmiar, zastosowane funkcje aktywacji, funkcję kosztu, wersję GD, metodę regularyzacji. Podaj wynik ewaluacji na zbiorze testowym.\n",
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"\n",
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"#### 2. MNIST\n",
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"\n",
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"Uruchom przykład `mnist_mlp.py` z [katalogu oficjalnych przykładów]( https://github.com/fchollet/keras/tree/master/examples) (warto ew. zmienić liczbę epok do 5). Posiłkując się dokumentacją, przeanalizuj kod i opisz:\n",
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"\n",
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"* Do jakiej postaci sprowadzane są dane `Y_train` i `Y_test`?\n",
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"* Przedstaw wzór matematyczny na zastosowaną funkcję błędu.\n",
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"* Jaka jest architektura sieci neuronowej? Ile ma warstw, jakie są rozmiary macierzy warstw? Czy można uzyskać dostęp do tych wag?\n",
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"* Jakie funkcje aktywacji użyto? Podaj ich wzory.\n",
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"* Czym jest `Dropout`? Czemu służy? Jakie znaczenie ma parametr?\n",
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"\n",
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"Zmodyfikuj model z przykładu `mnist_mlp.py` i wykonaj:\n",
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"\n",
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"* Usuń warstwy `Dropout`, jaki jest efekt?\n",
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"* Stwórz 6-cio warstwowy model o rozmiarach warstw 2500, 2000, 1500, 1000, 500 oraz 10 bez `Dropout`, użyj wszędzie funkcji aktywacji `tanh` z wyjątkiem ostatniej warstwy, gdzie należy użyć `softmax`. Trenuj model przez 10 epok.\n",
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"* Dodaj warstwy `Dropout`, porównaj jakość po 10 epokach, krótko opisz wnioski.\n",
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"* Zamiast `RMSprop` użyj algorytm `Adagrad`, porównaj jakość, krótko opisz wnioski. "
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]
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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lab/Untitled.ipynb
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lab/Untitled.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_list = [34.6, -203.4, 45, 8.2, -12.3, 44.6, 12.7]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"output_list = [x * x for x in input_list if x > 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1197.16, 2025, 67.24, 1989.16, 161.29]\n"
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]
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}
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],
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"source": [
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"print(output_list)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"67.24"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"8.2 ** 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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10
lab/data1.csv
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lab/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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lab/data1.tsv
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lab/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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178
lab/data2.csv
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lab/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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|
||||
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
lab/data6.tsv
Normal file
20
lab/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
lab/fires_thefts.csv
Normal file
42
lab/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
|
||||
|
488
lab/flats-predicted.tsv
Normal file
488
lab/flats-predicted.tsv
Normal file
@ -0,0 +1,488 @@
|
||||
283505.6268716706
|
||||
286807.81791486836
|
||||
1070822.8900455572
|
||||
291046.92186160106
|
||||
140742.41438722756
|
||||
499423.3316692108
|
||||
289618.3089864515
|
||||
702312.4665604271
|
||||
891758.620733487
|
||||
551573.3411180723
|
||||
1188342.9805958075
|
||||
307778.6267529641
|
||||
542577.1455850884
|
||||
640989.1946802845
|
||||
518524.64774014754
|
||||
164758.77251739136
|
||||
284405.90478238475
|
||||
278183.5653281631
|
||||
505499.3787911708
|
||||
415315.2007055009
|
||||
249646.11355068226
|
||||
156835.43489528715
|
||||
279685.20054993266
|
||||
357965.71224453143
|
||||
520717.3458796448
|
||||
696508.5570921783
|
||||
1049074.8704929457
|
||||
973233.9630660774
|
||||
413541.1801909984
|
||||
617900.1698498171
|
||||
480055.6191585947
|
||||
424484.2238776721
|
||||
144181.2933548356
|
||||
415105.98571251455
|
||||
609781.6901043693
|
||||
289163.24400765775
|
||||
634003.9354090634
|
||||
407983.3683475788
|
||||
296013.47607986047
|
||||
295840.89607969497
|
||||
351445.84617400414
|
||||
271615.57059556665
|
||||
279685.20054993266
|
||||
289518.75133290584
|
||||
172127.23986813438
|
||||
408056.39069419866
|
||||
342430.25689915166
|
||||
449394.5063358207
|
||||
360697.52929069113
|
||||
348841.99156168924
|
||||
232789.50305631894
|
||||
471204.29650383024
|
||||
507398.4290893693
|
||||
236285.5366482477
|
||||
673235.2919599946
|
||||
562931.0729317521
|
||||
1297643.8083903352
|
||||
284897.8523930212
|
||||
470354.2757695722
|
||||
231626.59599684452
|
||||
813426.0818321182
|
||||
482596.54474251246
|
||||
554200.9048645728
|
||||
285797.8826647134
|
||||
415943.340962504
|
||||
285797.8826647134
|
||||
273352.9561172483
|
||||
395711.2694393011
|
||||
1067660.883268814
|
||||
471102.3683978715
|
||||
364864.07297880325
|
||||
288126.77345057717
|
||||
488056.66773908347
|
||||
636369.4611877479
|
||||
479389.55991534586
|
||||
285661.69001834694
|
||||
569147.3021980671
|
||||
419418.359644889
|
||||
530550.6490235961
|
||||
672728.5567183747
|
||||
363026.4200765549
|
||||
299907.1723872403
|
||||
351202.119001588
|
||||
369903.8971827045
|
||||
642178.7746957783
|
||||
279957.58584266563
|
||||
518278.5501153184
|
||||
277665.3300496228
|
||||
772360.6139796954
|
||||
497236.74159552064
|
||||
1492497.1421237364
|
||||
259970.86902722623
|
||||
283887.66950384446
|
||||
402388.6738723164
|
||||
280412.6508214593
|
||||
287362.68818622956
|
||||
552337.4263824199
|
||||
232178.64009546567
|
||||
218685.54057369017
|
||||
518906.69037232135
|
||||
141124.45701940136
|
||||
296222.9387118688
|
||||
631069.714795073
|
||||
352456.49115118023
|
||||
152387.11595556804
|
||||
400578.2660040149
|
||||
336623.90017860616
|
||||
898123.2545322096
|
||||
465940.4753769551
|
||||
152978.37358072822
|
||||
413959.85781599313
|
||||
439002.80338841927
|
||||
346431.4045650252
|
||||
533434.410080821
|
||||
283778.0121644036
|
||||
608572.3855404289
|
||||
156662.60725609973
|
||||
339978.4591429831
|
||||
495833.2573085991
|
||||
355744.1536044081
|
||||
549417.277971396
|
||||
492198.33047798945
|
||||
352166.7390270692
|
||||
449394.5063358207
|
||||
168060.96356058907
|
||||
396375.5494110812
|
||||
177203.6990648565
|
||||
532879.5398094598
|
||||
409129.4962441002
|
||||
345796.70688956056
|
||||
253057.96193332074
|
||||
550809.2558537247
|
||||
404508.3496651937
|
||||
281485.75637136085
|
||||
692688.1307704402
|
||||
1253882.4585806779
|
||||
404053.28468640003
|
||||
284897.8523930212
|
||||
161247.11884218533
|
||||
286807.81791486836
|
||||
536563.7734848312
|
||||
390571.88758185424
|
||||
492508.77591858146
|
||||
269532.7774345322
|
||||
345923.2562370354
|
||||
292402.5123901308
|
||||
282249.84163570846
|
||||
164413.11723901654
|
||||
171745.4448749825
|
||||
377289.3581453778
|
||||
1201439.6076718224
|
||||
466078.7965377086
|
||||
155479.84436675743
|
||||
398322.645203793
|
||||
287362.68818622956
|
||||
394884.2615142288
|
||||
369861.8638510977
|
||||
232456.43009465688
|
||||
239011.5948715186
|
||||
423065.9583274397
|
||||
345677.4062512281
|
||||
401452.00860780326
|
||||
296222.9387118688
|
||||
1014275.1821450489
|
||||
523846.70928365504
|
||||
379927.0215777735
|
||||
252084.41403696482
|
||||
576788.1548415431
|
||||
610352.7617597604
|
||||
358141.3717574547
|
||||
353357.47242124315
|
||||
296222.9387118688
|
||||
335716.2174733155
|
||||
221571.17043761205
|
||||
162639.09672451403
|
||||
242695.82854689012
|
||||
341504.76086498133
|
||||
358054.4675195096
|
||||
276901.2447852752
|
||||
349291.90584071906
|
||||
241623.21827503244
|
||||
379063.37865988025
|
||||
171080.66962515857
|
||||
911505.5433358644
|
||||
409966.6038550677
|
||||
274363.1390064251
|
||||
922419.4668142219
|
||||
627682.7480283173
|
||||
371223.5681464302
|
||||
345018.2833468843
|
||||
416461.57624104427
|
||||
471102.3683978715
|
||||
414551.36308017524
|
||||
664041.8435589442
|
||||
163576.00962804904
|
||||
292920.5000296491
|
||||
323491.6815168518
|
||||
359378.5737549824
|
||||
222623.58267103252
|
||||
291811.0071259487
|
||||
287671.46083276154
|
||||
362206.9383379788
|
||||
400305.8807112819
|
||||
655941.6930452369
|
||||
271406.85088062414
|
||||
590104.5015878724
|
||||
273217.0111099037
|
||||
167469.45829640696
|
||||
237537.33026412176
|
||||
209912.82546051973
|
||||
328411.508090744
|
||||
413404.9875446319
|
||||
317008.2091677958
|
||||
288126.77345057717
|
||||
618539.556803723
|
||||
408020.25097942166
|
||||
296222.9387118688
|
||||
407946.7333547579
|
||||
400441.8257186265
|
||||
280894.49874620064
|
||||
271615.57059556665
|
||||
399677.7404542789
|
||||
352756.8432735494
|
||||
649670.509500929
|
||||
287744.7308184033
|
||||
359132.72376917506
|
||||
935829.0070194959
|
||||
397976.9899254182
|
||||
916442.2739864588
|
||||
608189.6395489062
|
||||
518634.3050795884
|
||||
226294.7783093643
|
||||
423065.9583274397
|
||||
478243.43201882445
|
||||
757053.7737368431
|
||||
272207.0758597488
|
||||
672506.4286020978
|
||||
474244.4425448631
|
||||
280339.6284748394
|
||||
590104.5015878724
|
||||
493704.18518917536
|
||||
1050680.2962596985
|
||||
360791.94831314095
|
||||
340846.3413199864
|
||||
279957.58584266563
|
||||
453597.22929642675
|
||||
404754.19965100096
|
||||
268977.907163171
|
||||
609279.2055238511
|
||||
567200.4540443772
|
||||
787924.0945182545
|
||||
546606.7868998129
|
||||
1367849.2924584588
|
||||
386086.9336493143
|
||||
492402.85619253456
|
||||
391718.01547837566
|
||||
406836.9928120355
|
||||
1056786.1669493234
|
||||
392937.4133605389
|
||||
1051579.8708110638
|
||||
165140.81514956514
|
||||
354923.6910291293
|
||||
170599.06933943916
|
||||
288126.77345057717
|
||||
539274.7069028688
|
||||
491846.1114134807
|
||||
366883.4813911138
|
||||
272107.51820620324
|
||||
539274.7069028688
|
||||
240805.7124872386
|
||||
910183.3059070886
|
||||
132573.47441833798
|
||||
1281392.3542699316
|
||||
574113.8564163265
|
||||
440026.81084154046
|
||||
282113.6489893419
|
||||
555078.2821445055
|
||||
286980.64555405575
|
||||
286043.73265052075
|
||||
287671.46083276154
|
||||
1135717.478260357
|
||||
322499.3543694245
|
||||
290382.394250799
|
||||
475754.999269835
|
||||
389425.7596853328
|
||||
951304.3368660425
|
||||
290664.87922942726
|
||||
423065.9583274397
|
||||
297086.33399074007
|
||||
525783.4577514498
|
||||
639816.0750898337
|
||||
290419.02924361994
|
||||
209912.82546051973
|
||||
278047.3726817966
|
||||
471204.29650383024
|
||||
393356.0909855336
|
||||
286216.56028970814
|
||||
610243.1164889877
|
||||
258024.0208735363
|
||||
485621.6555135402
|
||||
494416.8605650635
|
||||
492934.2643020555
|
||||
387714.16655351117
|
||||
513175.80325069244
|
||||
392717.60340361344
|
||||
620425.3511033851
|
||||
481556.08256534376
|
||||
596661.9020232063
|
||||
258616.02141576228
|
||||
405518.2849153486
|
||||
398531.86019677937
|
||||
476477.05120257585
|
||||
750140.3713648937
|
||||
523082.6240193075
|
||||
364326.44395839423
|
||||
812913.9539527084
|
||||
367640.7552303055
|
||||
414624.3854267951
|
||||
520717.3458796448
|
||||
290382.394250799
|
||||
353191.4562072116
|
||||
346933.89484653913
|
||||
412049.6446551241
|
||||
472004.30703397753
|
||||
288854.22372210387
|
||||
415660.8559838757
|
||||
286043.73265052075
|
||||
371312.86867696344
|
||||
388033.78180300415
|
||||
402770.7165044902
|
||||
636131.847115458
|
||||
299907.1723872403
|
||||
330763.86673944385
|
||||
279685.20054993266
|
||||
348441.18174711213
|
||||
570638.8377339414
|
||||
350473.95900304016
|
||||
283778.0121644036
|
||||
1147659.1977649378
|
||||
404262.4996793864
|
||||
548899.0426928557
|
||||
373930.2566043619
|
||||
258616.02141576228
|
||||
487639.65083948075
|
||||
604255.5706353118
|
||||
161492.96882799262
|
||||
362535.18219293957
|
||||
285797.8826647134
|
||||
346690.62339444994
|
||||
408920.2812511138
|
||||
898898.1364741289
|
||||
1310957.3359982735
|
||||
331242.1914997686
|
||||
288372.6234363844
