缩进

linear/train.py

@@ -14,17 +14,17 @@ from draw import draw_util
 
 
 def curce_data(x,y,y_pred):
-	x=x.tolist()
-	y=y.tolist()
-	y_pred=y_pred.tolist()
-	results=zip(x,y,y_pred)
-	results=["{},{},{}".format(s[0][0],s[1][0],s[2][0]) for s in results ]
-	return results
+    x=x.tolist()
+    y=y.tolist()
+    y_pred=y_pred.tolist()
+    results=zip(x,y,y_pred)
+    results=["{},{},{}".format(s[0][0],s[1][0],s[2][0]) for s in results ]
+    return results
 
 
 def read_data(path):
-	with open(path) as f :
-		lines=f.readlines()
+    with open(path) as f :
+        lines=f.readlines()
 	lines=[eval(line.strip()) for line in lines]
 	X,y=zip(*lines)
 	X=np.array(X)
@@ -33,44 +33,44 @@ def read_data(path):
 
 
 def demo():
-	X_train,y_train=read_data("../bbztx/train_data")
-	X_test,y_test=read_data("../bbztx/test_data")
+    X_train,y_train=read_data("../bbztx/train_data")
+    X_test,y_test=read_data("../bbztx/test_data")
 
 	#一个对象，它代表的线性回归模型，它的成员变量，就已经有了w，b. 刚生成w和b的时候 是随机的
-	model = LinearRegression()
-	#一调用这个函数，就会不停地找合适的w和b 直到误差最小
-	model.fit(X_train, y_train)
-	#打印W
-	print(model.coef_)
-	#打印b
-	print(model.intercept_)
-	#模型已经训练完毕,用模型看下在训练集的表现
-	y_pred_train = model.predict(X_train)
-	#sklearn 求解训练集的mse
-	# y_train 在训练集上 真实的y值
-	# y_pred_train 通过模型预测出来的y值
-	#计算  (y_train-y_pred_train)^2/n
-	train_mse = metrics.mean_squared_error(y_train, y_pred_train)
-	print("训练集MSE:", train_mse)
-
-	#看下在测试集上的效果
-	y_pred_test = model.predict(X_test)
-	test_mse = metrics.mean_squared_error(y_test, y_pred_test)
-	print("测试集MSE:",test_mse)
-
-	# train_curve = curce_data(X_train,y_train,y_pred_train)
-	test_curve = curce_data(X_test,y_test,y_pred_test)
-	print("推广mse差", test_mse-train_mse)
-
-	'''
+    model = LinearRegression()
+    #一调用这个函数，就会不停地找合适的w和b 直到误差最小
+    model.fit(X_train, y_train)
+    #打印W
+    print(model.coef_)
+    #打印b
+    print(model.intercept_)
+    #模型已经训练完毕,用模型看下在训练集的表现
+    y_pred_train = model.predict(X_train)
+    #sklearn 求解训练集的mse
+    # y_train 在训练集上 真实的y值
+    # y_pred_train 通过模型预测出来的y值
+    #计算  (y_train-y_pred_train)^2/n
+    train_mse = metrics.mean_squared_error(y_train, y_pred_train)
+    print("训练集MSE:", train_mse)
+
+    #看下在测试集上的效果
+    y_pred_test = model.predict(X_test)
+    test_mse = metrics.mean_squared_error(y_test, y_pred_test)
+    print("测试集MSE:",test_mse)
+
+    # train_curve = curce_data(X_train,y_train,y_pred_train)
+    test_curve = curce_data(X_test,y_test,y_pred_test)
+    print("推广mse差", test_mse-train_mse)
+
+    '''
 	with open("train_curve.csv","w") as f :
 		f.writelines("\n".join(train_curve))
 	保存数据
 	with open("test_curve.csv","w") as f :
 		f.writelines("\n".join(test_curve))
 	'''
-	for x in test_curve:
-		print(x)
+    for x in test_curve:
+        print(x)
 
 	return X_train,y_train, model.coef_, model.intercept_