human
What is a Python code to calculate the similarity) given the following:
I am working on the file "HW4.ipynb".
The first task is:
Making a database of similarities
We now move to calculating a global database of pairwise restaurant similarities.
We provide you here with a function to make a database of the similarities for each pair of restaurants in the database. The class Database is initialized in its constructor by taking as arguments a dataframe of reviews. The method populate_by calculating iterates over every possible pair of business_id's in the dataframe and populates the database with similarities and common supports. It takes as arguments a function the similarity function similarity_func like pearson_sim (calculate_similarity then uses this to calculate the similarity). The get method on the database can be used to retrieve the similarity for two business ids.
(See Thu Oct 17th's class video for information about classes)
Can you write Python code for it?
assistant
#Here is Python code to calculate the similarity).

class Database:
    "A class representing a database of similaries and common supports"
    
    def __init__(self, df):
        "the constructor, takes a reviews dataframe like smalldf as its argument"
        database={}
        self.df=df
        self.uniquebizids={v:k for (k,v) in enumerate(df.business_id.unique())}
        keys=self.uniquebizids.keys()
        l_keys=len(keys)
        self.database_sim=np.zeros([l_keys,l_keys])
        self.database_sup=np.zeros([l_keys, l_keys], dtype=np.int)
        
    def populate_by_calculating(self, similarity_func):
        """
        a populator for every pair of businesses in df. takes similarity_func like
        pearson_sim as argument
        """
        items=self.uniquebizids.items()
        for b1, i1 in items:
            for b2, i2 in items:
                if i1 < i2:
                    sim, nsup=calculate_similarity(b1, b2, self.df, similarity_func)
                    self.database_sim[i1][i2]=sim
                    self.database_sim[i2][i1]=sim
                    self.database_sup[i1][i2]=nsup
                    self.database_sup[i2][i1]=nsup
                elif i1==i2:
                    nsup=self.df[self.df.business_id==b1].user_id.count()
                    self.database_sim[i1][i1]=1.
                    self.database_sup[i1][i1]=nsup
                    

    def get(self, b1, b2):
        "returns a tuple of similarity,common_support given two business ids"
        sim=self.database_sim[self.uniquebizids[b1]][self.uniquebizids[b2]]
        nsup=self.database_sup[self.uniquebizids[b1]][self.uniquebizids[b2]]
        return (sim, nsup)