Add. Define the data and labels

机器学习算法理论及应用/第二章——手写线性回归算法/.idea/第二章——手写线性回归算法.iml

@@ -1,7 +1,10 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$" isTestSource="false" />
+      <sourceFolder url="file://$MODULE_DIR$/LinearRegression" isTestSource="false" />
+    </content>
     <orderEntry type="inheritedJdk" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>

机器学习算法理论及应用/第二章——手写线性回归算法/LinearRegression/UnivariateLinearRegression.py

@@ -0,0 +1,30 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from linear_regression import LinearRegression
+data = pd.read_csv('../data/world-happiness-report-2017.csv')  # 导入数据
+# 得到训练和测试数据，以8：2切分
+train_data = data.sample(frac=0.8)
+test_data = data.drop(train_data.index)
+
+input_param_name = 'Economy..GDP.per.Capita.'  # 特征features
+output_param_name = 'Happiness.Score'  # 标签label
+
+x_train = train_data[[input_param_name]].values  # 构建数据
+y_train = train_data[[output_param_name]].values
+
+x_test = test_data[[input_param_name]].values
+y_test = test_data[[output_param_name]].values
+
+# 可视化展示 run, 可以看到训练数据和预测数据的分布
+plt.scatter(x_train, y_train, label='Train data')
+plt.scatter(x_test, y_test, label='Test data')
+plt.xlabel(input_param_name)
+plt.ylabel(output_param_name)
+plt.title('Happy')
+plt.legend()
+plt.show()
+
+num_iterations = 500
+learning_rate = 0.01