change: docstring in module00 (#253)

Co-authored-by: Amir Mahla <ammah@MBP-de-Amir-2.lan>

Author: A-Mahla

module05/en.subject.tex

@@ -612,10 +612,10 @@ \section*{Instructions}
 def simple_predict(x, theta):
     """Computes the vector of prediction y_hat from two non-empty numpy.ndarray.
     Args:
-      x: has to be an numpy.ndarray, a one-dimensional vector of size m.
-      theta: has to be an numpy.ndarray, a one-dimensional vector of size 2.
+      x: has to be an numpy.ndarray, a one-dimensional array of size m.
+      theta: has to be an numpy.ndarray, a one-dimensional array of size 2.
     Returns:
-      y_hat as a numpy.ndarray, a one-dimensional vector of size m.
+      y_hat as a numpy.ndarray, a one-dimensional array of size m.
       None if x or theta are empty numpy.ndarray.
       None if x or theta dimensions are not appropriate.
     Raises:
@@ -694,7 +694,7 @@ \section*{Instructions}
 def add_intercept(x):
     """Adds a column of 1's to the non-empty numpy.array x.
     Args:
-      x: has to be a numpy.array. x can be a one-dimensional (m * 1) or two-dimensional (m * n) vector.
+      x: has to be a numpy.array. x can be a one-dimensional (m * 1) or two-dimensional (m * n) array.
     Returns:
       X, a numpy.array of dimension m * (n + 1).
       None if x is not a numpy.array.
@@ -811,10 +811,10 @@ \section*{Instructions}
 def predict_(x, theta):
     """Computes the vector of prediction y_hat from two non-empty numpy.array.
     Args:
-      x: has to be an numpy.array, a one-dimensional vector of size m.
-      theta: has to be an numpy.array, a two-dimensional vector of shape 2 * 1.
+      x: has to be an numpy.array, a one-dimensional array of size m.
+      theta: has to be an numpy.array, a two-dimensional array of shape 2 * 1.
     Returns:
-      y_hat as a numpy.array, a two-dimensional vector of shape m * 1.
+      y_hat as a numpy.array, a two-dimensional array of shape m * 1.
       None if x and/or theta are not numpy.array.
       None if x or theta are empty numpy.array.
       None if x or theta dimensions are not appropriate.
@@ -895,9 +895,9 @@ \section*{Instructions}
 def plot(x, y, theta):
     """Plot the data and prediction line from three non-empty numpy.array.
     Args:
-      x: has to be an numpy.array, a one-dimensional vector of size m.
-      y: has to be an numpy.array, a one-dimensional vector of size m.
-      theta: has to be an numpy.array, a two-dimensional vector of shape 2 * 1.
+      x: has to be an numpy.array, a one-dimensional array of size m.
+      y: has to be an numpy.array, a one-dimensional array of size m.
+      theta: has to be an numpy.array, a two-dimensional array of shape 2 * 1.
     Returns:
         Nothing.
     Raises:
@@ -1011,10 +1011,10 @@ \section*{Instructions}
 	Description:
 		Calculates all the elements (y_pred - y)^2 of the loss function.
 	Args:
-      y: has to be an numpy.array, a two-dimensional vector of shape m * 1.
-      y_hat: has to be an numpy.array, a two-dimensional vector of shape m * 1.
+      y: has to be an numpy.array, a two-dimensional array of shape m * 1.
+      y_hat: has to be an numpy.array, a two-dimensional array of shape m * 1.
 	Returns:
-		J_elem: numpy.array, a vector of dimension (number of the training examples,1).
+		J_elem: numpy.array, a array of dimension (number of the training examples, 1).
 		None if there is a dimension matching problem.
 		None if any argument is not of the expected type.
 	Raises:
@@ -1027,8 +1027,8 @@ \section*{Instructions}
 	Description:
 		Calculates the value of loss function.
 	Args:
-      y: has to be an numpy.array, a two-dimensional vector of shape m * 1.
-      y_hat: has to be an numpy.array, a two-dimensional vector of shape m * 1.
+      y: has to be an numpy.array, a two-dimensional array of shape m * 1.
+      y_hat: has to be an numpy.array, a two-dimensional array of shape m * 1.
 	Returns:
 		J_value : has to be a float.
 		None if there is a dimension matching problem.
@@ -1141,8 +1141,8 @@ \section*{Instructions}
     """Computes the half mean squared error of two non-empty numpy.array, without any for loop.
     The two arrays must have the same dimensions.
     Args:
-      y: has to be an numpy.array, a one-dimensional vector of size m.
-      y_hat: has to be an numpy.array, a one-dimensional vector of size m.
+      y: has to be an numpy.array, a one-dimensional array of size m.
+      y_hat: has to be an numpy.array, a one-dimensional array of size m.
     Returns:
       The half mean squared error of the two vectors as a float.
       None if y or y_hat are empty numpy.array.
@@ -1334,7 +1334,7 @@ \section*{Instructions}
 	Description:
 		Calculate the MSE between the predicted output and the real output.
 	Args:
-        y: has to be a numpy.array, a two-dimensional vector of shape m * 1.
+        y: has to be a numpy.array, a two-dimensional array of shape m * 1.
         y_hat: has to be a numpy.array, a two-dimensional vector of shape m * 1.		
 	Returns:
 		mse: has to be a float.
@@ -1350,8 +1350,8 @@ \section*{Instructions}
 	Description:
 		Calculate the RMSE between the predicted output and the real output.
 	Args:
-	        y: has to be a numpy.array, a two-dimensional vector of shape m * 1.
-        y_hat: has to be a numpy.array, a two-dimensional vector of shape m * 1.		
+	      y: has to be a numpy.array, a two-dimensional array of shape m * 1.
+        y_hat: has to be a numpy.array, a two-dimensional array of shape m * 1.		
 	Returns:
 		rmse: has to be a float.
 		None if there is a matching dimension problem.
@@ -1366,8 +1366,8 @@ \section*{Instructions}
 	Description:
 		Calculate the MAE between the predicted output and the real output.
 	Args:
-        y: has to be a numpy.array, a two-dimensional vector of shape m * 1.
-        y_hat: has to be a numpy.array, a two-dimensional vector of shape m * 1.		
+        y: has to be a numpy.array, a two-dimensional array of shape m * 1.
+        y_hat: has to be a numpy.array, a two-dimensional array of shape m * 1.		
 	Returns:
 		mae: has to be a float.
 		None if there is a matching dimension problem.
@@ -1382,8 +1382,8 @@ \section*{Instructions}
 	Description:
 		Calculate the R2score between the predicted output and the output.
 	Args:
-        y: has to be a numpy.array, a two-dimensional vector of shape m * 1.
-        y_hat: has to be a numpy.array, a two-dimensional vector of shape m * 1.		
+        y: has to be a numpy.array, a two-dimensional array of shape m * 1.
+        y_hat: has to be a numpy.array, a two-dimensional array of shape m * 1.		
 	Returns:
 		r2score: has to be a float.
 		None if there is a matching dimension problem.

version

@@ -1 +1 @@
-v5.0.2
+v5.0.2.post1