# Method variables
# n (path length) should be either 1 or 2
# note: tried 3, but didn't give good results, so now code is limited to 1 or 2
n = 1
input_file = "test-full-new.txt"
output_file = "test-local-path-1.txt"
# Generate graph from edges file
G = ex.read_graph(c.output_dir + "edges-directed-6.txt")
# Get feature space and target words
features_index = u.read_features_file(c.output_dir + "feat-space.txt")
target_words = u.get_target_words()
# Get sentences/vectors of data to expand
sentences, data = ex.get_expansion_data(input_file, features_index)
# Get matrix of weight vectors
print "generating weight matrix..."
W, b_arr = u.get_weight_matrix(target_words)
# Iterate over instances and expand them
print "expanding feature vectors..."
i = 0
for vect in data:
#if i == 3:
# break
# Store array of current feats
feats = [f.split(":")[0] for f in sentences[i][1:]]
if __name__ == "__main__":
# Method variables
#predict_threshold = 0.9 # prediction threshold
input_file = "test-full.txt"
output_file = "test-expanded-all-neighb.txt"
# Generate graph from edges file
G = ex.read_graph(c.output_dir + "edges-directed-6.txt")
# Get feature space and target words
features_index = u.read_features_file(c.output_dir + "feat-space.txt")
target_words = u.get_target_words()
# Get sentences/vectors of data to expand
sentences, data = ex.get_expansion_data(input_file, features_index)
# Get matrix of weight vectors
print "generating weight matrix..."
W, b_arr = u.get_weight_matrix(target_words)
print "expanding feature vectors..."
i = 0
for vect in data:
#if i == 5:
# break
# Store array of current feats
feats = [f.split(":")[0] for f in sentences[i][1:]]