def find_nearest_instance_subprocess(test_instance_start_index, test_instance_end_index,\
classified_results):
# print test_instance_start_index, test_instance_end_index
for test_instance_index in range(test_instance_start_index, test_instance_end_index):
# find the nearest training instance with cosine similarity
maximal_cosine_similarity = -1.0
maximal_cosine_similarity_index = 0
for training_instance, training_instance_index in\
zip(training_data_instances, range(len(training_data_instances))):
# compute the cosine similarity
# first, compute the inner product
inner_product = np.inner(test_data_instances[test_instance_index], training_instance)
# second, normalize the inner product
normalized_inner_product = inner_product / test_data_lengths[test_instance_index]\
/ training_data_lengths[training_instance_index]
if normalized_inner_product > maximal_cosine_similarity:
maximal_cosine_similarity = normalized_inner_product
maximal_cosine_similarity_index = training_instance_index
classified_results[test_instance_index] =\
training_data_labels[int(maximal_cosine_similarity_index)]
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