Update jacobi_iteration_method.py

Edits upon request.

arithmetic_analysis/jacobi_iteration_method.py

@@ -133,40 +133,33 @@ def jacobi_iteration_method(
         init_val = new_val
     """
 
-    """
-    denom - a list of values along the diagonal
-    """
-    denom = np.diag(coefficient_matrix)
-    """
-    val_last - values of the last column of the table array
-    """
+    # denominator - a list of values along the diagonal
+    denominator = np.diag(coefficient_matrix)
+
+    #val_last - values of the last column of the table array
     val_last = table[:, -1]
-    """
-    masks - boolean mask of all strings without diagonal
-    elements array coefficient_matrix
-    """
+
+    #masks - boolean mask of all strings without diagonal
+    #elements array coefficient_matrix
     masks = ~np.eye(coefficient_matrix.shape[0], dtype=bool)
-    """
-    no_diag - coefficient_matrix array values without diagonal elements
-    """
-    no_diag = coefficient_matrix[masks].reshape(-1, rows - 1)
-    """
-    Here we get 'i_col' - these are the column numbers, for each row
-    without diagonal elements, except for the last column.
-    """
+
+    #no_diagonals - coefficient_matrix array values without diagonal elements
+    no_diagonals = coefficient_matrix[masks].reshape(-1, rows - 1)
+
+    #Here we get 'i_col' - these are the column numbers, for each row
+    #without diagonal elements, except for the last column.
     i_row, i_col = np.where(masks)
     ind = i_col.reshape(-1, rows - 1)
-    """
-    'i_col' is converted to a two-dimensional list 'ind',
-    which will be used to make selections from 'init_val'
-    ('arr' array see below).
-    """
+
+    #'i_col' is converted to a two-dimensional list 'ind', which will be
+    #used to make selections from 'init_val' ('arr' array see below).
+
 
     # Iterates the whole matrix for given number of times
     for _ in range(iterations):
         arr = np.take(init_val, ind)
-        temp = np.sum((-1) * no_diag * arr, axis=1)
-        new_val = (temp + val_last) / denom
+        sum_product_rows = np.sum((-1) * no_diagonals * arr, axis=1)
+        new_val = (sum_product_rows + val_last) / denominator
         init_val = new_val
 
     return new_val.tolist()