update

2023-09-13 14:32:08 +02:00
parent 5f89e9d242
commit 8aab9c6367
2 changed files with 79 additions and 100 deletions
@@ -294,7 +294,7 @@ _Scikit-Learn_.  Here we limit ourselves to Ridge regression only.
 !bc pycod
 from sklearn import linear_model
 np.random.seed(2018)
-n = 100
+n = 10
 d = 2
 Lambda = 0.01

@@ -310,19 +310,20 @@ for p in range(d):
 #Split data in train and test
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

+print(X_train)
 # Scale data by subtracting mean value using scikit-learn
 from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
+print(X_train_scaled)

 #Calculate beta
 OLS = LinearRegression()
-OLS.fit(X_train,y_train)
+OLS.fit(X_train,y_train_scaled)
 ypredictOLS = OLS.predict(X_test)
 RegRidge = linear_model.Ridge(Lambda)
-RegRidge.fit(X_train,y_train)
+RegRidge.fit(X_train_scaled,y_train)
 ypredictRidge = RegRidge.predict(X_test)
 print(OLS.coef_)
 print(RegRidge.coef_)