線性回歸器無法預測一組值； 錯誤：ValueError：形狀（100,1）和（2,1）未對齊：1（dim 1）！= 2（dim 0）

Question

我有 2 個 numpy 數組：

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

我想使用這些數據集訓練線性回歸器。 為了比較復雜性和泛化之間的關系，我對一組 4 度(1, 3, 6, 9)使用了 h 多項式特征預處理。 擬合模型后，我想測試一個數組x = np.linspace(1, 10, 100)

經過多次嘗試，我發現 x 和 y 數組需要重新整形，我就這樣做了。 但是，當我創建要預測的新 x 數據集時，它會抱怨維度未對齊。 估計器正在處理原始 x 數組的測試拆分。

下面是我的代碼

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

不同數組的形狀（@循環中的度數 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 = 4, 25, 1 的預測

x_predict 的預測 = 不工作：

錯誤 = ValueError：形狀 (100,1) 和 (2,1) 未對齊：1 (dim 1) != 2 (dim 0)

Answer 1

你忘了轉換你的x_predict 。 我已在下面更新了您的代碼：

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

現在，當您調用fn_one()您將獲得預測。

希望這可以幫助！