Implement stack with Pyhon list

class Stack:
    '''Implement a stack using list'''
    def __init__(self, L=[]):
        self.data = L

    def push(self, item):
        self.data.append(item)

    def pop(self):
        return self.data.pop()

    def peek(self):
        return self.data[-1]

    def is_empty(self):
        return len(self.data) == 0

    def size(self):
        return len(self.data)

    def clear(self):
        self.data = []

if __name__ == '__main__':
    s = Stack()
    s.push(123)
    s.push('Hello')
    s.push(True)
    print('Size of s: ', s.size)
    print('Pop an item: ', s.pop())
    print('Peek an item:', s.peek())
    print('Size of s: ', s.size())
    print('Pop an item: ', s.pop())
    print('Pop an item: ', s.pop())
    print('Size of s: ', s.size())
    print('s is empty?: ', s.is_empty())
# Balanced Parentheses Using Stack data structure
from stack import Stack

def BalancedParentheses(strParens):
    s = Stack()
    for item in strParens:
        if item == '(':
            s.push(item)
        else:
            if s.is_empty():
                return False
            s.pop()

    return s.is_empty()

if __name__ == '__main__':
    print(BalancedParentheses('(()()()())'))
    print(BalancedParentheses('(((()))))'))
    print(BalancedParentheses('(()((())())))'))
    print(BalancedParentheses('(()()(()())))'))
# A more general brackets matching
from stack import Stack

def balancedSymbol(symStr):
    s = Stack()
    s.clear()   # I'm not sure why this is required 
    for item in symStr:
        if item in '([{':
            s.push(item)
        else:
            if s.is_empty():
                return False
            else:
                top = s.pop()
                if not match(top, item):
                    return False
    return s.is_empty()

def match(left, right):
    opening = '([{'
    closing = ')]}'
    return opening.index(left) == closing.index(right)


if __name__ == '__main__':
    print(balancedSymbol('(()[])'))         # Should be True
    print(balancedSymbol('(()[]){{'))       # Should be False
    print(balancedSymbol('({([])[]})'))     # Should be True
    print(balancedSymbol('(()[])[{}'))      # Should be False




Here's an implementation of the k-nearest neighbors (KNN) classifier in Python: ``` import numpy as np def knn_classifier(X_train, y_train, X_test, k): """ K-nearest neighbors classifier Parameters: X_train (numpy.ndarray): Training data features y_train (numpy.ndarray): Training data labels X_test (numpy.ndarray): Test data features k (int): Number of nearest neighbors to consider Returns: y_pred (numpy.ndarray): Predicted labels for test data """ # Calculate distances between test data and training data dists = np.sqrt(np.sum((X_train - X_test[:, np.newaxis])**2, axis=2)) # Get indices of k nearest neighbors for each test data point knn_indices = np.argsort(dists, axis=1)[:, :k] # Get labels of k nearest neighbors knn_labels = y_train[knn_indices] # Predict labels based on majority vote y_pred = np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=1, arr=knn_labels) return y_pred ``` This function takes in the training data features (`X_train`) and labels (`y_train`), test data features (`X_test`), and the number of nearest neighbors to consider (`k`). It first calculates the distances between each test data point and each training data point using the Euclidean distance metric. It then finds the indices of the k nearest neighbors for each test data point, and gets the corresponding labels. Finally, it predicts the label of each test data point based on the majority vote of its k nearest neighbors. Note that this implementation assumes that the input data is in the form of numpy arrays. If your data is in a different format, you may need to modify the function accordingly. Also, this implementation uses the `np.apply_along_axis()` function to apply a function to each row of a 2D array. This can be slower than using a loop, but is more concise and often easier to read. If performance is a concern, you may want to consider using a loop instead.
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