import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
#原始数据集维度
data = diabetes.data
print(data.shape)
#np.newaxis放在第一个参数位置
data_1 = data[np.newaxis,:]
print(data_1.shape)
#np.newaxis放在第二个参数位置
data_2 = data[:,np.newaxis,:]#等价于data_2 = data[:,np.newaxis]和data_2 = data[:,np.newaxis,...]
print(data_2.shape)
#np.newaxis放在第三个参数位置
data_3 = data[:,:,np.newaxis]
print(data_3.shape)
#此处相当于把data数据集转换成442*1*10后，在第三个维度中的10个系列中选取第3个系列(index为2)。
diabetes_X =data[:, np.newaxis, 2]
print(diabetes_X.shape)
print(data,diabetes_X,sep='\n')
print(data[:,2])#此处data[:,2]和data[:, np.newaxis, 2]的内容一样，只是显示的排列方式不一样。都是原data的第三列data[:2]的内容。
(442, 10)
(1, 442, 10)
(442, 1, 10)
(442, 10, 1)
(442, 1)
[[ 0.03807591 0.05068012 0.06169621 ... -0.00259226 0.01990842
-0.01764613]
[-0.00188202 -0.04464164 -0.05147406 ... -0.03949338 -0.06832974
-0.09220405]
[ 0.08529891 0.05068012 0.04445121 ... -0.00259226 0.00286377
-0.02593034]
...
[ 0.04170844 0.05068012 -0.01590626 ... -0.01107952 -0.04687948
0.01549073]
[-0.04547248 -0.04464164 0.03906215 ... 0.02655962 0.04452837
-0.02593034]
[-0.04547248 -0.04464164 -0.0730303 ... -0.03949338 -0.00421986
0.00306441]]
[[ 0.06169621]
[-0.05147406]
[ 0.04445121]
[-0.01159501]
[-0.03638469]
[-0.04069594]
[-0.04716281]
[-0.00189471]
[ 0.06169621]
[ 0.03906215]
[-0.08380842]
[ 0.01750591]
[-0.02884001]
[-0.00189471]
[-0.02560657]
[-0.01806189]
[ 0.04229559]
[ 0.01211685]
[-0.0105172 ]
[-0.01806189]
[-0.05686312]
[-0.02237314]
[-0.00405033]
[ 0.06061839]
[ 0.03582872]
[-0.01267283]
[-0.07734155]
[ 0.05954058]
[-0.02129532]
[-0.00620595]
[ 0.04445121]
[-0.06548562]
[ 0.12528712]
[-0.05039625]
[-0.06332999]
[-0.03099563]
[ 0.02289497]
[ 0.01103904]
[ 0.07139652]
[ 0.01427248]
[-0.00836158]
[-0.06764124]
[-0.0105172 ]
[-0.02345095]
[ 0.06816308]
[-0.03530688]
[-0.01159501]
[-0.0730303 ]
[-0.04177375]
[ 0.01427248]
[-0.00728377]
[ 0.0164281 ]
[-0.00943939]
[-0.01590626]
[ 0.0250506 ]
[-0.04931844]
[ 0.04121778]
[-0.06332999]
[-0.06440781]
[-0.02560657]
[-0.00405033]
[ 0.00457217]
[-0.00728377]
[-0.0374625 ]
[-0.02560657]
[-0.02452876]
[-0.01806189]
[-0.01482845]
[-0.02991782]
[-0.046085 ]
[-0.06979687]
[ 0.03367309]
[-0.00405033]
[-0.02021751]
[ 0.00241654]
[-0.03099563]
[ 0.02828403]
[-0.03638469]
[-0.05794093]
[-0.0374625 ]
[ 0.01211685]
[-0.02237314]
[-0.03530688]
[ 0.00996123]
[-0.03961813]
[ 0.07139652]
[-0.07518593]
[-0.00620595]
[-0.04069594]
[-0.04824063]
[-0.02560657]
[ 0.0519959 ]
[ 0.00457217]
[-0.06440781]
[-0.01698407]
[-0.05794093]
[ 0.00996123]
[ 0.08864151]
[-0.00512814]
[-0.06440781]
[ 0.01750591]
[-0.04500719]
[ 0.02828403]
[ 0.04121778]