|
||||
346428.83809997514
|
||||
167469.45829640696
|
||||
482185.38826969493
|
||||
966213.4818884054
|
||||
398913.90282895317
|
||||
360486.4575596759
|
||||
472093.77773697267
|
||||
771536.8815800177
|
||||
157808.7351526211
|
||||
471341.19887721795
|
||||
947300.7355802
|
||||
685429.320759138
|
||||
624133.3002806145
|
||||
345450.2106027431
|
||||
625923.0651254668
|
||||
823183.0807363826
|
||||
286043.73265052075
|
||||
378053.4434097254
|
||||
209912.82546051973
|
||||
285797.8826647134
|
||||
292402.5123901308
|
||||
227786.0662062167
|
||||
388169.72681034874
|
||||
301817.3855481093
|
||||
403398.8567614932
|
||||
408056.6383332206
|
||||
288372.6234363844
|
||||
477027.941922517
|
||||
489928.3771004346
|
||||
488854.10610318504
|
||||
468297.7766441138
|
||||
413468.15784437855
|
||||
470354.2757695722
|
||||
476048.28662703064
|
||||
182552.2959152897
|
||||
153151.20121991565
|
||||
279230.13557113893
|
||||
499884.0540278038
|
||||
470354.2757695722
|
||||
254759.70301826912
|
||||
514950.07140421687
|
||||
409584.5612228939
|
||||
324706.13678055606
|
||||
291183.11450796755
|
||||
224274.66017003258
|
||||
824251.8546184632
|
||||
686957.4912878332
|
||||
363294.1288261913
|
||||
350516.91014297307
|
||||
774117.7240498235
|
||||
355270.5117548522
|
||||
356939.56990935095
|
||||
163403.18198886164
|
||||
389771.1673246858
|
||||
478752.4029189165
|
||||
415660.8559838757
|
||||
272834.96847772994
|
||||
396757.8396822769
|
||||
276901.2447852752
|
||||
540593.6624385776
|
||||
752377.9290087378
|
||||
542331.0479602593
|
||||
381737.6770850969
|
||||
253576.19721186106
|
||||
284405.90478238475
|
||||
373150.93939069565
|
||||
286425.7752826945
|
||||
156071.34963093954
|
||||
498731.10330413503
|
||||
284269.7121360182
|
||||
271615.57059556665
|
||||
582898.2675790543
|
||||
1188698.1246094392
|
||||
353239.8566456388
|
||||
209912.82546051973
|
||||
390571.88758185424
|
||||
467918.56588567584
|
||||
477799.28863135166
|
||||
229487.0643740993
|
||||
443314.2178326511
|
||||
164167.26725320923
|
||||
423065.9583274397
|
||||
367949.0721565106
|
||||
694598.3439313092
|
||||
566818.4114122035
|
||||
409614.6154486634
|
||||
287671.46083276154
|
||||
279611.93056429084
|
||||
549480.2006321207
|
||||
424484.2238776721
|
||||
805049.5544055897
|
||||
771303.9567861691
|
||||
364708.48659056803
|
||||
195740.8607162901
|
||||
267339.33637796924
|
||||
293339.17765464383
|
||||
1005711.7485086942
|
||||
241911.92349787214
|
||||
411248.9243979556
|
||||
289937.42895790056
|
||||
281485.75637136085
|
||||
674386.7194189187
|
||||
612810.7861278128
|
||||
282113.6489893419
|
||||
528949.2085092592
|
||||
283778.0121644036
|
||||
281731.6063571681
|
||||
405837.6348782257
|
||||
364415.1160658166
|
||||
630638.8217051409
|
||||
702621.239206959
|
||||
642002.6651635239
|
||||
355932.916552263
|
||||
408056.39069419866
|
||||
222456.15337063096
|
||||
489789.6179890181
|
||||
358141.82177678583
|
||||
405026.584943734
|
||||
350507.767823121
|
||||
474268.6144337246
|
||||
403289.1994220524
|
||||
523109.1593262332
|
||||
694598.3439313092
|
||||
403990.1143866534
|
||||
895763.127092311
|
||||
488056.66773908347
|
||||
351939.6625913376
|
||||
288854.22372210387
|
||||
335334.1748411417
|
||||
307778.6267529641
|
||||
335716.2174733155
|
||||
476922.73259016784
|
||||
362153.13956076576
|
||||
383129.65496742557
|
||||
|
4939
lab/flats-simple-train.csv
Normal file
4939
lab/flats-simple-train.csv
Normal file
File diff suppressed because it is too large
Load Diff
488
lab/flats-test.tsv
Normal file
488
lab/flats-test.tsv
Normal file
File diff suppressed because one or more lines are too long
4939
lab/flats-train.tsv
Normal file
4939
lab/flats-train.tsv
Normal file
File diff suppressed because one or more lines are too long
4939
lab/flats_for_clustering.tsv
Normal file
4939
lab/flats_for_clustering.tsv
Normal file
File diff suppressed because it is too large
Load Diff
18
lab/generate_data6.py
Normal file
18
lab/generate_data6.py
Normal file
@ -0,0 +1,18 @@
|
||||
#! /usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
x = np.random.rand(20) * 200
|
||||
d = np.random.normal(size=20) * 500
|
||||
y = 0.1 * x**2 + 0.9 * x + 3 + d
|
||||
|
||||
np.set_printoptions(suppress=True, precision=3)
|
||||
|
||||
plt.scatter(x, y)
|
||||
plt.show()
|
||||
|
||||
data = np.vstack((x, y)).T
|
||||
print(data)
|
||||
np.savetxt('data6.tsv', data, delimiter='\t', fmt='%.3f')
|
||||
2019
lab/gratkapl-centrenrm.csv
Normal file
2019
lab/gratkapl-centrenrm.csv
Normal file
File diff suppressed because it is too large
Load Diff
5001
lab/gratkapl.csv
Normal file
5001
lab/gratkapl.csv
Normal file
File diff suppressed because it is too large
Load Diff
46
lab/knn.py
Normal file
46
lab/knn.py
Normal file
@ -0,0 +1,46 @@
|
||||
#! /usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from seaborn import relplot
|
||||
from sklearn.cluster import KMeans
|
||||
from sklearn.decomposition import PCA
|
||||
|
||||
data = pd.read_csv('mieszkania4.tsv', sep='\t')
|
||||
|
||||
data = data[
|
||||
(data['Powierzchnia w m2'] < 10000)
|
||||
& (data['cena'] < 10000000)
|
||||
]
|
||||
X_columns = [
|
||||
'cena',
|
||||
'Powierzchnia w m2',
|
||||
'Liczba pokoi',
|
||||
'Liczba pięter w budynku',
|
||||
'Piętro',
|
||||
]
|
||||
data['Piętro'] = data['Piętro'].apply(lambda x: 0 if x in ['parter', 'niski parter'] else x)
|
||||
data['Piętro'] = data['Piętro'].apply(pd.to_numeric, errors='coerce')
|
||||
data=data[:100]
|
||||
X = data[X_columns].dropna().reset_index().drop(['index'], axis=1)
|
||||
|
||||
kmeans = KMeans(n_clusters=5).fit(X.values)
|
||||
labels = pd.DataFrame(kmeans.labels_, columns=['label'])
|
||||
labeled = pd.concat([labels, X], axis=1)
|
||||
|
||||
print(labeled)
|
||||
|
||||
relplot(data=labeled, x='Liczba pokoi', y='Piętro', hue='label')
|
||||
relplot(data=labeled, x='Powierzchnia w m2', y='cena', hue='label')
|
||||
plt.show()
|
||||
|
||||
pca = PCA(n_components=2)
|
||||
pca.fit(X)
|
||||
|
||||
X_transformed = pd.DataFrame(pca.transform(X), columns=['x1', 'x2'])
|
||||
labeled_transformed = pd.concat([labels, X_transformed, X], axis=1)
|
||||
|
||||
relplot(data=labeled_transformed, x='x1', y='x2', hue='label')
|
||||
plt.show()
|
||||
792
lab/mushrooms-test.tsv
Normal file
792
lab/mushrooms-test.tsv
Normal file
@ -0,0 +1,792 @@
|
||||
e b s w t l f c b n e c s s w w p w o p n n m
|
||||
p x y n t p f c n n e e s s w w p w o p n v g
|
||||
e b y w t a f c b w e c s s w w p w o p n n m
|
||||
e b s w t l f c b g e c s s w w p w o p k s m
|
||||
e x y y t l f c b n e c s s w w p w o p n n m
|
||||
e s f g f n f c n k e e s s w w p w o p k v u
|
||||
e x s w t l f c b w e c s s w w p w o p n n m
|
||||
e x s w t l f c b n e c s s w w p w o p k s g
|
||||
p x y n t p f c n w e e s s w w p w o p n v u
|
||||
p x y w t p f c n n e e s s w w p w o p n v u
|
||||
e x y y t l f c b p e r s y w w p w o p n s g
|
||||
e f s g f n f w b k t e s s w w p w o e n a g
|
||||
e x y w t l f c b g e c s s w w p w o p n s g
|
||||
e x f n f n f c n k e e s s w w p w o p n y u
|
||||
e f y y t l f c b p e r s y w w p w o p n s g
|
||||
e f f n f n f c n g e e s s w w p w o p k v u
|
||||
e x f w f n f w b h t e f s w w p w o e k s g
|
||||
e f y n t l f c b n e r s y w w p w o p k y p
|
||||
e b s w t a f c b n e c s s w w p w o p n n g
|
||||
e b s w t a f c b k e c s s w w p w o p k s m
|
||||
e x f g f n f c n n e e s s w w p w o p n y u
|
||||
e x s w t a f c b n e c s s w w p w o p n n m
|
||||
e x f w t l f w n w t b s s w w p w o p n v d
|
||||
e b s w t a f c b k e c s s w w p w o p n s m
|
||||
e b y y t a f c b g e c s s w w p w o p k s m
|
||||
p x y w t p f c n p e e s s w w p w o p k s g
|
||||
e b y w t a f c b n e c s s w w p w o p k s g
|
||||
e f y n t a f c b p e r s y w w p w o p k y g
|
||||
e x f n t n f c b p t b s s p w p w o p k y d
|
||||
e x y n t l f c b n e r s y w w p w o p k y g
|
||||
e x f y t l f w n n t b s s w w p w o p n v d
|
||||
e x y y t a f c b p e r s y w w p w o p n y p
|
||||
e f y n t a f c b n e r s y w w p w o p n s p
|
||||
e x f y t a f w n p t b s s w w p w o p u v d
|
||||
e x s w t a f c b k e c s s w w p w o p k s m
|
||||
e x f g f n f c n k e e s s w w p w o p n v u
|
||||
e x f g f n f w b h t e f f w w p w o e n a g
|
||||
e b s y t l f c b w e c s s w w p w o p n s m
|
||||
e x y w t l f c b n e c s s w w p w o p k s g
|
||||
e x s w t a f w n n t b s s w w p w o p n v d
|
||||
e x s w f n f w b p t e f f w w p w o e k a g
|
||||
e x y n t l f c b p e r s y w w p w o p k s g
|
||||
e x y w t l f c b k e c s s w w p w o p n s m
|
||||
e x f y t l f w n p t b s s w w p w o p n v d
|
||||
e b s w t a f c b k e c s s w w p w o p n n g
|
||||
e b y w t a f c b w e c s s w w p w o p n n g
|
||||
e b y y t l f c b g e c s s w w p w o p k s g
|
||||
e f f y t a f w n n t b s s w w p w o p u v d
|
||||
p x s n t p f c n n e e s s w w p w o p n v g
|
||||
e x f n f n f c n k e e s s w w p w o p n v u
|
||||
e x y n t l f c b n e r s y w w p w o p k y p
|
||||
e b s y t l f c b w e c s s w w p w o p n s g
|
||||
e x f w f n f w b k t e f s w w p w o e k s g
|
||||
e x y y t a f c b g e c s s w w p w o p n n g
|
||||
p x s n t p f c n p e e s s w w p w o p k v u
|
||||
p x s n t p f c n n e e s s w w p w o p k s u
|
||||
e f y n t l f c b n e r s y w w p w o p n s p
|
||||
e f f g f n f c n n e e s s w w p w o p k v u
|
||||
e b s w t l f c b g e c s s w w p w o p k s g
|
||||
e f f g f n f c n p e e s s w w p w o p k y u
|
||||
p x s n t p f c n k e e s s w w p w o p n s g
|
||||
e f f n f n f w b p t e s s w w p w o e k a g
|
||||
e f f g f n f c n k e e s s w w p w o p n v u
|
||||
e x y w t a f c b n e c s s w w p w o p n n m
|
||||
e b s y t a f c b w e c s s w w p w o p k s g
|
||||
p x y n t p f c n p e e s s w w p w o p n s g
|
||||
e b y w t a f c b n e c s s w w p w o p n s g
|
||||
e b s y t a f c b w e c s s w w p w o p n s m
|
||||
e x y y t a f c b n e r s y w w p w o p k s p
|
||||
e b s w t a f c b n e c s s w w p w o p n s m
|
||||
e x f w t a f w n w t b s s w w p w o p n v d
|
||||
e x s w t l f c b g e c s s w w p w o p k s g
|
||||
e x s w f n f w b n t e f f w w p w o e n a g
|
||||
e f f g f n f w b h t e f s w w p w o e k a g
|
||||
e x y w t a f c b k e c s s w w p w o p k n g
|
||||
e b y y t l f c b n e c s s w w p w o p k n g
|
||||
e x s w f n f w b h t e f s w w p w o e k s g
|
||||
e x y y t a f c b w e c s s w w p w o p k s g
|
||||
e f s y t a f w n w t b s s w w p w o p u v d
|
||||
e b s w t l f c b k e c s s w w p w o p n s g
|
||||
e s f n f n f c n n e e s s w w p w o p k v u
|
||||
e x s w t l f c b k e c s s w w p w o p k n g
|
||||
e x f n t n f c b u t b s s g p p w o p n y d
|
||||
e b s y t a f c b g e c s s w w p w o p n s m
|
||||
e x s w f n f w b h t e f s w w p w o e n s g
|
||||
p f s n t p f c n n e e s s w w p w o p k s g
|
||||
e x s w f n f w b h t e s f w w p w o e k s g
|
||||
e x f g f n f c n k e e s s w w p w o p n y u
|
||||
e f f g f n f w b n t e s f w w p w o e n s g
|
||||
e f s w f n f w b p t e s f w w p w o e k s g
|
||||
e x y y t a f c b w e c s s w w p w o p k n m
|
||||
e f s g f n f w b n t e f s w w p w o e k a g
|
||||
e f f w f n f w b h t e f s w w p w o e k a g
|
||||
e f f n t n f c b n t b s s p w p w o p k v d
|
||||
e f f g f n f w b h t e s s w w p w o e k s g
|
||||
p f y w t p f c n p e e s s w w p w o p k v u
|
||||
e x f n f n f w b k t e s s w w p w o e n a g
|
||||
e x s w t l f c b k e c s s w w p w o p n s g
|
||||
e x f n t n f c b p t b s s p w p w o p n y d
|
||||
p x s n t p f c n p e e s s w w p w o p k s u
|
||||
e x f n t n f c b p t b s s g w p w o p k v d
|
||||
e f s g f n f w b k t e s f w w p w o e n s g
|
||||
e x y w t a f c b k e c s s w w p w o p k s g
|
||||
p f y w t p f c n n e e s s w w p w o p n v g
|
||||
p f y n t p f c n n e e s s w w p w o p k v g
|
||||
e f f y t l f w n n t b s s w w p w o p u v d
|
||||
e x f n f n f w b k t e f f w w p w o e n a g
|
||||
e f s w f n f w b p t e f f w w p w o e k a g
|
||||
e f f n f n f c n g e e s s w w p w o p n v u
|
||||
e x y n t l f c b p e r s y w w p w o p k y p
|
||||
e f s w f n f w b n t e f f w w p w o e n a g
|
||||
p f s n t p f c n p e e s s w w p w o p k v u
|
||||
e b s w t a f c b g e c s s w w p w o p k n g
|
||||
e b s w t a f c b w e c s s w w p w o p k n g
|
||||
p f y w t p f c n p e e s s w w p w o p k s u
|
||||
e x f w f n f w b k t e s s w w p w o e k s g
|
||||
p f y w t p f c n w e e s s w w p w o p n s g
|
||||
e f f g f n f w b k t e f s w w p w o e k a g
|
||||
e f f n f n f w b h t e s f w w p w o e k s g
|
||||
e f f g f n f w b p t e f s w w p w o e k a g
|
||||
e x s w f n f w b h t e s s w w p w o e k s g
|
||||
e x f n t n f c b n t b s s w w p w o p n y d
|
||||
e f f n f n f w b k t e f f w w p w o e n s g
|
||||
e f s g f n f w b k t e f f w w p w o e n a g
|
||||
e x f g f n f w b k t e s s w w p w o e k s g
|
||||
e x y y t a f c b n e c s s w w p w o p n n g
|
||||
e f f w f n f w b h t e s f w w p w o e k a g
|
||||
e x f n t n f c b p t b s s g w p w o p n y d
