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- Using GNU Octave, create a row vector that has following elements: 3, 4*2.55, 68/16, 45, nthroot (100,3), cos(25) and 0.05Creates a 15 element vector in pandas Plot the vector Remove 5 values from the vector Replot the vector Interpolate the missing values in the vector Replot the vector Copy the plots into Word and write a few sentences explaining the results Creates a 4x8 array in pandas that consists of 4 variables with 8 data points for each Adds axis labels to the 4x8 array Use the describe command on the 4x8 array Plot the 4x8 array Using the attached random_picker.py program select 6 locations to replace with NA in your 4x8 array (Run the picker program seperatly and then hard code the deletions) Use the describe command on the 4x8 array Using the interpolate method, fill in the missing data. If you still have missing data use the appropriate fillna method (ffill/bfill) to fix Describe the 4x8 array Plot the 4x8 array Copy the results of the describe command and the plots to the Word file and write up the resultsCreate a row vector of 1's that has 4 rows. Now Create a column vector of 1's that has 4 columns. Using matrix multiplication, multiply both of these to get a 4x4 matrix and display the resulting matrix. Using matlab, do this. Please answer it correctly and don’t copy from anywhere otherwise it will be downvoted.
- Create a 3-by-3 matrix: A = [1 2 3;4 5 6;7 8 10] (a)Extract a submatrix B consisting of rows 1 and 3 and columns 1 and 2 of the matrix A (b)Interchange rows 1 and 3 of A and put it in a variable C. Hint: use the vector of row indices together with the colon operator (c)Create a vector version of the original matrix A. (d)Delete the 2nd column of the original matrix A and put it in a variable called A. (e)Insert a 2nd column to the new matrix A to restore it back to its original form A. (f) Extract those entries of the original matrix A that are greater than 5Creates a 15 element vector in pandas Plot the vector Remove 5 values from the vector Replot the vector Interpolate the missing values in the vector Replot the vector Copy the plots into Word and write a few sentences explaining the results Creates a 4x8 array in pandas that consists of 4 variables with 8 data points for each Adds axis labels to the 4x8 array Use the describe command on the 4x8 array Plot the 4x8 arrayCreate a 1-by-1000 row vector called M containing random integer numbers between 8 and 256. Using logical array indexing, assign all elements with value greater than 200 to a variable called up200. Display the dimension of variable up200.MatLab Continuing previous task, create another variable called up100 containing all elements in M that are greater than 100 but less than 200. Display the dimension of vector up100. Append variable up200 to variable up100.Please give code for MatLab
- IN MATLAB SOFTWARE Task 5 Create a 1-by-1000 row vector called M containing random integer numbers between 8 and 256. Using logical array indexing, assign all elements with value greater than 200 to a variable called up200. Display the dimension of variable up200. Task 6 Continuing previous task, create another variable called up100 containing all elements in M that are greater than 100 but less than 200. Display the dimension of vector up100. Append variable up200 to variable up100.If a and b are two non-empty numerical row vectors (of different lengths) present in Matlab Workspace, what command would you use to create a new vector, called c, that is a combination of a and b (b elements are appended at the end of a)? c = a + b c = transpose(a,b) c = [a; b] c = [a, b]def large_position(matrix: list[list[int]], row: int, col: int) -> int: """ Returns the area of the largest rectangle whose top left corner is at position <row>, <col> in <matrix>. You MUST make use of the helper <longest_chain> here as you loop through each row of the matrix. Do not modify (i.e., mutate) the input matrix. >>> case100 = [[1, 0, 1, 0, 0], ... [1, 0, 1, 1, 1], ... [1, 1, 1, 1, 1], ... [1, 0, 0, 1, 0]] >>> large_position(case1, 0, 0) 4 """ Helper: def longest_chain(lst: List[int]) -> int: count = 0 # defining count variable as 0. Using it for counting the length of the sequence.i = 0 # defining i variable as 0. Using it for index positionwhile (i < len(lst) and lst[i] != 0): # iterating through out the list while i is lesser than the length of the list and until we encounter any 0i += 1 # increasing the index valuecount += 1 # increasing the countreturn…
- def large_position(matrix: list[list[int]], row: int, col: int) -> int: """ Returns the area of the largest rectangle whose top left corner is at position <row>, <col> in <matrix>. You MUST make use of the helper <longest_chain> here as you loop through each row of the matrix. Do not modify (i.e., mutate) the input matrix. >>> case100 = [[1, 0, 1, 0, 0], ... [1, 0, 1, 1, 1], ... [1, 1, 1, 1, 1], ... [1, 0, 0, 1, 0]] >>> large_position(case1, 0, 0) 4 """ Helper: def longest_chain(lst: List[int]) -> int: count = 0 # defining count variable as 0. Using it for counting the length of the sequence.i = 0 # defining i variable as 0. Using it for index positionwhile (i < len(lst) and lst[i] != 0): # iterating through out the list while i is lesser than the length of the list and until we encounter any 0i += 1 # increasing the index valuecount += 1 # increasing the countreturn…ou have been provided with the 4-by-4 matrix attached on the picture: Write a Java application using NetBeans that reads the above 4-by-4 matrix and displays the sum of all its elements on the major diagonal. Output is as follows:(see attached output.def perceptron_single_step_update(feature_vector,label,current_theta,current_theta_0):"""Properly updates the classification parameter, theta and theta_0, on asingle step of the perceptron algorithm. Args:feature_vector - A numpy array describing a single data point.label - The correct classification of the feature vector.current_theta - The current theta being used by the perceptronalgorithm before this update.current_theta_0 - The current theta_0 being used by the perceptronalgorithm before this update. Returns: A tuple where the first element is a numpy array with the value oftheta after the current update has completed and the second element is areal valued number with the value of theta_0 after the current updated hascompleted.