線形回帰のバッチ勾配降下が収束しないのはなぜですか？

Question

sklearn.linear_model.LinearRegressionと同様のLinearRegressionモデルを作成したいと思います。で起動するには、私はsklearn.linear_model.LinearRegressionを使用して、標準的な線形回帰モデルを訓練：

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn import linear_model 

# training data 
train_x = np.array([1,2,3,4,5,6], dtype=np.float64).reshape(6,1) 
train_y = np.array([1,2,3,3.25,3.5,3.8], dtype=np.float64) 
# test data 
predict_x = np.arange(0, 7, 0.1) 
predict_x = predict_x.reshape(predict_x.size, 1) 

# Simple regression 
model1 = linear_model.LinearRegression() 
model1.fit(train_x, train_y); 
print model1.coef_, model1.intercept_ 
# Quadratic regression 
model2 = linear_model.LinearRegression() 
model2.fit(np.concatenate((train_x, train_x**2), axis=1), train_y); 
print model2.coef_, model2.intercept_ 
# Five-order polynomial regression 
model5 = linear_model.LinearRegression() 
model5.fit(np.concatenate((train_x, train_x**2, train_x**3, train_x**4, train_x**5), axis=1), train_y); 
print model5.coef_, model5.intercept_ 
# Predict 
predict_y1 = model1.predict(predict_x) 
predict_y2 = model2.predict(np.concatenate((predict_x, predict_x**2), axis=1)) 
predict_y5 = model5.predict(np.concatenate((predict_x, predict_x**2, predict_x**3, predict_x**4, predict_x**5), axis=1)) 

# plot 
plt.figure(figsize = (10,10)) 
plt.scatter(train_x, train_y, color='black') 
plt.plot(predict_x, predict_y1, color='blue', label='underfitting') 
plt.plot(predict_x, predict_y2, color='green', label='fair') 
plt.plot(predict_x, predict_y5, color='red', label='overfitting') 
plt.axis([0,7,0,5]) 
plt.legend(loc=2) 
plt.show() 

その後、私は良い結果を得る：

[0.53571429] 0.883333333333

[1.34821429 -0.11607143] -0.2

[-8.52333333 7.0625 -2.30833333 0.3375 -0.01833333] 4.45

その後、モデルMyLinearRegressionを実装します。最初は、バッチ勾配降下と固定反復番号を選択して、自分のコードが正しいかどうかをテストします。

# center data 
def center_matrix(X): 
    assert(isinstance(X, np.ndarray)) 
    X_offset = np.average(X, axis=0) 
    return X - X_offset, X_offset 

class MyLinearRegression(object): 
    def __init__(self): 
     self.coef_ = None 
     self.intercept_ = None 
     self.learning_rate = None 

    def fit(self, X, y): 
     n_samples, n_features = X.shape 
     n_samples_, = y.shape 
     assert(n_samples == n_samples_) 
     X, X_offset = center_matrix(X) 
     y, y_offset = center_matrix(y) 
     self.coef_ = np.ones((n_features,), dtype=np.float64) 

     self.learning_rate = -0.0001 

     error = None 
     # using fixed iteration number 
     for epoch in np.arange(500000): 
      y_hat = X.dot(self.coef_) 
      error_ = y_hat-y 
      if error is not None and sum(error_**2) > sum(error**2): # if square error is increasing, then half learning_rate. 
       self.learning_rate /= 2. 
       continue 
      error = error_ 
      coef = self.coef_ + self.learning_rate * (X.T.dot(error)) 
      if np.isfinite(coef).all(): # if overflow happen, half learning_rate. 
       self.coef_ = coef 
      else: 
       self.learning_rate /= 2. 

     self.intercept_ = y_offset - self.coef_.dot(X_offset.T) 
     return self 

    def predict(self, X): 
     n_samples, n_features = X.shape 
     assert(n_features == self.coef_.size) 
     return X.dot(self.coef_) + self.intercept_ 

# Simple regression 
my_model1 = MyLinearRegression() 
my_model1.fit(train_x, train_y) 
print my_model1.coef_, my_model1.intercept_ 
# Quadratic regression 
my_model2 = MyLinearRegression() 
my_model2.fit(np.concatenate((train_x, train_x**2), axis=1), train_y); 
print my_model2.coef_, my_model2.intercept_ 

# Five-order polynomial regression 
my_model5 = MyLinearRegression() 
my_model5.fit(np.concatenate((train_x, train_x**2, train_x**3, train_x**4, train_x**5), axis=1), train_y); 
print my_model5.coef_, my_model5.intercept_ 

# predict 
my_predict_y1 = my_model1.predict(predict_x) 
my_predict_y2 = my_model2.predict(np.concatenate((predict_x, predict_x**2), axis=1)) 
my_predict_y5 = my_model5.predict(np.concatenate((predict_x, predict_x**2, predict_x**3, predict_x**4, predict_x**5), axis=1)) 

# plot 
plt.figure(figsize = (10,10)) 
plt.scatter(train_x, train_y, color='black') 
plt.plot(predict_x, my_predict_y1, color='blue', label='underfitting') 
plt.plot(predict_x, my_predict_y2, color='green', label='fair') 
plt.plot(predict_x, my_predict_y5, color='red', label='overfitting') 
plt.axis([0,7,0,5]) 
plt.legend(loc=2) 
plt.show() 

その後、私は悪い結果を得る：

[0.53571433] 0.883333191266

[1.34821275 -0.11607122] -0.199997815791

[-1.95681250e + 00 -2.20847875e + 01 -1.48602362e + 02 -9.20144807e + 02 -5.56577136e + 03] 11678151.1386

私は MyLinearRegression上 my_model1と my_model2に良い結果を得ることができます

、それらはsklearn.linear_model.LinearRegressionにこれらに閉鎖されています。しかし、どのように調整するにしても、learning_rateと繰り返し番号my_model5は収束しません。誰も助けることができますか？

Answer 1

ここでは機能のスケーリングが必要です。

参照：

• About Feature Scaling and Normalization – and the effect of standardization for machine learning algorithms

• Andrew Ng: Linear Regression II: Feature Scaling