Damian Schwyrz Damian89

## simple-tfidf-count-lsi-example.py
#!/usr/bin/env python
# coding: utf8

from sklearn import feature_extraction, decomposition

stoplist = []

docs = [
    "Maschinelles lernen ist eine Disziplien die irgendwas mit Künstlicher Intelligenz zu tun hat",
    "Künstliche Intelligenz ist ein interessantes Themengebiet",

## example_tfidf.py
print("TF-IDF + LDA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.LatentDirichletAllocation(n_components=2)
tfidf_lsi.fit(tfidf)

## example_tfidf.py
print()
print("TF-IDF + NMF")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.NMF(n_components=2)
tfidf_lsi.fit(tfidf)

## example_tfidf.py
print()
print("TF-IDF + FastICA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.FastICA(n_components=2)
tfidf_lsi.fit(tfidf.toarray())

## example_tfidf.py
print()
print("TF-IDF + IncrementalPCA")

tfidf_vect = feature_extraction.text.TfidfVectorizer(stop_words=stoplist)
tfidf_vect.fit(docs)
features = tfidf_vect.get_feature_names()

tfidf = tfidf_vect.transform(docs)
tfidf_lsi = decomposition.IncrementalPCA(n_components=2)
tfidf_lsi.fit(tfidf.toarray())

## example_tfidf.py
import warnings

from sklearn import ensemble

warnings.filterwarnings("ignore")

features = [
    [1, 1, 0],
    [1, 1, 0],
    [1, 1, 0],

## sklearn-numerai-solution.py
#!/usr/bin/env python
# coding: utf8

""" Example for numer.ai competition """

import math
import os
import sys

import numpy

## gridsearchcv.py
import pandas

# Einlesen der Datei
training_data = pandas.read_csv("data/numerai_training_data.csv")

# Die ersten 5 Zeilen samt Header ausgeben
print(training_data.head())

# Aus wie vielen Zeilen und Spalten besteht die Datei?
print(training_data.shape)

## sklearn-one-method-git.py
preprocessor = pipeline.Pipeline(
    [
        ('ss', preprocessing.StandardScaler()),
        ('ex', preprocessing.PolynomialFeatures(degree=3)),
    ]
)

## app.js
/*
 * Author: Damian Schwyrz <mail@damianschwyrz.de>
 * URL: https://www.damianschwyrz.de
 * Copyright (c) 2017.
 */

/**
 * Main table component
 */
Vue.component('table-keywords', {