dimuthnc

{
   "request_parameter_supported":true,
   "claims_parameter_supported":true,
   "scopes_supported":[
      "openid",
      "accounts",
      "payments"
   ],
   "check_session_iframe":"https://host.docker.internal:9446/oidc/checksession",
   "issuer":"https://host.docker.internal:9446/oauth2/token",

dimuthnc

package org.dimuth;

import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import javax.servlet.ServletOutputStream;
import javax.servlet.http.HttpServletResponse;
import javax.servlet.http.HttpServletResponseWrapper;

dimuthnc

perform_basic_authentication()

fraud_score = calculate_fraud_score( event_inputs )

if( fraud_score < 0.25 ) :
    complete_authentication()
Else if(fraud_score < 0.75 ):
    perform_biomatrix_authentication()
Else:
    set_authentication_failed(error_message)

dimuthnc

function onInitialRequest(context) {
   executeStep(1, {
       onSuccess: function (context) {
           var isAllowed = isWithinSessionLimit(context, {"sessionLimit":"2"});
            if (isAllowed) {
                Log.info('Authentication Successfull ');
            }
            else {
                executeStep(2);
                Log.info('Authentication Successfull ');

dimuthnc

from sklearn import linear_model
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split

def evaluate(train_set,features,a):
    total_score =0
    for x in range(10):
        train, test = train_test_split(train_set, train_size = 0.8)

dimuthnc

import pandas as pd

train_features = pd.read_csv('Data/dengue_features_train.csv',
                             index_col=[0,1,2])

train_labels = pd.read_csv('Data/dengue_labels_train.csv',
                           index_col=[0,1,2])

# Seperate data for San Juan
sj_train_features = train_features.loc['sj']