import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing, cross_validation, svm
df = pd.read_csv('table.csv')
print (df.head())
df = df[['Price', 'Asset']]
x = np.array(df.Price)
y = np.array(df.Asset)
x_train, x_test, y_train, y_test = cross_validation.train_test_split(
x, y, test_size=0.2)
x_train = np.pad(x, [(0,0)], mode='constant')
x_train.reshape((23,1))
y_train = np.pad(y, [(0,0)], mode ='constant')
y_train.reshape((23,1))
np.reshape(-1, 1)
錯誤:NumPy的重塑問題
runfile('C:/Users/HP/Documents/linear.py', wdir='C:/Users/HP/Documents')
Price Asset
0 87.585859 191
1 87.839996 232
2 87.309998 245
3 88.629997 445
4 88.379997 393
C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py:386:
DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
Traceback (most recent call last):
File "<ipython-input-124-030ffa933525>", line 1, in <module>
runfile('C:/Users/HP/Documents/linear.py', wdir='C:/Users/HP/Documents')
File "C:\Users\HP\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 714, in runfile
execfile(filename, namespace)
File "C:\Users\HP\Anaconda3\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 89, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/HP/Documents/linear.py", line 38, in <module>
clf.fit(x_train, y_train)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\linear_model\base.py", line 427, in fit
y_numeric=True, multi_output=True)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 520, in check_X_y
check_consistent_length(X, y)
File "C:\Users\HP\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 176, in check_consistent_length
"%s" % str(uniques))
ValueError: Found arrays with inconsistent numbers of samples: [ 1 23]
我的數據框尺寸:23,2
我填補我x_train和y_train爲[23.1]因爲我得到這個初始誤差ValueError異常:發現樣本數不一致的陣列:[1 18]。 填充後我的錯誤消息:ValueError:發現樣本數量不一致的數組:[1 23]。
然後我試圖重塑它,仍然收到錯誤信息:ValueError:發現樣本數不一致的陣列:[1 23]。
我該如何解決這個問題?
你想重塑什麼? 'np.reshape'不知道你想要重塑的東西。像'array.reshape((x,y))'一樣使用它。 – Ian
@mwormser說,你需要在數組對象上調用'reshape',比如'x_train'和'y_train'; 'x_train.reshape((23,1))' – Dartmouth
嘗試仍然收到錯誤消息:ValueError:找到的數組樣本數不一致:[1 23] – Bolajio