改错model = fitcnb(train_data(:,1:7),train_data(:,8)); model = train(model);

这段代码中存在一个错误，fitcnb()函数应该将第八列作为响应变量而不是训练数据。修改后的代码应该为:

model = fitcnb(train_data(:,1:7),train_data(:,8),'ResponseName','Response');
model = train(model);

相关问题

import torch import torch.nn as nn from torchtext.datasets import AG_NEWS from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator # 数据预处理 tokenizer = get_tokenizer('basic_english') train_iter = AG_NEWS(split='train') counter = Counter() for (label, line) in train_iter: counter.update(tokenizer(line)) vocab = build_vocab_from_iterator([counter], specials=["<unk>"]) word2idx = dict(vocab.stoi) # 设定超参数 embedding_dim = 64 hidden_dim = 128 num_epochs = 10 batch_size = 64 # 定义模型 class RNN(nn.Module): def __init__(self, vocab_size, embedding_dim, hidden_dim): super(RNN, self).__init__() self.embedding = nn.Embedding(vocab_size, embedding_dim) self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True) self.fc = nn.Linear(hidden_dim, 4) def forward(self, x): x = self.embedding(x) out, _ = self.rnn(x) out = self.fc(out[:, -1, :]) return out # 初始化模型、优化器和损失函数 model = RNN(len(vocab), embedding_dim, hidden_dim) optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() # 定义数据加载器 train_iter = AG_NEWS(split='train') train_data = [] for (label, line) in train_iter: label = torch.tensor([int(label)-1]) line = torch.tensor([word2idx[word] for word in tokenizer(line)]) train_data.append((line, label)) train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True) # 开始训练 for epoch in range(num_epochs): total_loss = 0.0 for input, target in train_loader: model.zero_grad() output = model(input) loss = criterion(output, target.squeeze()) loss.backward() optimizer.step() total_loss += loss.item() * input.size(0) print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))改错

在代码中出现了一个错误：缺少 `Counter` 的导入语句。需要在开头添加 `from collections import Counter`。

另外，在模型训练时，需要将模型设置为训练模式（`model.train()`），以启用 dropout 和 batch normalization 等功能。同时，还需要将输入和目标数据转移到 GPU 上进行计算，以加速训练过程。具体修改如下：

import torch
import torch.nn as nn
from torchtext.datasets import AG_NEWS
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from collections import Counter

# 数据预处理
tokenizer = get_tokenizer('basic_english')
train_iter = AG_NEWS(split='train')
counter = Counter()
for (label, line) in train_iter:
    counter.update(tokenizer(line))
vocab = build_vocab_from_iterator([counter], specials=["<unk>"])
word2idx = dict(vocab.stoi)

# 设定超参数
embedding_dim = 64
hidden_dim = 128
num_epochs = 10
batch_size = 64

# 定义模型
class RNN(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(RNN, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.RNN(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, 4)

    def forward(self, x):
        x = self.embedding(x)
        out, _ = self.rnn(x)
        out = self.fc(out[:, -1, :])
        return out

# 初始化模型、优化器和损失函数
model = RNN(len(vocab), embedding_dim, hidden_dim)
optimizer = torch.optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss()

# 将模型设置为训练模式
model.train()

# 将数据转移到 GPU 上
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

train_iter = AG_NEWS(split='train')
train_data = []
for (label, line) in train_iter:
    label = torch.tensor([int(label)-1]).to(device)
    line = torch.tensor([word2idx[word] for word in tokenizer(line)]).to(device)
    train_data.append((line, label))
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)

# 开始训练
for epoch in range(num_epochs):
    total_loss = 0.0
    for input, target in train_loader:
        optimizer.zero_grad()
        output = model(input)
        loss = criterion(output, target.squeeze())
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * input.size(0)
    print("Epoch: {}, Loss: {:.4f}".format(epoch+1, total_loss/len(train_data)))

declare l_src_data varchar2(20); l_type pls_integer :=DBMS_CRYPTO.ENCRYPT_AES128 + DBMS_CRYPTO.CHAIN_CBC + DBMS_CRYPTO.PAD_PKCS5; l_key varchar2(20) :='0123456789123456'; l_encval raw(2000); CURSOR secret_cursor IS select phonenumber from customer; begin OPEN secret_cursor; LOOP FETCH secret_cursor INTO l_src_data; l_encval :=dbms_crypto.encrypt( src=>utl_i18n.string_to_raw(l_src_data,'AL32UTF8'), typ=>l_type, key=>utl_i18n.string_to_raw(l_key,'AL32UTF8')); DBMS_OUTPUT.PUT_LINE (l_encval); EXIT WHEN secret_cursor%NOTFOUND; END LOOP; CLOSE secret_cursor; end; declare ;_src_data row(100) :=hextoraw("190248129038903853275ijdkvjkad'); l_type pls_integer :=dbms_crypto.encrypt_aes128+ + DBMS_CRYPTO.CHAIN_CBC + DBMS_CRYPTO.PAD_PKCS5; l_key varchar2(20) :='0123456789123456'; l_decval raw(200); begin l_decval :=dbms_crypto.decrypt( src=>l_src_data, typ=>l_type, key=>utl_i18n.string_to_raw(l_key,'AL32UTF8')); end;改错

有几个错误需要改正：

1. 在第一个代码块中，`raw(2000)` 应该改为 `raw(200)`，因为加密后的数据不会超过 200 个字节。

2. 在第二个代码块中，`row(100)` 应该改为 `raw(100)`，因为需要将十六进制字符串转换为原始二进制数据。

3. 在第一个代码块和第二个代码块中，加密类型 `DBMS_CRYPTO.ENCRYPT_AES128` 应该改为 `DBMS_CRYPTO.ENCRYPT_AES128 + DBMS_CRYPTO.CHAIN_CBC + DBMS_CRYPTO.PAD_PKCS5`，以保证使用 AES128 算法、CBC 模式和 PKCS5 填充。

4. 在第二个代码块中，`l_src_data` 应该改为 `_src_data`，因为变量名不一致。

修改后的代码如下：

declare
  l_src_data varchar2(20);
  l_type pls_integer := DBMS_CRYPTO.ENCRYPT_AES128
                       + DBMS_CRYPTO.CHAIN_CBC
                       + DBMS_CRYPTO.PAD_PKCS5;
  l_key varchar2(20) := '0123456789123456';
  l_encval raw(200);
  
  CURSOR secret_cursor IS
    select phonenumber from customer;
begin
  OPEN secret_cursor;
  LOOP
    FETCH secret_cursor INTO l_src_data;
    EXIT WHEN secret_cursor%NOTFOUND;
    
    l_encval := dbms_crypto.encrypt(
      src => utl_i18n.string_to_raw(l_src_data, 'AL32UTF8'),
      typ => l_type,
      key => utl_i18n.string_to_raw(l_key, 'AL32UTF8')
    );
    
    DBMS_OUTPUT.PUT_LINE(l_encval);
  END LOOP;
  
  CLOSE secret_cursor;
end;

declare
  _src_data raw(100) := hextoraw('190248129038903853275ijdkvjkad');
  l_type pls_integer := DBMS_CRYPTO.ENCRYPT_AES128
                       + DBMS_CRYPTO.CHAIN_CBC
                       + DBMS_CRYPTO.PAD_PKCS5;
  l_key varchar2(20) := '0123456789123456';
  l_decval raw(200);
begin
  l_decval := dbms_crypto.decrypt(
    src => _src_data,
    typ => l_type,
    key => utl_i18n.string_to_raw(l_key, 'AL32UTF8')
  );
end;