Appearance
question:Given an array with n integers, you need to find the maximum sum of lengths of non-overlapping subarrays with the given integers, such that every number is smaller than all numbers on its right side. Implement a function named `maxSumOfSubarraysWithMinAbsoluteDifference` that takes in a list of integers and an integer k. The function should return the maximum sum of lengths of k such subarrays. If it is not possible to divide the array into k such subarrays, return -1.
answer:def maxSumOfSubarraysWithMinAbsoluteDifference(arr, k): n = len(arr) if k > n: return -1 # Function to find the length of subarray where all # elements are in a strictly increasing order def subarray_lengths(): lengths = [] length = 1 for i in range(1, n): if arr[i] > arr[i-1]: length += 1 else: if length > 1: lengths.append(length) length = 1 if length > 1: lengths.append(length) return lengths lengths = subarray_lengths() if len(lengths) < k: return -1 lengths.sort(reverse=True) return sum(lengths[:k])
question:Train a neural network with PyTorch for multiclass classification with weighted classes.
answer:import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset class SimpleNN(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(SimpleNN, self).__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.fc2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.fc2(out) return out def train_model(train_loader, model, criterion, optimizer, num_epochs): for epoch in range(num_epochs): for inputs, labels in train_loader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() return model def get_train_loader(data, targets, batch_size=16): tensor_x = torch.Tensor(data) tensor_y = torch.Tensor(targets).long() dataset = TensorDataset(tensor_x, tensor_y) train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) return train_loader def get_model_and_optimizer(input_size, hidden_size, num_classes, learning_rate): model = SimpleNN(input_size, hidden_size, num_classes) optimizer = optim.Adam(model.parameters(), lr=learning_rate) return model, optimizer def get_loss_function(class_weights): return nn.CrossEntropyLoss(weight=class_weights)
question:Given a list of words, write a Python program that finds and returns the word that has the greatest number of distinct characters. If there is a tie, return the word that appears first in the list.
answer:def word_with_most_unique_chars(words): Given a list of words, returns the word that has the greatest number of distinct characters. If there is a tie, returns the word that appears first in the list. :param words: List of words :return: Word with the most distinct characters max_unique_chars = 0 result_word = words[0] if words else "" for word in words: unique_chars = len(set(word)) if unique_chars > max_unique_chars: max_unique_chars = unique_chars result_word = word return result_word
question:I have a list of tuples representing a binary tree in Python. Each tuple has three elements: the value of the node, and two nested trees which represent the node's left and right children. Note these trees themselves are represented as tuples in the same way. I want to write a recursive function that returns the deepest node on the left side of the tree.
answer:def deepest_left_node(tree, is_left=False, depth=0): if tree is None: return (None, depth) value, left, right = tree if left is None and right is None: return (value, depth) if is_left else (None, depth) left_result, left_depth = deepest_left_node(left, True, depth + 1) right_result, right_depth = deepest_left_node(right, False, depth + 1) if left_result is None or (right_result is not None and right_depth > left_depth): return right_result, right_depth else: return left_result, left_depth def find_deepest_left_node(tree): result, _ = deepest_left_node(tree) return result