|
||||
e f f w f n f w b p t e f f w w p w o e n s g
|
||||
e x f w f n f w b h t e s f w w p w o e k s g
|
||||
p f s n t p f c n p e e s s w w p w o p k v g
|
||||
e f f n f n f w b k t e s s w w p w o e k s g
|
||||
e x f g f n f w b h t e s f w w p w o e k a g
|
||||
e x f n f n f w b k t e s f w w p w o e n s g
|
||||
e x s n f n f w b h t e f s w w p w o e k s g
|
||||
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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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p x y n f s f c n b t ? k s w w p w o e w v d
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p x f y f f f c b g e b k k p n p w o l h y d
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p x y n f s f c n b t ? k s w p p w o e w v p
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p x y n f f f c n b t ? k k w p p w o e w v p
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p x y n f f f c n b t ? k s w p p w o e w v d
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p f s b t f f c b h t b s f w w p w o p h s g
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p x y e f y f c n b t ? s k p p p w o e w v d
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p f y y f f f c b h e b k k p p p w o l h y g
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p x y n f s f c n b t ? k s p w p w o e w v d
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|
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p x y n f f f c n b t ? k s w w p w o e w v l
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p x s b t f f c b w t b s f w w p w o p h v u
|
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e x s p t n f c b e e ? s s e w p w t e w c w
|
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p x y g f f f c b h e b k k b b p w o l h v g
|
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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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p x y e f f f c n b t ? k k w w p w o e w v d
|
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||||
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|
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p x y e f s f c n b t ? k k p p p w o e w v l
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p x y e f s f c n b t ? k k p p p w o e w v p
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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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p x s e f f f c n b t ? k k p w p w o e w v p
|
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|
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p x s e f y f c n b t ? s s p w p w o e w v l
|
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p x y e f f f c n b t ? s k w p p w o e w v d
|
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p f s n f f f c n b t ? k k w w p w o e w v p
|
||||
p k s n f s f c n b t ? k k p p p w o e w v d
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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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p x y e f s f c n b t ? k s w w p w o e w v l
|
||||
p f y e f y f c n b t ? s s w w p w o e w v d
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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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p f y e f f f c n b t ? s s p p p w o e w v p
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p x s n f y f c n b t ? k k p w p w o e w v p
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p k s e f y f c n b t ? k s p p p w o e w v p
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p f y n f y f c n b t ? k k w w p w o e w v d
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p k y n f f f c n b t ? s k p w p w o e w v d
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p k s e f f f c n b t ? s k p p p w o e w v p
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p f y n f y f c n b t ? s s w p p w o e w v p
|
||||
p f y n f s f c n b t ? k s p w p w o e w v d
|
||||
p x y e f s f c n b t ? k k p p p w o e w v d
|
||||
p x y e f f f c n b t ? s k p w p w o e w v d
|
||||
p x s e f y f c n b t ? s k w p p w o e w v l
|
||||
p f y n f y f c n b t ? k s w w p w o e w v l
|
||||
p x y e f f f c n b t ? s s p w p w o e w v p
|
||||
p x s n f y f c n b t ? k s w w p w o e w v p
|
||||
p f y n f s f c n b t ? k k p w p w o e w v d
|
||||
p x s e f s f c n b t ? k s p p p w o e w v d
|
||||
p f y e f s f c n b t ? k s p p p w o e w v p
|
||||
p f s n f y f c n b t ? s k p w p w o e w v d
|
||||
p f y n f f f c n b t ? s k p w p w o e w v p
|
||||
p f s e f y f c n b t ? k k p p p w o e w v l
|
||||
p x s e f s f c n b t ? k s w p p w o e w v d
|
||||
p f s e f y f c n b t ? k s w w p w o e w v d
|
||||
p x s e f s f c n b t ? k s w w p w o e w v l
|
||||
e k f g f n f w b p e ? k s w w p w t p w s g
|
||||
p f y e f f f c n b t ? k k p w p w o e w v l
|
||||
p x s n f f f c n b t ? k k p w p w o e w v d
|
||||
p f s n f y f c n b t ? s s p w p w o e w v p
|
||||
p x s n f f f c n b t ? s s p w p w o e w v d
|
||||
p f s n f f f c n b t ? s s w w p w o e w v l
|
||||
p f s n f y f c n b t ? k s p p p w o e w v p
|
||||
p x y e f y f c n b t ? s k w w p w o e w v p
|
||||
p f s e f s f c n b t ? k s p p p w o e w v l
|
||||
p f s e f f f c n b t ? s k p w p w o e w v l
|
||||
p x s e f f f c n b t ? k s w p p w o e w v d
|
||||
p x s e f y f c n b t ? s k w w p w o e w v l
|
||||
p x s n f y f c n b t ? k s p w p w o e w v d
|
||||
p f y n f y f c n b t ? s s w w p w o e w v d
|
||||
p x s e f f f c n b t ? k s w w p w o e w v p
|
||||
p f s n f s f c n b t ? s s p w p w o e w v d
|
||||
p k y e f f f c n b t ? s s w w p w o e w v p
|
||||
e f s n f n a c b o e ? s s o o p n o p n c l
|
||||
p x s e f f f c n b t ? s k w p p w o e w v d
|
||||
p k y e f s f c n b t ? s s p p p w o e w v p
|
||||
p f y e f f f c n b t ? s s w w p w o e w v p
|
||||
p k y n f s f c n b t ? s s p p p w o e w v l
|
||||
p x s e f y f c n b t ? s k p w p w o e w v p
|
||||
p x s e f s f c n b t ? s s p w p w o e w v p
|
||||
p x s e f f f c n b t ? s s w w p w o e w v d
|
||||
e k f w f n f w b g e ? k k w w p w t p w n g
|
||||
p x s n f y f c n b t ? s k w w p w o e w v l
|
||||
p x y e f s f c n b t ? s k p w p w o e w v p
|
||||
p k s e f f f c n b t ? k k p p p w o e w v l
|
||||
p k s e f y f c n b t ? s s w p p w o e w v l
|
||||
e b s w f n f w b p e ? k s w w p w t p w n g
|
||||
p f s n f s f c n b t ? k k w w p w o e w v l
|
||||
p x s e f y f c n b t ? s s p w p w o e w v p
|
||||
p k s n f f f c n b t ? s k p p p w o e w v l
|
||||
p x s e f s f c n b t ? k k p p p w o e w v l
|
||||
p k y n f s f c n b t ? s s w w p w o e w v d
|
||||
e k y n f n f c b w e b y y n n p w t p w y d
|
||||
p f y n f y f c n b t ? s s w w p w o e w v p
|
||||
e k f g f n f w b w e ? s s w w p w t p w n g
|
||||
p x s e f s f c n b t ? k s w p p w o e w v p
|
||||
p k s n f s f c n b t ? s k w p p w o e w v p
|
||||
p k s e f y f c n b t ? s s w w p w o e w v d
|
||||
p k y n f f f c n b t ? k k p w p w o e w v d
|
||||
e k s w f n f w b w e ? k s w w p w t p w s g
|
||||
p k s e f s f c n b t ? k k p p p w o e w v l
|
||||
e k s g f n f w b p e ? k s w w p w t p w n g
|
||||
p k s e f s f c n b t ? k s p w p w o e w v d
|
||||
p k y n f f f c n b t ? k s w p p w o e w v l
|
||||
p x y e f f f c n b t ? s s p w p w o e w v d
|
||||
p k y c f m f c b y e c k y c c p w n n w c d
|
||||
p f y n f s f c n b t ? s k p w p w o e w v l
|
||||
e b s g f n f w b p e ? s k w w p w t p w s g
|
||||
p f s n f y f c n b t ? k s p p p w o e w v d
|
||||
e b s g f n f w b p e ? k k w w p w t p w s g
|
||||
p c y y f n f w n y e c y y y y p y o e w c l
|
||||
p k y e f f f c n b t ? k k p p p w o e w v l
|
||||
e x s g t n f c b w e b s s w w p w t p w v p
|
||||
e x f g f n f w b w e ? k s w w p w t p w s g
|
||||
p k y n f y f c n b t ? k k p p p w o e w v d
|
||||
p k y e f s f c n b t ? k k p p p w o e w v l
|
||||
p f s e f s f c n b t ? k s w p p w o e w v p
|
||||
p k s e f s f c n b t ? s s p p p w o e w v l
|
||||
e x f g f n f w b p e ? s k w w p w t p w s g
|
||||
p k y e f y f c n b t ? s s w w p w o e w v l
|
||||
p k s n f y f c n b t ? k k p p p w o e w v p
|
||||
p f y n f y f c n b t ? s s p p p w o e w v d
|
||||
p k s n f s f c n b t ? k k w p p w o e w v p
|
||||
p k s n f y f c n b t ? k s p p p w o e w v p
|
||||
p k y n f s f c n b t ? k k w p p w o e w v l
|
||||
e f s n f n a c b o e ? s s o o p o o p n c l
|
||||
p k s n f y f c n b t ? s s p p p w o e w v p
|
||||
e k s n f n f c b w e b y y n n p w t p w y p
|
||||
p k y n f f f c n b t ? s k p p p w o e w v l
|
||||
e k f w f n f w b g e ? s k w w p w t p w n g
|
||||
e b f w f n f w b g e ? k s w w p w t p w s g
|
||||
e b s w f n f w b w e ? s k w w p w t p w s g
|
||||
e k s w f n f w b p e ? s k w w p w t p w s g
|
||||
e x f g f n f w b p e ? k s w w p w t p w n g
|
||||
p k s e f y f c n b t ? k s w w p w o e w v d
|
||||
e b s w f n f w b p e ? s s w w p w t p w s g
|
||||
p k y e f s f c n b t ? k s p p p w o e w v p
|
||||
p x s n f y f c n b t ? k s w w p w o e w v d
|
||||
p k y n f s f c n b t ? k k w w p w o e w v p
|
||||
e k s n f n a c b o e ? s s o o p n o p o v l
|
||||
p k s e f s f c n b t ? k s w p p w o e w v p
|
||||
e k s n f n a c b y e ? s s o o p n o p n c l
|
||||
p k s n f y f c n b t ? s k p w p w o e w v d
|
||||
p f s n f s f c n b t ? s s p p p w o e w v l
|
||||
e x y n t n f c b w e b s s w w p w t p w v p
|
||||
e k s n f n a c b o e ? s s o o p n o p y c l
|
||||
e k f w f n f w b p e ? k s w w p w t p w n g
|
||||
e k f g f n f w b p e ? s s w w p w t p w s g
|
||||
p k s e f f f c n b t ? k k w p p w o e w v p
|
||||
e x s n f n a c b o e ? s s o o p o o p y v l
|
||||
p k s e f s f c n b t ? k s p w p w o e w v p
|
||||
e x s n f n a c b y e ? s s o o p o o p n v l
|
||||
p k y n f s f c n b t ? k k p w p w o e w v p
|
||||
p k s e f y f c n b t ? s s w w p w o e w v l
|
||||
p k s n f s f c n b t ? s s p p p w o e w v p
|
||||
e x s n f n a c b y e ? s s o o p o o p y v l
|
||||
p k y n f s f c n b t ? k k w p p w o e w v d
|
||||
e k s g f n f w b w e ? k k w w p w t p w n g
|
||||
e k s n f n a c b y e ? s s o o p o o p n c l
|
||||
e f s n f n a c b n e ? s s o o p o o p b v l
|
||||
e b s w f n f w b p e ? k k w w p w t p w n g
|
||||
p k s n f s f c n b t ? s s w p p w o e w v d
|
||||
p k y e f f f c n b t ? k s p w p w o e w v d
|
||||
e k s w f n f w b p e ? k k w w p w t p w n g
|
||||
e k s n f n a c b o e ? s s o o p o o p n c l
|
||||
p k s n f s f c n b t ? k k w w p w o e w v d
|
||||
p k y e f f f c n b t ? s k p w p w o e w v l
|
||||
p k y n f s f c n b t ? s k p w p w o e w v p
|
||||
p k s n f y f c n b t ? s k p w p w o e w v p
|
||||
p k s e f f f c n b t ? s s w w p w o e w v l
|
||||
e b s n f n a c b n e ? s s o o p o o p o v l
|
||||
e f s n f n a c b o e ? s s o o p o o p o v l
|
||||
p k s n f f f c n b t ? s k w w p w o e w v p
|
||||
e k s n f n a c b o e ? s s o o p n o p o c l
|
||||
e x y c t n f c b w e b s s w w p w t p w v p
|
||||
p k y e f s f c n b t ? s k p w p w o e w v p
|
||||
p k s n f y f c n b t ? s s p p p w o e w v d
|
||||
p k s e f y f c n b t ? s k p w p w o e w v d
|
||||
e k s n f n a c b n e ? s s o o p n o p n c l
|
||||
p k y e f f f c n b t ? k k w w p w o e w v p
|
||||
p k y e f f f c n b t ? s s w w p w o e w v l
|
||||
p k y e f y f c n b t ? s s p p p w o e w v p
|
||||
p x s n f y f c n b t ? k k w w p w o e w v d
|
||||
e f s n f n a c b o e ? s s o o p n o p b v l
|
||||
e k s n f n a c b y e ? s s o o p o o p n v l
|
||||
p k y e f y f c n b t ? k s p w p w o e w v l
|
||||
e x s n f n a c b y e ? s s o o p n o p b v l
|
||||
|
6465
lab/mushrooms-train.tsv
Normal file
6465
lab/mushrooms-train.tsv
Normal file
File diff suppressed because it is too large
Load Diff
172
lab/wzor.py
Normal file
172
lab/wzor.py
Normal file
@ -0,0 +1,172 @@
|
||||
#! /usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from seaborn import relplot
|
||||
|
||||
|
||||
def safeSigmoid(x, eps=0):
|
||||
y = 1.0/(1.0 + np.exp(-x))
|
||||
if eps > 0:
|
||||
y[y < eps] = eps
|
||||
y[y > 1 - eps] = 1 - eps
|
||||
return y
|
||||
|
||||
def h(theta, X, eps=0.0):
|
||||
return safeSigmoid(X*theta, eps)
|
||||
|
||||
def J(h, theta, X, y):
|
||||
m = len(y)
|
||||
h_val = h(theta, X)
|
||||
s1 = np.multiply(y, np.log(h_val))
|
||||
s2 = np.multiply((1 - y), np.log(1 - h_val))
|
||||
return -np.sum(s1 + s2, axis=0) / m
|
||||
|
||||
def dJ(h, theta, X, y):
|
||||
return 1.0 / y.shape[0] * (X.T * (h(theta, X) - y))
|
||||
|
||||
def GD(h, fJ, fdJ, theta, X, y, alpha=0.01, eps=10**-3, maxSteps=10000):
|
||||
errorCurr = fJ(h, theta, X, y)
|
||||
errors = [[errorCurr, theta]]
|
||||
while True:
|
||||
# oblicz nowe theta
|
||||
theta = theta - alpha * fdJ(h, theta, X, y)
|
||||
# raportuj poziom błędu
|
||||
errorCurr, errorPrev = fJ(h, theta, X, y), errorCurr
|
||||
# kryteria stopu
|
||||
if errorCurr > errorPrev:
|
||||
raise Exception('Zbyt duży krok!')
