chat

🔍

question:# Question: Implement a Custom Activation Function in a Neural Network You are tasked with implementing a custom activation function for a feedforward neural network. The neural network class is already provided, but currently, it only supports the ReLU activation function. You need to extend this functionality by implementing a custom activation function that can handle multiple types of activations. Your task is to: 1. Implement the `custom_activation` method that performs different activation functions based on the input parameter. 2. Integrate this custom activation function into the feedforward process of the neural network class. Requirements: - Your `custom_activation` method should accept two parameters: - `x`: A numpy array representing the input to the activation function. - `activation_type`: Type of activation function to apply, which can be either "relu", "sigmoid", or "tanh". Input: - `x`: Numpy array. - `activation_type` (str): Activation function type, either "relu", "sigmoid", or "tanh". Output: - `activated_x`: Numpy array after applying the specified activation function. Constraints: - Assume that the input array `x` can be of any shape. # Example: ```python # Example input: x = np.array([-1, 0, 1]) activation_type = 'sigmoid' # Expected output: # activated_x = np.array([0.26894142, 0.5, 0.73105858]) ``` Function signature: ```python def custom_activation(x: np.ndarray, activation_type: str) -> np.ndarray: pass ``` Example Neural Network Class: ```python class NeuralNetwork: def __init__(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.weights1 = np.random.rand(self.input_size, self.hidden_size) self.weights2 = np.random.rand(self.hidden_size, self.output_size) def feedforward(self, X, activation_type='relu'): self.hidden = custom_activation(np.dot(X, self.weights1), activation_type) self.output = custom_activation(np.dot(self.hidden, self.weights2), activation_type) return self.output # Demonstrating the activation function within the class: nn = NeuralNetwork(3, 3, 2) X = np.array([[1, 2, 3]]) activation_type = 'tanh' output = nn.feedforward(X, activation_type) print(output) ``` You should integrate your `custom_activation` method into the provided neural network class, ensuring it can handle the specified activation functions during the feedforward process.

answer:import numpy as np def custom_activation(x: np.ndarray, activation_type: str) -> np.ndarray: if activation_type == 'relu': return np.maximum(0, x) elif activation_type == 'sigmoid': return 1 / (1 + np.exp(-x)) elif activation_type == 'tanh': return np.tanh(x) else: raise ValueError("Unsupported activation type. Supported types: 'relu', 'sigmoid', 'tanh'.") class NeuralNetwork: def __init__(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.weights1 = np.random.rand(self.input_size, self.hidden_size) self.weights2 = np.random.rand(self.hidden_size, self.output_size) def feedforward(self, X, activation_type='relu'): self.hidden = custom_activation(np.dot(X, self.weights1), activation_type) self.output = custom_activation(np.dot(self.hidden, self.weights2), activation_type) return self.output

question:# Maximum Subarray Sum with Dynamic Programming **Scenario/Context:** You are developing an automated trading system and need to analyze past stock price data to identify the most profitable trading periods. One way to do this is by finding the maximum sum of a contiguous subarray within a one-dimensional array of stock price changes. **Task:** Implement a function that uses Dynamic Programming to find the maximum sum of a contiguous subarray within a given array of integers. **Function Signature:** ```python def max_subarray_sum(arr: list) -> int: pass ``` # Input 1. **arr** (list): A list of integers representing the changes in stock prices. # Output - An integer representing the maximum sum of any contiguous subarray. # Constraints - `1 ≤ len(arr) ≤ 10^5` - `-10^3 ≤ arr[i] ≤ 10^3` for all elements in `arr` - The input list can contain both positive and negative integers. # Performance Requirements - The solution should have a time complexity of O(n) and space complexity of O(1). # Example ```python arr = [-2, 1, -3, 4, -1, 2, 1, -5, 4] # Expected Output: # 6 # Explanation: # The subarray [4,-1,2,1] has the largest sum 6. print(max_subarray_sum(arr)) ``` # Edge Cases - Handle arrays with only one element. - Consider arrays with all negative numbers. - Include scenarios where the maximum sum is achieved by taking the entire array. # Validation Ensure that the input is a list of integers and not empty. Implement validation checks for the input constraints and handle any potential corner cases.

