fix: converted single to triple backticks11 (#36238)

guide/arabic/machine-learning/linear-regression/index.md

@@ -10,31 +10,32 @@ localeTitle: الانحدارالخطي
 
 في بايثون:
 
- `#Price of wheat/kg and the average price of bread 
- wheat_and_bread = [[0.5,5],[0.6,5.5],[0.8,6],[1.1,6.8],[1.4,7]] 
- 
- def step_gradient(b_current, m_current, points, learningRate): 
-    b_gradient = 0 
-    m_gradient = 0 
-    N = float(len(points)) 
-    for i in range(0, len(points)): 
-        x = points[i][0] 
-        y = points[i][1] 
-        b_gradient += -(2/N) * (y - ((m_current * x) + b_current)) 
-        m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current)) 
-    new_b = b_current - (learningRate * b_gradient) 
-    new_m = m_current - (learningRate * m_gradient) 
-    return [new_b, new_m] 
- 
- def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations): 
-    b = starting_b 
-    m = starting_m 
-    for i in range(num_iterations): 
-        b, m = step_gradient(b, m, points, learning_rate) 
-    return [b, m] 
- 
- gradient_descent_runner(wheat_and_bread, 1, 1, 0.01, 100) 
-` 
+```py
+#Price of wheat/kg and the average price of bread
+wheat_and_bread = [[0.5,5],[0.6,5.5],[0.8,6],[1.1,6.8],[1.4,7]]
+
+def step_gradient(b_current, m_current, points, learningRate):
+    b_gradient = 0
+    m_gradient = 0
+    N = float(len(points))
+    for i in range(0, len(points)):
+        x = points[i][0]
+        y = points[i][1]
+        b_gradient += -(2/N) * (y - ((m_current * x) + b_current))
+        m_gradient += -(2/N) * x * (y - ((m_current * x) + b_current))
+    new_b = b_current - (learningRate * b_gradient)
+    new_m = m_current - (learningRate * m_gradient)
+    return [new_b, new_m]
+
+def gradient_descent_runner(points, starting_b, starting_m, learning_rate, num_iterations):
+    b = starting_b
+    m = starting_m
+    for i in range(num_iterations):
+        b, m = step_gradient(b, m, points, learning_rate)
+    return [b, m]
+
+gradient_descent_runner(wheat_and_bread, 1, 1, 0.01, 100)
+``` 
 
 المثال رمز من [هذه المقالة](http://blog.floydhub.com/coding-the-history-of-deep-learning/) . كما يشرح نزول التدرج والمفاهيم الأساسية الأخرى للتعلم العميق.
 
@@ -42,23 +43,24 @@ localeTitle: الانحدارالخطي
 
 في بايثون: تطبيق مباشرة باستخدام مكتبة scikit ، مما يجعل من السهل استخدام الانحدار الخطي حتى على مجموعات البيانات الكبيرة.
 
- `import pandas as pd 
- from sklearn.cross_validation import train_test_split 
- from sklearn.linear_model import LinearRegression as lr 
- train = pd.read_csv('../input/train.csv') 
- test = pd.read_csv('../input/test.csv') 
- X = train.iloc[:, 0:4].values 
- y = train.iloc[:, 4].values 
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) 
- X_train 
- model = lr() 
- model.fit(X_train, y_train) 
- print(model.score(X_train,y_train)) 
- y_pred_class = model.predict(X_test) 
- model.score(X_train,y_train) 
- print(model.coef_) 
- print(model.intercept_) 
- # calculate accuracy 
- from sklearn import metrics 
- print(metrics.accuracy_score(y_test, y_pred_class)) 
-`
+```py
+import pandas as pd
+from sklearn.cross_validation import train_test_split
+from sklearn.linear_model import LinearRegression as lr
+train = pd.read_csv('../input/train.csv')
+test = pd.read_csv('../input/test.csv')
+X = train.iloc[:, 0:4].values
+y = train.iloc[:, 4].values
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
+X_train
+model = lr()
+model.fit(X_train, y_train)
+print(model.score(X_train,y_train))
+y_pred_class = model.predict(X_test)
+model.score(X_train,y_train)
+print(model.coef_)
+print(model.intercept_)
+# calculate accuracy
+from sklearn import metrics
+print(metrics.accuracy_score(y_test, y_pred_class))
+```