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

Posted 2023-03-12

【中文标题】线性回归器无法预测一组值；错误：ValueError：形状（100,1）和（2,1）未对齐：1（dim 1）！= 2（dim 0）

【英文标题】：Linear Regressor unable to predict a set of values; Error: ValueError: shapes (100,1) and (2,1) not aligned: 1 (dim 1) != 2 (dim 0)

【发布时间】：2019-11-23 10:29:29

【问题描述】：

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器。为了比较复杂度和泛化之间的关系，我对一组 4 度 (1, 3, 6, 9) 使用 h 多项式特征预处理。 拟合模型后，我想在数组上测试x = np.linspace(1, 10, 100)

经过多次尝试，我发现 x 和 y 数组需要重新整形，我就这样做了。但是，当我创建要预测的新 x 数据集时，它抱怨维度未对齐。估计器正在对原始 x 数组进行测试拆分。

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

不同数组的形状（@degree 3 in the loop）

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

x_predict 的预测 = 不工作：

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

【参考方案1】：

您忘记转换您的x_predict。我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，您将获得预测。

希望这会有所帮助！

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