| import random | |
| import sys | |
| def build_chain(text, chain = {}): | |
| words = text.split(' ') | |
| index = 1 | |
| for word in words[index:]: | |
| key = words[index - 1] | |
| if key in chain: | |
| chain[key].append(word) |
| from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer | |
| from sklearn.decomposition import NMF, LatentDirichletAllocation | |
| import numpy as np | |
| def display_topics(H, W, feature_names, documents, no_top_words, no_top_documents): | |
| for topic_idx, topic in enumerate(H): | |
| print "Topic %d:" % (topic_idx) | |
| print " ".join([feature_names[i] | |
| for i in topic.argsort()[:-no_top_words - 1:-1]]) | |
| top_doc_indices = np.argsort( W[:,topic_idx] )[::-1][0:no_top_documents] |
| from gensim.models import KeyedVectors | |
| # Load gensim word2vec | |
| w2v_path = '<Gensim File Path>' | |
| w2v = KeyedVectors.load_word2vec_format(w2v_path) | |
| import io | |
| # Vector file, `\t` seperated the vectors and `\n` seperate the words | |
| """ |
| # CoffeeScript version of Google Spreadsheet Driver for Tableau Data Web Connector | |
| init = -> | |
| if !tableau | |
| alert 'init- tableau NOT defined!' | |
| return | |
| tableau.scriptVersion = '1.0' | |
| tableau.log 'init' | |
| tableau.initCallback() |
| #!/bin/bash | |
| # | |
| #This script assumes very little other than a fresh Ubuntu install (using the Windows store) on Win10 1709 or newer with WSL installed already | |
| #In Powershell, run the following to install WSL and Ubuntu | |
| # | |
| #Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Windows-Subsystem-Linux | |
| #Invoke-WebRequest -Uri https://aka.ms/wsl-ubuntu-1604 -OutFile ~/Ubuntu.zip -UseBasicParsing | |
| #Expand-Archive ~/Ubuntu.zip ~/Ubuntu | |
| #~/Ubuntu/ubuntu.exe | |
| # |
| from collections import OrderedDict | |
| import numpy as np | |
| import spacy | |
| from spacy.lang.en.stop_words import STOP_WORDS | |
| nlp = spacy.load('en_core_web_sm') | |
| class TextRank4Keyword(): | |
| """Extract keywords from text""" | |
| import string | |
| import nltk | |
| from nltk.tokenize import RegexpTokenizer | |
| from nltk.corpus import stopwords | |
| import re | |
| def preprocess(sentence): | |
| sentence = sentence.lower() | |
| tokenizer = RegexpTokenizer(r'\w+') | |
| tokens = tokenizer.tokenize(sentence) |
| #coding:utf-8 | |
| import psutil | |
| from mpl_toolkits.mplot3d import Axes3D | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import matplotlib.animation as animation | |
| TIME_NUM = 90 | |
| FRAMES = 360 |
refer to: https://stackoverflow.com/questions/39371772/how-to-install-anaconda-on-raspberry-pi-3-model-b
{USER}: pi
Install Miniconda 3:
wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-armv7l.sh
sudo md5sum Miniconda3-latest-Linux-armv7l.sh # (optional) check md5
sudo /bin/bash Miniconda3-latest-Linux-armv7l.sh # -> change default directory to /home/pi/miniconda3
| docker ps | awk {' print $1 '} | tail -n+2 > tmp.txt; for line in $(cat tmp.txt); do docker kill $line; done; rm tmp.txt |