Group 2 X=11 3 5 7 9]: Y=12:4:68:101: Group3 X= diag(eye(3)). *3: K=ones(3)+eye(3): Group 4 X= zeros (3): X=X+diag(1:
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- function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= Run the function for L=3 and N=7 and upload the image of the graph plotted USE MATLAPCreate a nfa for L = {w ∈ {a, b}∗|w begins and ends with different letters.}function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend 2-Run the function for L=3 and N=3 and upload the image of the graph plotted use matlap
- function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= Which one is true? a. When the N increases, the approximated graph tends to be closer to the original square wave b. When the L increases, the approximated graph tends to be closer to the original square wave c. When the L decreases, the approximated graph tends to be closer to the original square wave d. When the N decreases, the approximated graph tends to be closer to the original square wave e. Nonefunction [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= In which case the bandwidth decreases? a. When N decreases b. When L increases c. When L decreases d. When N increases e. NoneGiven: (3,6) int numi, num2, newNum; double x, y; Which of the following assignments are valid? If an assignment is not valid, state the reason. a. numl = 35; b.newNum = numl - num2; c.numl = 5;num2 = 2 + numl;numl = num2 I 3; d. numl * num2 = newNum; e.x = 12 * numl - 15.3; f.numl * 2 = newNum + num2; g. x / y = x * y; h. num2 = numl % 2.0; i.newNum = static_cast<int> (x) % 5; j.x = x + y - 5; k. newNum = numl + static_cast<int> (4.6/2);
- function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend 1-Run the function with L=3 and N=1 and upload the plotted graph use matlapTopText Publishing is a textbook publishing company with a headquarters location, a warehouse, and three sales offices that each have a sales manager and sales reps. TopText sells to schools, colleges, and individual customers. Many authors write more than one book for TopText, and more than one author writes some books. TopText maintains an active list of more than 100 books, each identified by a universal code called an ISBN. You have been asked to draw an ERD for the TopText information system and to include cardinality notation in your diagram.# A set of constants, each representing a list index for station information. ID = 0 NAME = 1 LATITUDE %3D LONGITUDE 3 САРАСITY 4 BIKES_AVAILABLE %3D DOCKS_AVAILABLE %3D NO_KIOSK "SMART' # For use in the get_lat_lon_distance helper function EARTH_RADIUS = 6371 # SAMPLE DATA TO USE IN DOCSTRING EXAMPLES SAMPLE_STATIONS [7090, 'Danforth Ave / Lamb Ave', 43.681991, -79.329455, 15, 4, 10], [7486, 'Gerrard St E / Ted Reeve Dr', 43.684261, -79.299332, 24, 5, 19], [7571, 'Highfield Rd / Gerrard St E - SMART', 43.671685, -79.325176, 19, 14, 5]] HANDOUT_STATIONS [7000, 'Ft. York / Capreol Crt.', 43.639832, -79.395954, 31, 20, 11], [7001, 'Lower Jarvis St SMART / The Esplanade', 43.647992, -79.370907, 15, 5, 10]]
- def upgrade_stations(threshold: int, num_bikes: int, stations: List["Station"]) -> int: II II I| "" "Modify each station in stations that has a capacity that is less than threshold by adding num_bikes to the capacity and bikes available counts. Modify each station at most once. Return the total number of bikes that were added to the bike share network. Precondition: num_bikes >= 0 » handout_copy = [HANDOUT_STATIONS[0][:], HANDOUT_STATIONS[1][:]] » upgrade_stations(25, 5, handout_copy) %3D » handout_copy[0] HANDOUT_STATIONS[0] True » handout_copy[1] [7001, 'Lower Jarvis St SMART / The Esplanade', \ 43.647992, -79.370907, 20, 10, =%3D 10] True II II IIget_total_cases() takes the a 2D-list (similar to database) and an integer x from this set {0, 1, 2} as input parameters. Here, 0 represents Case_Reported_Date, 1 represents Age_Group and 2 represents Client_Gender (these are the fields on the header row, the integer value represents the index of each of these fields on that row). This function computes the total number of reported cases for each instance of x in the text file, and it stores this information in a dictionary in this form {an_instance_of_x : total_case}. Finally, it returns the dictionary and the total number of all reported cases saved in this dictionary.def classify(classifier, point): """ return the classification probability for the given point using the given classifier :params: classifier: list of floats, representing the weights for a logistic classifier point: list of values, representing features of a data point. will be same length as list of weights return: float, representing probability that point is from class 1. """ # TODO: implement this! # placeholder; say "50-50" for any point return 0.5