Github Copilot VS Stable Code 3B

"""This is a simple, but efficient In-Memory Key-Value Database implementation written in Python.It has support for creating, committing, and rolling back transactions.When a transaction is created, the size of the database does not double.It can do basic CRUD operations and a count operation.All operations are O(log n) or better."""class InMemoryKeyValueDatabase():

class InMemoryKeyValueDatabase(): def __init__(self): # The following list will contain all the key-value pairs that make up your database. # Each of these items is a dictionary containing 'key' and 'value'. self.__listOfAllData__ = [] """ The InMemoryKeyValueDatabase has an internal variable called "databaseName". This holds the name of this particular database. It is set once when you create your database using one of the factory functions (InMemoryKeyValueFactory)). """ self.__internalVariable__ = None # The following dictionary will contain all the key-value pairs that make up your database. # Each of these items is a dictionary containing 'key' and 'value'. self.__dictionaryOfAllDataByKeys__ = {} def addNewKeyValuePairToDatabase( self, newKey, newValue ): """ This will take in the key as well as its value. If these items are already present within this InMemoryKeyValue database then no changes to anything would happen and nothing would be updated or changed. The only thing that could possibly change is if a new 'key' and/or 'value' pair was added into one of the dictionarys ((self.__dictionaryOfAllDataByKeys__) that are used within this InMemoryKeyValue database. """ # If either of these items already exist then no changes to anything would happen and nothing would be updated or changed. if newKey in self.__dictionaryOfAllDataByKeys__.keys(): return False # This will add the given 'key' and its associated 'value'. self.__dictionaryOfAllDataByKeys__[newKey] = newValue; """ The InMemoryKeyValueDatabase has an internal variable called "databaseName". This holds the name of this particular database. It is set once when you create your database using one of the factory functions (InMemoryKeyValueFactory)). """ return True def getAllData( self ): # The following list will contain all the key-value pairs that make up your database. # Each of these items is a dictionary containing 'key' and 'value'. self.__listOfAllData__ = [] """ The InMemoryKeyValueDatabase has an internal variable called "databaseName". This holds the name of this particular database. It is set once when you create your database using one of the factory functions (InMemoryKeyValueFactory)). """ # The following dictionary will contain all the key-value pairs that make up your database. # Each of these items is a dictionary containing 'key' and 'value'. self.__dictionaryOfAllDataByKeys__ = {} def removeSingleItem( self, itemKeyToRemove ): """ This will take in the key as well as its value. If these items are already present within this InMemoryKeyValue database then no changes to anything would happen and nothing would be updated or changed. The only thing that could possibly change is if a new 'key' and/or 'value' pair was added into one of the dictionarys ((self.__dictionaryOfAllDataByKeys__) that are used within this InMemoryKeyValue database. """ # If either of these items already exist then no changes to anything would happen and nothing would be updated or changed. if itemKeyToRemove in self.__dictionaryOfAllDataByKeys__.keys(): return False del self.__dictionaryOfAllDataByKeys__[itemKeyToRemove] # If either of these items already exist then no changes to anything would happen and nothing would be updated or changed. if itemKeyToRemove in self.__dictionaryOfAllDataByKeys__.keys(): return False del self.__dictionaryOfAllDataByKeys__[itemKeyToRemove] """ The InMemoryKeyValueDatabase has an internal variable called "databaseName". This holds the name of this particular database. It is set once when you create your database using one of the factory functions (InMemoryKeyValueFactory)). """def clearAllData( self ) : self.__listOfAllData__ = [] del self.__dictionaryOfAllDataByKeys__ # If either of these items already exist then no changes to anything would happen and nothing would be updated or changed. if "databaseName" in self.__internalVariable__.keys(): return False self.__internalVariable__ = {}

class InMemoryDB: def __init__(self): self.data = {} self.transactions = [] def begin(self): self.transactions.append({}) def commit(self): if not self.transactions: raise Exception("No transaction") self.data.update(self.transactions.pop()) def rollback(self): if not self.transactions: raise Exception("No transaction") self.transactions.pop() def set(self, key, value): if self.transactions: self.transactions[-1][key] = value else: self.data[key] = value def get(self, key): if self.transactions: for transaction in reversed(self.transactions): if key in transaction: return transaction[key] return self.data.get(key) def delete(self, key): if self.transactions: self.transactions[-1][key] = None else: if key in self.data: del self.data[key] def count(self, value): count = sum(1 for v in self.data.values() if v == value) for transaction in self.transactions: count += sum(1 for v in transaction.values() if v == value) return count

# Property-based Test Suite for a Pearson-r correlation coefficient function

Create a property-based Test Suite for a Pearson-r correlation coefficient function

from hypothesis import given, strategies as stimport numpy as npfrom scipy import statsdef pearson_r(x, y): """Calculate Pearson correlation coefficient.""" mean_x = np.mean(x) mean_y = np.mean(y) num = np.sum((x - mean_x) * (y - mean_y)) den = np.sqrt(np.sum((x - mean_x)**2) * np.sum((y - mean_y)**2)) return num / den@given(st.lists(st.floats(allow_nan=False, allow_infinity=False), min_size=2), st.lists(st.floats(allow_nan=False, allow_infinity=False), min_size=2))def test_pearson_r(x, y): """Property-based test for the Pearson correlation coefficient function.""" # Ensure the lists have the same size if len(x) > len(y): x = x[:len(y)] else: y = y[:len(x)] # Convert lists to numpy arrays x = np.array(x) y = np.array(y) # Calculate Pearson correlation coefficients our_result = pearson_r(x, y) scipy_result, _ = stats.pearsonr(x, y) # Assert that the results are approximately equal np.testing.assert_almost_equal(our_result, scipy_result, decimal=5)

def pearson_r(x, y):