Suppose you are doing dimensionality reduction, and you chose to use FactorAnalysis module from the scikit learn library as a tool. What will be the suitable transformation statement if you want to reduce the dimension of the DataFrame df to two factors? Note: Assume totol n columns and you take only n-1 columns for reduction and exclude the last column. O a. fa = FactorAnalysis(n_components = n).fit_transform(df.iloc[; :-1].values) O b. fa = FactorAnalysis(n_components = n-2).fit_transform(df.iloc[; :-1].values) O . fa = FactorAnalysis(n_components = 2).fit_transform(df.iloc[;, :-1].values) O d. fa = FactorAnalysis(n_components = 2).fit_transform(df.iloc[-1, :-1].values)

Suppose you are doing dimensionality reduction, and you chose to use FactorAnalysis module from the scikit learn library as a tool. What will be the suitable transformation statement if you want to reduce the dimension of the DataFrame df to two factors? Note: Assume totol n columns and you take only n-1 columns for reduction and exclude the last column. O a. fa = FactorAnalysis(n_components = n).fit_transform(df.iloc[; :-1].values) O b. fa = FactorAnalysis(n_components = n-2).fit_transform(df.iloc[; :-1].values) O . fa = FactorAnalysis(n_components = 2).fit_transform(df.iloc[;, :-1].values) O d. fa = FactorAnalysis(n_components = 2).fit_transform(df.iloc[-1, :-1].values)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Which implementation of the size and isEmpty methods would you choose if you had the choice of having a million-element list? (Think about the problem with the University of Michigan's alumni database.) How would you select between making a million different lists? Consider the difficulty of monitoring dependents for a million income tax returns.)
Modify 1. Replace the elements of r_vec with the indices 4, 6, 8, 10 and 15 with 3, 6, 9, 12 and 15, respectively. 2. Replace the elements of mat with the indices (4, 5), (4, 17), (20, 5) and (20,17) with 5, 8, 2 and 9, respectively. 3. Replace the all the elements of the eleventh column of mat with ones. 4. Replace the characters 11 to 19 of chr with the words white cat. 5. Replace the first entry of str with the value of sclr.
As we've seen, it takes linear time to remove a value from the PriorityVector. This might be lowered to constant time if the elements were stored in descending order. Discuss the situations that favour using one implementation over the other as you compare the ascending and descending implementations.
We've seen that deleting a value from the PriorityVector costs linear time. This might be lowered to constant time if elements were stored in descending order. Compare the ascending and descending implementations, and discuss the circumstances that favour using one over the other.
b. Given 1st = [30, 1, 12, 14, 10, 0], how many elements are in 1st? What is the index of the first element in 1st? What is the index of the last element in 1st? What is 1st [2]? What is 1st[-2]?
link to MINST code: https://www.dropbox.com/s/i21pedejv0sknnq/MINST.pdf?dl=0 1) Add a confusion matrix to the MNIST code example from the link provided. Set the epochs parameter to 5. Explain which entry of the matrix yields the number of ones (the number 1) that are falsely recognized as eights (the number 8). (If you use indices in your explanation, use the Python index notation, i.e., start at zero.) Run your code a few times. Is the confusion matrix always the same? Briefly explain.
At various points throughout the other functions, we’re going to want to see the state of the board not as a list of 9 elements, but in the more traditional 3x3 grid. Write a function print_board(board) that takes in a 9-element list as explained above, and prints out a graphical representation of the board. It doesn’t have to exactly match the examples below so long as you can clearly distinguish the rows and columns. (Hint: It’s probably going to be faster to just manually get the three list elements that make up each row and concatenate them together than to do something fancy with a loop in this case)
What you need to Implement? Your task is to implement two versions of the example reducer from MDN, so that these new functions can handle non-numeric values in the array in a graceful manner. Here are the 2 functions you need to implement: reducer1 o If this function is passed to reduce, any non-numeric values in the array will be skipped and the sum of only the numeric values in the array will be returned. o If all the values in the array are non-numeric, then the value O will be returned. reducer2 ○ If this function is passed to reduce and the array contains any non-numeric values, this function will throw the exception TypeError. o Otherwise, the sum of all the values in the array will be returned. you can use o To throw the exception TypeError with the message 'My error message' the statement throw new TypeError('My error message'). See TypeError on MDN for more discussion of this exception.
Victor, a level 200 undergraduate student in AIT remarked that array elements can be accessed in constant time by using the index of the element as the subscript. Do you agree with him on this statement? and if so, explain the rationality of this statement.
Write a C function, void sum_rows( int a*,int sum*,int n) to compute the sum of row elements in a nxn matrix and store the result in vector sum[]. Can this function be optimized by the compiler? If No, why? Rewrite the function to remove the optimization blocker.
Trace the match_pattern module (including match_segment) for the pattern and input given below; >>>match ( '?*actor have got the ?*action of ?*object on LMS ?*duration', 'students have got the final paper of AI on LMS today'))
g. Include a delete() function in LinearProbingHashST that deletes a key-value pair by changing the value to null (while leaving the key in place) and then deleting the pair from the table in resize(). The most difficult decision you must make is when to use resize(). Note: If a future put() action associates a new value with the key, you should replace the null value. Make sure that while deciding whether to enlarge or shrink the table, your programme takes into consideration both the number of such tombstone items and the number of vacant places.