Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN: 9780133594140
Author: James Kurose, Keith Ross
Publisher: PEARSON
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Coding in python, one the fist question please.
![In the implementation above, we have factored together what would have been the common part of the implementation of + and – in the
method _binary_op. The _binary_op method is a static method: it does not refer to a specific object. In this way, we can decide separately
what to provide to it as the left and right hand sides; this will be useful later, as we shall see. To call it, we have to refer to it via AD._binary_op
rather than self._binary_op, as would be the case for a normal (object) method. Furthermore, in the _binary_op method, we do not have
access to self, since self refers to a specific object, while the method is generic, defined for the class.
The _binary_op method combines values from its left and right operands using operator op, and the default neutral if a value is not
found. The two operands left and right are interpreted as dictionaries if they are a subclass of dict, and are interpreted as constants
otherwise.
We have defined our class AD as a subclass of the dictionary class dict. This enables us to apply to an AD all the methods that are defined for
a dictionary, which is really quite handy.
[] d1 = AD()
d1['red'] - 2
print(d1)
d2 = AD({'blue': 4, 'green': 5})
print(d2)
In the code above, note how we can write d1['red']: our ability to access ADs via the square-bracket notation is due to the fact that ADs, like
dictionaries, implement the getitem method. Note also that AD inherits from dict even the initializer methods, so that we can pass to our
dictionary another dictionary, and obtain an AD that contains a copy of that dictionary.
We can see that addition and subtraction also work:
[] print(AD(red-2, green-3) + AD(red-1, blue-4))
print(AD(red=2, green=3)
- AD(red=1, blue-4))
We let multiplication and division to you to implement. You need to implement the operators:
mul
truediv_
_floordiv
[] ### Question 1: Implement multiplication and division
# Define _mul_
def ad_mul(self, other):
# YOUR CODE HERE
AD._mul_ = ad_mul
# Define below _truediv_, similarly.
# YOUR CODE HERE
# And finally, define below _floordiv_, for INTEGER division.
# YOUR CODE HERE
We will let you also implement max_items and min_items properties. The value of these properties should be a pair, consisting of the
maximum/minimum value, and of the
De a set, because there are multiple key, value pairs](https://content.bartleby.com/qna-images/question/293032ce-89a7-4d5a-8a29-aae96cb1d151/3ed2fc22-f134-4aef-b1c4-1f946a9f21f3/7du0znb_processed.jpeg)
Transcribed Image Text:In the implementation above, we have factored together what would have been the common part of the implementation of + and – in the
method _binary_op. The _binary_op method is a static method: it does not refer to a specific object. In this way, we can decide separately
what to provide to it as the left and right hand sides; this will be useful later, as we shall see. To call it, we have to refer to it via AD._binary_op
rather than self._binary_op, as would be the case for a normal (object) method. Furthermore, in the _binary_op method, we do not have
access to self, since self refers to a specific object, while the method is generic, defined for the class.
The _binary_op method combines values from its left and right operands using operator op, and the default neutral if a value is not
found. The two operands left and right are interpreted as dictionaries if they are a subclass of dict, and are interpreted as constants
otherwise.
We have defined our class AD as a subclass of the dictionary class dict. This enables us to apply to an AD all the methods that are defined for
a dictionary, which is really quite handy.
[] d1 = AD()
d1['red'] - 2
print(d1)
d2 = AD({'blue': 4, 'green': 5})
print(d2)
In the code above, note how we can write d1['red']: our ability to access ADs via the square-bracket notation is due to the fact that ADs, like
dictionaries, implement the getitem method. Note also that AD inherits from dict even the initializer methods, so that we can pass to our
dictionary another dictionary, and obtain an AD that contains a copy of that dictionary.
We can see that addition and subtraction also work:
[] print(AD(red-2, green-3) + AD(red-1, blue-4))
print(AD(red=2, green=3)
- AD(red=1, blue-4))
We let multiplication and division to you to implement. You need to implement the operators:
mul
truediv_
_floordiv
[] ### Question 1: Implement multiplication and division
# Define _mul_
def ad_mul(self, other):
# YOUR CODE HERE
AD._mul_ = ad_mul
# Define below _truediv_, similarly.
# YOUR CODE HERE
# And finally, define below _floordiv_, for INTEGER division.
# YOUR CODE HERE
We will let you also implement max_items and min_items properties. The value of these properties should be a pair, consisting of the
maximum/minimum value, and of the
De a set, because there are multiple key, value pairs
![- Arithmetic Dictionaries
At the core of our sock drawer implementation is a fundamental data structure which, for some reason, does not come standard in
programming languages: a dictionary that has numerical values (and arbitrary keys).
A number is a number. An array, or vector, is a list of numbers that can be indexed by their position in the vector. An arithmetic dictionary is a set
of numbers that can be addressed by their associated key. Just as numbers, and arrays, can be combined via +, -, *, /, and compared with >, <,
etc, so can arithmetic dictionaries. Let us start defining them. For brevity, we call them simply AD rather than ArithmeticDictionary. We note that
Python includes, in the collections module, the Counter class, which supports addition. ADs are a more general version of the Counter class,
as they support (or you can make them to support) all math operations you wish.
