)
for coef in coeficientes_confianca:
arq_destino.write(
'=============================================================================\n'
)
arq_destino.write('Coeficiente de Confiança = %s\n' % (coef))
arq_destino.write(
'=============================================================================\n'
)
arq_destino.write('\n')
for prob in probs:
df_prob = udata.filtrarPorProbabilidadeErro(df, prob)
for size in sizes:
df_size = udata.filtrarPorTamanhoArray(df_prob, size)
for alg in algs:
df_alg = udata.filtrarPorAlgoritmo(df_size, alg)
if (df_alg.shape[0] > 0):
arq_destino.write(col)
arq_destino.write('\n')
arq_destino.write(
'Coef. Confiança = %s; Prob = %s; Size = %s; Alg = %s'
% (coef, prob, size, alg))
arq_destino.write('\n')
data = df_alg[col]
lim_inferior, mean, lim_superior, h, std_error, std_dev = mean_confidence_interval(
data, confidence=coef)
arq_csv_destino.write(
printCsvInfo(coef, prob, size, alg, col,
arq_destino.write(s + '\n')
s = ' - f_statistic = %.15f / p_value = %.15f' % (f_statistic, p_value)
print(s)
arq_destino.write(s+'\n')
colunas_desc = udata.COL_NAMES
for prob in udata.PROBABILIDADES:
for size in udata.TAMANHOS:
df = udata.obterDados2()
df = udata.filtrarPorProbabilidadeErro(df, prob=prob)
df = udata.filtrarPorTamanhoArray(df, tamanho=size)
if (df.shape[0] > 0):
arq_name = 'anova_prob_%s_tam_%s' % (prob, size)
arq_destino = os.path.join(path_arq_destino, '%s.txt' % (arq_name))
if os.path.exists(arq_destino):
os.remove(arq_destino)
arq_destino = open(arq_destino, 'w+')
csv_destino = os.path.join(path_arq_destino, 'csv/%s' % (arq_name))
head_n = 1000
df_bubble = df[df[udata.obterNomeColuna('algoritmo')] == 'bubble']#.head(head_n)
df_merge = df[df[udata.obterNomeColuna('algoritmo')] == 'merge']#.head(head_n)
df_insertion = df[df[udata.obterNomeColuna('algoritmo')] == 'insertion']#.head(head_n)
def salvarGrafico(df_p, file_title, tam):
# df_p.loc[:]['size_of_array'] = tam
print(df_p['size_of_array'].unique())
corr = df_p.corr() #.round(decimal=3)
print(corr)
fig = plt.figure()
sns.heatmap(corr, annot=True) #, cmap='coolwarm')
fig.savefig(
'graficos/_%s.png' % (file_title), bbox_inches='tight', pad_inches=2
) # , format='png', orientation='landscape', papertype='letter')
plt.close(fig)
q1 = udata.obterDados()
q1 = udata.filtrarPorTamanhoArray(q1, 100)
print(q1.corr())
df = udata.obterDados()
# salvarGrafico(df=df, file_title='teste-04-correlacao-0-geral')
for prob in udata.PROBABILIDADES:
df_prob = udata.filtrarPorProbabilidadeErro(df, prob)
# print( df_prob.shape )
# print(df_alg.shape)
for tam in udata.TAMANHOS:
# print('%.2f-%s-%s' % (prob, alg, tam))
df_tam = udata.filtrarPorTamanhoArray(df_prob, tam)
for alg in udata.ALGORTIMOS:
df_alg = udata.filtrarPorAlgoritmo(df_tam, alg)
# print(df_tam.head())