|
||||
if abs(errorPrev - errorCurr) <= eps:
|
||||
break
|
||||
if len(errors) > maxSteps:
|
||||
break
|
||||
errors.append([errorCurr, theta])
|
||||
return theta, errors
|
||||
|
||||
def classifyBi(h, theta, X):
|
||||
probs = h(theta, X)
|
||||
result = np.array(probs > 0.5, dtype=int)
|
||||
return result, probs
|
||||
|
||||
def plot_data_for_classification(X, Y, xlabel, ylabel):
|
||||
fig = plt.figure(figsize=(16*.6, 9*.6))
|
||||
ax = fig.add_subplot(111)
|
||||
fig.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9)
|
||||
X = X.tolist()
|
||||
Y = Y.tolist()
|
||||
X1n = [x[1] for x, y in zip(X, Y) if y[0] == 0]
|
||||
X1p = [x[1] for x, y in zip(X, Y) if y[0] == 1]
|
||||
X2n = [x[2] for x, y in zip(X, Y) if y[0] == 0]
|
||||
X2p = [x[2] for x, y in zip(X, Y) if y[0] == 1]
|
||||
ax.scatter(X1n, X2n, c='r', marker='x', s=50, label='Dane')
|
||||
ax.scatter(X1p, X2p, c='g', marker='o', s=50, label='Dane')
|
||||
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel(ylabel)
|
||||
ax.margins(.05, .05)
|
||||
return fig
|
||||
|
||||
def powerme(x1,x2,n):
|
||||
X = []
|
||||
for m in range(n+1):
|
||||
for i in range(m+1):
|
||||
X.append(np.multiply(np.power(x1,i),np.power(x2,(m-i))))
|
||||
return np.hstack(X)
|
||||
|
||||
def plot_decision_boundary(fig, h, theta, degree):
|
||||
ax = fig.axes[0]
|
||||
|
||||
xmin = 1860.0
|
||||
xmax = 2020.0
|
||||
xstep = 1.0
|
||||
xspan = int((xmax - xmin) / xstep)
|
||||
|
||||
ymin = 0.0
|
||||
ymax = 1200.0
|
||||
ystep = 1.0
|
||||
yspan = int((ymax - ymin) / ystep)
|
||||
|
||||
xx, yy = np.meshgrid(np.arange(xmin, xmax, xstep),
|
||||
np.arange(ymin, ymax, ystep))
|
||||
l = len(xx.ravel())
|
||||
C = powerme(yy.reshape(l, 1), xx.reshape(l, 1), degree)
|
||||
z = h(theta, C).reshape(yspan, xspan)
|
||||
# z = classifyBi(h, theta, C)[0].reshape(yspan, xspan)
|
||||
|
||||
# plt.contour(xx, yy, z, levels=[0.1,0.3,0.5,0.7,0.9], colors='m', lw=3)
|
||||
print(z)
|
||||
plt.contour(xx, yy, z)
|
||||
|
||||
data = pd.read_csv('wyk/mieszkania4.tsv', sep='\t')
|
||||
data['Czy blok'] = (data['Typ zabudowy'] == 'blok')
|
||||
print(data.columns)
|
||||
|
||||
data = data[
|
||||
(data['Powierzchnia w m2'] < 10000)
|
||||
& (data['cena'] < 10000000)
|
||||
]
|
||||
|
||||
relplot(data=data, x='Rok budowy', y='Powierzchnia w m2', hue='Czy blok')
|
||||
plt.show()
|
||||
|
||||
# X_columns = ['cena', 'Powierzchnia w m2', 'Rok budowy']
|
||||
X_columns = ['Rok budowy', 'Powierzchnia w m2']
|
||||
Y_column = ['Czy blok']
|
||||
|
||||
data = data[X_columns + Y_column].dropna()
|
||||
|
||||
m = len(data)
|
||||
n = len(X_columns)
|
||||
|
||||
X = np.matrix(np.concatenate((np.ones((m, 1)), data[X_columns].values), axis=1))
|
||||
Y = np.matrix(data[Y_column].values, dtype=int)
|
||||
|
||||
split_point = int(0.8 * m)
|
||||
|
||||
X_train = X[:split_point]
|
||||
X_test = X[split_point:]
|
||||
Y_train = Y[:split_point]
|
||||
Y_test = Y[split_point:]
|
||||
|
||||
thetaStartMx = np.ones((n + 1, 1))
|
||||
thetaBest, errors = GD(h, J, dJ, thetaStartMx, X_train, Y_train,
|
||||
alpha=0.1, eps=10**-7, maxSteps=10000)
|
||||
print(thetaBest)
|
||||
|
||||
Y_predicted, Y_probs = classifyBi(h, thetaBest, X_test)
|
||||
|
||||
print(Y_predicted.sum())
|
||||
print(Y_test.sum())
|
||||
|
||||
accuracy = np.array(Y_predicted == Y_test, dtype=int).sum() / Y_test.shape[0]
|
||||
print(accuracy)
|
||||
|
||||
fig = plot_data_for_classification(X, Y, xlabel=u'Rok budowy', ylabel=u'Powierzchnia w m2')
|
||||
plot_decision_boundary(fig, h, thetaBest, 1)
|
||||
plt.show()
|
||||
|
||||
|
||||
# More dimensions
|
||||
|
||||
dim = 6
|
||||
|
||||
X_train2 = powerme(X_train[:,1], X_train[:,2], dim)
|
||||
X_test2 = powerme(X_test[:,1], X_test[:,2], dim)
|
||||
thetaStart2 = np.ones((X_train2.shape[1], 1))
|
||||
thetaBest2, errors2 = GD(h, J, dJ, thetaStart2, X_train2, Y_train,
|
||||
alpha=0.1, eps=10**-7, maxSteps=100000)
|
||||
|
||||
print(thetaBest2)
|
||||
Y_predicted2, Y_probs2 = classifyBi(h, thetaBest2, X_test2)
|
||||
|
||||
print(Y_predicted2.sum())
|
||||
print(Y_test.sum())
|
||||
|
||||
accuracy2 = np.array(Y_predicted2 == Y_test, dtype=int).sum() / Y_test.shape[0]
|
||||
print(accuracy2)
|
||||
|
||||
fig2 = plot_data_for_classification(X, Y, xlabel=u'Rok budowy', ylabel=u'Powierzchnia w m2')
|
||||
plot_decision_boundary(fig2, h, thetaBest2, dim)
|
||||
plt.show()
|
||||
992
wyk/2001_Wprowadzenie.ipynb
Normal file
992
wyk/2001_Wprowadzenie.ipynb
Normal file
File diff suppressed because one or more lines are too long
42665
wyk/2002_Regresja_liniowa.ipynb
Normal file
42665
wyk/2002_Regresja_liniowa.ipynb
Normal file
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Load Diff
18795
wyk/2003_Regresja_logistyczna.ipynb
Normal file
18795
wyk/2003_Regresja_logistyczna.ipynb
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Load Diff
1274
wyk/2004_Metody_ewaluacji.ipynb
Normal file
1274
wyk/2004_Metody_ewaluacji.ipynb
Normal file
File diff suppressed because one or more lines are too long
1740
wyk/2005_Regresja_wielomianowa.ipynb
Normal file
1740
wyk/2005_Regresja_wielomianowa.ipynb
Normal file
File diff suppressed because one or more lines are too long
1450
wyk/2006_Naiwny_klasyfikator_bayesowski.ipynb
Normal file
1450
wyk/2006_Naiwny_klasyfikator_bayesowski.ipynb
Normal file
File diff suppressed because one or more lines are too long
784
wyk/2007_KNN.ipynb
Normal file
784
wyk/2007_KNN.ipynb
Normal file
File diff suppressed because one or more lines are too long
816
wyk/2007a_Reprezentacja_danych.ipynb
Normal file
816
wyk/2007a_Reprezentacja_danych.ipynb
Normal file
@ -0,0 +1,816 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Uczenie maszynowe UMZ 2019/2020\n",
|
||||
"### 28 kwietnia 2020\n",
|
||||
"# 7a. Reprezentacja danych"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Na tym wykładzie dowiemy się, w jaki sposób reprezentować różnego rodzaju dane tak, żeby można było używać ich do uczenia maszynowego."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Przydatne importy\n",
|
||||
"\n",
|
||||
"import ipywidgets as widgets\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas\n",
|
||||
"\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Plik *mieszkania4.tsv* zawiera dane wydobyte z serwisu *gratka.pl* dotyczące cen mieszkań w Poznaniu."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" cena Powierzchnia w m2 Liczba pokoi Garaż Liczba pięter w budynku \\\n",
|
||||
"0 290386 46 2 False 5.0 \n",
|
||||
"1 450000 59 2 False 3.0 \n",
|
||||
"2 375000 79 3 False 16.0 \n",
|
||||
"3 400000 63 3 True 2.0 \n",
|
||||
"4 389285 59 3 False 13.0 \n",
|
||||
"\n",
|
||||
" Piętro Typ zabudowy Materiał budynku Rok budowy \\\n",
|
||||
"0 parter apartamentowiec cegła 2017.0 \n",
|
||||
"1 2 kamienica cegła 1902.0 \n",
|
||||
"2 5 blok płyta 1990.0 \n",
|
||||
"3 2 blok cegła 2009.0 \n",
|
||||
"4 12 blok NaN NaN \n",
|
||||
"\n",
|
||||
" opis \n",
|
||||
"0 Polecam mieszkanie 2 pokojowe o metrażu 46,68 ... \n",
|
||||
"1 Ekskluzywna oferta - tylko u nas! Projekt arch... \n",
|
||||
"2 Polecam do kupna przestronne mieszkanie trzypo... \n",
|
||||
"3 Dla rodziny albo pod wynajem. Świetna lokaliza... \n",
|
||||
"4 NaN \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Wczytanie danych (mieszkania) przy pomocy biblioteki pandas\n",
|
||||
"\n",
|
||||
"alldata = pandas.read_csv(\n",
|
||||
" 'mieszkania4.tsv', header=0, sep='\\t',\n",
|
||||
" usecols=['cena', 'Powierzchnia w m2', 'Liczba pokoi', 'Garaż', 'Liczba pięter w budynku', 'Piętro', 'Typ zabudowy', 'Materiał budynku', 'Rok budowy', 'opis'])\n",
|
||||
"\n",
|
||||
"print(alldata[:5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Jak widać powyżej, w pliku *mieszkania4.tsv* znajdują się dane różnych typów:\n",
|
||||
"* dane numeryczne (po prostu liczby):\n",
|
||||
" * cena\n",
|
||||
" * powierzchnia w m<sup>2</sup>\n",
|
||||
" * liczba pokoi\n",
|
||||
"* dane częściowo numeryczne (liczby oraz wartości specjalne):\n",
|
||||
" * liczba pięter w budynku\n",
|
||||
" * piętro\n",
|
||||
" * rok budowy\n",
|
||||
"* dane boole'owskie (prawda/fałsz):\n",
|
||||
" * garaż\n",
|
||||
"* dane kategoryczne (wybór jednej z kilku kategorii):\n",
|
||||
" * typ zabudowy\n",
|
||||
"* dane tekstowe (dowolny tekst):\n",
|
||||
" * opis"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Algorytmy uczenia maszynowego działają na danych liczbowych. Z tego powodu musimy znaleźć właściwy sposób reprezentowania pozostałych danych."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dane numeryczne"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Dane numeryczne to takie, które są liczbami. W większości przypadków możemy na nich operować bezpośrednio. Przykładem takich danych jest kolumna *Powierzchnia w m2* z powyższego przykładu:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[ 46 59 79 63 90 66 32 38 68 43 185 64\n",
|
||||
" 165 71 73 51 70 48 42 33 203 88 41 31\n",
|
||||
" 45 62 60 295 53 84 170 56 47 228 44 67\n",
|
||||
" 49 37 87 36 55 57 118 65 30 28 230 54\n",
|
||||
" 52 95 50 26 171 282 77 40 150 300 39 145\n",
|
||||
" 370 140 225 29 61 135 27 270 177 85 92 132\n",
|
||||
" 75 200 74 219 220 96 235 20 153 318 104 58\n",
|
||||
" 72 117 189 81 111 35 280 141 195 120 250 97\n",
|
||||
" 154 114 76 287 34 180 160 176 148 98 217 86\n",
|
||||
" 260 198 78 183 80 163 82 100 156 320 89 103\n",
|
||||
" 159 125 340 149 175 237 110 182 186 106 233 197\n",
|
||||
" 136 162 157 240 211 83 196 69 102 91 108 130\n",
|
||||
" 510 143 1200 178 226 190 151 138 161 142 683 146\n",
|
||||
" 94 109 263 112 855 376 218 113 215 264 139 129\n",
|
||||
" 167 600 24 174 296 315 232 298 330 93 301 127\n",
|
||||
" 290 275 375 124 252 173 158 25 269 128 192 155\n",
|
||||
" 99 126 147 288 119 206 105 224 346 339 204 1100\n",
|
||||
" 392 243 101 18 202 205 107 199 137 134 144 216\n",
|
||||
" 172 239 116 364 121 23 267 369 11930 122 400 209\n",
|
||||
" 210 268 500 123 245 15 22 335 262 438 307 184\n",
|
||||
" 354 249 431 214 164 328 800 16 229 152 650 241\n",
|
||||
" 187 276 297 443 353 360 350 213 19 265]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(pandas.unique(alldata['Powierzchnia w m2']))\n",
|
||||
"\n",
|
||||
"# (funkcja `pandas.unique` służy do pomijania duplikatów wartości)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Czasami w danej kolumnie oprócz liczb występują również inne wartości. Przykładem takiej cechy może być *Piętro*:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['parter' '2' '5' '12' '1' '3' nan '8' '4' '16' '7' '6' 'poddasze' '9'\n",
