def main():
G = GloveEmbedding("glove.6B.50d.txt")
word_to_idx, idx_to_word, embedding = G.read_embedding()
#print("locon: ", word_to_idx["locon"])
s = "I love New York and music locon"
s = s.lower()
print("Sentence: ", s)
S = SentenceToIndices(word_to_idx)
sentence = S.map_sentence(s)
print("Sentence to indices: ", sentence)
print("Padded: ", PadSentences(10).pad(sentence))
SE = SentenceToEmbedding(word_to_idx, idx_to_word, embedding)
matrix = SE.map_sentence(s, max_len=10)
print("Matrix: ", matrix)
print("Matrix.shape: ", matrix.shape)
print("Embedding i: ", embedding[word_to_idx["i"]])
sentences = []
sentences.append("I esta malo".lower())
sentences.append("Love la musica salsa.".lower())
sentences.append("Uff, q mal te va nene".lower())
mapped, mlen = S.map_sentence_list(sentences)
print("mlen: ", mlen)
for s in mapped:
print(s)
def main():
G = GloveEmbedding("../test/data/glove.6B.50d.txt")
word_to_idx, idx_to_word, embedding = G.read_embedding()
#print("locon: ", word_to_idx["locon"])
print("Length dictionary: ", len(word_to_idx))
#s = "I love New York and music locon"
s = "The flu is making me sad"
s = s.lower()
print("Sentence: ", s)
S = SentenceToIndices(word_to_idx)
sentence = S.map_sentence(s)
print("Sentence to indices: ", sentence)
print("Padded: ", PadSentences(10).pad(sentence))
SE = SentenceToEmbeddingWithEPSILON(word_to_idx, idx_to_word, embedding)
matrix1 = SE.map_sentence(s, max_len=len(s))
s2 = "The flu is making me sad".lower()
matrix2 = SE.map_sentence(s2, max_len=len(s2))
print("Matrix 1: ", matrix1)
print("Matrix.shape: ", matrix1.shape)
print("\n Matrix 2: ", matrix2)
print("Matrix.shape: ", matrix2.shape)
print("\n Self Similarity: ", matrix_cosine_similary(matrix1, matrix1))
M1 = np.array([-1, 40, 0.04]).reshape((3, 1))
M2 = np.array([100, 2, 3]).reshape((3, 1))
print("M1: \n ", M1)
print("M2: \n", M2)
SimM = matrix_cosine_similary(M1, M2)
print("SimM: \n", SimM)
D = distance_similarity_matrix(SimM)
print("D: ", D)
M3 = np.array([[1, 2, 3, 1], [4, 5, 6, 2], [7, 8, 9, 1]])
M4 = np.array([[1, 2, 3.000001, 1], [4, 5, 6, 2], [7, 8, 9, 1]])
SimM = matrix_cosine_similary(M3, M3)
print("SimM: \n", SimM)
D = distance_similarity_matrix(SimM)
print("D: ", D)
SimM = matrix_cosine_similary(M3, M4)
print("\nSimM: \n", SimM)
Up = np.triu(SimM)
D = distance_similarity_matrix(SimM)
print("D: ", D)
print("Up: ", Up)
print("sum Up: ", np.sum(Up))
print("up I: ", np.triu(np.ones(Up.shape)))
print("sum I: ", np.sum(np.triu(np.ones(Up.shape))))
