我正在尝试让计算机预先猜测玩家将要选择的内容，具体取决于玩家在先前尝试中选择的内容：
import random
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
from sklearn.model_selection import train_test_split
iris_dataset=load_iris()
while True:
print()
print("Do you choose rock, paper or scissors?")
guessArray = []
resultArray = []
guess = str(input())
guess = guess.lower()
guessNum = 0
if guess == 'rock':
guessArray.append(1)
elif guess == 'paper':
guessArray.append(2)
elif guess == 'scissors':
guessArray.append(3)
else:
guessArray.append(4)
if guess == 'rock':
guessNum = 1
elif guess == 'paper':
guessNum = 2
elif guess == 'scissors':
guessNum = 3
else:
guessNum = 4
choices = ['rock', 'paper', 'scissors']
print('You guessed', guess)
X_train, X_test, y_train, y_test = train_test_split(iris_dataset["data"], iris_dataset["target"], random_state=0)
kn = KNeighborsClassifier(n_neighbors=1)
kn.fit(X_train, y_train)
x_new = np.array([guessArray])
prediction = kn.predict(x_new)
computer_guess = format(prediction)
print('Computer guess', computer_guess)
if guess in choices:
if guessNum == computer_guess:
result = 'Tie'
elif guessNum == 1:
if computer_guess == 3:
result = 'Win'
elif computer_guess == 2:
result = 'Loose'
elif guess == 2:
if computer_guess == 2:
result = 'Loose'
elif computer_guess == 1:
result = 'Win'
elif guess == 3:
if computer_guess == 2:
result = 'Win'
elif computer_guess == 1:
result = 'Loose'
else:
result = 'Invalid'
resultArray.append(result)
#Print result
#print("Predicted target value: {}\n".format(prediction))
#print("Predicted feature name: {}\n".format
# (iris_dataset["target_names"][prediction]))
#print("Test score: {:.2f}".format(kn.score(X_test, y_test))
代码可以正常运行，甚至可以正常打印所有内容，直到“ prediction = kn.predict（x_new）”行，然后一切都变得混乱。我发现我需要以某种方式重塑x_new变量，但是由于我今天才开始使用python编程，除了.net和c＃的真正基础知识之外，从未与其他机器学习合作过。
Traceback (most recent call last):
File "rockpaperscissors.py", line 40, in <module>
prediction = kn.predict(x_new)
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\neighbors\classification.py", line 149, in predict
neigh_dist, neigh_ind = self.kneighbors(X)
File "C:\Users\viktor.landstrom.LEARNET\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\neighbors\base.py", line 454, in kneighbors
for s in gen_even_slices(X.shape[0], n_jobs)
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\parallel.py", line 1003, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\parallel.py", line 834, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\parallel.py", line 753, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\_parallel_backends.py", line 201, in apply_async
result = ImmediateResult(func)
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\_parallel_backends.py", line 582, in __init__
self.results = batch()
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\parallel.py", line 256, in __call__
for func, args, kwargs in self.items]
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\joblib\parallel.py", line 256, in <listcomp>
for func, args, kwargs in self.items]
File "C:\Users\user\AppData\Local\Programs\Python\Python37-32\lib\site-packages\sklearn\neighbors\base.py", line 291, in _tree_query_parallel_helper
return tree.query(data, n_neighbors, return_distance)
File "sklearn\neighbors\binary_tree.pxi", line 1317, in sklearn.neighbors.kd_tree.BinaryTree.query
ValueError: query data dimension must match training data dimension