def predict(X, clf, vec, feat_obj=None):
"""
predict()
"""
# Data -> features
if feat_obj == None: feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
# predict
return predict_vectorized(feats, clf, vec)
def predict( X, clf, vec, feat_obj=None ):
"""
predict()
"""
# Data -> features
if feat_obj == None: feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
# predict
return predict_vectorized(feats, clf, vec)
def extract_features(X, feat_obj=None):
# Data -> features
if feat_obj == None: feat_obj = FeaturesWrapper()
return feat_obj.extract_features(X)
def extract_features(X, feat_obj=None):
# Data -> features
if feat_obj == None: feat_obj = FeaturesWrapper()
return feat_obj.extract_features(X)