answer:def max_subarray_sum(arr: list) -> int: Returns the maximum sum of a contiguous subarray within the given array. # Edge case: If the array has only one element, return that element. if len(arr) == 1: return arr[0] # Initialize the variables. max_so_far = arr[0] max_ending_here = arr[0] # Iterate through the array starting from the second element. for i in range(1, len(arr)): # Update max_ending_here by either starting a new subarray or extending the existing one. max_ending_here = max(arr[i], max_ending_here + arr[i]) # Update max_so_far to keep track of the maximum sum found so far. max_so_far = max(max_so_far, max_ending_here) return max_so_far

question:# Coding Assessment Question Scenario You are tasked with creating a library management system that includes categorizing books by their genres and checking their availability. One specific feature needed is to determine the least populous genre when a new book is added, to ensure a balanced collection. Task Implement a function `find_least_populous_genre(library: dict, new_book: dict) -> str` that determines and returns the genre with the fewest books in the library when a new book is added. The library's data structure is a dictionary where the keys are genre names and the values are lists of book titles. The new book's data is a dictionary containing the book's title and genre. Requirements * **Input**: Two dictionaries: - `library`: keys are genre names (str), and values are lists of book titles (list of str) - `new_book`: keys are 'title' (str) and 'genre' (str) * **Output**: Genre name (str) with the fewest books after adding the new book. Constraints * Handle cases where the library initially has multiple genres with the same number of books. * If the new book's genre does not exist in the library, add it with the new book as its first entry. * The input dictionaries will always have a valid structure. Example ```python library = { 'Science Fiction': ['Dune', 'Neuromancer'], 'Fantasy': ['Harry Potter', 'The Hobbit'], 'Mystery': ['The Hound of the Baskervilles'] } new_book = {'title': 'Ender's Game', 'genre': 'Science Fiction'} find_least_populous_genre(library, new_book) # Output: 'Mystery' ``` Notes * Update the library with the new book before determining the least populous genre. * In case of a tie among multiple genres, return any one of the least populous genres. * Use appropriate data structures to efficiently determine the genre with the fewest books.

answer:def find_least_populous_genre(library, new_book): Determines and returns the genre with the fewest books in the library when a new book is added. :param library: dict, keys are genre names (str), values are lists of book titles (str) :param new_book: dict, keys are 'title' (str) and 'genre' (str) :return: str, genre with the fewest books after adding the new book genre = new_book['genre'] title = new_book['title'] if genre not in library: library[genre] = [] library[genre].append(title) min_genre = min(library, key=lambda g: len(library[g])) return min_genre

question:# Problem Statement Write a function `reorganize_string` that takes a string `s` and returns a new string such that no two adjacent characters are the same. If it is not possible to reorganize the string to satisfy this condition, return an empty string. # Requirements * The function should raise a ValueError with the message "Input string contains invalid characters" if the string `s` contains non-alphabetic characters. * The function should preserve the case of the original characters. * The function should not modify the input string `s`. * The solution must have a time complexity of O(n log n) or better for a string of length n. # Input * A string `s` containing alphabetic characters (both uppercase and lowercase). # Output * A new string with the same characters but reorganized such that no two adjacent characters are the same, or an empty string if such a reorganization is not possible. # Function Signature ```python def reorganize_string(s: str) -> str: pass ``` # Example ```python >>> reorganize_string("aab") "aba" >>> reorganize_string("aaab") "" >>> reorganize_string("aabbcc") "abcabc" >>> reorganize_string("abc123") Traceback (most recent call last): ... ValueError: Input string contains invalid characters ``` # Constraints * The length of the input string `s` can be between 0 and 10^5. * The input string `s` will only contain alphabetic characters and spaces.

answer:import heapq from collections import Counter def reorganize_string(s: str) -> str: # Check for invalid characters if not s.isalpha(): raise ValueError("Input string contains invalid characters") # Count the frequencies of each character counter = Counter(s) max_heap = [(-count, char) for char, count in counter.items()] # Create a max heap based on character frequencies heapq.heapify(max_heap) prev_char = None prev_count = 0 result = [] while max_heap: count, char = heapq.heappop(max_heap) if prev_count < 0: heapq.heappush(max_heap, (prev_count, prev_char)) result.append(char) prev_char = char prev_count = count + 1 # Decrease count (since it was negative, increase towards zero) if len(result) != len(s): return "" return ''.join(result)