[英]How to solve “Exception: Data must be 1-dimensional” in linear regression?
我需要在不使用 scikit 的情况下对波士顿住房数据集进行线性回归。
这是我到现在为止的想法
import pandas as pd
import numpy as np
import matplotlib.pyplot as mlt
from sklearn.cross_validation import train_test_split
data = pd.read_csv("housing.csv", delimiter=' ',
skipinitialspace=True,
names=['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE',
'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
)
df_x = data.drop('MEDV', axis = 1)
df_y = data['MEDV']
x_train, x_test, y_train, y_test = train_test_split(df_x, df_y,
test_size=0.2,
random_state=4
)
def hypothesis(x, theta):
return np.dot(x, theta.T)
def costfn(predictions, y, x):
a = 1 / (2 * len(x)) * np.sum((prediction - y) ** 2)
return a
def gradient(theta, alpha, predictions, x, y):
theta = np.subtract(theta, (alpha / len(x)) * np.dot(np.subtract(predictions, y).T, x))
return theta
alpha = 0.001
iters = 1000
theta = np.zeros([1, 13])
predictions = hypothesis(x_train, theta)
for i in range(iters):
predictions = hypothesis(x_train, theta)
theta = gradient(theta, alpha, predictions, x_train, y_train)
predictions = hypothesis(x_test, theta)
print(predictions)
我已经输入并分离了测试和训练用例,所有这些都运行良好。 但是我收到了这个错误-
Exception Traceback (most recent call last)
<ipython-input-33-36492e2820ce> in <module>
6 for i in range(iters):
7 predictions = hypothesis(x_train, theta)
----> 8 theta = gradient(theta, alpha, predictions, x_train, y_train)
9
10 predictions = hypothesis(x_test, theta)
<ipython-input-32-15d0b5b7bf16> in gradient(theta, alpha, predictions, x, y)
9
10
---> 11 theta = np.subtract(theta, (alpha / len(x)) * np.dot(np.subtract(predictions, y).T, x))
12 return theta
/usr/lib/python3/dist-packages/pandas/core/series.py in __array_wrap__(self, result, context)
502 """
503 return self._constructor(result, index=self.index,
--> 504 copy=False).__finalize__(self)
505
506 def __array_prepare__(self, result, context=None):
/usr/lib/python3/dist-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
262 else:
263 data = _sanitize_array(data, index, dtype, copy,
--> 264 raise_cast_failure=True)
265
266 data = SingleBlockManager(data, index, fastpath=True)
/usr/lib/python3/dist-packages/pandas/core/series.py in _sanitize_array(data, index, dtype, copy, raise_cast_failure)
3273 elif subarr.ndim > 1:
3274 if isinstance(data, np.ndarray):
-> 3275 raise Exception('Data must be 1-dimensional')
3276 else:
3277 subarr = _asarray_tuplesafe(data, dtype=dtype)
Exception: Data must be 1-dimensional
请帮忙。 另外,如果我的逻辑是错误的,请告诉我,因为我是初学者。
pandas
非常适合数据管理,但我倾向于坚持使用 NumPy 对象进行数学步骤。 pandas
正在尝试在这里做一些聪明的事情,我不知道是什么,但是如果您将df_x.values
和df_y.values
传递给train_test_split()
,您的代码将运行:
x_train, x_test, y_train, y_test = train_test_split(df_x.values,
df_y.values,
test_size=0.2,
random_state=4
)
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.