Add second year

second/semester2/CT248/Assignments/Assignment-02/Lab1.m

@@ -0,0 +1,8 @@
+clear;
+d = roll_2_dice(10000, 100);
+[freq, prop] = tabulate_2_dice(d);
+
+disp(freq);
+disp(prop);
+
+

second/semester2/CT248/Assignments/Assignment-02/roll_2_dice.m

@@ -0,0 +1,7 @@
+function d = roll_2_dice(N, seed)
+  % function to roll 2 dice and return the combination of each device row in a vector
+  rng(seed);
+
+  % generating two 1 * N vectors of 6 simulated dice rolls and adding them
+  d = randi([1 6], 1, N) + randi([1 6], 1, N);
+end

second/semester2/CT248/Assignments/Assignment-02/tabulate_2_dice.m

@@ -0,0 +1,14 @@
+function [freq, prop] = tabulate_2_dice(d)
+  % function to calculate the frequency and proportion of each outcome based on a set of dice throws
+  freq = zeros(1,12);
+  % looping through all the values in d and incrementing the corresponding counter in freq
+  for i = d
+    freq(i) = freq(i) + 1;
+  end
+
+  % looping through each index in prop and calculating the proportion pertaining to that index
+  prop = zeros(1,12);
+  for i = [1:12]
+    prop(i) = freq(i) / sum(freq); % proportion of i is the frequency of i divided by the sum of all freqs
+  end
+end

second/semester2/CT248/Assignments/Assignment-03/mystack.m

@@ -0,0 +1,28 @@
+function[push, pop, peek] = mystack()
+% function to return handles to the subfunction push, pop, & peek
+    push = @push;
+    pop = @pop;
+    peek = @peek;
+end
+
+function [stack] = push(stack, value)
+% function to push a value onto the stack at location 1 and return the
+% stack
+    stack = [value; stack];
+end
+
+function [stack] = pop(stack)
+% function to pop the value at location 1 off the stack and return the
+% stack
+    stack(1) = [];
+end
+
+function [value] = peek(stack)
+% function to return the top value from the stack (arrau location 1)
+% returns NaN if there is no value at location 1
+    if isempty(stack)
+        value = NaN;
+    else
+        value = stack(1);
+    end
+end

second/semester2/CT248/Assignments/Assignment-03/test.m

@@ -0,0 +1,13 @@
+% test script as specified in assignment spec
+[push, pop, peek] = mystack(); 
+
+stack = []
+stack = push(stack, 100)
+
+stack = push(stack, 200) 
+
+peek(stack) 
+
+stack = pop(stack) 
+
+peek(stack)

second/semester2/CT248/Assignments/Assignment-04/imageprocessing.m

@@ -0,0 +1,15 @@
+clear;
+eng1 = imread("Engineering-Building.jpg");
+eng1_gs = pic2grayscale(eng1);
+eng1_gs_inv = transform_pic(eng1_gs);
+eng1_gs_bin_50 = transform_threshold(eng1_gs,50);
+eng1_gs_bin_75 = transform_threshold(eng1_gs,75);
+eng1_gs_bin_100 = transform_threshold(eng1_gs,100);
+
+% plotting images
+subplot(3,2,1),imshow(eng1),title("Original Picture"); 
+subplot(3,2,2),imshow(eng1_gs),title("Greyscale"); 
+subplot(3,2,3),imshow(eng1_gs_inv),title("Inverted Greyscale"); 
+subplot(3,2,4),imshow(eng1_gs_bin_50),title("Binary Threshold = 50"); 
+subplot(3,2,5),imshow(eng1_gs_bin_75),title("Binary Threshold = 75"); 
+subplot(3,2,6),imshow(eng1_gs_bin_100),title("Binary Threshold = 100"); 

second/semester2/CT248/Assignments/Assignment-04/pic2grayscale.m

@@ -0,0 +1,17 @@
+function [returnImg] = pic2grayscale(img) 
+% which uses the NTSC Standard transformation to convert RGB to grayscale. 
+    %0.2989 * R + 0.5870 * G + 0.1140 * B
+
+    % img to be returned
+    returnImg = zeros(size(img,1), size(img, 2)); 
+
+    % looping through the RGB image and calculating the grayscale value for
+    % each pixel in the corresponding returnImg
+    for i = 1:size(img,1) 
+        for j = 1:size(img,2)
+            returnImg(i,j) = (0.2989 * img(i,j,1)) + (0.5870 * img(i,j,2)) + (0.1140 * img(i,j,3));
+        end 
+    end
+    
+    returnImg = uint8(returnImg);
+end

second/semester2/CT248/Assignments/Assignment-04/transform_pic.m

@@ -0,0 +1,5 @@
+function [img] = transform_pic(img)
+% function which converts a 255 colour code to 0, 254 to 1, etc, and 0 to
+% 255. 
+    img = 255 - img;
+end

