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saiplanner / kittler.py
Created May 16, 2019 — forked from al42and/kittler.py
Kittler-Illingworth Thresholding
View kittler.py
import numpy as np
def Kittler(im, out):
"""
The reimplementation of Kittler-Illingworth Thresholding algorithm by Bob Pepin
Works on 8-bit images only
Original Matlab code: https://www.mathworks.com/matlabcentral/fileexchange/45685-kittler-illingworth-thresholding
Paper: Kittler, J. & Illingworth, J. Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986).
"""
h,g = np.histogram(im.ravel(),256,[0,256])
@saiplanner
saiplanner / naivebayes
Created May 7, 2019 — forked from vickyqian/naivebayes
Naive Bayes Classifier
View naivebayes
import nltk
training_set = nltk.classify.util.apply_features(extract_features, tweets)
# Train the classifier Naive Bayes Classifier
NBClassifier = nltk.NaiveBayesClassifier.train(training_set)
#ua is a dataframe containing all the united airline tweets
ua['sentiment'] = ua['tweets'].apply(lambda tweet: NBClassifier.classify(extract_features(getFeatureVector(processTweet2(tweet)))))
View preprocesstweet.txt
###Preprocess tweets
def processTweet2(tweet):
# process the tweets
#Convert to lower case
tweet = tweet.lower()
#Convert www.* or https?://* to URL
tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))','URL',tweet)
#Convert @username to AT_USER
tweet = re.sub('@[^\s]+','AT_USER',tweet)
View getfeaturevector
def getFeatureVector(tweet):
featureVector = []
#split tweet into words
words = tweet.split()
for w in words:
#replace two or more with two occurrences
w = replaceTwoOrMore(w)
#strip punctuation
w = w.strip('\'"?,.')
#check if the word stats with an alphabet
@saiplanner
saiplanner / twitter crawler.txt
Created May 7, 2019 — forked from vickyqian/twitter crawler.txt
A Python script to download all the tweets of a hashtag into a csv
View twitter crawler.txt
import tweepy
import csv
import pandas as pd
####input your credentials here
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
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