Question

我有2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线性回归。为了比较复杂度和泛化之间的关系，我使用h多项式特征对一组4度(1, 3, 6, 9)进行了预处理。拟合模型后，我要在数组x = np.linspace(1, 10, 100)

上进行测试

经过大量的尝试，我发现x和y数组需要重塑，我做到了。但是，当我创建要预测的新x数据集时，它抱怨尺寸未对齐。估算器正在对原始x数组进行测试分割。

下面是我的代码

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

不同阵列的形状（循环中的@度3）

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的预测= 4、25、1

x_predict的预测=不起作用：

Error = ValueError：形状（100,1）和（2,1）不对齐：1（尺寸1）！= 2（暗淡0）

Answer 1

您忘记转换x_predict。我已经在下面更新了您的代码：

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)
    x_predict_transformed = pf.fit_transform(x_predict)
    predictions.append(linReg.predict(x_predict_transformed))

 np.array(predictions)
 return predictions

现在，当您致电fn_one()时，您将得到预测。

希望这会有所帮助！

线性回归无法预测一组值；错误：ValueError：形状（100,1）和（2,1）不对齐：1（dim 1）！= 2（dim 0）

1 个答案: