import preprocess
from sklearn.externals import joblib
tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3, "START": 4}
reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"}
word_dict = None
numFeatures = None
if __name__ == "__main__":
train = sys.argv[1]
test = sys.argv[2]
model_number = sys.argv[3]
X = None
y = None
retagged_train, word_dict, totalNumWords = preprocess.iob(train)
global numFeatures
numDistinctWords = len(word_dict)
numFeatures = numDistinctWords + 1 + 9 + 12 + 2
X = scipy.sparse.lil_matrix((totalNumWords, numFeatures))
y = np.empty([totalNumWords, ])
#training_seen = [0] * totalNumWords
with open(retagged_train) as f:
identifier_line = True
split_line = None
word = None
tag = None
length = None
alpha = None
allCaps = None
hasNumeric = None
deltaO[index] = scoreI, "I", counts, phi, wordIndex
if tag == "B":
deltaB[index] = scoreI, "I", counts, phi, wordIndex
if tag == "I":
deltaI[index] = scoreI, "I", counts, phi, wordIndex
return scoreI, "I", counts, phi, wordIndex
if __name__ == "__main__":
train = sys.argv[1]
test = sys.argv[2]
model_number = sys.argv[3]
X = None
y = None
retagged_train, word_dict, totalNumWords = preprocess.iob(train)
global numFeatures
numDistinctWords = len(word_dict)
numFeatures = numDistinctWords + 1 + 6 + 8 + 1
X = scipy.sparse.lil_matrix((totalNumWords, numFeatures))
y = np.empty([
totalNumWords,
])
with open(retagged_train) as f:
identifier_line = True
split_line = None
word = None
tag = None
length = None
alpha = None
allCaps = None
import preprocess
from sklearn.externals import joblib
tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3}
reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"}
word_dict = None
numFeatures = None
if __name__ == "__main__":
train = sys.argv[1]
test = sys.argv[2]
model_number = sys.argv[3]
X = None
y = None
retagged_train, word_dict = preprocess.iob(train)
global numFeatures
numWords = len(word_dict)
numFeatures = numWords + 3 + 4
X = scipy.sparse.lil_matrix((numWords, numFeatures))
y = np.empty([
numWords,
])
training_seen = [0] * numWords
with open(retagged_train) as f:
identifier_line = True
split_line = None
word = None
tag = None
length = None
alpha = None
import preprocess
from sklearn.externals import joblib
tags = {"UNKNOWN": 0, "O": 1, "B": 2, "I": 3}
reverse_tags = {0: "UNKNOWN", 1: "O", 2: "B", 3: "I"}
word_dict = None
numFeatures = None
if __name__ == "__main__":
train = sys.argv[1]
test = sys.argv[2]
model_number = sys.argv[3]
X = None
y = None
retagged_train, word_dict = preprocess.iob(train)
global numFeatures
numWords = len(word_dict)
numFeatures = numWords + 3 + 4
X = scipy.sparse.lil_matrix((numWords, numFeatures))
y = np.empty([numWords, ])
training_seen = [0] * numWords
with open(retagged_train) as f:
identifier_line = True
split_line = None
word = None
tag = None
length = None
alpha = None
allCaps = None
hasNumeric = None
