我有两个numpy数组：

x= np.linspace(1,10,100) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10

我想使用这些数据集训练一个线性回归函数。为了比较复杂性和泛化之间的关系，我使用h多项式特征对一组4次(1, 3, 6, 9)进行预处理。拟合模型后，我想在数组x = np.linspace(1, 10, 100)上进行测试
经过多次尝试，我发现x和y数组需要重新调整，我也这样做了。但是，当我创建要预测的新x数据集时，它抱怨维度没有对齐。估计器正在处理原始x数组的测试拆分。

• • 以下是我的代码**

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

np.random.seed(0)
n = 100
x = np.linspace(0,10,n) + np.random.randn(n)/5
y = np.sin(x)+x/6 + np.random.randn(n)/10

X_train, X_test, y_train, y_test = train_test_split(x, y, random_state=0)

def fn_one():
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import PolynomialFeatures

 x_predict = np.linspace(0,10,100)
 x_predict = x_predict.reshape(-1, 1)
 degrees = [1, 3, 6, 9]
 predictions = []

  for i, deg in enumerate(degrees):
    linReg = LinearRegression()
    pf = PolynomialFeatures(degree=deg)
    xt = x.reshape(-1, 1)
    yt = y.reshape(-1, 1)

    X_transformed = pf.fit_transform(xt)
    X_train_transformed, X_test_transformed, y_train_temp, y_test_temp = train_test_split(X_transformed, yt, random_state=0)
    linReg.fit(X_train_transformed, y_train_temp)
    predictions.append(linReg.predict(x_predict))

 np.array(predictions)
 return predictions

• • 不同数组的形状（循环中的3阶）**

x_predict = (100, 1)

xt = 100, 1

yt = 100, 1

X_train_transformed = 75, 4

y_train_temp = 75, 1

X_test_transformed = 25, 4

y_train_temp = 25, 1

X_test_transformed = 4、25、1的预测值
x_predict的预测=不工作：
错误=值错误：形状（100，1）和（2，1）未对齐：1（维度1）!= 2（维度0）