[ 0.06492964]
[-0.03207344]
[-0.07626374]
[ 0.04984027]
[ 0.04552903]
[-0.00943939]
[-0.03207344]
[ 0.00457217]
[ 0.02073935]
[ 0.01427248]
[ 0.11019775]
[ 0.00133873]
[ 0.05846277]
[-0.02129532]
[-0.0105172 ]
[-0.04716281]
[ 0.00457217]
[ 0.01750591]
[ 0.08109682]
[ 0.0347509 ]
[ 0.02397278]
[-0.00836158]
[-0.06117437]
[-0.00189471]
[-0.06225218]
[ 0.0164281 ]
[ 0.09618619]
[-0.06979687]
[-0.02129532]
[-0.05362969]
[ 0.0433734 ]
[ 0.05630715]
[-0.0816528 ]
[ 0.04984027]
[ 0.11127556]
[ 0.06169621]
[ 0.01427248]
[ 0.04768465]
[ 0.01211685]
[ 0.00564998]
[ 0.04660684]
[ 0.12852056]
[ 0.05954058]
[ 0.09295276]
[ 0.01535029]
[-0.00512814]
[ 0.0703187 ]
[-0.00405033]
[-0.00081689]
[-0.04392938]
[ 0.02073935]
[ 0.06061839]
[-0.0105172 ]
[-0.03315126]
[-0.06548562]
[ 0.0433734 ]
[-0.06225218]
[ 0.06385183]
[ 0.03043966]
[ 0.07247433]
[-0.0191397 ]
[-0.06656343]
[-0.06009656]
[ 0.06924089]
[ 0.05954058]
[-0.02668438]
[-0.02021751]
[-0.046085 ]
[ 0.07139652]
[-0.07949718]
[ 0.00996123]
[-0.03854032]
[ 0.01966154]
[ 0.02720622]
[-0.00836158]
[-0.01590626]
[ 0.00457217]
[-0.04285156]
[ 0.00564998]
[-0.03530688]
[ 0.02397278]
[-0.01806189]
[ 0.04229559]
[-0.0547075 ]
[-0.00297252]
[-0.06656343]
[-0.01267283]
[-0.04177375]
[-0.03099563]
[-0.00512814]
[-0.05901875]
[ 0.0250506 ]
[-0.046085 ]
[ 0.00349435]
[ 0.05415152]
[-0.04500719]
[-0.05794093]
[-0.05578531]
[ 0.00133873]
[ 0.03043966]
[ 0.00672779]
[ 0.04660684]
[ 0.02612841]
[ 0.04552903]
[ 0.04013997]
[-0.01806189]
[ 0.01427248]
[ 0.03690653]
[ 0.00349435]
[-0.07087468]
[-0.03315126]
[ 0.09403057]
[ 0.03582872]
[ 0.03151747]
[-0.06548562]
[-0.04177375]
[-0.03961813]
[-0.03854032]
[-0.02560657]
[-0.02345095]
[-0.06656343]
[ 0.03259528]
[-0.046085 ]
[-0.02991782]
[-0.01267283]
[-0.01590626]
[ 0.07139652]
[-0.03099563]
[ 0.00026092]
[ 0.03690653]
[ 0.03906215]
[-0.01482845]
[ 0.00672779]
[-0.06871905]
[-0.00943939]
[ 0.01966154]
[ 0.07462995]
[-0.00836158]
[-0.02345095]
[-0.046085 ]
[ 0.05415152]
[-0.03530688]
[-0.03207344]
[-0.0816528 ]
[ 0.04768465]
[ 0.06061839]
[ 0.05630715]
[ 0.09834182]
[ 0.05954058]
[ 0.03367309]
[ 0.05630715]
[-0.06548562]
[ 0.16085492]
[-0.05578531]
[-0.02452876]
[-0.03638469]
[-0.00836158]
[-0.04177375]
[ 0.12744274]
[-0.07734155]
[ 0.02828403]
[-0.02560657]
[-0.06225218]
[-0.00081689]
[ 0.08864151]
[-0.03207344]
[ 0.03043966]
[ 0.00888341]
[ 0.00672779]
[-0.02021751]
[-0.02452876]
[-0.01159501]
[ 0.02612841]
[-0.05901875]
[-0.03638469]
[-0.02452876]
[ 0.01858372]
[-0.0902753 ]
[-0.00512814]
[-0.05255187]
[-0.02237314]
[-0.02021751]
[-0.0547075 ]
[-0.00620595]
[-0.01698407]
[ 0.05522933]
