y_Standard = StandardScaler_y.fit_transform(y) 这句话出错了
##出错信息如下
---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-21-0dc73e1ea680> in <module>()----> 1 y_Standard = StandardScaler_y.fit_transform(y)~/anaconda3/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params) 515 if y is None: 516 # fit method of arity 1 (unsupervised transformation)--> 517 return self.fit(X, **fit_params).transform(X) 518 else: 519 # fit method of arity 2 (supervised transformation)~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py in fit(self, X, y) 588 # Reset internal state before fitting 589 self._reset()--> 590 return self.partial_fit(X, y) 591 592 def partial_fit(self, X, y=None):~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py in partial_fit(self, X, y) 610 """ 611 X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,--> 612 warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES) 613 614 # Even in the case of `with_mean=False`, we update the mean anyway~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator) 439 "Reshape your data either using array.reshape(-1, 1) if " 440 "your data has a single feature or array.reshape(1, -1) "--> 441 "if it contains a single sample.".format(array)) 442 array = np.atleast_2d(array) 443 # To ensure that array flags are maintainedValueError: Expected 2D array, got 1D array instead:
array=[ 24. 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15. 18.9
21.7 20.4 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5
15.6 13.9 16.6 14.8 18.4 21. 12.7 14.5 13.2 13.1 13.5 18.9
20. 21. 24.7 30.8 34.9 26.6 25.3 24.7 21.2 19.3 20. 16.6
14.4 19.4 19.7 20.5 25. 23.4 18.9 35.4 24.7 31.6 23.3 19.6
18.7 16. 22.2 25. 33. 23.5 19.4 22. 17.4 20.9 24.2 21.7
22.8 23.4 24.1 21.4 20. 20.8 21.2 20.3 28. 23.9 24.8 22.9
23.9 26.6 22.5 22.2 23.6 28.7 22.6 22. 22.9 25. 20.6 28.4
21.4 38.7 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4
19.8 19.4 21.7 22.8 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3
22. 20.3 20.5 17.3 18.8 21.4 15.7 16.2 18. 14.3 19.2 19.6
23. 18.4 15.6 18.1 17.4 17.1 13.3 17.8 14. 14.4 13.4 15.6
11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4 17. 15.6
13.1 41.3 24.3 23.3 27. 50. 50. 50. 22.7 25. 50. 23.8
23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2
39.8 36.2 37.9 32.5 26.4 29.6 50. 32. 29.8 34.9 37. 30.5
36.4 31.1 29.1 50. 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5
50. 22.6 24.4 22.5 24.4 20. 21.7 19.3 22.4 28.1 23.7 25.
23.3 28.7 21.5 23. 26.7 21.7 27.5 30.1 44.8 50. 37.6 31.6
46.7 31.5 24.3 31.7 41.7 48.3 29. 24. 25.1 31.5 23.7 23.3
22. 20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8
29.6 42.8 21.9 20.9 44. 50. 36. 30.1 33.8 43.1 48.8 31.
36.5 22.8 30.7 50. 43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.
33.2 33.1 29.1 35.1 45.4 35.4 46. 50. 32.2 22. 20.1 23.2
22.3 24.8 28.5 37.3 27.9 23.9 21.7 28.6 27.1 20.3 22.5 29.
24.8 22. 26.4 33.1 36.1 28.4 33.4 28.2 22.8 20.3 16.1 22.1
19.4 21.6 23.8 16.2 17.8 19.8 23.1 21. 23.8 23.1 20.4 18.5
25. 24.6 23. 22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1
19.5 18.5 20.6 19. 18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1
24.5 26.6 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6
25. 19.9 20.8 16.8 21.9 27.5 21.9 23.1 50. 50. 50. 50.
50. 13.8 13.8 15. 13.9 13.3 13.1 10.2 10.4 10.9 11.3 12.3
8.8 7.2 10.5 7.4 10.2 11.5 15.1 23.2 9.7 13.8 12.7 13.1
12.5 8.5 5. 6.3 5.6 7.2 12.1 8.3 8.5 5. 11.9 27.9
17.2 27.5 15. 17.2 17.9 16.3 7. 7.2 7.5 10.4 8.8 8.4
16.7 14.2 20.8 13.4 11.7 8.3 10.2 10.9 11. 9.5 14.5 14.1
16.1 14.3 11.7 13.4 9.6 8.7 8.4 12.8 10.5 17.1 18.4 15.4
10.8 11.8 14.9 12.6 14.1 13. 13.4 15.2 16.1 17.8 14.9 14.1
12.7 13.5 14.9 20. 16.4 17.7 19.5 20.2 21.4 19.9 19. 19.1
19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3 16.7 12. 14.6 21.4
23. 23.7 25. 21.8 20.6 21.2 19.1 20.6 15.2 7. 8.1 13.6
20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9
22. 11.9].
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.630
收起
正在回答 回答被采纳积分+3
1回答
慕粉2107308463
2017-11-20 00:03:01
