def main():
t_c_matrix = open("t_c_matrix", "r")
t_set = open("t_set", "r")
word_dict = defaultdict(float)
word_context_matrix = pickle.load(t_c_matrix)
word_set = pickle.load(t_set)
word_set = list(word_set)
# print word_set.index("Spain")
# print word_set.index("England")
# print word_set.index("Athens")
vec1 = word_context_matrix[word_set.index("Spain")]
vec2 = word_context_matrix[word_set.index("England")]
vec3 = word_context_matrix[word_set.index("Athens")]
vec = vec1 - vec2 + vec3
for index, word in enumerate(word_set):
sim = cos_sim(word_context_matrix[index], vec)
word_dict[word] = sim
count = 0
for word, sim in sorted(word_dict.items(), key = lambda x:-x[1]):
print word + "\t" + str(sim)
count += 1
if count == 9:
break
def main():
t_c_matrix = open("t_c_matrix", "r")
t_set = open("t_set", "r")
word_dict = defaultdict(float)
word_context_matrix = pickle.load(t_c_matrix)
word_set = pickle.load(t_set)
word_set = list(word_set)
Index_specific_word = word_set.index("England")
England_representation = word_context_matrix[Index_specific_word]
# "England"の単語表現
for index, word in enumerate(word_set):
if word == "England":
continue
else:
# print England_representation
# print word_context_matrix[index]
# print word,
sim = cos_sim(England_representation, word_context_matrix[index])
word_dict[word] = sim
count = 0
for word, sim in sorted(word_dict.items(), key=lambda x: -x[1]):
print word + "\t" + str(sim)
count += 1
if count == 9:
break
def main():
t_c_matrix = open("t_c_matrix", "r")
t_set = open("t_set", "r")
word_dict = defaultdict(float)
word_context_matrix = pickle.load(t_c_matrix)
word_set = pickle.load(t_set)
word_set = list(word_set)
Index_specific_word = word_set.index("England")
England_representation = word_context_matrix[Index_specific_word]
# "England"の単語表現
for index, word in enumerate(word_set):
if word == "England":
continue
else:
# print England_representation
# print word_context_matrix[index]
# print word,
sim = cos_sim(England_representation, word_context_matrix[index])
word_dict[word] = sim
count = 0
for word, sim in sorted(word_dict.items(), key = lambda x:-x[1]):
print word + "\t" + str(sim)
count += 1
if count == 9:
break
def main():
t_c_matrix = open("t_c_matrix", "r")
t_set = open("t_set", "r")
word_dict = defaultdict(float)
word_context_matrix = pickle.load(t_c_matrix)
word_set = pickle.load(t_set)
word_set = list(word_set)
# print word_set.index("Spain")
# print word_set.index("England")
# print word_set.index("Athens")
vec1 = word_context_matrix[word_set.index("Spain")]
vec2 = word_context_matrix[word_set.index("England")]
vec3 = word_context_matrix[word_set.index("Athens")]
vec = vec1 - vec2 + vec3
for index, word in enumerate(word_set):
sim = cos_sim(word_context_matrix[index], vec)
word_dict[word] = sim
count = 0
for word, sim in sorted(word_dict.items(), key=lambda x: -x[1]):
print word + "\t" + str(sim)
count += 1
if count == 9:
break
def make_sim_dict(word_context_matrix, word_set, objective_word, word_dict):
Index_specific_word = word_set.index(objective_word)
objective_representation = word_context_matrix[Index_specific_word]
# 受け取った単語のベクトル表現
for index, word in enumerate(word_set):
if word == objective_word:
continue
else:
sim = cos_sim(word_context_matrix[index], objective_representation)
word_dict[word] = sim
return word_dict
def make_sim_dict(word_context_matrix, word_set, objective_word, word_dict):
Index_specific_word = word_set.index(objective_word)
objective_representation = word_context_matrix[Index_specific_word]
# 受け取った単語のベクトル表現
for index, word in enumerate(word_set):
if word == objective_word:
continue
else:
sim = cos_sim(word_context_matrix[index], objective_representation)
word_dict[word] = sim
return word_dict
