Python脚本错误“预期的二维数组，而不是1D数组：”？

Question

我下面这个教程，使这个ML预测：使用Python 3.6

Link Tutorial

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import style 

style.use("ggplot") 
from sklearn import svm 

x = [1, 5, 1.5, 8, 1, 9] 
y = [2, 8, 1.8, 8, 0.6, 11] 

plt.scatter(x,y) 
plt.show() 

X = np.array([[1,2], 
      [5,8], 
      [1.5,1.8], 
      [8,8], 
      [1,0.6], 
      [9,11]]) 

y = [0,1,0,1,0,1] 
X.reshape(1, -1) 

clf = svm.SVC(kernel='linear', C = 1.0) 
clf.fit(X,y) 

print(clf.predict([0.58,0.76])) 

Im和我得到错误“预期二维数组，得到了一维数组，而不是：”我 觉得剧本是为旧版本，但我不知道如何将其转换为3.6版本。

已经与尝试：

X.reshape(1, -1) 

Answer 1

你只是应该提供predict方法用相同的二维数组，但要处理（或更多）的一个值。总之，只需更换

[0.58,0.76] 

随着

[[0.58,0.76]] 

，它应该工作

Answer 2

问题发生时，你的阵列[0.58,0.76]上运行的预测。在调用之前通过重塑它来解决问题predict()：

import numpy as np 
import matplotlib.pyplot as plt 
from matplotlib import style 

style.use("ggplot") 
from sklearn import svm 

x = [1, 5, 1.5, 8, 1, 9] 
y = [2, 8, 1.8, 8, 0.6, 11] 

plt.scatter(x,y) 
plt.show() 

X = np.array([[1,2], 
      [5,8], 
      [1.5,1.8], 
      [8,8], 
      [1,0.6], 
      [9,11]]) 

y = [0,1,0,1,0,1] 

clf = svm.SVC(kernel='linear', C = 1.0) 
clf.fit(X,y) 

test = np.array([0.58, 0.76]) 
print test  # Produces: [ 0.58 0.76] 
print test.shape # Produces: (2,) meaning 2 rows, 1 col 

test = test.reshape(1, -1) 
print test  # Produces: [[ 0.58 0.76]] 
print test.shape # Produces (1, 2) meaning 1 row, 2 cols 

print(clf.predict(test)) # Produces [0], as expected