second/semester2/CT248/Assignments/Assignment-04/transform_threshold.m

@@ -0,0 +1,18 @@
+function [img] = transform_threshold(img, threshold) 
+% function which converts the picture to binary format where any value 
+% above the threshold is white (1), and all values equal to or below are
+% black (0). 
+
+    % looping through each element in the matrix, and setting it to 1 if
+    % above the threshold, 0 otherwise
+    for i = 1:numel(img) 
+        if img(i) > threshold 
+            img(i) = 1; 
+        else 
+            img(i) = 0; 
+        end
+    end
+    
+    % casting the matrix to type logical once each element is either 1 or 0
+    img = logical(img);
+end

second/semester2/CT248/Assignments/Assignment-06/shark_tuna_model.m

@@ -0,0 +1,14 @@
+function dydt = shark_tuna_model(t,x)
+% function to model the prey-predator population relations over time of
+% sharks & tuna 
+
+    global k;   % mentioning global variable x so that it can be used here 
+
+    dydt = [0; 0]; 
+    
+    % dS/dt = k1 S T - k2 S
+    dydt(1) = k(1)*x(1)*x(2)-k(2)*x(1); 
+    
+    % dT/dt = k3 T -k4 S T
+    dydt(2) = k(3)*x(2) - k(4) * x(1) * x(2); 
+end

second/semester2/CT248/Assignments/Assignment-06/test.m

@@ -0,0 +1,12 @@
+clear; 
+
+global k; 
+
+k = [0.015 0.7 0.5 0.01].'; 
+
+[t,y] = ode45(@shark_tuna_model, [0 50], [100 100]);
+plot(t,y); 
+title("SHARK-TUNA POPULATION DYNAMICS LIMIT CYCLING");
+xlabel("TIME"); 
+ylabel("POPULATION NUMBERS");
+legend("SHARKS", "TUNA", "Location", "northwest");

second/semester2/CT248/Assignments/Assignment-07/SIR.m

@@ -0,0 +1,15 @@
+function dydt = SIR(t, x, c, i, alpha, beta, gamma)
+    dydt = [0; 0; 0; 0; 0];
+    S = x(1);
+    I = x(2);
+    R = x(3);
+    H = x(4);
+    RH = x(5);
+    N = S + I + R + H + RH;
+
+    dydt(1) = (-c*S) * (I/N) * i;
+    dydt(2) = (c*S) * (I/N) * i - (alpha*I);
+    dydt(3) = (alpha*I) - (beta*R);
+    dydt(4) = (beta*R) - (gamma*H);
+    dydt(5) = gamma*H;
+end

second/semester2/CT248/Assignments/Assignment-07/test.m

@@ -0,0 +1,40 @@
+clear; 
+
+i = 0.125; 
+alpha = 0.25; 
+beta = 0.02; 
+gamma = 0.10; 
+
+time_vec = 0:.25:100;
+init_vec = [9999 1 0 0 0];
+c = linspace(3, 8, 20); 
+
+infected_stock = zeros(length(time_vec), 20); 
+in_hospital = zeros(length(time_vec), 20); 
+
+for loopcounter = 1:20
+    [t,y] = ode45(@SIR, ...
+                   time_vec, ...
+                   init_vec, ...
+                   odeset, ...
+                   c(loopcounter), ...
+                   i, ...
+                   alpha, ...
+                   beta, ...
+                   gamma); 
+    
+    infected_stock(:, loopcounter) = y(:,2); 
+    in_hospital(:,loopcounter) = y(:,4); 
+end
+
+subplot(3, 1, 1);
+plot(time_vec, infected_stock); 
+title("Infected Stock"); 
+
+subplot(3, 1, 2);
+plot(time_vec, infected_stock); 
+title("People in Hospital"); 
+
+subplot(3,1,3); 
+scatter(c, max(in_hospital)); 
+title("Contacts v Peak in Hospital");

