from data_parser import DataParser
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
parser = DataParser('europe.csv')
matrix_standarized = parser.get_standarized_matrix()
# Create a figure instance
fig = plt.figure(1, figsize=(9, 6))
# Create an axes instance
ax = fig.add_subplot(111)
## Custom x-axis labels
ax.set_xticklabels([
'Area', 'GDP', 'Inflation', 'Life.expect', 'Military', 'Pop.growth',
'Unemployment'
])
## add patch_artist=True option to ax.boxplot()
## to get fill color
bp = ax.boxplot(matrix_standarized, patch_artist=True)
## change outline color, fill color and linewidth of the boxes
for box in bp['boxes']:
# change outline color
box.set(color='#7570b3', linewidth=2)
# change fill color
box.set(facecolor='#1b9e77')
from kohonen_network import Kohonen
from data_parser import DataParser
import seaborn as sn
import matplotlib.pyplot as plt
import numpy as np
parser = DataParser('europe.csv')
standarized_matrix = np.array(parser.get_standarized_matrix()).T
k_neurons = 15
kohonen = Kohonen(standarized_matrix,
k_neurons,
iteration_limit=3000,
eta=0.0001)
kohonen.train()
som_map = kohonen.construct_nodes_map()
u_matrix = kohonen.build_u_matrix()
countries_string = parser.parse_as_class()
neurons = [[[] for x in range(k_neurons)] for y in range(k_neurons)]
for i in range(len(standarized_matrix)):
best_i, best_j, best_difference = kohonen.get_best_matching(
standarized_matrix[i])
neurons[best_i][best_j].append(countries_string[i][0])
print("Country: {}, Best neuron: {},{}, Best difference: {}".format(
countries_string[i][0], best_i, best_j, best_difference))
print('Paises por neurona')
print(neurons)
from data_parser import DataParser
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
from sklearn.covariance import EmpiricalCovariance
from sklearn.decomposition import PCA
parser = DataParser('europe.csv')
# print(parser.get_numerical_csv())
# print(np.matrix(parser.get_numerical_csv()).T)
# matrix_for_correlation = np.array(parser.get_numerical_csv(), dtype=float).T
matrix_for_correlation = np.array(parser.get_standarized_matrix())
print('MEDIA (Tiene que tender a 0):', np.mean(matrix_for_correlation), '| STD (Tiene que tender a 1):', np.std(matrix_for_correlation))
# print('ESTANDARIZADA',parser.get_standarized_matrix())
# 'Area','GDP','Inflation','Life.expect','Military','Pop.growth','Unemployment'
matrix_for_correlation_with_keys = {
'Area': matrix_for_correlation[0],
'GDP': matrix_for_correlation[1],
'Inflation': matrix_for_correlation[2],
'Life.expect': matrix_for_correlation[3],
'Military': matrix_for_correlation[4],
'Pop.growth': matrix_for_correlation[5],
'Unemployment': matrix_for_correlation[6]
}
df = pd.DataFrame(matrix_for_correlation_with_keys, columns=[