|
||||
" '11' '13' '14' '10' '15' 'niski parter']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(pandas.unique(alldata['Piętro']))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Jak widać powyżej, tutaj oprócz liczb pojawiają się pewne tekstowe wartości specjalne, takie jak `parter`, `poddasze` czy `niski parter`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Takie wartości należy zamienić na liczby. Jak?\n",
|
||||
"* Wydaje się, że `parter` czy `niski parter` można z powodzeniem potraktować jako piętro „zerowe” i zamienić na `0`.\n",
|
||||
"* Z poddaszem sytuacja nie jest już tak oczywista. Czy mają Państwo jakieś propozycje?\n",
|
||||
" * Może zamienić `poddasze` na wartość NaN (zobacz poniżej)?\n",
|
||||
" * Może wykorzystać w tym celu wartość z sąsiedniej kolumny *Liczba pięter w budynku*?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Można w tym celu wykorzystać funkcje [apply](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html?highlight=apply#pandas.DataFrame.apply) i [to_numeric](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html) z biblioteki `pandas`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Przed zamianą:\n",
|
||||
"122 1\n",
|
||||
"123 2\n",
|
||||
"124 poddasze\n",
|
||||
"125 5\n",
|
||||
"126 parter\n",
|
||||
"127 3\n",
|
||||
"Name: Piętro, dtype: object\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('Przed zamianą:')\n",
|
||||
"print(alldata['Piętro'][122:128])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Po zamianie:\n",
|
||||
"122 1.0\n",
|
||||
"123 2.0\n",
|
||||
"124 NaN\n",
|
||||
"125 5.0\n",
|
||||
"126 0.0\n",
|
||||
"127 3.0\n",
|
||||
"Name: Piętro, dtype: float64\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Zamiana wartości 'parter' i 'niski parter' w kolumnie 'Piętro' na 0.\n",
|
||||
"alldata['Piętro'] = alldata['Piętro'].apply(lambda x: 0 if x in ['parter', 'niski parter'] else x)\n",
|
||||
"\n",
|
||||
"# Zamiana wszystkich wartości w kolumnie 'Piętro' na numeryczne.\n",
|
||||
"# Parametr errors='coerce' powoduje, że napotkane nieliczbowe wartości będą zamieniane na NaN.\n",
|
||||
"alldata['Piętro'] = alldata['Piętro'].apply(pandas.to_numeric, errors='coerce')\n",
|
||||
"\n",
|
||||
"print()\n",
|
||||
"print('Po zamianie:')\n",
|
||||
"print(alldata['Piętro'][122:128])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Wartości NaN"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Wartość NaN (zob. też na [Wikipedii](https://pl.wikipedia.org/wiki/NaN)) – to wartość numeryczna oznaczająca „nie-liczbę”, „wartość niezdefiniowaną”, np. niezdefiniowany wynik działania lub brak danych:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"nan\n",
|
||||
"nan\n",
|
||||
"nan\n",
|
||||
"nan\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-20-d4d317707dde>:1: RuntimeWarning: invalid value encountered in sqrt\n",
|
||||
" print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n",
|
||||
"\n",
|
||||
"print(alldata['Piętro'][14]) # brak danych na temat piętra w rekordzie 14.\n",
|
||||
"\n",
|
||||
"# Jak uzyskać wartość NaN?\n",
|
||||
"print(float('NaN'))\n",
|
||||
"print(np.nan)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Co można zrobić z wartością NaN?\n",
|
||||
"* Czasami można wartość NaN zamienić na `0`, np. być może w kolumnie „przychód” wartość NaN oznacza brak przychodu. Należy jednak być z tym ostrożnym. **W większości przypadków wstawienie 0 zamiast NaN będzie niepoprawne**, np. „rok 0” to nie to samo co „rok nieznany”. Nawet w kolumnie „cena” wartość NaN raczej oznacza, że cena jest nieznana, a to przecież nie to samo, co „cena równa 0 zł”.\n",
|
||||
"* **Najbezpieczniej jest usunąć cały rekord (wiersz), który zawiera jakąkolwiek wartość NaN**. Należy przy tym pamiętać, że pozbywamy się w ten sposób (być może wartościowych) danych. Jest to istotne zwłaszcza wtedy, gdy nasze dane zawierają dużo wartości niezdefiniowanych.\n",
|
||||
"* Wartość NaN można też zamienić na średnią, medianę, modę itp. z pozostałych wartości w zbiorze danych. To dobra opcja, jeżeli usunięcie całych wierszy zawierających NaN pozbawiłoby nas zbyt wielu rekordów.\n",
|
||||
"* Można użyć też bardziej zaawansowanych technik, np. [MICE](https://stats.stackexchange.com/questions/421545/multiple-imputation-by-chained-equations-mice-explained) czy KNN."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Przydatne artykuły na temat usuwania wartości niezdefiniowanych ze zbioru danych:\n",
|
||||
"* [Working with missing data in machine learning](https://towardsdatascience.com/working-with-missing-data-in-machine-learning-9c0a430df4ce)\n",
|
||||
"* [What’s the best way to handle NaN values?](https://towardsdatascience.com/whats-the-best-way-to-handle-nan-values-62d50f738fc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Biblioteka `pandas` dostarcza narzędzi do automatycznego usuwania wartości NaN: [dropna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Liczba rekordów przed usunięciem NaN: 4938\n",
|
||||
"Liczba rekordów po usunięciu NaN: 888\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('Liczba rekordów przed usunięciem NaN:', len(alldata))\n",
|
||||
"\n",
|
||||
"alldata = alldata.dropna() # usunięcie rekordów zawierających NaN\n",
|
||||
"\n",
|
||||
"print('Liczba rekordów po usunięciu NaN:', len(alldata))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dane boole'owskie"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"W przypadku danych typu prawda/fałsz, wystarczy zamienić wartości `True` na `1`, a `False` na `0`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Przed zamianą:\n",
|
||||
"0 False\n",
|
||||
"1 False\n",
|
||||
"2 False\n",
|
||||
"3 True\n",
|
||||
"13 False\n",
|
||||
" ... \n",
|
||||
"4909 False\n",
|
||||
"4917 True\n",
|
||||
"4918 False\n",
|
||||
"4920 False\n",
|
||||
"4937 False\n",
|
||||
"Name: Garaż, Length: 888, dtype: bool\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('Przed zamianą:')\n",
|
||||
"print(alldata['Garaż'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"Po zamianie:\n",
|
||||
"0 0\n",
|
||||
"1 0\n",
|
||||
"2 0\n",
|
||||
"3 1\n",
|
||||
"13 0\n",
|
||||
" ..\n",
|
||||
"4909 0\n",
|
||||
"4917 1\n",
|
||||
"4918 0\n",
|
||||
"4920 0\n",
|
||||
"4937 0\n",
|
||||
"Name: Garaż, Length: 888, dtype: int64\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"alldata['Garaż'] = alldata['Garaż'].apply(lambda x: 1 if x == True else 0)\n",
|
||||
"\n",
|
||||
"print()\n",
|
||||
"print('Po zamianie:')\n",
|
||||
"print(alldata['Garaż'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dane kategoryczne"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"O danych kategorycznych mówimy, jeżeli dane mogą przyjmować wartości ze skończonej listy („kategorii”), np.:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['apartamentowiec', 'kamienica', 'blok', 'dom wielorodzinny/szeregowiec', 'plomba']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# \"Typ zabudowy\" może przyjmować jedną z następujących wartości:\n",
|
||||
"\n",
|
||||
"typ_zabudowy_values = list(pandas.unique(alldata['Typ zabudowy']))\n",
|
||||
"\n",
|
||||
"print(typ_zabudowy_values)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"['cegła', 'płyta', 'inne', 'pustak', 'silikat', 'beton']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# \"Materiał budynku\" może przyjmować jedną z następujących wartości:\n",
|
||||
"\n",
|
||||
"material_budynku_values = list(pandas.unique(alldata['Materiał budynku']))\n",
|
||||
"\n",
|
||||
"print(material_budynku_values)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Cechę kategoryczną można rozbić na skończoną liczbę cech boole'owskich:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Skopiujmy dane, żeby przedstawić 2 alternatywne rozwiązania\n",
|
||||
"\n",
|
||||
"alldata_1 = alldata.copy()\n",
|
||||
"alldata_2 = alldata.copy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Nowo utworzone kolumny (cechy boole'owskie):\n",
|
||||
"['Czy apartamentowiec?', 'Czy kamienica?', 'Czy blok?', 'Czy dom wielorodzinny/szeregowiec?', 'Czy plomba?']\n",
|
||||
"\n",
|
||||
" Typ zabudowy Czy apartamentowiec? Czy kamienica? \\\n",
|
||||
"0 apartamentowiec True False \n",
|
||||
"1 kamienica False True \n",
|
||||
"2 blok False False \n",
|
||||
"3 blok False False \n",
|
||||
"13 blok False False \n",
|
||||
"... ... ... ... \n",
|
||||
"4909 apartamentowiec True False \n",
|
||||
"4917 dom wielorodzinny/szeregowiec False False \n",
|
||||
"4918 blok False False \n",
|
||||
"4920 dom wielorodzinny/szeregowiec False False \n",
|
||||
"4937 dom wielorodzinny/szeregowiec False False \n",
|
||||
"\n",
|
||||
" Czy blok? Czy dom wielorodzinny/szeregowiec? Czy plomba? \n",
|
||||
"0 False False False \n",
|
||||
"1 False False False \n",
|
||||
"2 True False False \n",
|
||||
"3 True False False \n",
|
||||
"13 True False False \n",
|
||||
"... ... ... ... \n",
|
||||
"4909 False False False \n",
|
||||
"4917 False True False \n",
|
||||
"4918 True False False \n",
|
||||
"4920 False True False \n",
|
||||
"4937 False True False \n",
|
||||
"\n",
|
||||
"[888 rows x 6 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Rozwiązanie 1\n",
|
||||
"\n",
|
||||
"select_column_names = []\n",
|
||||
"for typ_zabudowy in typ_zabudowy_values:\n",
|
||||
" new_column_name = 'Czy {}?'.format(typ_zabudowy)\n",
|
||||
" alldata_1[new_column_name] = (alldata_1['Typ zabudowy'] == typ_zabudowy)\n",
|
||||
" select_column_names.append(new_column_name)\n",
|
||||
"\n",
|
||||
"print(\"Nowo utworzone kolumny (cechy boole'owskie):\")\n",
|
||||
"print(select_column_names)\n",
|
||||
"\n",
|
||||
"select_column_names = ['Typ zabudowy'] + select_column_names\n",
|
||||
"\n",
|
||||
"print()\n",
|
||||
"\n",
|
||||
"print(alldata_1[select_column_names])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Nie trzeba tego robić ręcznie. Można do tego celu użyć funkcji [get_dummies](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html) z biblioteki `pandas`:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" cena Powierzchnia w m2 Liczba pokoi Garaż Liczba pięter w budynku \\\n",
|
||||
"0 290386 46 2 0 5.0 \n",
|
||||
"1 450000 59 2 0 3.0 \n",
|
||||
"2 375000 79 3 0 16.0 \n",
|
||||
"3 400000 63 3 1 2.0 \n",
|
||||
"13 450000 64 3 0 4.0 \n",
|
||||
"... ... ... ... ... ... \n",
|
||||
