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i have qustion in pythone program i sent to here but the answer i get is wrong i attached the answer i token from here pleas help me for get good answer

i have qustion in pythone program i sent to here but the answer i get is wrong i attached the answer i token from here pleas help me for get good answer

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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i have qustion in pythone program i sent to here but the answer i get is wrong i attached the answer i token from here pleas help me for get good answer 

 

tahnk you

+ X Untitled2 - Jupyter Notebe X Home Page - Select or cre C x pandas - Google G X Answered: Write a Python b X و علامة تبويب جديدة f localhost:8889/notebooks/Untitled2.ipynb?kernel_name=python3 0 قائمة القراءة SQL for Beginners:. û B مرحتّار فياض - . ..Black Google hiljs A YouTube Gmail M التطبيقات Cjupyter Untitled Last Checkpoint: as o ius (autosaved) Logout File Edit View Insert Cell Kernel Widgets Help Trusted | Python 3 O + > Run Code # Defining a empty list , to store passing index pass_index = [] # for each instance s in s, looping instance with help of index for index in S.index: # fetching s and sLabel from respective index s = 5.loc[index] sLabel trainy.loc[index] # finding euclidean distance between s and all instances in s # using apply method of pandas dataframe, and passing euclidean function in it # using axis =1 for row wise apply and second argument as s2 euclDistances = S.apply (euclidean, s2 = s, axis=1) # first k instance have the lowest euclidean distances # sorting euclDistances and fetching first k instances firstk = euclDistances.sort_values ().iloc[:k] # Label (L) for majority of k points # fetching Labels of firstk by matching index with trainy firstKLabels = trainy.loc[firstK.index.tolist()] # finding most occuring label using mode method of pandas majorLabel = firstKLabels.mode().iloc[0] # if s label doesn't belong to majorlabel removing from s, i.e. not storing # its index in pass index if slabel -- majorlabel: pass_index.append (index) # otherwise not including # filteredData using pass_index filterTrainx = S.loc[pass_index] filterTrainy = trainy.loc[pass_index] # printing size of both print("\nAfter removing noise: ") print(filterTrainx.shape, filterTrainy.shape) File "<ipython-input-2-5d04f7dacd67>", line 52 s = S.loc[index] IndentationError: expected an indented block 3:36 PM O Type here to search 95°F O G ») ENG 9/27/2021 (8)
Transcribed Image Text:+ X Untitled2 - Jupyter Notebe X Home Page - Select or cre C x pandas - Google G X Answered: Write a Python b X و علامة تبويب جديدة f localhost:8889/notebooks/Untitled2.ipynb?kernel_name=python3 0 قائمة القراءة SQL for Beginners:. û B مرحتّار فياض - . ..Black Google hiljs A YouTube Gmail M التطبيقات Cjupyter Untitled Last Checkpoint: as o ius (autosaved) Logout File Edit View Insert Cell Kernel Widgets Help Trusted | Python 3 O + > Run Code # Defining a empty list , to store passing index pass_index = [] # for each instance s in s, looping instance with help of index for index in S.index: # fetching s and sLabel from respective index s = 5.loc[index] sLabel trainy.loc[index] # finding euclidean distance between s and all instances in s # using apply method of pandas dataframe, and passing euclidean function in it # using axis =1 for row wise apply and second argument as s2 euclDistances = S.apply (euclidean, s2 = s, axis=1) # first k instance have the lowest euclidean distances # sorting euclDistances and fetching first k instances firstk = euclDistances.sort_values ().iloc[:k] # Label (L) for majority of k points # fetching Labels of firstk by matching index with trainy firstKLabels = trainy.loc[firstK.index.tolist()] # finding most occuring label using mode method of pandas majorLabel = firstKLabels.mode().iloc[0] # if s label doesn't belong to majorlabel removing from s, i.e. not storing # its index in pass index if slabel -- majorlabel: pass_index.append (index) # otherwise not including # filteredData using pass_index filterTrainx = S.loc[pass_index] filterTrainy = trainy.loc[pass_index] # printing size of both print("\nAfter removing noise: ") print(filterTrainx.shape, filterTrainy.shape) File "<ipython-input-2-5d04f7dacd67>", line 52 s = S.loc[index] IndentationError: expected an indented block 3:36 PM O Type here to search 95°F O G ») ENG 9/27/2021 (8)
