'''
Design of an artificial neuron using perceptron learning rule
'''
import numpy
from neuron import Neuron, bipolar_step
numpy.set_printoptions(precision=2)
w = [1, -1, 0, 0.5]
C = 0.1
D = [-1, -1, 1]
X = numpy.array([[1, -2, 0, -1], [0, 1.5, -0.5, -1], [-1, 1, 0.5, -1]])
neuron = Neuron(activation_fn=bipolar_step, weights=w)
for epoch in range(5):
print(f'\nepoch {epoch+1}')
for x, di in zip(X, D):
neuron.inputs = x
o = neuron.calc_out()
neuron.weights += C * (di - o) * x
print(f'weights: {neuron.weights}')
# nand gate
print('\nnand')
nand_gate = Neuron(lambda net: net > -2, weights=[-1, -1])
show_output(nand_gate, gate_inputs)
# nor gate
print('\nnor')
nor_gate = Neuron(lambda net: net > -1, weights=[-1, -1])
show_output(nor_gate, gate_inputs)
# not
print('\nnot')
not_gate = Neuron(lambda net: net > -1, weights=[-1])
for g_inputs in [[0], [1]]:
not_gate.inputs = numpy.array(g_inputs)
print(f'x:{g_inputs} O:', not_gate.calc_out())
# xor
print('\nxor')
o1 = Neuron(signum, weights=[-2, 1], bias=-1 / 2)
o2 = Neuron(signum, weights=[1, -1], bias=-1 / 2)
xor_gate = Neuron(signum, weights=[1, 1], bias=-1 / 2)
def sh_xor_output(gate_inputs_list):
'''helper fuction to display xor output'''
gate_inputs_array = numpy.array(gate_inputs_list)
o1.inputs = o2.inputs = gate_inputs_array
xor_gate.inputs = numpy.array([o1.calc_out(), o2.calc_out()])
print(f'x1:{gate_inputs_array[0]} x2:{gate_inputs_array[1]} O:',
xor_gate.calc_out())
