def var_4():
n1 = n([-1], 0, linear_activation_double)
n2 = n([0.2], 0, one_to_one_activation)
n3 = n([0.2], 0, one_to_one_activation)
n4 = n([2, -1.5], 0, linear_activation_double)
def f(i):
r1 = n1.calc([i], log=True)
r2 = n2.calc([r1], log=True)
r3 = n3.calc([r1], log=True)
r4 = n4.calc([r2, r3], log=True)
return r4
def plot(dots):
import matplotlib.pyplot as plt
results = [f(dot) for dot in dots]
fig = plt.figure()
plt.plot(dots, results, 'ro')
fig.suptitle('Task 5, Var 4', fontsize=20)
plt.grid(True)
plt.show()
# print(f(0.5))
import numpy as np
plot(np.arange(-10., 10., 0.05))
def var_3():
n1 = n([2], 0.5, modular_activation)
n2 = n([2], 0.5, modular_activation)
n3 = n([1, -0.5], 0.5, linear_activation_double_plus_one)
n4 = n([1, -0.5], 0.5, linear_activation_double_plus_one)
def f(i):
r1 = n1.calc([i], log=True)
r2 = n2.calc([i], log=True)
r3 = n3.calc([r1, r2], log=True)
r4 = n4.calc([r1, r2], log=True)
return r3, r4
def plot(dots):
import matplotlib.pyplot as plt
results = [f(dot)[0] for dot in dots]
fig = plt.figure()
plt.plot(dots, results, 'ro')
fig.suptitle('Task 5, Var 3', fontsize=20)
plt.grid(True)
plt.show()
# print(f(-1.5))
import numpy as np
plot(np.arange(-10., 10., 0.05))
def var_3():
n1 = n([1, -1, 0, 0], -0.5)
n2 = n([-1, 0, 0, 1], -0.5)
n3 = n([1, 1], 1.5, binary_activation_reversed)
def f(i):
r = n3.calc([n1.calc(i), n2.calc(i)])
return r
dimension_test(4, f, True)
def var_2():
n1 = n([1, 1, -1], -2.5)
n2 = n([1, -1, -1], -2.5)
n3 = n([-1, 1, -1], -2.5)
n4 = n([1, 1, 1], 2.5, binary_activation_reversed)
def f(i):
r = n4.calc([n1.calc(i), n2.calc(i), n3.calc(i)])
return r
dimension_test(3, f, True)
def var_4():
n1 = n([-1, 1, 1], 0.5, linear_activation)
n2 = n([1, -1, 1], 0.5, linear_activation)
n3 = n([-1, -1], 0.5)
def f(i):
r1 = n1.calc(i)
r2 = n2.calc(i)
r3 = n3.calc([r1, r2])
return r3
dimension_test(3, f)
def var_4(x1, x2):
n1 = n([1, -1], 0, linear_activation)
n2 = n([1, -1], 0, linear_activation)
n3 = n([0.5, 0.5], 0, linear_activation)
n4 = n([1], 0, binary_activation)
n5 = n([1], 0, binary_activation)
def f(i, j):
r1 = n1.calc([i, j])
r2 = n2.calc([j, i])
r3 = n3.calc([i, j])
r4 = n4.calc([r1])
r5 = n5.calc([r2])
return r3, r4, r5
halfsumm, max_1, max_2 = f(x1, x2)
print("Mean " + str(x1) + " and " + str(x2) + " = " + str(halfsumm))
if max_2 > max_1:
print("Number " + str(x2) + " is greater than " + str(x1))
else:
print("Number " + str(x1) + " is greater than " + str(x2))
def var_2():
n1 = n([0.5, -1, 0.2], 0.5, linear_activation)
n2 = n([1.5], 0.5, sqrt_activation)
n3 = n([1.5], 0.5, sqrt_activation)
def f(i, j, k):
r1 = n1.calc([i, j, k], log=True)
r2 = n2.calc([r1], log=True)
r3 = n3.calc([r1], log=True)
return r2, r3
def plot(dots):
import matplotlib.pyplot as plt
results = [f(dot, 0, 0)[0] for dot in dots]
fig = plt.figure()
plt.plot(dots, results, 'ro')
fig.suptitle('Task 5, Var 2', fontsize=20)
plt.grid(True)
plt.show()
# print(f(1, -1, 1))
import numpy as np
plot(np.arange(-10., 10., 0.05))
def var_3(x1, x2):
n1 = n([1, -1], 0, linear_activation)
n2 = n([1, -1], 0, linear_activation)
n3 = n([1, -1], 0, modular_activation)
n4 = n([1], 0, binary_activation_reversed)
n5 = n([1], 0, binary_activation_reversed)
def f(i, j):
r1 = n1.calc([i, j])
r2 = n2.calc([j, i])
r3 = n3.calc([i, j])
r4 = n4.calc([r1])
r5 = n5.calc([r2])
return r3, r4, r5
difference_module, max_1, max_2 = f(x1, x2)
print("Difference modulo " + str(x1) + " и " + str(x2) + " = " +
str(difference_module))
if max_2 > max_1:
print("Number " + str(x2) + " is smaller than " + str(x1))
else:
print("Number " + str(x1) + " is smaller than " + str(x2))