"预期的二维数组，得到一维数组而不是错误"
[英]Expected 2D array, got 1D array instead error
问题描述
Iam getting the error as
我收到错误为
# 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()<\/code><\/pre>"
3 个解决方案
解决方案1
6 已采纳 2018-10-24 06:26:16
Seems, expected dimension is wrong.似乎，预期的尺寸是错误的。 Could you try:你能不能试试：
regressor = SVR(kernel='rbf')
regressor.fit(X.reshape(-1, 1), y)
解决方案2
0 2020-07-25 04:54:08
The problem is if you type y.ndim, you will see the dimension as 1, and if you type X.ndim, you will see the dimension as 2.问题是如果你输入 y.ndim，你会看到维度为 1，如果你输入 X.ndim，你会看到维度为 2。
So to solve this problem you have to change the result of y.ndim from 1 to 2.所以要解决这个问题，你必须将 y.ndim 的结果从 1 改为 2。
For this just use the reshape function that comes under numpy class.为此，只需使用 numpy 类下的 reshape 函数。
data=pd.read_csv("Position_Salaries.csv")
X=data.iloc[:,1:2].values
y=data.iloc[:,2].values
y=np.reshape(y,(10,1))
It should solve the problem caused due to dimension.它应该解决由于尺寸引起的问题。 Do the regular Feature Scaling after the above code and it will work for sure.在上面的代码之后做常规的特征缩放，它肯定会起作用。
Do vote if it works for you.如果它对你有用，请投票。
Thanks.谢谢。
解决方案3
0 2022-02-05 05:12:27
from sklearn.preprocessing import StandardScaler
#Creating two objects for dependent and independent variable
ss_X = StandardScaler()
ss_y = StandardScaler()
X = ss_X.fit_transform(X)
y = ss_y.fit_transform(y.reshape(-1,1))
After Reshape thing it will work fine重塑后它会正常工作
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