YannAhlgrim
676 lines
56 KiB
Plaintext
676 lines
56 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7ba05a39",
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"metadata": {},
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"source": [
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"# Simulationsstudie: Diebstahlerkennung eines Autos in Echtzeit\n",
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"\n",
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"**Name:** Yann Ahlgrim \n",
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"\n",
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"In dieser Simulationsstudie wird ein Modell entwickelt, das anhand von Fahrverhaltensdaten erkennt, ob ein anderer Fahrer als üblich am Steuer sitzt. Ziel ist es, eine Wahrscheinlichkeit für einen Fahrerwechsel zu schätzen und bei hoher Wahrscheinlichkeit eine Diebstahlwarnung auszugeben. Die Studie umfasst die Generierung einer synthetischen Grundgesamtheit, die Simulation der Data Scientist Perspektive und die Analyse der Modellgüte."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f04292ef",
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"metadata": {},
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"source": [
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"## Erzeugung der Grundgesamtheit"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a5fe9381",
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"metadata": {},
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"source": [
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"### Datengrundlage\n",
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"\n",
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"#### Erklärende Variablen:\n",
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"\n",
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"1. **Durchschnittsgeschwindigkeit (metrisch):** \n",
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" Die Werte verteilen sich natürlicherweise ungefähr normal um ca. 47 km/h (29 mph) mit großer Streuung. Aggressive Fahrer tendieren zu höheren Durchschnittsgeschwindigkeiten. Diese Variable ist erklärend, da ein anderer Fahrer oft abweichende Geschwindigkeitsprofile aufweist. \n",
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" → Die Verteilung basiert auf: [NHTSA 100-Car Naturalistic Driving Study](https://www.nhtsa.gov/sites/nhtsa.gov/files/100carmain.pdf)\n",
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"\n",
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"2. **Schaltverhalten (ordinal):** \n",
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" Charakteristisches Schaltmuster bei Handschaltgetrieben, insbesondere der Drehzahlbereich beim Hochschalten. Fahrer unterscheiden sich deutlich: \n",
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" - *frueh* (ökonomisches Fahren), \n",
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" - *normal*, \n",
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" - *spaet* (sportliches/aggressives Fahren). \n",
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" Diese Variable dürfte prädiktiv sein, da Schaltgewohnheiten fahrerspezifisch sind. Studien zeigen, dass sie zur Fahreridentifikation genutzt werden können. \n",
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" → Quelle: [Driver Identification Based on Gear Shift Events and Attention-Based Bidirectional Long Short-Term Memory for Manual Transmission System)](https://ieeexplore.ieee.org/document/10034086)\n",
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"\n",
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"3. **Harte Bremsmanöver (metrisch):** \n",
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" Anzahl starker Bremsvorgänge mit hoher Verzögerung (>0,3 g). Modellierung z.B. als Poisson-verteilte Zufallsvariable mit geringem Mittelwert (wenige Ereignisse pro 100 km). Harte Bremsmanöver korrelieren mit aggressiver Fahrweise und sind prädiktiv für Fahrerwechsel. \n",
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" → Quelle: [Tesla Safety Score Definition](https://www.findmyelectric.com/blog/tesla-safety-score-beta-explained)\n",
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"\n",
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"4. **Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien):** \n",
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" haeufigkeit bzw. Ausmaß, mit dem Tempolimits überschritten werden. Kategorisierung z.B. in: \n",
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" - *selten*, \n",
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" - *manchmal*, \n",
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" - *haeufig* zu schnell. \n",
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" Diese Variable ist prädiktiv, da ein fremder Fahrer oft ein anderes Risikoverhalten beim Speeding zeigt.\n",
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" Laut einer AAA-Verkehrssicherheitsstudie geben nahezu 50 % der Fahrer an, auf Autobahnen im letzten Monat ~15 mph (~24 km/h) über dem Tempolimit gefahren zu sein.\n",
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" → Quelle: [87 Percent of Drivers Engage in Unsafe Behaviors While Behind the Wheel](https://newsroom.aaa.com/2016/02/87-percent-of-drivers-engage-in-unsafe-behaviors-while-behind-the-wheel/)\n",
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"\n",
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"---\n",
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"\n",
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"#### Nicht erklärende Variablen:\n",
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"\n",
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"\n",
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"5. **Wetterbedingungen (nominal, 3 Kategorien):** \n",
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" - *trocken* (~70–75 %) \n",
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" - *nass* (~20–25 %) \n",
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" - *winterlich* (<5 %) \n",
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" → Modelliert als Multinomial. Kontextvariable. \n",
