def __init__(self): self.criterias = { "track": ['Khomri', 'Paris', 'Travail', 'Myriam', 'ElKhomri'], "locations": [2.138472,48.774288,2.518187,48.965338], "lang": ["fr"] } FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self): self.criterias = { "track": user_cat, "locations": [-0.6389644,44.8111222,-0.5334955,44.9163535], "lang": ["fr"] } FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self): self.criterias = { "track": ['Khomri', 'Paris', 'Travail', 'Myriam', 'ElKhomri'], "locations": [2.138472, 48.774288, 2.518187, 48.965338], "lang": ["fr"] } FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self): # Tweets from Bordeaux OR mentioning 'Paris' self.criterias = { "track": ['remaniement', 'Paris'], "locations": [-0.6389644,44.8111222,-0.5334955,44.9163535] } FilteredStream.__init__(self, self.criterias, "config.json")
def __init__(self): # Tweets from Bordeaux OR mentioning 'Paris' self.criterias = { "track": ['remaniement', 'Paris'], "locations": [-0.6389644, 44.8111222, -0.5334955, 44.9163535] } FilteredStream.__init__(self, self.criterias, "config.json")
def __init__(self): self.criterias = { #You can choose some key words to look for by chaging the track's content "track": ['remaniement', 'Paris'], "locations": [-0.6389644,44.8111222,-0.5334955,44.9163535], "lang": [*] } FilteredStream.__init__(self, self.criterias, 10, "config.json")
def __init__(self): # Tweets from Bordeaux OR mentioning 'Paris', in ANY language self.criterias = { "track": ["Paris"], # Keywords filter "locations": [-0.6389644,44.8111222,-0.5334955,44.9163535], # Bordeaux bounding box # "lang": ["fr", "en"] # In French OR English "lang": ["*"] # Any language } FilteredStream.__init__(self, self.criterias, 5, config)
def __init__(self): # Tweets from Bordeaux OR mentioning 'Paris', in ANY language self.criterias = { "track": ["Paris"], # Keywords filter "locations": [-0.6389644, 44.8111222, -0.5334955, 44.9163535], # Bordeaux bounding box # "lang": ["fr", "en"] # In French OR English "lang": ["*"] # Any language } FilteredStream.__init__(self, self.criterias, 5, config)
def __init__(self): # Search criterias self.criterias = { "track": ["Paris", "Bordeaux"], "locations": [-0.6389644, 44.8111222, -0.5334955, 44.9163535], "lang": ["fr"] } FilteredStream.__init__(self, self.criterias, SIZE, "../config.json") self.tweets = [] self.num_export = 0
def __init__(self): # Search criterias self.criterias = { "track": ["Paris", "Bordeaux"], "locations": [-0.6389644,44.8111222,-0.5334955,44.9163535], "lang": ["fr"] } FilteredStream.__init__(self, self.criterias, SIZE, "../config.json") self.tweets = [] self.num_export = 0
def __init__(self): # Tweets from Paris OR mentioning 'Paris', in French self.criterias = { "track": ["Paris"], # Keywords filter "locations": [2.138472, 48.774288, 2.518187, 48.965338], # Paris bounding box "lang": ["fr"] # In French } self.corpus_tot = [] self.labels = np.asarray([]) # Creates the classificator : threshold affect the clustering self.brc = birch.clf_init(threshold=1.5) self.mon_fichier = open("FinalDictionary.txt", "r") self.vectorizer = CountVectorizer() X = self.vectorizer.fit_transform(self.mon_fichier) FilteredStream.__init__(self, self.criterias, 10, config)