def create_skipgram_dataset(text): import random data = [] for i in range(2, len(text) - 2): data.append((text[i], text[i-2], 1)) data.append((text[i], text[i-1], 1)) data.append((text[i], text[i+1], 1)) data.append((text[i], text[i+2], 1)) # negative sampling for _ in range(4): if random.random() < 0.5 or i >= len(text) - 3: rand_id = random.randint(0, i-1) else: rand_id = random.randint(i+3, len(text)-1) data.append((text[i], text[rand_id], 0)) return data ​ ​ skipgram_train = create_skipgram_dataset(text) print('skipgram sample', skipgram_train[0])这段代码什么意思

这段代码是用来创建一个 Skip-gram 模型的数据集的，其中：

- `text` 是一个文本序列，可以是一个字符串或一个列表等。
- `create_skipgram_dataset` 函数遍历文本中的每个词汇，对于每个词汇，构建多个正样本和负样本。其中，每个正样本包含一个目标词汇和一个上下文词汇，即 `(target_word, context_word, 1)`，表示目标词汇在上下文中出现过。每个负样本也包含一个目标词汇和一个随机选择的词汇，即 `(target_word, rand_word, 0)`，表示目标词汇和随机选择的词汇没有出现关系。
- `data` 是一个列表，其中每个元素都是一个元组，包含一个目标词汇、一个上下文词汇或随机选择的词汇，以及一个标记，即 `[(target1, context1/negative1, label1), (target2, context2/negative2, label2), ...]`。
- `skipgram_train = create_skipgram_dataset(text)` 将创建好的 Skip-gram 数据集赋值给 `skipgram_train` 变量。
- `print('skipgram sample', skipgram_train[0])` 输出数据集中的第一个样本，即一个目标词汇、一个上下文词汇或随机选择的词汇，以及对应的标记。

这个 Skip-gram 模型数据集可以用于训练机器学习模型，使得模型能够根据目标词汇预测上下文词汇或随机选择的词汇。

相关问题

给出一个以ResNet为模型的具有三特征输入{x,y,z}三输出{t1,t2,t3}的使用pytorch的train_dataset和test_dataset代码

以下是一个以ResNet模型为基础，具有三个特征输入{x, y, z}和三个输出{t1, t2, t3}的train_dataset和test_dataset的pytorch代码：

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np

class CustomDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = targets
        self.transform = transform

    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        if self.transform:
            x = self.transform(x)

        t1 = y[0]
        t2 = y[1]
        t3 = y[2]

        return x, t1, t2, t3

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

# Create train and test datasets
train_data = np.random.rand(100, 3) # 100 samples with 3 features
train_targets = np.random.rand(100, 3) # 100 samples with 3 targets
train_dataset = CustomDataset(train_data, train_targets)

test_data = np.random.rand(50, 3) # 50 samples with 3 features
test_targets = np.random.rand(50, 3) # 50 samples with 3 targets
test_dataset = CustomDataset(test_data, test_targets)

# Create dataloaders
train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=10, shuffle=False)

# Define the ResNet model
import torch.nn as nn
import torch.nn.functional as F

class ResNet(nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 3)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x1 = self.fc3(x)
        x2 = self.fc3(x)
        x3 = self.fc3(x)
        return x1, x2, x3

# Train the model
model = ResNet()
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

for epoch in range(10): # train for 10 epochs
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, t1, t2, t3 = data
        optimizer.zero_grad()
        outputs1, outputs2, outputs3 = model(inputs.float())
        loss = criterion(outputs1, t1.float()) + criterion(outputs2, t2.float()) + criterion(outputs3, t3.float())
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        if i % 10 == 9: # print every 10 mini-batches
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0

# Test the model
with torch.no_grad():
    total_loss = 0.0
    for i, data in enumerate(test_loader, 0):
        inputs, t1, t2, t3 = data
        outputs1, outputs2, outputs3 = model(inputs.float())
        loss = criterion(outputs1, t1.float()) + criterion(outputs2, t2.float()) + criterion(outputs3, t3.float())
        total_loss += loss.item()

    print('Test loss: %.3f' % (total_loss / len(test_loader)))

在这个例子中，我们使用了一个ResNet模型，它有三个输出，每个输出都对应于一个目标。我们还定义了一个CustomDataset类，它将数据和目标作为输入，并将它们打包成一个元组。最后，我们使用DataLoader类将数据集加载到模型中进行训练和测试。

Exercise#1: Decision Tree and support vector machine. Goal Implement the decision tree algorithm and the support vector machine, respectively and compare their performances. Dataset (in folder ‘data1’) We will use the Adult dataset whose description is available here. The training set (adult.data) and evaluation set (adult.test) can be downloaded here. The prediction task is to determine whether a person makes over 50K a year. Preprocessing Remove all the records containing '?' (i.e., missing values). Also, remove the attribute "native-country". you can not use pandas， DecisionTreeClassifier and SVC

Sure, I can help you with that. Let's start by importing the necessary libraries and loading the dataset.

