Octave 7.1.0 on CoCalc Ubuntu 20.04

import matplotlib.pyplot as plt
 
x  = [1, 2, 3, 4, 5, 6, 7, 8, 9]
y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]
y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]
plt.plot(x, y1, label="line L")
plt.plot(x, y2, label="line H")
plt.plot()

plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("Line Graph Example")
plt.legend()
plt.show()

version()

function sqs = squares(n)
    # Compute the squares of the numbers from 1 to n.

    ### BEGIN SOLUTION
    # Put correct code here. This code is removed for the student version, but is
    # used to confirm that your tests are valid.
    if (n <= 0)
        error("n must be positive")
    endif
    sqs = (1:n).^2;
    ### END SOLUTION
endfunction

# [Modify the tests below for your own problem]
# Check that squares returns the correct output for several inputs:
assert(squares(1), [1])
assert(squares(2), [1 4])

# Check that squares raises an error for invalid input:
number_of_errors = 0;
for n = [0 -1]
    try
        squares(n);
    catch
      number_of_errors++;
    end_try_catch
endfor
assert(number_of_errors, 2)

### BEGIN HIDDEN TESTS
# students will NOT see these extra tests
assert(squares(10), [1 4 9 16 25 36 49 64 81 100])
### END HIDDEN TESTS

function s = foo(a, b)
    # Compute the sum of a and b.

    ### BEGIN SOLUTION
    s = a + b;
    ### END SOLUTION
endfunction

foo(23,23)

[2 3 4]' * [4 3 -1]

x = rand(3,3)^3

save r-octave.mat x -7

scatter(sort(rand(1000, 1)), sort(randn(1000, 1)))

i = 0:.1:2*pi;
plot(i, sin(i))

pkg load symbolic;
syms x;
f = sin(x);
diff(f,x)

pkg load symbolic; syms x
f = 2 * (cos(x) + sin(x)^2)
f1 = diff(f, x)

xx = -10:0.1:10;
plot(xx, f(xx))

pkg load image

a = ones(100, 100);
b = ones(100, 100);
b(3, 1) = .5;
psnr(a, b)

tx = ty = linspace (-8, 8, 41)';
[xx, yy] = meshgrid (tx, ty);
r = sqrt (xx .^ 2 + yy .^ 2) + eps;
tz = sin (r) ./ r;
mesh (tx, ty, tz);
xlabel ("tx");
ylabel ("ty");
zlabel ("tz");
title ("3-D Sombrero plot");

[x,y] = meshgrid(-16:0.5:16);
r = hypot(x,y)/2 + eps;
figure;
surf(sin(r)./r);
colormap(jet);

ezplot (@(x, y) x.^2 + 3 * (y - 1).^2 + .5 * x .* y - 6)

x = 0:3;
y = 0:4;
[xx, yy] = meshgrid(x, y);
xx + 2*yy

pkg load symbolic
syms y(x)
de = diff(y) == x;
f = dsolve(de, y(1) == 1)

pkg load odepkg;

dxdt = @(t, x) - 0.24 * x.^2 + t;
tsteps = [0:0.1:5];
[t, x] = ode45(dxdt, tsteps, [-1:0.5:3]);
plot(t, x)

pkg load optim;

function [obj_value, gradient] = objective(theta, location)
  x = theta - location + ones(rows(theta),1); # move minimizer to "location"
  [obj_value, gradient] = rosenbrock(x);
endfunction

dim = 20;                 # dimension of Rosenbrock function
theta0 = zeros(dim+1,1);  # starting values
location = (0:dim)/dim;   # true values
location = location';
control = {Inf,1};        # maxiters, verbosity

bfgsmin("objective", {theta0, location}, control);

__have_feature__ JAVA

javaclasspath

a = 1.001;
b = javaObject ("java.math.BigDecimal", a);

isjava (b)
b.toString()

b.add(b).toString()

[3 4 5]

randn(5)

javaObject('java.math.BigInteger','0')

randn(6,6) \ randn(6,1)

randn(6,6)

41 \ 4