[ 0.07678558]
[ 0.01858372]
[-0.02237314]
[ 0.09295276]
[-0.03099563]
[ 0.03906215]
[-0.06117437]
[-0.00836158]
[-0.0374625 ]
[-0.01375064]
[ 0.07355214]
[-0.02452876]
[ 0.03367309]
[ 0.0347509 ]
[-0.03854032]
[-0.03961813]
[-0.00189471]
[-0.03099563]
[-0.046085 ]
[ 0.00133873]
[ 0.06492964]
[ 0.04013997]
[-0.02345095]
[ 0.05307371]
[ 0.04013997]
[-0.02021751]
[ 0.01427248]
[-0.03422907]
[ 0.00672779]
[ 0.00457217]
[ 0.03043966]
[ 0.0519959 ]
[ 0.06169621]
[-0.00728377]
[ 0.00564998]
[ 0.05415152]
[-0.00836158]
[ 0.114509 ]
[ 0.06708527]
[-0.05578531]
[ 0.03043966]
[-0.02560657]
[ 0.10480869]
[-0.00620595]
[-0.04716281]
[-0.04824063]
[ 0.08540807]
[-0.01267283]
[-0.03315126]
[-0.00728377]
[-0.01375064]
[ 0.05954058]
[ 0.02181716]
[ 0.01858372]
[-0.01159501]
[-0.00297252]
[ 0.01750591]
[-0.02991782]
[-0.02021751]
[-0.05794093]
[ 0.06061839]
[-0.04069594]
[-0.07195249]
[-0.05578531]
[ 0.04552903]
[-0.00943939]
[-0.03315126]
[ 0.04984027]
[-0.08488624]
[ 0.00564998]
[ 0.02073935]
[-0.00728377]
[ 0.10480869]
[-0.02452876]
[-0.00620595]
[-0.03854032]
[ 0.13714305]
[ 0.17055523]
[ 0.00241654]
[ 0.03798434]
[-0.05794093]
[-0.00943939]
[-0.02345095]
[-0.0105172 ]
[-0.03422907]
[-0.00297252]
[ 0.06816308]
[ 0.00996123]
[ 0.00241654]
[-0.03854032]
[ 0.02612841]
[-0.08919748]
[ 0.06061839]
[-0.02884001]
[-0.02991782]
[-0.0191397 ]
[-0.04069594]
[ 0.01535029]
[-0.02452876]
[ 0.00133873]
[ 0.06924089]
[-0.06979687]
[-0.02991782]
[-0.046085 ]
[ 0.01858372]
[ 0.00133873]
[-0.03099563]
[-0.00405033]
[ 0.01535029]
[ 0.02289497]
[ 0.04552903]
[-0.04500719]
[-0.03315126]
[ 0.097264 ]
[ 0.05415152]
[ 0.12313149]
[-0.08057499]
[ 0.09295276]
[-0.05039625]
[-0.01159501]
[-0.0277622 ]
[ 0.05846277]
[ 0.08540807]
[-0.00081689]
[ 0.00672779]
[ 0.00888341]
[ 0.08001901]
[ 0.07139652]
[-0.02452876]
[-0.0547075 ]
[-0.03638469]
[ 0.0164281 ]
[ 0.07786339]
[-0.03961813]
[ 0.01103904]
[-0.04069594]
[-0.03422907]
[ 0.00564998]
[ 0.08864151]
[-0.03315126]
[-0.05686312]
[-0.03099563]
[ 0.05522933]
[-0.06009656]
[ 0.00133873]
[-0.02345095]
[-0.07410811]
[ 0.01966154]
[-0.01590626]
[-0.01590626]
[ 0.03906215]
[-0.0730303 ]]
[ 0.06169621 -0.05147406 0.04445121 -0.01159501 -0.03638469 -0.04069594
-0.04716281 -0.00189471 0.06169621 0.03906215 -0.08380842 0.01750591
-0.02884001 -0.00189471 -0.02560657 -0.01806189 0.04229559 0.01211685
-0.0105172 -0.01806189 -0.05686312 -0.02237314 -0.00405033 0.06061839
0.03582872 -0.01267283 -0.07734155 0.05954058 -0.02129532 -0.00620595
0.04445121 -0.06548562 0.12528712 -0.05039625 -0.06332999 -0.03099563
0.02289497 0.01103904 0.07139652 0.01427248 -0.00836158 -0.06764124
