Question

使用梯度下降的线性回归

参考：Linear Regression Using Gradient Descent in 10 Lines of Code

数据集：

房价卧室数

编码环境：Google合作实验室

代码：

import numpy as np
import pandas as pd
import io
from google.colab import files 
uploaded = files.upload()
df = pd.read_csv(io.BytesIO(uploaded['datasets_46927_85203_data.csv'])) 
house_price=df.price
No_of_bedrooms=df.bedrooms
# Mean of house_price and No_of_bedrooms
mean_x= np.mean(house_price)
mean_y=np.mean(No_of_bedrooms)

#Total number of datapoints
n=len(house_price)
numerator = 0;
denominator = 0;
for i in range (n):
  numerator += (house_price[i] - mean_x) * (No_of_bedrooms[i] - mean_y)
  denominator += (house_price[i] - mean_x) ** 2
slope= numerator / denominator
constant = mean_y - (slope*mean_x)
def linear_regression(house_price[], y, m_current=slope, b_current=constant, epochs=1000,
learning_rate=0.0001):
     N = float(len(No_of_bedrooms))
     for i in range(epochs):
          y_current = (m_current * house_price[i]) + b_current
          cost = sum([data**2 for data in (No_of_bedrooms[i]-y_current)]) / N
          m_gradient = -(2/N) * sum(house_price[i] * (No_of_bedrooms[i] - y_current))
          b_gradient = -(2/N) * sum(No_of_bedrooms[i] - y_current)
          m_current = m_current - (learning_rate * m_gradient)
          b_current = b_current - (learning_rate * b_gradient)
     return m_current, b_current, cost

错误：def linear_regression(house_price[], y, m_current=slope, b_current=constant, epochs=1000, learning_rate=0.0001):

Error Statement: File "<ipython-input-6-864b867cbc7a>", line 31
    

def linear_regression(house_price[], y, m_current=slope, b_current=constant, epochs=1000, learning_rate=0.0001):
                                     ^
SyntaxError: invalid syntax

我不知道如何消除此错误。

此post与我的查询无关。

如果有人帮助我，那将很棒。谢谢。

使用梯度下降实现线性回归

0 个答案: