eustin

scores_dict = {x: np.random.randn(1)[0] for x in ['shirt', 'pants', 'dress']}  

print(scores_dict)

eustin

pi = random.choice(all_permutations)

print(pi)

eustin

obj_pos_1, obj_pos_2, obj_pos_3 = pi

print(f"object at position 1 is '{obj_pos_1}'")
print(f"object at position 2 is '{obj_pos_2}'")
print(f"object at position 3 is '{obj_pos_3}'")

eustin

first_term_numerator = np.exp(score_obj_pos_1)
first_term_denominator = np.exp(score_obj_pos_1) + np.exp(score_obj_pos_2) + np.exp(score_obj_pos_3)

first_term = first_term_numerator / first_term_denominator

print(f"first term is {first_term}"

eustin

second_term_numerator = np.exp(score_obj_pos_2)
second_term_denominator = np.exp(score_obj_pos_2) + np.exp(score_obj_pos_3)

second_term = second_term_numerator / second_term_denominator

print(f"second term is {second_term}")

eustin

third_term = 1.0

eustin

prob_of_permutation = first_term * second_term * third_term

print(f"probability of permutation is {prob_of_permutation}")

eustin

np.exp(scores_dict['shirt']) / sum(np.exp(list(scores_dict.values())))

eustin

ordered_scores = np.array([scores_dict[x] for x in xlabs]).astype(np.float32)
predicted_prob_dist = tf.nn.softmax(ordered_scores)

print(predicted_prob_dist)

eustin

raw_relevance_grades = tf.constant([3.0, 1.0, 0.0], dtype=tf.float32)
true_prob_dist = tf.nn.softmax(raw_relevance_grades)

print(true_prob_dist)