In Python, to be able to have two ADs d1 and d2 and compute their sum via the syntax d1 + d2 , we need to define the
Ladd operation, and
similarly for the other arithmetic operations.
There is a fine point to be discussed. Arrays can be combined with arithmetical operations only when they are of the same dimensions, or when
one can be "broadcast" to fit the dimension of the other. For dictionaries, however, the restriction to be able to combine only arithmetic
dictionaries with the same keys would be too restrictive. If we have an AD d representing a sock drawer, and we want to add to it one yellow
and two blue socks, we want to be able to write something like:
d +- AD({'yellow': 1, 'blue': 2})
rather than worry that d might have different keys from {yellow', 'blue'}. Thus, in the definition of an operation, we will need to define suitable
defaults for the missing values. In general, the suitable defaults are the neutral elements with respect to the operation: 0 for + and –, and 1 for
X and /.
Let us begin from addition and subtraction.
[] class AD(dict):
init_(self, *args, **kwargs):
"""This initializer simply passes all arguments to dict, so that
we can create an AD with the same ease with which we can create
def
There is no need, indeed, to repeat the initializer,
but we leave it here in case we like to create attributes specific
a dict.
of an AD later."""
super ()._init_(*args, **kwargs)
def
add_(self, other):
return AD._binary_op(self, other, lambda x, y: x + y, e)
def _sub_(self, other):
return AD._binary_op(self, other, lambda x, y: x y, e)
@staticmethod
def _binary_op(left, right, op, neutral):
r = AD()
1_keys = set(left.keys()) if isinstance (left, dict) else set()
r_keys
%3D
set (right.keys(0) if isinstance(right, dict) else set()
%3D
for k in l_keys | r_keys:
# If the right (or left) element is a dictionary (or an AD),
# we get the elements from the dictionary; else we use the right
# or left value itself. This implements a sort of dictionary
# broadcasting.
1_val = left.get(k, neutral) if isinstance(left, dict) else left
r_val =
r[k] = op(1_val, r_val)
right.get(k, neutral) if isinstance(right, dict) else right
return r](https://content.bartleby.com/qna-images/question/293032ce-89a7-4d5a-8a29-aae96cb1d151/3ed2fc22-f134-4aef-b1c4-1f946a9f21f3/hie5br7_processed.jpeg)
Transcribed Image Text:- Arithmetic Dictionaries
At the core of our sock drawer implementation is a fundamental data structure which, for some reason, does not come standard in
programming languages: a dictionary that has numerical values (and arbitrary keys).
A number is a number. An array, or vector, is a list of numbers that can be indexed by their position in the vector. An arithmetic dictionary is a set
of numbers that can be addressed by their associated key. Just as numbers, and arrays, can be combined via +, -, *, /, and compared with >, <,
etc, so can arithmetic dictionaries. Let us start defining them. For brevity, we call them simply AD rather than ArithmeticDictionary. We note that
Python includes, in the collections module, the Counter class, which supports addition. ADs are a more general version of the Counter class,
as they support (or you can make them to support) all math operations you wish.
In Python, to be able to have two ADs d1 and d2 and compute their sum via the syntax d1 + d2 , we need to define the
Ladd operation, and
similarly for the other arithmetic operations.
There is a fine point to be discussed. Arrays can be combined with arithmetical operations only when they are of the same dimensions, or when
one can be "broadcast" to fit the dimension of the other. For dictionaries, however, the restriction to be able to combine only arithmetic
dictionaries with the same keys would be too restrictive. If we have an AD d representing a sock drawer, and we want to add to it one yellow
and two blue socks, we want to be able to write something like:
d +- AD({'yellow': 1, 'blue': 2})
rather than worry that d might have different keys from {yellow', 'blue'}. Thus, in the definition of an operation, we will need to define suitable
defaults for the missing values. In general, the suitable defaults are the neutral elements with respect to the operation: 0 for + and –, and 1 for
X and /.
Let us begin from addition and subtraction.
[] class AD(dict):
init_(self, *args, **kwargs):
"""This initializer simply passes all arguments to dict, so that
we can create an AD with the same ease with which we can create
def
There is no need, indeed, to repeat the initializer,
but we leave it here in case we like to create attributes specific
a dict.
of an AD later."""
super ()._init_(*args, **kwargs)
def
add_(self, other):
return AD._binary_op(self, other, lambda x, y: x + y, e)
def _sub_(self, other):
return AD._binary_op(self, other, lambda x, y: x y, e)
@staticmethod
def _binary_op(left, right, op, neutral):
r = AD()
1_keys = set(left.keys()) if isinstance (left, dict) else set()
r_keys
%3D
set (right.keys(0) if isinstance(right, dict) else set()
%3D
for k in l_keys | r_keys:
# If the right (or left) element is a dictionary (or an AD),
# we get the elements from the dictionary; else we use the right
# or left value itself. This implements a sort of dictionary
# broadcasting.
1_val = left.get(k, neutral) if isinstance(left, dict) else left
r_val =
r[k] = op(1_val, r_val)
right.get(k, neutral) if isinstance(right, dict) else right
return r
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