+ X Untitled2 - Jupyter Notebe X Home Page - Select or cre C x pandas - Google G X Answered: Write a Python b X و علامة تبويب جديدة f localhost:8889/notebooks/Untitled2.ipynb?kernel_name=python3 0 قائمة القراءة SQL for Beginners:. û B مرحبّا, فياض - . ..Black Google hiljs A YouTube Gmail M التطبيقات C jupyter UntitledY Last Checkpoint as s ie (autosaved) Logout File Edit View Insert Cll Kernel Widgets Help | Python 3 O Trusted + > Run Code In [2]: # import pandas as pd import pandas as pd import random import math # reading training data data = pd.read_csv("fashion-mnist_train.csv") # creating training set trainx = data trainy = trainx.pop("label") ## A. selecting 3000 images 300 for each Label # using list comprehension for fetching list of (trainx, trainy) of 300 rows for each # Label data_list = [(trainX[trainy==label].iloc[:300], trainy[trainy==label].iloc[:300]) for label in trainy.unique()] # concatinating all trainx and trainy in one using pd.concat and passing data_list, using list comprehension to fetch # trainx and trainy from tuples list trainx = pd.concat ([df[0] for df in data_list]) trainy = pd.concat ([df[1] for df in data_list]) print("Shape of Data: ") print(trainx.shape, trainy.shape) ## B. Shuffling the training set # first fetching the index from trainx index_list = trainx.index.tolist() # using random.shuffle to shuffle index_list random.shuffle(index_list) # using shuffle_index to get shuffled trainX and y suffle_trainx = trainx.loc[index_list] suffle_trainy = trainy.loc[index_list] ## C. Apply ENN # defining lambda function to calculate euclidean distance, it takes two pd.Series # or list as argument # using list comprehension to find (a-b)^2 for every value in s1 and s2 # adding all squared differences using sum # using math.sqrt to find square root of sum to get distance euclidean = lambda s1, s2:math.sqrt(sum([(a-b)*(a-b) for a, b in zip(s1, s2)])) # set s = I S = suffle_trainx # chooc valun nf h 3:35 PM O Type here to search 95°F O G ) ENG 9/27/2021 (8)
Transcribed Image Text:+ X Untitled2 - Jupyter Notebe X Home Page - Select or cre C x pandas - Google G X Answered: Write a Python b X و علامة تبويب جديدة f localhost:8889/notebooks/Untitled2.ipynb?kernel_name=python3 0 قائمة القراءة SQL for Beginners:. û B مرحبّا, فياض - . ..Black Google hiljs A YouTube Gmail M التطبيقات C jupyter UntitledY Last Checkpoint as s ie (autosaved) Logout File Edit View Insert Cll Kernel Widgets Help | Python 3 O Trusted + > Run Code In [2]: # import pandas as pd import pandas as pd import random import math # reading training data data = pd.read_csv("fashion-mnist_train.csv") # creating training set trainx = data trainy = trainx.pop("label") ## A. selecting 3000 images 300 for each Label # using list comprehension for fetching list of (trainx, trainy) of 300 rows for each # Label data_list = [(trainX[trainy==label].iloc[:300], trainy[trainy==label].iloc[:300]) for label in trainy.unique()] # concatinating all trainx and trainy in one using pd.concat and passing data_list, using list comprehension to fetch # trainx and trainy from tuples list trainx = pd.concat ([df[0] for df in data_list]) trainy = pd.concat ([df[1] for df in data_list]) print("Shape of Data: ") print(trainx.shape, trainy.shape) ## B. Shuffling the training set # first fetching the index from trainx index_list = trainx.index.tolist() # using random.shuffle to shuffle index_list random.shuffle(index_list) # using shuffle_index to get shuffled trainX and y suffle_trainx = trainx.loc[index_list] suffle_trainy = trainy.loc[index_list] ## C. Apply ENN # defining lambda function to calculate euclidean distance, it takes two pd.Series # or list as argument # using list comprehension to find (a-b)^2 for every value in s1 and s2 # adding all squared differences using sum # using math.sqrt to find square root of sum to get distance euclidean = lambda s1, s2:math.sqrt(sum([(a-b)*(a-b) for a, b in zip(s1, s2)])) # set s = I S = suffle_trainx # chooc valun nf h 3:35 PM O Type here to search 95°F O G ) ENG 9/27/2021 (8)
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