"4909 141300 46 2 0 1.0 \n",
|
||||
"4917 710000 120 4 1 1.0 \n",
|
||||
"4918 858000 120 5 0 3.0 \n",
|
||||
"4920 399000 69 3 0 2.0 \n",
|
||||
"4937 127900 36 2 0 2.0 \n",
|
||||
"\n",
|
||||
" Piętro Rok budowy opis \\\n",
|
||||
"0 0.0 2017.0 Polecam mieszkanie 2 pokojowe o metrażu 46,68 ... \n",
|
||||
"1 2.0 1902.0 Ekskluzywna oferta - tylko u nas! Projekt arch... \n",
|
||||
"2 5.0 1990.0 Polecam do kupna przestronne mieszkanie trzypo... \n",
|
||||
"3 2.0 2009.0 Dla rodziny albo pod wynajem. Świetna lokaliza... \n",
|
||||
"13 2.0 1992.0 Witam,Mam na imię Jędrzej i w biurze Platan po... \n",
|
||||
"... ... ... ... \n",
|
||||
"4909 0.0 2014.0 !!! Apartamenty w Lusówku oddawane w stanie de... \n",
|
||||
"4917 0.0 2013.0 Sprzedam lokal mieszkalny odrębna własność w b... \n",
|
||||
"4918 3.0 1993.0 Polecam do kupna mieszkanie 5 pokojowe o pow. ... \n",
|
||||
"4920 1.0 2008.0 Przestronne mieszkanie z pięknym widokiem!Dwup... \n",
|
||||
"4937 2.0 2018.0 Sprzedaż nowego mieszkania w FAŁKOWIE - Osiedl... \n",
|
||||
"\n",
|
||||
" Typ zabudowy_apartamentowiec Typ zabudowy_blok \\\n",
|
||||
"0 1 0 \n",
|
||||
"1 0 0 \n",
|
||||
"2 0 1 \n",
|
||||
"3 0 1 \n",
|
||||
"13 0 1 \n",
|
||||
"... ... ... \n",
|
||||
"4909 1 0 \n",
|
||||
"4917 0 0 \n",
|
||||
"4918 0 1 \n",
|
||||
"4920 0 0 \n",
|
||||
"4937 0 0 \n",
|
||||
"\n",
|
||||
" Typ zabudowy_dom wielorodzinny/szeregowiec Typ zabudowy_kamienica \\\n",
|
||||
"0 0 0 \n",
|
||||
"1 0 1 \n",
|
||||
"2 0 0 \n",
|
||||
"3 0 0 \n",
|
||||
"13 0 0 \n",
|
||||
"... ... ... \n",
|
||||
"4909 0 0 \n",
|
||||
"4917 1 0 \n",
|
||||
"4918 0 0 \n",
|
||||
"4920 1 0 \n",
|
||||
"4937 1 0 \n",
|
||||
"\n",
|
||||
" Typ zabudowy_plomba Materiał budynku_beton Materiał budynku_cegła \\\n",
|
||||
"0 0 0 1 \n",
|
||||
"1 0 0 1 \n",
|
||||
"2 0 0 0 \n",
|
||||
"3 0 0 1 \n",
|
||||
"13 0 0 1 \n",
|
||||
"... ... ... ... \n",
|
||||
"4909 0 0 1 \n",
|
||||
"4917 0 0 1 \n",
|
||||
"4918 0 0 1 \n",
|
||||
"4920 0 0 1 \n",
|
||||
"4937 0 0 1 \n",
|
||||
"\n",
|
||||
" Materiał budynku_inne Materiał budynku_pustak Materiał budynku_płyta \\\n",
|
||||
"0 0 0 0 \n",
|
||||
"1 0 0 0 \n",
|
||||
"2 0 0 1 \n",
|
||||
"3 0 0 0 \n",
|
||||
"13 0 0 0 \n",
|
||||
"... ... ... ... \n",
|
||||
"4909 0 0 0 \n",
|
||||
"4917 0 0 0 \n",
|
||||
"4918 0 0 0 \n",
|
||||
"4920 0 0 0 \n",
|
||||
"4937 0 0 0 \n",
|
||||
"\n",
|
||||
" Materiał budynku_silikat \n",
|
||||
"0 0 \n",
|
||||
"1 0 \n",
|
||||
"2 0 \n",
|
||||
"3 0 \n",
|
||||
"13 0 \n",
|
||||
"... ... \n",
|
||||
"4909 0 \n",
|
||||
"4917 0 \n",
|
||||
"4918 0 \n",
|
||||
"4920 0 \n",
|
||||
"4937 0 \n",
|
||||
"\n",
|
||||
"[888 rows x 19 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"alldata_2 = pandas.get_dummies(alldata_2, columns=['Typ zabudowy', 'Materiał budynku'])\n",
|
||||
"\n",
|
||||
"print(alldata_2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Zwróćmy uwagę, że dzięki użyciu `get_dummies` nowe kolumny zostały utworzone i nazwane automatycznie, nie trzeba też już ręcznie konwertować wartości boole'owskich do numerycznych."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Funkcja `get_dummies` do określenia, na ile i jakich kolumn podzielić daną kolumnę kategoryczną, używa bieżącej zawartości tabeli. Dlatego należy jej użyć przed dokonaniem podziału na zbiory uczący i testowy.\n",
|
||||
"\n",
|
||||
"Więcej na ten temat można przeczytać w artykule [How to use pandas.get_dummies with the test set](http://fastml.com/how-to-use-pd-dot-get-dummies-with-the-test-set)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dane tekstowe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Przetwarzanie danych tekstowych to szeroki temat, którym można zapełnić cały wykład. Dlatego tutaj przedstawię tylko najważniejsze metody."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Możemy na przykład tworzyć cechy sprawdzające występowanie poszczególnych wyrazów lub ciągów znaków w tekście:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" nowe_w_opisie opis\n",
|
||||
"0 True Polecam mieszkanie 2 pokojowe o metrażu 46,68 ...\n",
|
||||
"1 False Ekskluzywna oferta - tylko u nas! Projekt arch...\n",
|
||||
"2 False Polecam do kupna przestronne mieszkanie trzypo...\n",
|
||||
"3 False Dla rodziny albo pod wynajem. Świetna lokaliza...\n",
|
||||
"13 False Witam,Mam na imię Jędrzej i w biurze Platan po...\n",
|
||||
"... ... ...\n",
|
||||
"4920 True Przestronne mieszkanie z pięknym widokiem!Dwup...\n",
|
||||
"4925 True BEZ 2% PCC, BEZ PROWIZJI. Nowe mieszkanie 48,6...\n",
|
||||
"4928 True Polecam do sprzedaży słoneczne mieszkanie dwup...\n",
|
||||
"4934 False OKAZJA!! LUKSUSOWY APARTAMENT W SĄSIEDZTWIE PA...\n",
|
||||
"4937 True Sprzedaż nowego mieszkania w FAŁKOWIE - Osiedl...\n",
|
||||
"\n",
|
||||
"[1333 rows x 2 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"alldata['nowe_w_opisie'] = alldata['opis'].apply(lambda x: True if 'nowe' in x.lower() else False)\n",
|
||||
"print(alldata[['nowe_w_opisie', 'opis']])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Można też zamienić tekst na wektory używając algorytmów TF–IDF, Word2Vec lub podobnych."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Ciekawy artykuł na temat przygotowywania danych tekstowych do uczenia maszynowego można znaleźć na przykład tutaj: https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Slideshow",
|
||||
"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": "3.8.3"
|
||||
},
|
||||
"livereveal": {
|
||||
"start_slideshow_at": "selected",
|
||||
"theme": "amu"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
808
wyk/2008_Uczenie_nienadzorowane.ipynb
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808
wyk/2008_Uczenie_nienadzorowane.ipynb
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951
wyk/2009_Sieci_neuronowe.ipynb
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wyk/2009_Sieci_neuronowe.ipynb
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wyk/2010_Propagacja_wsteczna.ipynb
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1073
wyk/2010_Propagacja_wsteczna.ipynb
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Load Diff
1772
wyk/2011_Wielowarstwowe_sieci_neuronowe.ipynb
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1772
wyk/2011_Wielowarstwowe_sieci_neuronowe.ipynb
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254
wyk/2012_RNN.ipynb
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254
wyk/2012_RNN.ipynb
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wyk/2013_CNN.ipynb
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851
wyk/2013_CNN.ipynb
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411
wyk/2014_Autoencoder.ipynb
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411
wyk/2014_Autoencoder.ipynb
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@ -0,0 +1,411 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Uczenie maszynowe UMZ 2019/2020\n",
|
||||
"### 16 czerwca 2020\n",
|
||||
"# 14. Autoencoder. Tłumaczenie neuronowe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## 14.1. Autoencoder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"* Uczenie nienadzorowane\n",
|
||||
"* Dane: zbiór nieanotowanych przykładów uczących $\\{ x^{(1)}, x^{(2)}, x^{(3)}, \\ldots \\}$, $x^{(i)} \\in \\mathbb{R}^{n}$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Autoencoder (encoder-decoder)\n",
|
||||
"\n",
|
||||
"Sieć neuronowa taka, że:\n",
|
||||
"* warstwa wejściowa ma $n$ neuronów\n",
|
||||
"* warstwa wyjściowa ma $n$ neuronów\n",
|
||||
"* warstwa środkowa ma $k < n$ neuronów\n",
|
||||
"* $y^{(i)} = x^{(i)}$ dla każdego $i$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"60%\" src=\"http://ufldl.stanford.edu/tutorial/images/Autoencoder636.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Co otrzymujemy dzięki takiej sieci?\n",
|
||||
"\n",
|
||||
"* $y^{(i)} = x^{(i)} \\; \\Longrightarrow \\;$ Autoencoder próbuje nauczyć się funkcji $h(x) \\approx x$, czyli funkcji identycznościowej.\n",
|
||||
"* Warstwy środkowe mają mniej neuronów niż warstwy zewnętrzne, więc żeby to osiągnąć, sieć musi znaleźć bardziej kompaktową (tu: $k$-wymiarową) reprezentację informacji zawartej w wektorach $x_{(i)}$.\n",
|
||||
"* Otrzymujemy metodę kompresji danych."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Innymi słowy:\n",
|
||||
"* Ograniczenia nałożone na reprezentację danych w warstwie ukrytej pozwala na „odkrycie” pewnej **struktury** w danych.\n",
|
||||
"* _Decoder_ musi odtworzyć do pierwotnej postaci reprezentację danych skompresowaną przez _encoder_."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"70%\" src=\"https://upload.wikimedia.org/wikipedia/commons/2/28/Autoencoder_structure.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"70%\" src=\"autoencoder_schema.jpg\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"* Całkowita liczba warstw w sieci autoencodera może być większa niż 3.\n",
|
||||
"* Jako funkcji kosztu na ogół używa się błędu średniokwadratowego (_mean squared error_, MSE) lub entropii krzyżowej (_binary crossentropy_).\n",
|
||||
"* Autoencoder może wykryć ciekawe struktury w danych nawet jeżeli $k \\geq n$, jeżeli na sieć nałoży się inne ograniczenia.\n",
|
||||
"* W wyniku działania autoencodera uzyskujemy na ogół kompresję **stratną**."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Autoencoder a PCA\n",
|
||||
"\n",
|
||||
"Widzimy, że autoencoder można wykorzystać do redukcji liczby wymiarów. Podobną rolę pełni poznany na jednym z poprzednich wykładów algorytm PCA (analiza głównych składowych, _principal component analysis_). Faktycznie, jeżeli zastosujemy autoencoder z liniowymi funkcjami aktywacji i pojedynczą sigmoidalną warstwą ukrytą, to na podstawie uzyskanych wag można odtworzyć główne składowe używając rozkładu według wartości osobliwych (_singular value decomposition_, SVD)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Autoencoder odszumiający\n",
|
||||
"\n",
|
||||
"Jeżeli na wejściu zamiast „czystych” danych użyjemy danych zaszumionych, to otrzymamy sieć, która może usuwać szum z danych:\n",
|
||||
"\n",
|
||||
"<img style=\"margin: auto\" width=\"70%\" src=\"denoising_autoencoder.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"70%\" src=\"denoising.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Autoencoder – zastosowania\n",
|
||||
"\n",
|
||||
"Autoencoder sprawdza się gorzej niż inne algorytmy kompresji, więc nie stosuje się go raczej jako metody kompresji danych, ale ma inne zastosowania:\n",