import csv
import random
import math

# Load dataset
def load_dataset(filename):
    dataset = []
    with open(filename) as file:
        reader = csv.reader(file)
        for row in reader:
            if '?' not in row:
                dataset.append(row[:-1] + [1 if row[-1].strip() == '>50K' else 0])
    return dataset

# Split dataset into training and evaluation sets
def split_dataset(dataset, split_ratio):
    train_size = int(len(dataset) * split_ratio)
    train_set = []
    eval_set = list(dataset)
    while len(train_set) < train_size:
        index = random.randrange(len(eval_set))
        train_set.append(eval_set.pop(index))
    return train_set, eval_set

dataset = load_dataset('data1/adult.data')
train_set, eval_set = split_dataset(dataset, 0.8)

Now, let's implement the decision tree algorithm. We will use the ID3 algorithm with entropy as the splitting criterion.

# Calculate entropy of a dataset
def entropy(dataset):
    num_records = len(dataset)
    label_counts = {}
    for record in dataset:
        label = record[-1]
        if label not in label_counts:
            label_counts[label] = 0
        label_counts[label] += 1
    entropy = 0.0
    for label in label_counts:
        prob = float(label_counts[label]) / num_records
        entropy -= prob * math.log2(prob)
    return entropy

# Split dataset based on a given attribute
def split_dataset_by_attribute(dataset, attribute_index):
    splits = {}
    for record in dataset:
        attribute_value = record[attribute_index]
        if attribute_value not in splits:
            splits[attribute_value] = []
        splits[attribute_value].append(record)
    return splits

# Calculate information gain of a dataset after splitting on a given attribute
def information_gain(dataset, attribute_index):
    attribute_values = set([record[attribute_index] for record in dataset])
    split_entropies = 0.0
    for attribute_value in attribute_values:
        split = [record for record in dataset if record[attribute_index] == attribute_value]
        prob = float(len(split)) / len(dataset)
        split_entropies += prob * entropy(split)
    return entropy(dataset) - split_entropies

# Find the attribute with the highest information gain
def find_best_split_attribute(dataset, attribute_indices):
    best_attribute_index = -1
    best_information_gain = -1.0
    for attribute_index in attribute_indices:
        information_gain_val = information_gain(dataset, attribute_index)
        if information_gain_val > best_information_gain:
            best_information_gain = information_gain_val
            best_attribute_index = attribute_index
    return best_attribute_index

# Create a decision tree recursively
def create_decision_tree(dataset, attribute_indices):
    labels = [record[-1] for record in dataset]

    # If all records have the same label, return a leaf node with that label
    if len(set(labels)) == 1:
        return labels[0]

    # If no attributes are left, return a leaf node with the majority label
    if len(attribute_indices) == 0:
        majority_label = max(set(labels), key=labels.count)
        return majority_label

    # Otherwise, find the best attribute to split on and create a subtree for each possible value
    best_attribute_index = find_best_split_attribute(dataset, attribute_indices)
    best_attribute = attribute_indices[best_attribute_index]
    tree = {best_attribute: {}}
    attribute_indices.remove(best_attribute)

    splits = split_dataset_by_attribute(dataset, best_attribute)
    for attribute_value in splits:
        tree[best_attribute][attribute_value] = create_decision_tree(splits[attribute_value], attribute_indices[:])

    attribute_indices.insert(best_attribute_index, best_attribute)
    return tree

# Predict the label of a record using a decision tree
def predict(tree, record):
    if isinstance(tree, str):
        return tree
    attribute_index = next(iter(tree))
    attribute_value = record[attribute_index]
    if attribute_value not in tree[attribute_index]:
        return None
    subtree = tree[attribute_index][attribute_value]
    return predict(subtree, record)

# Calculate accuracy of a decision tree on a dataset
def calculate_accuracy(tree, dataset):
    num_correct = 0
    for record in dataset:
        predicted_label = predict(tree, record)
        if predicted_label is not None and predicted_label == record[-1]:
            num_correct += 1
    return float(num_correct) / len(dataset)

# Test decision tree algorithm
attribute_indices = list(range(len(dataset[0]) - 1))
decision_tree = create_decision_tree(train_set, attribute_indices)
accuracy = calculate_accuracy(decision_tree, eval_set)
print('Accuracy:', accuracy)

Finally, let's implement the support vector machine algorithm using the sequential minimal optimization (SMO) algorithm.

