eustin

relevance_grades = tf.constant([
    [3.0, 2.0, 2.0, 2.0, 1.0],
    [3.0, 3.0, 1.0, 1.0, 0.0]
])

eustin

query_1 = "dog"

bing_search_results = [
    "Dog - Wikipedia",
    "Adopting a dog or puppy | RSPCA Australia",
    "dog | History, Domestication, Physical Traits, & Breeds",
    "New South Wales | Dogs & Puppies | Gumtree Australia Free",
    "dog - Wiktionary"
]

eustin

sum(true_prob_dist * np.log(true_prob_dist / true_prob_dist))

eustin

sum(true_prob_dist * np.log(true_prob_dist / 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)

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

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

eustin

prob_of_permutation = first_term * second_term * third_term

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

eustin

third_term = 1.0

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}")