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" Die Einteilung basiert auf Daten des Statistischen Bundesamtes: Etwa 70–75 % aller Fahrten finden unter trockenen Bedingungen statt, 20–25 % bei Nässe und unter 5 % bei Schnee oder Glätte.\n",
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"\n",
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"6. **Fahrstrecke (metrisch):** \n",
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" Länge der Fahrt (z.B. lognormalverteilt um 12 km). Kontextvariable, nicht direkt erklärend. \n",
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" → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n",
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"\n",
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" \n",
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"7. **Straßentyp (nominal, 3 Kategorien):** \n",
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" - *Autobahn* (~30–33 %) \n",
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" - *Außerorts* (~43 %) \n",
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" - *Innerorts* (~24–27 %) \n",
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" → Modelliert als Multinomialverteilung. Kontextvariable ohne direkte Prädiktionswirkung. \n",
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" → Quelle: [Klimaschutzinstrumente im Verkehr](https://openumwelt.de/server/api/core/bitstreams/07bd23f9-ff0a-41e5-b89b-8d7baf0a5c36/content)\n",
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"\n",
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"8. **Wochentag (nominal, 2–3 Kategorien):** \n",
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" Z.B. *Werktag* vs *Wochenende* oder *Mo–Fr*, *Sa*, *So*. Modellierung als kategorische Variable. Kontextvariable. \n",
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" → Quelle: [Mobilität in Deutschland 2017 – Ergebnisbericht](https://www.bmv.de/SharedDocs/DE/Anlage/G/mid-ergebnisbericht.pdf)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1468207b",
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"metadata": {},
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"source": [
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"### Bibliotheken Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"id": "97ba29a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import der benötigten Bibliotheken\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from scipy.stats import norm, multinomial, poisson, lognorm, bernoulli\n",
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"import statsmodels.api as sm\n",
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"from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
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"\n",
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"np.random.seed(11)\n",
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"N = 1_000_000"
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]
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},
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{
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"cell_type": "markdown",
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"id": "27947656",
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"metadata": {},
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"source": [
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"### Verteilungen der Variablen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"id": "61247da5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1. Durchschnittsgeschwindigkeit (metrisch, normalverteilt)\n",
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"avg_speed = np.clip(np.random.normal(loc=47, scale=10, size=N), 10, 130)\n",
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"\n",
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"# 2. Schaltverhalten (ordinal, 3 Kategorien)\n",
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"shift_behavior = np.random.choice(['frueh', 'normal', 'spaet'], size=N, p=[0.4, 0.4, 0.2])\n",
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"\n",
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"# 3. Harte Bremsmanöver (metrisch, Poisson)\n",
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"hard_brakes = np.random.poisson(lam=2, size=N)\n",
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"\n",
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"# 4. Geschwindigkeitsüberschreitungen (ordinal, 3 Kategorien) – Abhängigkeit von avg_speed\n",
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"def softmax(x):\n",
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" e_x = np.exp(x - np.max(x, axis=1, keepdims=True))\n",
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" return e_x / e_x.sum(axis=1, keepdims=True)\n",
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"\n",
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"speed_scaled = (avg_speed - avg_speed.min()) / (avg_speed.max() - avg_speed.min())\n",
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"strength = 0.18\n",
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"logits = np.zeros((N, 3))\n",
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"logits[:, 0] = 1 - strength * speed_scaled # selten\n",
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"logits[:, 1] = 1 # manchmal\n",
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"logits[:, 2] = 1 + strength * speed_scaled # haeufig\n",
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"probs = softmax(logits)\n",
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"speeding = [np.random.choice(['selten', 'manchmal', 'haeufig'], p=p) for p in probs]\n",
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"\n",
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"# 5. Wetterbedingungen (nominal, Kontext)\n",
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"weather = np.random.choice(['trocken', 'nass', 'winterlich'], size=N, p=[0.75, 0.2, 0.05])\n",
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"\n",
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"# 6. Fahrstrecke (metrisch, lognormal)\n",
