# Path: scratch/nlp.py
# def cosine_similarity(v1: Vector, v2: Vector) -> float:
#     return dot(v1, v2) / math.sqrt(dot(v1, v1) * dot(v2, v2))


from collections import Counter
from typing import Dict, List, Tuple
from scratch.nlp import cosine_similarity
from collections import defaultdict
    from typing import NamedTuple
    from scratch.deep_learning import random_tensor
    from typing import List
    from scratch.linear_algebra import dot
    from scratch.working_with_data import pca, transform
    import csv
    import re
    import random
    import tqdm
                   for interest, frequency in popular_interests.most_common()
                   if interest not in user_interests]
    return suggestions[:max_results]

unique_interests = sorted({interest
                           for user_interests in users_interests
                           for interest in user_interests})

assert unique_interests[:6] == [
    'Big Data',
    'C++',
    'Cassandra',
    'HBase',
    'Hadoop',
    'Haskell',
    # ...
]

def make_user_interest_vector(user_interests: List[str]) -> List[int]:
    """
    Given a list ofinterests, produce a vector whose ith element is 1
    if unique_interests[i] is in the list, 0 otherwise
    """
    return [1 if interest in user_interests else 0
            for interest in unique_interests]

user_interest_vectors = [make_user_interest_vector(user_interests)
                         for user_interests in users_interests]



# Path: scratch/nlp.py
# def cosine_similarity(v1: Vector, v2: Vector) -> float:
#     return dot(v1, v2) / math.sqrt(dot(v1, v1) * dot(v2, v2))


# Path: scratch/recommender_systems.py
from collections import Counter
from typing import Dict, List, Tuple
from scratch.nlp import cosine_similarity
from collections import defaultdict
    from typing import NamedTuple
    from scratch.deep_learning import random_tensor
    from typing import List
    from scratch.linear_algebra import dot
    from scratch.working_with_data import pca, transform
    import csv
    import re
    import random
    import tqdm

                   for interest, frequency in popular_interests.most_common()
                   if interest not in user_interests]
    return suggestions[:max_results]

unique_interests = sorted({interest
                           for user_interests in users_interests
                           for interest in user_interests})

assert unique_interests[:6] == [
    'Big Data',
    'C++',
    'Cassandra',
    'HBase',
    'Hadoop',
    'Haskell',
    # ...
]

def make_user_interest_vector(user_interests: List[str]) -> List[int]:
    """
    Given a list ofinterests, produce a vector whose ith element is 1
    if unique_interests[i] is in the list, 0 otherwise
    """
    return [1 if interest in user_interests else 0
            for interest in unique_interests]

user_interest_vectors = [make_user_interest_vector(user_interests)
                         for user_interests in users_interests]



user_similarities = [[cosine_similarity(interest_vector_i, interest_vector_j)