class Test: def_init_(self,value): self. value =value defget(self): return self._yalue defset(self,v): self. value=v value = property(__get,_set) def show(self): print(self._value)
时间: 2024-01-27 13:03:56 浏览: 37
这段代码有一些语法错误,应该是:
```
class Test:
def __init__(self, value):
self._value = value
def get(self):
return self._value
def set(self, v):
self._value = v
value = property(get, set)
def show(self):
print(self._value)
```
这是一个简单的类,它有一个 `value` 属性和一个 `show` 方法。`value` 属性可以通过 `get` 和 `set` 方法来获取和设置。同时,我们使用 `property` 方法将 `get` 和 `set` 方法绑定到 `value` 属性上,使得我们可以通过 `test.value` 的方式来获取和设置属性值。`show` 方法用于输出 `value` 的值。
相关问题
import tensorflow as tf from tensorflow.keras import optimizers, layers, Model class linearModel(Model): def __init__(self, ndim): super(linearModel, self).__init__() self.w = tf.Variable( shape=[ndim, 1], initial_value=tf.random.uniform( [ndim,1], minval=-0.1, maxval=0.1, dtype=tf.float32)) @tf.function def call(self, x): y = tf.squeeze(tf.matmul(x, self.w), axis=1) return y (xs, ys), (o_x, o_y) = load_data('train.txt') ndim = xs.shape[1] model = linearModel(ndim=ndim) 将上述代码的tensflow框架改为pytorch框架,并能在jupyter上运行
import torch
import torch.nn as nn
import torch.optim as optim
class LinearModel(nn.Module):
def __init__(self, ndim):
super(LinearModel, self).__init__()
self.w = nn.Parameter(torch.rand(ndim, 1) * 0.2 - 0.1)
def forward(self, x):
y = torch.mm(x, self.w).squeeze(dim=1)
return y
def load_data(file_path):
# 加载数据的代码请自行实现
pass
# 加载数据
(xs, ys), (o_x, o_y) = load_data('train.txt')
ndim = xs.shape[1]
# 定义模型、损失函数和优化器
model = LinearModel(ndim=ndim)
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# 训练模型
for epoch in range(1000):
optimizer.zero_grad()
y_pred = model(xs)
loss = criterion(y_pred, ys)
loss.backward()
optimizer.step()
if epoch % 100 == 0:
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 1000, loss.item()))
# 测试模型
with torch.no_grad():
y_test = model(o_x)
test_loss = criterion(y_test, o_y)
print('Test Loss: {:.4f}'.format(test_loss.item()))
class KnnRegressorCV: def __init__(self, ks=list(range(1, 21)), cv=LFold(5)): self.ks = ks self.cv = cv # YOUR CODE HERE def fit(self, x, y): df = pd.DataFrame(columns=["KValue", "LFoldID", "MSE_train", "MSE_test"]) count = 0 for k in self.ks: for index, each in enumerate(self.cv.split(x, y)): mse_train, mse_test = runknn(k, each[0], each[1], each[2].reshape(-1), each[3].reshape(-1)) df.at[count, "KValue"] = k df.at[count, "LFoldID"] = index df.at[count, "MSE_train"] = mse_train df.at[count, "MSE_test"] = mse_test count += 1 self.bestK = find_best_k(df, self.ks) self.y_train_ = y self.x_train_kdtree_ = KDTree(x) print("bestK internal:", self.bestK) return self # YOUR CODE HERE def predict(self, x): _, neighbours = self.x_train_kdtree_.query(x, k=self.bestK) neighbours = neighbours.reshape(len(x), self.k) neighbour_labels = self.y_train_[neighbours] pred = np.mean(neighbour_labels, axis=1) return pred给这段代码加注释
```
# 导入需要的库
import pandas as pd
from sklearn.neighbors import KDTree
# 定义 KNN 回归交叉验证类
class KnnRegressorCV:
def __init__(self, ks=list(range(1, 21)), cv=LFold(5)): # 初始化方法,参数包括 k 值列表和交叉验证方法
self.ks = ks # k 值列表
self.cv = cv # 交叉验证方法
def fit(self, x, y): # 模型训练方法,参数包括自变量和因变量
# 定义空的数据框用于存储模型评估结果
df = pd.DataFrame(columns=["KValue", "LFoldID", "MSE_train", "MSE_test"])
count = 0 # 定义计数器
for k in self.ks: # 遍历 k 值列表
for index, each in enumerate(self.cv.split(x, y)): # 遍历交叉验证的训练集和测试集
# 调用 runknn 函数进行模型评估,返回训练集和测试集的 MSE
mse_train, mse_test = runknn(k, each[0], each[1], each[2].reshape(-1), each[3].reshape(-1))
# 将评估结果存入数据框
df.at[count, "KValue"] = k
df.at[count, "LFoldID"] = index
df.at[count, "MSE_train"] = mse_train
df.at[count, "MSE_test"] = mse_test
count += 1 # 计数器加 1
# 调用 find_best_k 函数找到 MSE 最小的 k 值
self.bestK = find_best_k(df, self.ks)
self.y_train_ = y # 存储因变量
self.x_train_kdtree_ = KDTree(x) # 存储自变量的 KDTree
print("bestK internal:", self.bestK) # 打印最优的 k 值
return self # 返回模型自身
def predict(self, x): # 预测方法,参数为自变量
_, neighbours = self.x_train_kdtree_.query(x, k=self.bestK) # 找到最近的 k 个邻居
neighbours = neighbours.reshape(len(x), self.k) # 将邻居的索引转为二维数组
neighbour_labels = self.y_train_[neighbours] # 找到邻居的因变量值
pred = np.mean(neighbour_labels, axis=1) # 对邻居因变量值求平均,作为预测值
return pred # 返回预测值
```