def printResults(self):
cr = criteria.Criteria()
cr.annualInterestPayment(self.fields)
cr.loanToIncome(self.fields)
if (cr.isValidLoan(self.fields)):
QMessageBox.information(
self, "Accepted",
self.fields["Last name"].displayText() + " can get the loan")
else:
QMessageBox.critical(
self, "Refused", self.fields["Last name"].displayText() +
" can not get the loan")
def fileReader(self):
filename = QFileDialog.getOpenFileName(self, 'Open File', '.',
"CSV file (*.csv)")
cr = criteria.Criteria()
with open(filename[0], 'r') as csvFile:
data = csv.reader(csvFile, delimiter=",")
fields = []
it = 0
for row in data:
if (it != 0):
cr.annualInterestPayment(None, row[3], row[5])
cr.loanToIncome(None, row[3], row[6])
self.printResultsCSV(row,
cr.isValidLoan(None, row[6], row[7]))
it += 1
true = model.GMM(N, K0)
result = {'G': [], 'AIC': [], 'WAIC': [], 'CV': []}
for t in range(ex):
# 真の分布からのサンプリング
X = [true.data_sampling(a0, b0) for i in range(n)]
X_t, X_v = X[:n], X[n:]
# 変分ベイズによる学習
for K in range(1, 10):
gmm = model.GMM(K, N)
for _ in range(iter):
gmm.update(X_t)
# 各種規準の計算
params = [gmm.poserior_sampling() for _ in range(T)]
crt = criteria.Criteria(gmm, true, X_t, X_v, params, (a0, b0))
L = crt.L
Ln = crt.Ln
G = crt.G
AIC = crt.AIC
WAIC = crt.WAIC
CV = crt.CV
result['G'].append(G - L)
result['AIC'].append(AIC - Ln)
result['WAIC'].append(WAIC - Ln)
result['CV'].append(CV - Ln)
# 結果をcsvに保存
pd.DataFrame(result).to_csv('GMM/result.csv', index=False)
for t in range(T):
# データ生成
nn0 = model.NN(M, H0, N)
XY = [nn0.generate(a0, b0) for _ in range(2 * n)]
XYt, XYv = XY[:n], XY[n:]
Xt = [xy[0] for xy in XYt]
Xv = [xy[0] for xy in XYv]
Yt = [xy[1] for xy in XYt]
Yv = [xy[1] for xy in XYv]
# MCMC
nn = model.NN(M, H, N)
params = nn.sampling(Xt, Yt, size=size, burn=burn)
# 各種規準の計算
crt = criteria.Criteria(nn, nn0, XYt, XYv, params, (a0, b0))
L = crt.L
Ln = crt.Ln
G = crt.G
AIC = crt.AIC
WAIC = crt.WAIC
DIC1 = crt.DIC1
DIC2 = crt.DIC2
CV = crt.CV
logger.info(
f'loss={G-L:.3g}, AIC={AIC-Ln:.3g},WAIC={WAIC-Ln:.3g}, DIC1={DIC1-Ln:.3g}, DIC2={DIC2-Ln:.3g}, CV={CV-Ln:.3g}'
)
result['G'].append(G - L)
result['AIC'].append(AIC - Ln)
result['WAIC'].append(WAIC - Ln)
result['DIC1'].append(DIC1 - Ln)
# Add Gsys server startup group to builder
log_sifter_builder.add_group_rule(
event_group_rule.EventGroupRule(tag="gsys_startup",
trigger_event_tags=[
"gsysd_version_published",
"gsys_server_start"
],
context_event_tags=[]))
# Add PCIE error criteria to the builder
log_sifter_builder.add_criteria(
criteria.Criteria(
symptom_tag="PCIE Error",
signal=signals.ExistenceGroupSignal(tag="pcie_error_signal",
group_tag="pcie_error"),
evaluator=evaluators.ExistenceEvaluator(),
action_msg="Possible hardare issues exist. Please check the device."
))
# Add SMBus error criteria to the builder
log_sifter_builder.add_criteria(
criteria.Criteria(
symptom_tag="SMBus Error",
signal=signals.ExistenceGroupSignal(tag="smbus_error_signal",
group_tag="smbus_error"),
evaluator=evaluators.ExistenceEvaluator(),
action_msg="Possible hardare issues exist. Please check the device."
))
# Add rescan loop mode criteria to the builder