second/semester2/CT248/Assignments/Assignment-08/six.m

@@ -0,0 +1,76 @@
+clear;
+
+% 1. read in the file and confirm the number of records (336,766)
+flights = readtable("Flights.csv"); 
+numRecords = height(flights)
+
+% 2. convert "origin" & "dest" to strings (from cell type) 
+flights.origin = string(flights.origin); 
+flights.dest = string(flights.dest); 
+
+
+% 3. check the number of missing values for the departure time 
+numMissing = sum(ismissing(flights.dep_delay))
+
+% 4. filter all the missing values from the departure delay and check the
+% difference in the number of records
+flights_clean = flights(~isnan(flights.dep_delay), :);
+numRecordsClean = height(flights_clean);
+disp("Number of records in flights = " + numRecords); 
+disp("Number of records in flights_clean = " + numRecordsClean);
+
+% 5. confirm the difference in records between the two tables 
+diff = numRecords - numRecordsClean
+
+% 6. Remove any departure delay greater than 2 hours (120 minutes). This 
+% leaves 318,798 observations. 
+flights_final = flights_clean(flights_clean.dep_delay <= 120, :); 
+height(flights_final)
+
+% 7. Generate the following table and graph, showing the average delay per 
+% month. 
+months = unique(flights_final.Month);
+
+res1 = table(months, zeros(size(months)), 'VariableNames', {'Month', 'AvgDelayMonth'});
+for i = months(1):length(months)
+    month_delays = flights_final.dep_delay(flights_final.Month == i);
+    avg_delay_month = mean(month_delays); 
+    res1.AvgDelayMonth(i) = avg_delay_month;
+end
+res1
+plot(res1.Month, res1.AvgDelayMonth, '-o');
+title('Average Delay by Month');
+
+% 8. Generate the following table and graph, showing the average delay per
+% hour.
+hours = transpose(1:24);
+
+res2 = table(hours, zeros(size(hours)), 'VariableNames', {'Hour', 'AvgDelayHour'});
+for i = hours(1):length(hours)
+    hour_delays = flights_final.dep_delay(flights_final.hour == i);
+    avg_delay_hour = mean(hour_delays); 
+    res2.AvgDelayHour(i) = avg_delay_hour;
+end
+res2 = res2(~isnan(res2.AvgDelayHour),:);
+res2
+plot(res2.Hour, res2.AvgDelayHour, '-o');
+title('Average Delay by Hour of the Day');
+
+% 9. Generate the following table and graph, showing the average delay by
+% month and by origin 
+res3 = renamevars(removevars(groupsummary(flights_final,["Month","origin"],"mean","dep_delay"),'GroupCount'), 'mean_dep_delay', 'AvrDelayMonthOrigin')
+
+jfk = res3(res3.origin == 'JFK', {'Month', 'AvrDelayMonthOrigin'});
+subplot(3,1,1);
+plot(jfk.Month, jfk.AvrDelayMonthOrigin, '-o');
+title("JFK");
+
+ewr = res3(res3.origin == 'EWR', {'Month', 'AvrDelayMonthOrigin'});
+subplot(3,1,2);
+plot(ewr.Month, ewr.AvrDelayMonthOrigin, '-o');
+title("EWR");
+
+lga = res3(res3.origin == 'LGA', {'Month', 'AvrDelayMonthOrigin'});
+subplot(3,1,3);
+plot(lga.Month, lga.AvrDelayMonthOrigin, '-o');
+title("LGA");

second/semester2/CT248/Assignments/LabTest-1/clean_grades.m

@@ -0,0 +1,9 @@
+function [grades] = clean_grades(grades) 
+   for i = 1:(size(grades,1)*size(grades,2))
+       if grades(i) < 0 
+           grades(i)  = 0; 
+       elseif grades(i) > 100 
+           grades(i) = 0; 
+       end 
+   end
+end

second/semester2/CT248/Assignments/LabTest-1/create_grades.m

@@ -0,0 +1,4 @@
+function [grades] = create_grades(rows, columns, min, max, seed) 
+    rng(seed);
+    grades = randi([min max], rows, columns);
+end

second/semester2/CT248/Assignments/LabTest-1/fill_estimates.m

@@ -0,0 +1,25 @@
+function [grades] = fill_estimates(grades)
+    for i = 1:size(grades, 2) 
+        % making copy
+        copy = grades(:, i);
+        nonzero = 0; % count of nonzero elements to calculate avg
+        sum = 0;    % sum of nonzero elements
+
+        % looping through copy and counting number of nonzero elements
+        for j = 1:size(copy,1) 
+            if copy(j) ~= 0
+                nonzero = nonzero + 1;
+                sum = sum + copy(j);
+            end 
+        end
+        
+        avg = round(sum / nonzero);
+
+        % looping through original and replacing any nonzero with average 
+        for j = 1:size(copy,1)
+            if grades(j,i) == 0
+                grades(j,i) = avg; 
+            end
+        end
+    end
+end

second/semester2/CT248/Assignments/LabTest-1/test.m

@@ -0,0 +1,4 @@
+clear;
+gr1 = create_grades(10,5,-10,110,100);
+gr2 = clean_grades(gr1);
+gr3 = fill_estimates(gr2);

second/semester2/CT248/Assignments/LabTest-2/test.m

@@ -0,0 +1,22 @@
+clear;
+
+r = 0.2; % random val for r
+K = linspace(1000, 1000000, 50);
+time_vec = linspace(0, 100, 50);
+P = 100; % random val for P
+population = zeros(length(time_vec), 50);
+
+% implementing the model as an anonymous function 
+dpdt = @(t,P,r,K) r*P * (1 - (P/K)); 
+
+for loopcounter = 1:50
+    [t,y] = ode45(dpdt, ...
+                  time_vec, ...
+                  P, ...
+                  odeset, ...
+                  r, ...
+                  K(loopcounter));
+    population(:, loopcounter) = y(:,1); 
+end
+
+plot(time_vec, population);