|
||||
"* odszumianie danych\n",
|
||||
"* redukcja wymiarowości\n",
|
||||
"* VAE (_variational autoencoders_) – http://kvfrans.com/variational-autoencoders-explained/"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## 14.2. Word embeddings"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"_Word embeddings_ – sposoby reprezentacji słów jako wektorów liczbowych"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Znaczenie wyrazu jest reprezentowane przez sąsiednie wyrazy:\n",
|
||||
"\n",
|
||||
"“A word is characterized by the company it keeps.” (John R. Firth, 1957)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"* Pomysł pojawił sie jeszcze w latach 60. XX w.\n",
|
||||
"* _Word embeddings_ można uzyskiwać na różne sposoby, ale dopiero w ostatnim dziesięcioleciu stało się opłacalne użycie w tym celu sieci neuronowych."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Przykład – 2 zdania: \n",
|
||||
"* \"have a good day\"\n",
|
||||
"* \"have a great day\"\n",
|
||||
"\n",
|
||||
"Słownik:\n",
|
||||
"* {\"a\", \"day\", \"good\", \"great\", \"have\"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"* Aby wykorzystać metody uczenia maszynowego do analizy danych tekstowych, musimy je jakoś reprezentować jako liczby.\n",
|
||||
"* Najprostsza metoda to wektory jednostkowe:\n",
|
||||
" * \"a\" = $(1, 0, 0, 0, 0)$\n",
|
||||
" * \"day\" = $(0, 1, 0, 0, 0)$\n",
|
||||
" * \"good\" = $(0, 0, 1, 0, 0)$\n",
|
||||
" * \"great\" = $(0, 0, 0, 1, 0)$\n",
|
||||
" * \"have\" = $(0, 0, 0, 0, 1)$\n",
|
||||
"* Taka metoda nie uwzględnia jednak podobieństw i różnic między znaczeniami wyrazów."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Metody uzyskiwania _word embeddings_:\n",
|
||||
"* Common Bag of Words (CBOW)\n",
|
||||
"* Skip Gram\n",
|
||||
"\n",
|
||||
"Obie opierają się na odpowiednim użyciu autoencodera."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"90%\" src=\"we_autoencoder.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Common Bag of Words\n",
|
||||
"\n",
|
||||
"<img style=\"margin: auto\" width=\"60%\" src=\"cbow.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Skip Gram\n",
|
||||
"\n",
|
||||
"<img style=\"margin: auto\" width=\"50%\" src=\"skipgram.png\" />"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Skip Gram a CBOW\n",
|
||||
"\n",
|
||||
"* Skip Gram lepiej reprezentuje rzadkie wyrazy i lepiej działa, jeżeli mamy mało danych.\n",
|
||||
"* CBOW jest szybszy i lepiej reprezentuje częste wyrazy."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"### Popularne modele _word embeddings_\n",
|
||||
"* Word2Vec (Google)\n",
|
||||
"* GloVe (Stanford)\n",
|
||||
"* FastText (Facebook)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "slide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## 14.3. Tłumaczenie neuronowe\n",
|
||||
"\n",
|
||||
"_Neural Machine Translation_ (NMT)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"Neuronowe tłumaczenie maszynowe również opiera się na modelu _encoder-decoder_:\n",
|
||||
"* _Encoder_ koduje z języka źródłowego na abstrakcyjną reprezentację.\n",
|
||||
"* _Decoder_ odkodowuje z abstrakcyjnej reprezentacji na język docelowy."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"slideshow": {
|
||||
"slide_type": "subslide"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"<img style=\"margin: auto\" width=\"70%\" src=\"http://devblogs.nvidia.com/parallelforall/wp-content/uploads/2015/06/Figure2_NMT_system.png\"/>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Slideshow",
|
||||
"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": "3.8.3"
|
||||
},
|
||||
"livereveal": {
|
||||
"start_slideshow_at": "selected",
|
||||
"theme": "amu"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
417
wyk/2015_Uczenie_przez_wzmacnianie.ipynb
Normal file
417
wyk/2015_Uczenie_przez_wzmacnianie.ipynb
Normal file
File diff suppressed because one or more lines are too long
41
wyk/Untitled.ipynb
Normal file
41
wyk/Untitled.ipynb
Normal file
@ -0,0 +1,41 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
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|
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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
wyk/data02.tsv
Normal file
1675
wyk/data02.tsv
Normal file
File diff suppressed because it is too large
Load Diff
335
wyk/data02_test.tsv
Normal file
335
wyk/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
|
||||
252723.0 False 2 1 Winogrady 43
|
||||
285200.0 False 2 1 Winogrady 46
|
||||
249641.0 False 2 0 Ogrody 54
|
||||
302887.0 False 2 4 Górczyn 25
|
||||
242731.0 False 2 0 Podolany 74
|
||||
991380.0 False 5 1 Starołęka 94
|
||||
265000.0 True 2 2 Piątkowo 70
|
||||
344000.0 True 2 0 Stare 49
|
||||
313000.0 True 2 4 Stare 49
|
||||
315000.0 True 3 0 Stare 60
|
||||
315000.0 True 3 0 Rataje 63
|
||||
690000.0 True 2 1 Stare 20
|
||||
465000.0 True 2 2 Wilda 49
|
||||
368110.0 True 3 2 Nowe 64
|
||||
282994.0 True 2 0 Nowe 55
|
||||
350000.0 True 2 3 Rataje 52
|
||||
780000.0 True 3 0 Grunwald 87
|
||||
263000.0 True 2 0 Rataje 38
|
||||
219000.0 True 2 7 Winogrady 38
|
||||
250000.0 True 3 3 Rataje 53
|
||||
394000.0 True 4 0 Nowe 88
|
||||
535000.0 True 2 2 Centrum 55
|
||||
329000.0 True 3 13 Rataje 65
|
||||
192750.0 True 2 2 Nowe 55
|
||||
375408.0 True 3 0 Nowe 88
|
||||
649400.0 True 3 4 Nowe 50
|
||||
294906.0 True 2 3 Nowe 74
|
||||
291720.0 True 2 5 Stare 20
|
||||
290646.0 True 2 2 Stare 80
|
||||
285678.0 False 2 1 Winogrady 42
|
||||
156085.0 False 2 4 Nowe 54
|
||||
496573.0 False 3 2 Stare 78
|
||||
1156054.0 False 4 0 Stare 52
|
||||
380931.0 False 3 3 Starołęka 82
|
||||
285200.0 True 2 0 Stare 46
|
||||
434201.0 True 4 1 Nowe 54
|
||||
269984.0 True 3 0 Stare 76
|
||||
333024.0 True 2 1 Stare 45
|
||||
637037.0 True 4 3 Stare 79
|
||||
319060.0 True 3 3 Grunwald 56
|
||||
267700.0 True 2 2 Grunwald 45
|
||||
365690.0 True 4 1 Winogrady 63
|
||||
285678.0 True 2 4 Winogrady 48
|
||||
295363.0 True 3 1 Stare 48
|
||||
223750.0 True 2 0 Stare 37
|
||||
277823.0 True 2 1 Jeżyce 50
|
||||
333695.0 True 3 2 Jeżyce 68
|
||||
288000.0 True 2 4 Naramowice 22
|
||||
518000.0 True 4 0 Wilda 41
|
||||
235000.0 True 2 4 Dębiec 44
|
||||
364762.0 True 4 1 Winogrady 62
|
||||
320000.0 True 4 3 Stare 40
|
||||
449000.0 True 5 3 Wilda 97
|
||||
249000.0 True 2 10 Grunwald 42
|
||||
279000.0 True 2 3 Piątkowo 30
|
||||
289000.0 True 3 4 Winogrady 48
|
||||
318645.0 True 2 2 Stare 30
|
||||
289000.0 True 2 7 Jeżyce 80
|
||||
285000.0 True 2 2 Łazarz 20
|
||||
399000.0 True 3 4 Piątkowo 69
|
||||
350000.0 True 3 1 Rataje 63
|
||||
240000.0 True 2 2 Dębiec 48
|
||||
356160.0 False 4 4 Winogrady 36
|
||||
290970.0 False 2 0 Winogrady 52
|
||||
289000.0 True 3 4 Stare 47
|
||||
399000.0 True 3 4 Piątkowo 69
|
||||
315000.0 True 3 0 Piatkowo 50
|
||||
329000.0 True 2 1 Grunwald 66
|
||||
253000.0 True 2 4 Grunwald 70
|
||||
179000.0 True 2 0 Grunwald 34
|
||||
250000.0 True 2 0 Naramowice 50
|
||||
410000.0 True 2 4 Jeżyce 43
|
||||
360000.0 True 3 3 Jeżyce 62
|
||||
590000.0 True 3 3 Jeżyce 80
|
||||
325000.0 True 3 2 Jeżyce 64
|
||||
247705.0 False 2 1 Podolany 74
|
||||
247705.0 False 2 1 Piątkowo 74
|
||||
392861.0 False 3 3 Winogrady 42
|
||||
156085.0 False 2 4 Głuszyna 45
|
||||
249441.0 False 2 5 Jeżyce 51
|
||||
398000.0 True 3 3 Łazarz 96
|
||||
255000.0 True 2 2 Wilda 72
|
||||
329000.0 True 2 1 Naramowice 40
|
||||
232409.0 True 2 0 Stare 38
|
||||
296300.0 True 3 2 Stare 49
|
||||
364762.0 True 4 4 Winogrady 63
|
||||
239592.0 True 2 5 Grunwald 36
|
||||
395000.0 True 3 0 Naramowice 73
|
||||
333694.0 False 3 1 Piątkowo 38
|
||||
344500.0 False 3 6 Jeżyce 53
|
||||
377652.0 False 4 6 Jeżyce 22
|
||||
332145.0 False 3 0 Winogrady 45
|
||||
328241.0 False 3 3 Winogrady 81
|
||||
328241.0 False 3 5 Winogrady 81
|
||||
355041.6 False 3 6 Jeżyce 96
|
||||
318384.0 False 3 6 Jeżyce 24
|
||||
328241.0 False 3 1 Winogrady 81
|
||||
323340.0 False 3 0 Winogrady 89
|
||||
332145.0 False 3 1 Winogrady 45
|
||||
392607.0 False 4 5 Jeżyce 94
|
||||
345180.0 False 3 1 Jeżyce 30
|
||||
315810.0 False 4 8 Winogrady 20
|
||||
263581.0 False 2 2 Winogrady 21
|
||||
323340.0 False 3 2 Winogrady 89
|
||||
263581.0 False 2 6 Winogrady 21
|
||||
238948.0 False 2 5 Winogrady 54
|
||||
240875.0 False 2 7 Winogrady 54
|
||||
246379.0 False 2 0 Winogrady 39
|
||||
345240.0 False 4 1 Winogrady 54
|
||||
345240.0 False 4 0 Winogrady 54
|
||||
332145.0 False 3 3 Winogrady 45
|
||||
348117.0 False 4 5 Winogrady 54
|
||||
348117.0 False 4 6 Winogrady 54
|
||||
339282.0 False 3 7 Winogrady 62
|
||||
317200.0 False 3 8 Winogrady 52
|
||||
329422.5 False 3 6 Winogrady 45
|
||||
265741.5 False 2 7 Winogrady 21
|
||||
238948.0 False 2 6 Winogrady 54
|
||||
348117.0 False 4 7 Winogrady 54
|
||||
339282.0 False 3 5 Winogrady 62
|
||||
263581.0 False 2 4 Winogrady 21
|
||||
348117.0 False 4 4 Winogrady 54
|
||||
296277.0 False 3 0 Winogrady 57
|
||||
304133.5 False 3 1 Winogrady 27
|
||||
296277.0 False 3 2 Winogrady 57
|
||||
263581.0 False 2 3 Winogrady 21
|
||||
345240.0 False 4 2 Winogrady 54
|
||||
263581.0 False 2 1 Winogrady 21
|
||||
345240.0 False 4 3 Winogrady 54
|
||||
339282.0 False 3 6 Winogrady 62
|
||||
323340.0 False 3 3 Winogrady 89
|
||||
310020.0 False 3 0 Winogrady 67
|
||||
252723.0 False 2 1 Winogrady 43
|
||||
366850.0 False 4 1 Winogrady 25
|
||||
319875.0 False 2 8 Grunwald 18
|
||||
249641.0 False 2 0 Jeżyce 54
|
||||
356160.0 False 4 4 Winogrady 36
|
||||
156085.0 False 2 4 Nowe 54
|
||||
319875.0 False 2 7 Grunwald 18
|
||||
329763.26 False 2 9 Grunwald 94
|
||||
324400.97 False 2 7 Grunwald 94
|
||||
388352.0 False 3 7 Grunwald 60
|
||||
347633.0 False 3 10 Grunwald 20
|
||||
409713.0 False 3 11 Grunwald 62
|
||||
330368.0 False 2 12 Grunwald 62
|
||||
356251.9 False 3 14 Grunwald 20
|
||||
347839.75 False 2 12 Grunwald 58
|
||||
350506.16 False 3 11 Grunwald 20
|
||||
342405.13 False 2 11 Grunwald 58
|
||||
429699.0 False 3 15 Grunwald 62
|
||||
319875.0 False 2 9 Grunwald 18
|
||||
341703.96 False 3 8 Grunwald 21
|
||||
336969.98 False 2 10 Grunwald 58
|
||||
413044.0 False 3 12 Grunwald 62
|
||||
405104.0 False 3 1 Grunwald 34
|
||||
341703.96 False 3 9 Grunwald 21
|
||||
424977.14 False 4 5 Grunwald 20
|
||||
327787.0 False 2 11 Grunwald 62
|
||||
353378.74 False 3 12 Grunwald 20
|
||||
332949.0 False 2 13 Grunwald 62
|
||||
383760.0 False 3 6 Grunwald 60
|
||||
