LinearRegression()怎么输入Y预测X，怎么输出相关系数r

LinearRegression()怎么输入Y预测X，怎么输出相关系数r
这个只能输入x预测Y

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

rng=np.random.RandomState(1)
xtrain=10*rng.rand(30)
ytrain=8+4*xtrain+rng.rand(30)

fig=plt.figure(figsize=(12,8))
ax1=fig.add_subplot(121)
ax1.scatter(xtrain,ytrain,color='red')
ax1.grid()
ax1.set_title('1')
plt.show()

model=LinearRegression()

model.fit(xtrain[:,np.newaxis],ytrain)
print(model.coef_[0])
print(model.intercept_)
print(model.rank_)

xtest=np.linspace(1,10,20)
y=model.predict(xtest[:,np.newaxis])
print(y)

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
 
 
# Hyper-parameters 定义迭代次数， 学习率以及模型形状的超参数
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001
 
# Toy dataset  1. 准备数据集
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
                    [9.779], [6.182], [7.59], [2.167], [7.042], 
                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
 
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
                    [3.366], [2.596], [2.53], [1.221], [2.827], 
                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
 
# Linear regression model  2. 定义网络结构 y=w*x+b 其中w的size [1,1], b的size[1,]
model = nn.Linear(input_size, output_size)
 
# Loss and optimizer 3.定义损失函数， 使用的是最小平方误差函数
criterion = nn.MSELoss()
# 4.定义迭代优化算法， 使用的是随机梯度下降算法
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
loss_dict = []
# Train the model 5. 迭代训练
for epoch in range(num_epochs):
    # Convert numpy arrays to torch tensors  5.1 准备tensor的训练数据和标签
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
 
    # Forward pass  5.2 前向传播计算网络结构的输出结果
    outputs = model(inputs)
    # 5.3 计算损失函数
    loss = criterion(outputs, targets)
    
    # Backward and optimize 5.4 反向传播更新参数
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
 
    
    # 可选 5.5 打印训练信息和保存loss
    loss_dict.append(loss.item())
    if (epoch+1) % 5 == 0:
        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
 
# Plot the graph 画出原y与x的曲线与网络结构拟合后的曲线
predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()
 
# 画loss在迭代过程中的变化情况
plt.plot(loss_dict, label='loss for every epoch')
plt.legend()
plt.show()