-0.0105172 -0.02345095 0.06816308 -0.03530688 -0.01159501 -0.0730303
-0.04177375 0.01427248 -0.00728377 0.0164281 -0.00943939 -0.01590626
0.0250506 -0.04931844 0.04121778 -0.06332999 -0.06440781 -0.02560657
-0.00405033 0.00457217 -0.00728377 -0.0374625 -0.02560657 -0.02452876
-0.01806189 -0.01482845 -0.02991782 -0.046085 -0.06979687 0.03367309
-0.00405033 -0.02021751 0.00241654 -0.03099563 0.02828403 -0.03638469
-0.05794093 -0.0374625 0.01211685 -0.02237314 -0.03530688 0.00996123
-0.03961813 0.07139652 -0.07518593 -0.00620595 -0.04069594 -0.04824063
-0.02560657 0.0519959 0.00457217 -0.06440781 -0.01698407 -0.05794093
0.00996123 0.08864151 -0.00512814 -0.06440781 0.01750591 -0.04500719
0.02828403 0.04121778 0.06492964 -0.03207344 -0.07626374 0.04984027
0.04552903 -0.00943939 -0.03207344 0.00457217 0.02073935 0.01427248
0.11019775 0.00133873 0.05846277 -0.02129532 -0.0105172 -0.04716281
0.00457217 0.01750591 0.08109682 0.0347509 0.02397278 -0.00836158
-0.06117437 -0.00189471 -0.06225218 0.0164281 0.09618619 -0.06979687
-0.02129532 -0.05362969 0.0433734 0.05630715 -0.0816528 0.04984027
0.11127556 0.06169621 0.01427248 0.04768465 0.01211685 0.00564998
0.04660684 0.12852056 0.05954058 0.09295276 0.01535029 -0.00512814
0.0703187 -0.00405033 -0.00081689 -0.04392938 0.02073935 0.06061839
-0.0105172 -0.03315126 -0.06548562 0.0433734 -0.06225218 0.06385183
0.03043966 0.07247433 -0.0191397 -0.06656343 -0.06009656 0.06924089
0.05954058 -0.02668438 -0.02021751 -0.046085 0.07139652 -0.07949718
0.00996123 -0.03854032 0.01966154 0.02720622 -0.00836158 -0.01590626
0.00457217 -0.04285156 0.00564998 -0.03530688 0.02397278 -0.01806189
0.04229559 -0.0547075 -0.00297252 -0.06656343 -0.01267283 -0.04177375
-0.03099563 -0.00512814 -0.05901875 0.0250506 -0.046085 0.00349435
0.05415152 -0.04500719 -0.05794093 -0.05578531 0.00133873 0.03043966
0.00672779 0.04660684 0.02612841 0.04552903 0.04013997 -0.01806189
0.01427248 0.03690653 0.00349435 -0.07087468 -0.03315126 0.09403057
0.03582872 0.03151747 -0.06548562 -0.04177375 -0.03961813 -0.03854032
-0.02560657 -0.02345095 -0.06656343 0.03259528 -0.046085 -0.02991782
-0.01267283 -0.01590626 0.07139652 -0.03099563 0.00026092 0.03690653
0.03906215 -0.01482845 0.00672779 -0.06871905 -0.00943939 0.01966154
0.07462995 -0.00836158 -0.02345095 -0.046085 0.05415152 -0.03530688
-0.03207344 -0.0816528 0.04768465 0.06061839 0.05630715 0.09834182
0.05954058 0.03367309 0.05630715 -0.06548562 0.16085492 -0.05578531
-0.02452876 -0.03638469 -0.00836158 -0.04177375 0.12744274 -0.07734155
0.02828403 -0.02560657 -0.06225218 -0.00081689 0.08864151 -0.03207344
