-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
71 lines (51 loc) · 2.41 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
import matplotlib.pyplot as plt
import random
from math import e
from input_layer import InputLayer
from output_layer import OutputLayer
from settings import *
def vizual_sample(number_input, lenght_data, list_data):
coordinates = []
for k in range(number_input):
coordinates.append([])
for i in range(lenght_data):
coordinates[k].append(list_data[i][k])
return coordinates
def form_sample(size_sample):
input_vector = []
for i in range(size_sample):
t = [round((random.random()), 5) for _ in range(QUANTITY_INPUT)]
input_vector.append(t)
return input_vector
def form_weight_relation(quantity_cluster):
weight_relation_vector = []
for _ in range(quantity_cluster):
t = [round((random.uniform(0.4, 0.6)), 5) for _ in range(QUANTITY_INPUT)]
weight_relation_vector.append(t)
return weight_relation_vector
if __name__ == '__main__':
epoch = 10
size_sample = 100
quantity_cluster = 5
output_layer = OutputLayer(form_weight_relation(quantity_cluster), quantity_cluster)
input_layer = InputLayer(form_sample(size_sample), output_layer, size_sample=size_sample)
help_epoch = []
help_function = []
speed_function_start = 0.5
delta_function_start = 0.5
for i in range(epoch):
delta_function = (- i / (epoch + 1) + 1)
#delta_function = 1
speed_function = speed_function_start * e ** (i/epoch)
k = input_layer.epoch(output_layer.weight_relation_vector, output_layer.quantity_cluster, delta_function, speed_function)
coordinates_input_sample = vizual_sample(QUANTITY_INPUT, input_layer.size_sample, input_layer.input_vector)
coordinates_output_weight = vizual_sample(QUANTITY_INPUT, output_layer.quantity_cluster, output_layer.weight_relation_vector)
plt.plot(coordinates_input_sample[0], coordinates_input_sample[1], 'ro')
#plt.plot(input_neuron_obj.input_vector[k][0], input_neuron_obj.input_vector[k][1], 'go')
plt.plot(coordinates_output_weight[0], coordinates_output_weight[1], 'bo')
plt.plot(input_layer.number[0], input_layer.number[1], 'gx')
plt.plot(output_layer.new_number[0], output_layer.new_number[1], 'rx')
plt.show()
# создать класс Учитель и объединить слои
# передать учителю экземпляр сети (инстанцию)
# поменять функцию соседства