335530.0 False 2 14 Grunwald 62
|
||||
387040.0 False 3 5 Grunwald 60
|
||||
302985.6 False 2 4 Grunwald 18
|
||||
410780.0 False 3 3 Grunwald 44
|
||||
417773.83 False 4 3 Grunwald 20
|
||||
303286.5 False 2 3 Grunwald 13
|
||||
364080.0 False 3 1 Grunwald 60
|
||||
319055.27 False 3 3 Grunwald 21
|
||||
292100.0 False 2 1 Grunwald 80
|
||||
245830.0 False 2 2 Grunwald 67
|
||||
352390.0 False 2 12 Grunwald 80
|
||||
377200.0 False 3 3 Grunwald 60
|
||||
313080.0 False 3 5 Winogrady 18
|
||||
314089.0 False 2 5 Grunwald 49
|
||||
308237.0 False 2 1 Nowe 85
|
||||
339072.0 False 2 9 Grunwald 98
|
||||
321812.5 False 2 8 Grunwald 49
|
||||
322643.5 False 2 10 Grunwald 81
|
||||
500203.02 False 4 10 Grunwald 73
|
||||
343978.41 False 3 9 Grunwald 14
|
||||
325184.0 False 2 11 Grunwald 81
|
||||
351130.0 False 3 10 Grunwald 85
|
||||
297420.8 False 2 3 Grunwald 24
|
||||
267702.4 False 2 2 Grunwald 22
|
||||
506566.5 False 4 11 Grunwald 73
|
||||
297420.8 False 2 2 Grunwald 24
|
||||
495722.51 False 4 9 Grunwald 99
|
||||
426416.8 False 4 6 Grunwald 20
|
||||
327081.85 False 2 8 Grunwald 94
|
||||
307080.0 False 2 5 Grunwald 18
|
||||
305793.0 False 2 4 Grunwald 13
|
||||
312198.0 False 2 6 Grunwald 18
|
||||
333231.99 False 3 7 Grunwald 21
|
||||
514939.5 False 4 13 Grunwald 73
|
||||
317880.0 False 2 2 Grunwald 98
|
||||
267702.4 False 2 3 Grunwald 22
|
||||
519126.0 False 4 14 Grunwald 73
|
||||
510753.0 False 4 12 Grunwald 73
|
||||
347010.0 False 2 10 Grunwald 80
|
||||
324387.0 False 2 9 Grunwald 49
|
||||
311514.5 False 2 4 Grunwald 49
|
||||
298249.6 False 2 6 Grunwald 38
|
||||
333774.0 False 2 6 Grunwald 98
|
||||
495722.51 False 4 8 Grunwald 99
|
||||
331125.0 False 2 5 Grunwald 98
|
||||
336423.0 False 2 8 Grunwald 98
|
||||
325312.98 False 3 3 Grunwald 14
|
||||
298249.6 False 2 5 Grunwald 38
|
||||
495782.26 False 4 7 Grunwald 99
|
||||
341311.84 False 3 8 Grunwald 14
|
||||
328476.0 False 2 4 Grunwald 98
|
||||
316848.0 False 2 6 Grunwald 52
|
||||
328241.0 False 3 2 Winogrady 81
|
||||
332145.0 False 3 2 Winogrady 45
|
||||
348750.0 True 2 4 Centrum 50
|
||||
297600.0 True 2 3 Winogrady 50
|
||||
300160.0 True 2 3 Winogrady 90
|
||||
293760.0 True 2 3 Winogrady 90
|
||||
315819.0 False 2 5 Grunwald 13
|
||||
435784.0 False 3 5 Grunwald 44
|
||||
407208.0 False 3 2 Grunwald 44
|
||||
370640.0 False 3 2 Grunwald 60
|
||||
329422.5 False 3 4 Winogrady 45
|
||||
329422.5 False 3 7 Winogrady 45
|
||||
328241.0 False 3 4 Winogrady 81
|
||||
323340.0 False 3 1 Winogrady 89
|
||||
329422.5 False 3 5 Winogrady 45
|
||||
335000.0 True 3 1 Piątkowo 63
|
||||
391728.59 True 3 2 Winogrady 21
|
||||
285678.0 True 2 2 Winogrady 42
|
||||
287566.0 True 2 1 Winogrady 74
|
||||
421049.88 True 4 1 Grunwald 42
|
||||
332388.13 True 2 4 Grunwald 96
|
||||
330359.78 True 2 14 Grunwald 56
|
||||
301206.53 True 2 7 Grunwald 81
|
||||
307490.0 True 2 14 Grunwald 94
|
||||
307880.88 True 3 3 Grunwald 16
|
||||
319047.97 True 3 3 Grunwald 21
|
||||
495782.25 True 4 7 Grunwald 99
|
||||
413044.03 True 3 12 Grunwald 62
|
||||
380480.0 True 3 4 Grunwald 60
|
||||
426416.78 True 4 6 Grunwald 20
|
||||
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
|
||||
410000.0 True 2 5 Rataje 53
|
||||
365000.0 True 4 2 Łazarz 62
|
||||
257000.0 True 2 12 Winogrady 47
|
||||
240000.0 True 2 16 Rataje 36
|
||||
319055.0 True 3 3 Grunwald 56
|
||||
267702.0 True 2 2 Grunwald 45
|
||||
245830.0 True 2 2 Grunwald 40
|
||||
253193.0 True 2 6 Jeżyce 38
|
||||
318384.0 True 3 6 Jeżyce 48
|
||||
245875.0 True 2 1 Jeżyce 38
|
||||
347090.0 True 3 1 Winogrady 57
|
||||
238948.0 True 2 4 Stare 39
|
||||
355000.0 True 3 3 Łazarz 62
|
||||
330255.0 False 2 1 Stare 14
|
||||
442080.0 False 3 1 Starołęka 68
|
||||
277823.0 False 2 2 Podolany 70
|
||||
308039.0 False 2 0 Naramowice 71
|
||||
321656.0 False 2 1 Naramowice 50
|
||||
304140.0 False 3 5 Winogrady 18
|
||||
771672.0 False 4 4 Górczyn 66
|
||||
322497.0 False 2 6 Naramowice 60
|
||||
333024.0 False 2 1 Stare 51
|
||||
290000.0 True 3 14 Nowe 20
|
||||
515000.0 True 3 0 Rataje 71
|
||||
372000.0 True 4 1 Nadolnik 125
|
||||
410000.0 True 2 5 Rataje 53
|
||||
259000.0 True 2 0 Grunwald 52
|
||||
298950.0 True 2 0 Piątkowo 70
|
||||
495000.0 True 4 3 Naramowice 104
|
||||
245000.0 True 2 7 Jeżyce 53
|
||||
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
|
||||
299000.0 True 2 0 Piątkowo 20
|
||||
259000.0 True 3 1 Grunwald 48
|
||||
370000.0 True 4 4 Piątkowo 60
|
||||
249641.0 False 2 0 Ogrody 54
|
||||
375000.0 True 3 1 Nowe 63
|
||||
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
|
||||
326000.0 True 2 1 Sołacz 39
|
||||
350000.0 True 2 2 Jeżyce 47
|
||||
239000.0 True 2 4 Grunwald 50
|
||||
224343.0 False 2 4 Starołęka 35
|
||||
362220.0 False 3 3 Starołęka 60
|
||||
287394.0 False 2 2 Starołęka 50
|
||||
320739.0 False 3 1 Starołęka 56
|
||||
460499.0 False 3 1 Stare 51
|
||||
242731.0 False 2 0 Podolany 74
|
||||
156085.0 False 2 4 Nowe 54
|
||||
285200.0 False 2 1 Winogrady 46
|
||||
211190.0 False 2 0 Podolany 78
|
||||
309000.0 True 3 3 Wilda 80
|
||||
313000.0 True 2 1 Górczyn 48
|
||||
313000.0 True 2 1 Dębiec 48
|
||||
643720.0 True 4 0 Stare 60
|
||||
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
wyk/data02_train.tsv
Normal file
1340
wyk/data02_train.tsv
Normal file
File diff suppressed because it is too large
Load Diff
1675
wyk/data_flats.tsv
Normal file
1675
wyk/data_flats.tsv
Normal file
File diff suppressed because it is too large
Load Diff
4186
wyk/data_flats_with_outliers.tsv
Normal file
4186
wyk/data_flats_with_outliers.tsv
Normal file
File diff suppressed because it is too large
Load Diff
BIN
wyk/denoising.png
Normal file
BIN
wyk/denoising.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 34 KiB |
BIN
wyk/denoising_autoencoder.png
Normal file
BIN
wyk/denoising_autoencoder.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 72 KiB |
118
wyk/ex2data2.txt
Normal file
118
wyk/ex2data2.txt
Normal file
@ -0,0 +1,118 @@
|
||||
0.051267,0.69956,1
|
||||
-0.092742,0.68494,1
|
||||
-0.21371,0.69225,1
|
||||
-0.375,0.50219,1
|
||||
-0.51325,0.46564,1
|
||||
-0.52477,0.2098,1
|
||||
-0.39804,0.034357,1
|
||||
-0.30588,-0.19225,1
|
||||
0.016705,-0.40424,1
|
||||
0.13191,-0.51389,1
|
||||
0.38537,-0.56506,1
|
||||
0.52938,-0.5212,1
|
||||
0.63882,-0.24342,1
|
||||
0.73675,-0.18494,1
|
||||
0.54666,0.48757,1
|
||||
0.322,0.5826,1
|
||||
0.16647,0.53874,1
|
||||
-0.046659,0.81652,1
|
||||
-0.17339,0.69956,1
|
||||
-0.47869,0.63377,1
|
||||
-0.60541,0.59722,1
|
||||
-0.62846,0.33406,1
|
||||
-0.59389,0.005117,1
|
||||
-0.42108,-0.27266,1
|
||||
-0.11578,-0.39693,1
|
||||
0.20104,-0.60161,1
|
||||
0.46601,-0.53582,1
|
||||
0.67339,-0.53582,1
|
||||
-0.13882,0.54605,1
|
||||
-0.29435,0.77997,1
|
||||
-0.26555,0.96272,1
|
||||
-0.16187,0.8019,1
|
||||
-0.17339,0.64839,1
|
||||
-0.28283,0.47295,1
|
||||
-0.36348,0.31213,1
|
||||
-0.30012,0.027047,1
|
||||
-0.23675,-0.21418,1
|
||||
-0.06394,-0.18494,1
|
||||
0.062788,-0.16301,1
|
||||
0.22984,-0.41155,1
|
||||
0.2932,-0.2288,1
|
||||
0.48329,-0.18494,1
|
||||
0.64459,-0.14108,1
|
||||
0.46025,0.012427,1
|
||||
0.6273,0.15863,1
|
||||
0.57546,0.26827,1
|
||||
0.72523,0.44371,1
|
||||
0.22408,0.52412,1
|
||||
0.44297,0.67032,1
|
||||
0.322,0.69225,1
|
||||
0.13767,0.57529,1
|
||||
-0.0063364,0.39985,1
|
||||
-0.092742,0.55336,1
|
||||
-0.20795,0.35599,1
|
||||
-0.20795,0.17325,1
|
||||
-0.43836,0.21711,1
|
||||
-0.21947,-0.016813,1
|
||||
-0.13882,-0.27266,1
|
||||
0.18376,0.93348,0
|
||||
0.22408,0.77997,0
|
||||
0.29896,0.61915,0
|
||||
0.50634,0.75804,0
|
||||
0.61578,0.7288,0
|
||||
0.60426,0.59722,0
|
||||
0.76555,0.50219,0
|
||||
0.92684,0.3633,0
|
||||
0.82316,0.27558,0
|
||||
0.96141,0.085526,0
|
||||
0.93836,0.012427,0
|
||||
0.86348,-0.082602,0
|
||||
0.89804,-0.20687,0
|
||||
0.85196,-0.36769,0
|
||||
0.82892,-0.5212,0
|
||||
0.79435,-0.55775,0
|
||||
0.59274,-0.7405,0
|
||||
0.51786,-0.5943,0
|
||||
0.46601,-0.41886,0
|
||||
0.35081,-0.57968,0
|
||||
0.28744,-0.76974,0
|
||||
0.085829,-0.75512,0
|
||||
0.14919,-0.57968,0
|
||||
-0.13306,-0.4481,0
|
||||
-0.40956,-0.41155,0
|
||||
-0.39228,-0.25804,0
|
||||
-0.74366,-0.25804,0
|
||||
-0.69758,0.041667,0
|
||||
-0.75518,0.2902,0
|
||||
-0.69758,0.68494,0
|
||||
-0.4038,0.70687,0
|
||||
-0.38076,0.91886,0
|
||||
-0.50749,0.90424,0
|
||||
-0.54781,0.70687,0
|
||||
0.10311,0.77997,0
|
||||
0.057028,0.91886,0
|
||||
-0.10426,0.99196,0
|
||||
-0.081221,1.1089,0
|
||||
0.28744,1.087,0
|
||||
0.39689,0.82383,0
|
||||
0.63882,0.88962,0
|
||||
0.82316,0.66301,0
|
||||
0.67339,0.64108,0
|
||||
1.0709,0.10015,0
|
||||
-0.046659,-0.57968,0
|
||||
-0.23675,-0.63816,0
|
||||
-0.15035,-0.36769,0
|
||||
-0.49021,-0.3019,0
|
||||
-0.46717,-0.13377,0
|
||||
-0.28859,-0.060673,0
|
||||
-0.61118,-0.067982,0
|
||||
-0.66302,-0.21418,0
|
||||
-0.59965,-0.41886,0
|
||||
-0.72638,-0.082602,0
|
||||
-0.83007,0.31213,0
|
||||
-0.72062,0.53874,0
|
||||
-0.59389,0.49488,0
|
||||
-0.48445,0.99927,0
|
||||
-0.0063364,0.99927,0
|
||||
0.63265,-0.030612,0
|
||||
BIN
wyk/exp1.png
Normal file
BIN
wyk/exp1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 27 KiB |
BIN
wyk/exp2.png
Normal file
BIN
wyk/exp2.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
BIN
wyk/exp3.png
Normal file
BIN
wyk/exp3.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 50 KiB |
BIN
wyk/fit.png
Normal file
BIN
wyk/fit.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 138 KiB |
BIN
wyk/gru.png
Normal file
BIN
wyk/gru.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
151
wyk/iris.csv
Normal file
151
wyk/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
|
||||
|
BIN
wyk/learning-curves.png
Normal file
BIN
wyk/learning-curves.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 5.9 KiB |
BIN
wyk/lstm.jpg
Normal file
BIN
wyk/lstm.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 37 KiB |
4939
wyk/mieszkania4.tsv
Normal file
4939
wyk/mieszkania4.tsv
Normal file
File diff suppressed because one or more lines are too long
BIN
wyk/multireglog.png
Normal file
BIN
wyk/multireglog.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 56 KiB |
BIN
wyk/nn1.png
Normal file
BIN
wyk/nn1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 83 KiB |
BIN
wyk/nn2.png
Normal file
BIN
wyk/nn2.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 85 KiB |
BIN
wyk/nn3.png
Normal file
BIN
wyk/nn3.png
Normal file
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Reference in New Issue
Block a user