0.03043966 0.00888341 0.00672779 -0.02021751 -0.02452876 -0.01159501
0.02612841 -0.05901875 -0.03638469 -0.02452876 0.01858372 -0.0902753
-0.00512814 -0.05255187 -0.02237314 -0.02021751 -0.0547075 -0.00620595
-0.01698407 0.05522933 0.07678558 0.01858372 -0.02237314 0.09295276
-0.03099563 0.03906215 -0.06117437 -0.00836158 -0.0374625 -0.01375064
0.07355214 -0.02452876 0.03367309 0.0347509 -0.03854032 -0.03961813
-0.00189471 -0.03099563 -0.046085 0.00133873 0.06492964 0.04013997
-0.02345095 0.05307371 0.04013997 -0.02021751 0.01427248 -0.03422907
0.00672779 0.00457217 0.03043966 0.0519959 0.06169621 -0.00728377
0.00564998 0.05415152 -0.00836158 0.114509 0.06708527 -0.05578531
0.03043966 -0.02560657 0.10480869 -0.00620595 -0.04716281 -0.04824063
0.08540807 -0.01267283 -0.03315126 -0.00728377 -0.01375064 0.05954058
0.02181716 0.01858372 -0.01159501 -0.00297252 0.01750591 -0.02991782
-0.02021751 -0.05794093 0.06061839 -0.04069594 -0.07195249 -0.05578531
0.04552903 -0.00943939 -0.03315126 0.04984027 -0.08488624 0.00564998
0.02073935 -0.00728377 0.10480869 -0.02452876 -0.00620595 -0.03854032
0.13714305 0.17055523 0.00241654 0.03798434 -0.05794093 -0.00943939
-0.02345095 -0.0105172 -0.03422907 -0.00297252 0.06816308 0.00996123
0.00241654 -0.03854032 0.02612841 -0.08919748 0.06061839 -0.02884001
-0.02991782 -0.0191397 -0.04069594 0.01535029 -0.02452876 0.00133873
0.06924089 -0.06979687 -0.02991782 -0.046085 0.01858372 0.00133873
-0.03099563 -0.00405033 0.01535029 0.02289497 0.04552903 -0.04500719
-0.03315126 0.097264 0.05415152 0.12313149 -0.08057499 0.09295276
-0.05039625 -0.01159501 -0.0277622 0.05846277 0.08540807 -0.00081689
0.00672779 0.00888341 0.08001901 0.07139652 -0.02452876 -0.0547075
-0.03638469 0.0164281 0.07786339 -0.03961813 0.01103904 -0.04069594
-0.03422907 0.00564998 0.08864151 -0.03315126 -0.05686312 -0.03099563
0.05522933 -0.06009656 0.00133873 -0.02345095 -0.07410811 0.01966154
-0.01590626 -0.01590626 0.03906215 -0.0730303 ]
#再举一个例子，机器学习中大名鼎鼎的iris数据集
iris = datasets.load_iris()
iris_data = iris.data
iris_target = iris.target
print(iris_data.shape)
print(iris_target.shape)
# 如果想把特征数据和标签数据放在同一个array中，运行iris_ = np.hstack((iris_data, iris_target))，结果报错;
# iris_ = np.hstack((iris_data, iris_target))#报错
# 报错信息：ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has
# 2 dimension(s) and the array at index 1 has 1 dimension(s)；
#说我们输入的arrays的维度数不一样，这时我们的np.newaxis就可以派上用场了
print(iris_target[:, np.newaxis].shape)
iris = np.hstack((iris_data, iris_target[:,np.newaxis]))
print(iris.shape)# 成功了
#输出：
(150, 4)
(150,)
(150, 1)
(150, 5)
