def finalize(self):
        if self._model is None:
            self._train()
        self._vectors = []

#---------------------------------------------------------------------------------------------------
# "Nothing beats SVM + character n-grams."
# Character n-grams seem to capture all information: morphology, context, frequency, ...
# SVM will discover the most informative features.
# Each row in the CSV is a score (positive = +1, negative = –1) and a Dutch book review.
# Can we learn from this dataset to predict sentiment? Yes we can!
# The following script demonstrates sentiment analysis for Dutch book reviews,
# with 90% accuracy, in 10 lines of Python code:

#from pattern.db import CSV
#from pattern.vector import SVM, chngrams, kfoldcv
#
#def v(s):
#    return chngrams(s, n=4)
#
#data = CSV.load(os.path.join("..", "..", "test", "corpora", "polarity-nl-bol.com.csv"))
#data = map(lambda p, review: (v(review), int(p) > 0), data)
#
#print(kfoldcv(SVM, data, folds=3))

#---------------------------------------------------------------------------------------------------
# I hate to spoil your party..." by Lars Buitinck.
# As pointed out by Lars Buitinck, words + word-level bigrams with TF-IDF can beat the 90% boundary:

#from pattern.db import CSV
#from pattern.en import ngrams
#from pattern.vector import Model, SVM, gridsearch
#
#def v(s):
#    return count(words(s) + ngrams(s, n=2))
#    
#data = CSV.load(os.path.join("..", "..", "test", "corpora", "polarity-nl-bol.com.csv"))
#data = map(lambda p, review: Document(v(review), type=int(p) > 0), data)
#data = Model(data, weight="tf-idf")
#
#for p in gridsearch(SVM, data, c=[0.1, 1, 10], folds=3):
#    print(p)

# This reports 92% accuracy for the best run (c=10).
# Of course, it's optimizing for the same cross-validation 
# that it's testing on, so this is easy to overfit.
# In scikit-learn it will run faster (4 seconds <=> 20 seconds), see: http://goo.gl/YqlRa

#### GENETIC ALGORITHM #############################################################################
Plans route based on predicted current location and waypoints (to include final goal).
dataset/ETHPy150Open clips/pattern/pattern/vector/__init__.py/SVM.finalize--crossover with--dataset/ETHPy150Open FishPi/FishPi-POCV---Command---Control/fishpi/control/navigation.py/PathPlanner.plan_route