预期的2D阵列，而是出现了1D阵列错误 [英] Expected 2D array, got 1D array instead error

问题描述

我收到错误消息

"ValueError:预期的2D数组，取而代之的是1D数组:array = [45000.50000. 60000. 80000. 110000. 150000. 200000. 300000.500000. 1000000.].如果数据具有单个功能，则使用array.reshape(-1，1)重整数据；如果数据具有单个特征，则使用array.reshape(1，-1)重整数据包含一个样本."

"ValueError: Expected 2D array, got 1D array instead: array=[ 45000. 50000. 60000. 80000. 110000. 150000. 200000. 300000. 500000. 1000000.]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."

在执行以下代码时:

# SVR

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Position_S.csv')
X = dataset.iloc[:, 1:2].values
y = dataset.iloc[:, 2].values

 # Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

# Fitting SVR to the dataset
from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

# Visualising the SVR results
plt.scatter(X, y, color = 'red')
plt.plot(X, regressor.predict(X), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

# Visualising the SVR results (for higher resolution and smoother curve)
X_grid = np.arange(min(X), max(X), 0.01)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color = 'red')
plt.plot(X_grid, regressor.predict(X_grid), color = 'blue')
plt.title('Truth or Bluff (SVR)')
plt.xlabel('Position level')
plt.ylabel('Salary')
plt.show()

推荐答案

似乎，预期尺寸错误.您可以尝试:

Seems, expected dimension is wrong. Could you try:

regressor = SVR(kernel='rbf')
regressor.fit(X.reshape(-1, 1), y)

这篇关于预期的2D阵列，而是出现了1D阵列错误的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！