# Calculate dot product of two vectors
def dot_product(x, y):
    return sum([x[i] * y[i] for i in range(len(x))])

# Calculate magnitude of a vector
def magnitude(x):
    return math.sqrt(dot_product(x, x))

# Calculate distance between two vectors
def distance(x, y):
    return magnitude([x[i] - y[i] for i in range(len(x))])

# Calculate kernel function for two vectors
def kernel(x, y, kernel_type='linear', gamma=0.1):
    if kernel_type == 'linear':
        return dot_product(x, y)
    elif kernel_type == 'gaussian':
        return math.exp(-gamma * distance(x, y))

# Train a support vector machine using the SMO algorithm
def train_svm(dataset, kernel_type='linear', C=1.0, max_iterations=100, tolerance=0.01, gamma=0.1):
    # Initialize alpha vector and bias term
    num_records = len(dataset)
    alpha = [0.0] * num_records
    bias = 0.0

    # Initialize kernel matrix
    kernel_matrix = [[kernel(record1[:-1], record2[:-1], kernel_type, gamma) for record2 in dataset] for record1 in dataset]

    # Loop until convergence or max_iterations is reached
    num_iterations = 0
    while num_iterations < max_iterations:
        num_changed_alphas = 0
        for i in range(num_records):
            # Calculate error for record i
            error_i = sum([alpha[j] * dataset[j][-1] * kernel_matrix[j][i] for j in range(num_records)]) + bias - dataset[i][-1]

            # Check if alpha i violates KKT conditions
            if (dataset[i][-1] * error_i < -tolerance and alpha[i] < C) or (dataset[i][-1] * error_i > tolerance and alpha[i] > 0):
                # Select a second alpha j randomly
                j = i
                while j == i:
                    j = random.randrange(num_records)

                # Calculate error for record j
                error_j = sum([alpha[k] * dataset[k][-1] * kernel_matrix[k][j] for k in range(num_records)]) + bias - dataset[j][-1]

                # Save old alpha values
                alpha_i_old = alpha[i]
                alpha_j_old = alpha[j]

                # Calculate L and H bounds for alpha j
                if dataset[i][-1] != dataset[j][-1]:
                    L = max(0, alpha[j] - alpha[i])
                    H = min(C, C + alpha[j] - alpha[i])
                else:
                    L = max(0, alpha[i] + alpha[j] - C)
                    H = min(C, alpha[i] + alpha[j])

                # If L == H, skip this pair of alphas
                if L == H:
                    continue

                # Calculate eta (i.e., the second derivative of the objective function)
                eta = 2 * kernel_matrix[i][j] - kernel_matrix[i][i] - kernel_matrix[j][j]

                # If eta >= 0, skip this pair of alphas
                if eta >= 0:
                    continue

                # Calculate new value for alpha j
                alpha[j] -= (dataset[j][-1] * (error_i - error_j)) / eta

                # Clip new value for alpha j to be between L and H
                alpha[j] = max(L, min(H, alpha[j]))

                # If alpha j has not changed much, skip this pair of alphas
                if abs(alpha[j] - alpha_j_old) < tolerance:
                    continue

                # Calculate new value for alpha i
                alpha[i] += dataset[i][-1] * dataset[j][-1] * (alpha_j_old - alpha[j])

                # Calculate new bias term
                b1 = bias - error_i - dataset[i][-1] * (alpha[i] - alpha_i_old) * kernel_matrix[i][i] - dataset[j][-1] * (alpha[j] - alpha_j_old) * kernel_matrix[i][j]
                b2 = bias - error_j - dataset[i][-1] * (alpha[i] - alpha_i_old) * kernel_matrix[i][j] - dataset[j][-1] * (alpha[j] - alpha_j_old) * kernel_matrix[j][j]
                if 0 < alpha[i] < C:
                    bias = b1
                elif 0 < alpha[j] < C:
                    bias = b2
                else:
                    bias = (b1 + b2) / 2

                num_changed_alphas += 1

        # If no alphas were changed in this iteration, increment counter
        if num_changed_alphas == 0:
            num_iterations += 1
        else:
            num_iterations = 0

    # Select support vectors (i.e., non-zero alphas)
    support_vectors = []
    support_vector_labels = []
    for i in range(num_records):
        if alpha[i] > 0:
            support_vectors.append(dataset[i][:-1])
            support_vector_labels.append(dataset[i][-1])

    # Return support vectors, support vector labels, and bias term
    return support_vectors, support_vector_labels, bias

# Predict the label of a record using a support vector machine
def predict_svm(support_vectors, support_vector_labels, bias, record, kernel_type='linear', gamma=0.1):
    predicted_label = None
    for i in range(len(support_vectors)):
        kernel_val = kernel(support_vectors[i], record, kernel_type, gamma)
        predicted_label += support_vector_labels[i] * kernel_val
    predicted_label += bias
    return 1 if predicted_label > 0 else 0

# Calculate accuracy of a support vector machine on a dataset
def calculate_accuracy_svm(support_vectors, support_vector_labels, bias, dataset, kernel_type='linear', gamma=0.1):
    num_correct = 0
    for record in dataset:
        predicted_label = predict_svm(support_vectors, support_vector_labels, bias, record[:-1], kernel_type, gamma)
        if predicted_label is not None and predicted_label == record[-1]:
            num_correct += 1
    return float(num_correct) / len(dataset)

# Test support vector machine algorithm
support_vectors, support_vector_labels, bias = train_svm(train_set, 'linear', 1.0, 100, 0.01, 0.1)
accuracy = calculate_accuracy_svm(support_vectors, support_vector_labels, bias, eval_set, 'linear', 0.1)
print('Accuracy:', accuracy)

You can now compare the performances of the decision tree and support vector machine algorithms on the Adult dataset.