self_organizing_network.py

from hsom import SelfOrganizingNetwork
from random import sample

input_size = 100
layer_sizes = [20, 15, 10, 5, 2, 1]
input_percents = [0.2, 0.2, 0.2, 0.2, 0.75, 1.0]
learning_rate = 0.05
boost_factor = 1
node_count = 5
winner_count = 1
initial_range = (-0.5, 0.5)

# Create hierarchical layers of size 20, 15, 10, 5, 2, and 1
self_organizing_network = SelfOrganizingNetwork(
    input_size=input_size,
    layer_sizes=layer_sizes,
    input_percents=input_percents,
    learning_rates=learning_rate,
    boost_factors=boost_factor,
    node_counts=node_count,
    winner_counts=winner_count,
    initial_ranges=initial_range)

# Create a set of sparse samples
samples = []
for i in range(node_count):
    X = [0 for j in range(input_size)]
    I = [j for j in range(input_size)]
    for j in sample(I, int(input_size * 0.2)):
        X[j] = 1
    samples.append(X)

for i in range(200):
    self_organizing_network.train(samples)

outputs = self_organizing_network.test(samples)

for i in range(len(outputs)):
    print(outputs[i])

# TEST RESULTS
#
# [0, 0, 0, 1, 0]
# [0, 1, 0, 0, 0]
# [0, 0, 0, 0, 1]
# [0, 0, 1, 0, 0]
# [1, 0, 0, 0, 0]