Fix. Some rules of PE8

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

@@ -3,7 +3,8 @@
 import numpy as np
 from .normalize import normalize
 
-def generate_polynomials(dataset, polynomial_degree, normalize_data = False):
+
+def generate_polynomials(dataset, polynomial_degree, normalize_data=False):
     """变换方法：
     x1, x2, x1^2, x2^2, x1*x2, x1*x2^2, etc.
     """
@@ -40,4 +41,4 @@ def generate_polynomials(dataset, polynomial_degree, normalize_data = False):
     if normalize_data:
         polynomials = normalize(polynomials)[0]
 
-    return polynomials
+    return polynomials

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

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 def generate_sinusoids(dataset, sinusoid_degree):
     """
     sin(x).

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

@@ -3,8 +3,8 @@
 
 import numpy as np
 
-def normalize(features):
 
+def normalize(features):
     features_normalized = np.copy(features).astype(float)
 
     # 计算均值
@@ -21,4 +21,4 @@ def normalize(features):
     features_deviation[features_deviation == 0] = 1
     features_normalized /= features_deviation
 
-    return features_normalized, features_mean, features_deviation
+    return features_normalized, features_mean, features_deviation