'greyblue', 'lilac', 'green', 'red',
'cyan', 'violet', 'yellow', 'white',
'darkgrey', 'mediumgrey', 'lightgrey']
# +
# Graph setup
# -
amount_vertecies = 100
percent_edges = 0.5
graph1 = Graph()
matrix1 = graph1.create_matrix(amount_vertecies)
edges1 = graph1.generate_edges(amount_vertecies, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
# +
# Network configuration
data = rgb_colors
data_lables = color_names
batch_size = 2
length = 10
width = 10
number_iterations = 100
shuffle = True
# -
# for now amount_verticies has to be a multiplication of length and width
amount_nodes = 400
percent_edges = 0.5
# +
# using graph class to generate matrix
graph1 = Graph()
# +
# matrix genaration
matrix1 = graph1.create_matrix(amount_nodes)
edges1 = graph1.generate_edges(amount_nodes, percent_edges)
matrix1 = graph1.add_edges(matrix1, edges1)
list_edges = graph1.make_list_edges_distances(matrix1)
netxgraph1 = graph1.build_networkx_graph(list_edges)
# +
# matrix1
# -
# This initializes regular SOM grid matrix, it needs to be passes instead of matrix1 for it to work
# Also one needs to experiment with sigma to achieve good results learning rate
matrix2 = graph1.standard_som_distance_matrix(length, width)
matrix2
map1 = MapClass(data, length, width, learning_rate, number_epochs, matrix2,
sigma, data_lables, batch_size, shuffle, netxgraph1)