def get_approximations(a_approximator):
global num_of_samples
s = []
for i in range(0, num_of_samples):
x_sample = i * 2 * math.pi / num_of_samples
y_sample = a_approximator.approximation(x_sample)
s.append([x_sample, y_sample])
return s
samples = get_samples()
main_plot = Drawer()
main_plot.add_points(points=samples)
approximator = OneVariableApproximator(samples, 5)
progress = [approximator.objective()]
for i in range(10000):
approximator.one_gradient_descent_step()
progress.append(approximator.objective())
obj_function_plot = Drawer(xlimits=(0, len(progress)),
ylimits=(0, 1.2 * progress[0]))
for i, p in zip(range(len(progress)), progress):
pair = [i, p]
print(pair)
obj_function_plot.add_pkoints([pair])
for x1_ind in range(x1_range + 1):
for x2_ind in range(x2_range + 1):
x1 = (math.pi * x1_ind) / x1_range
x2 = (math.pi * x2_ind) / x2_range
y = math.sin(x1) + math.cos(x2)
s.append([x1, x2, y])
return s
refile = open("2varSamples.json", "r")
samples = json.loads(refile.read())
approximator = TwoVariableApproximator(samples, 3)
progress = [approximator.objective()]
for i in range(10000):
approximator.one_gradient_descent_step()
progress.append(approximator.objective())
print(progress[-1])
obj_function_plot = Drawer(xlimits=(0, len(progress)),
ylimits=(0, 1.2 * progress[0]))
for i in range(len(progress)):
obj_function_plot.add_points([[i, progress[i]]])
print("bettas", approximator.bettas)
print("estimate:", approximator.approximation([0.5, 0.5]),
approximator.approximation([0.75, 0.25]))
print("expected:",
math.sin(0.5) + math.cos(0.5),
math.sin(0.75) + math.cos(0.25))
obj_function_plot.draw()