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"mu, sigma = np.log(12), 0.7\n",
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"trip_distance = np.random.lognormal(mean=mu, sigma=sigma, size=N)\n",
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"\n",
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"# 7. Straßentyp (nominal, Kontext) – Abhängigkeit von trip_distance mit Softmax-Funktion\n",
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"# Skaliere trip_distance auf [0, 1]\n",
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"dist_scaled = (trip_distance - trip_distance.min()) / (trip_distance.max() - trip_distance.min())\n",
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"road_strength = 0.18\n",
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"road_logits = np.zeros((N, 3))\n",
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"road_logits[:, 0] = 1 - road_strength * dist_scaled # Innerorts\n",
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"road_logits[:, 1] = 1 # Außerorts\n",
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"road_logits[:, 2] = 1 + road_strength * dist_scaled # Autobahn\n",
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"road_probs = softmax(road_logits)\n",
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"road_type = [np.random.choice(['Innerorts', 'Außerorts', 'Autobahn'], p=p) for p in road_probs]\n",
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"\n",
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"# 8. Wochentag (nominal, Kontext)\n",
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"weekday = np.random.choice(['Mo-Fr', 'Sa', 'So'], size=N, p=[0.7, 0.15, 0.15])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "defebcc1",
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"metadata": {},
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"source": [
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"### Zusammenführen der Daten in ein DataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"id": "f8e0efb0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>avg_speed</th>\n",
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" <th>shift_behavior</th>\n",
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" <th>hard_brakes</th>\n",
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" <th>speeding</th>\n",
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" <th>weather</th>\n",
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" <th>trip_distance</th>\n",
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" <th>road_type</th>\n",
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" <th>weekday</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>64.494547</td>\n",
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" <td>frueh</td>\n",
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" <td>2</td>\n",
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" <td>manchmal</td>\n",
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" <td>trocken</td>\n",
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" <td>30.449962</td>\n",
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" <td>Innerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>44.139270</td>\n",
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" <td>frueh</td>\n",
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" <td>3</td>\n",
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" <td>haeufig</td>\n",
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" <td>trocken</td>\n",
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" <td>11.911103</td>\n",
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" <td>Autobahn</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>42.154349</td>\n",
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" <td>spaet</td>\n",
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" <td>2</td>\n",
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" <td>selten</td>\n",
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" <td>trocken</td>\n",
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" <td>6.086055</td>\n",
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" <td>Autobahn</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>20.466814</td>\n",
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" <td>normal</td>\n",
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" <td>5</td>\n",
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" <td>haeufig</td>\n",
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" <td>trocken</td>\n",
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" <td>5.732684</td>\n",
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" <td>Innerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>46.917154</td>\n",
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" <td>spaet</td>\n",
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" <td>2</td>\n",
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" <td>selten</td>\n",
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" <td>trocken</td>\n",
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" <td>7.256758</td>\n",
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" <td>Außerorts</td>\n",
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" <td>Mo-Fr</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n",
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"0 64.494547 frueh 2 manchmal trocken 30.449962 \n",
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"1 44.139270 frueh 3 haeufig trocken 11.911103 \n",
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"2 42.154349 spaet 2 selten trocken 6.086055 \n",
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"3 20.466814 normal 5 haeufig trocken 5.732684 \n",
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"4 46.917154 spaet 2 selten trocken 7.256758 \n",
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"\n",
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" road_type weekday \n",
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"0 Innerorts Mo-Fr \n",
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"1 Autobahn Mo-Fr \n",
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"2 Autobahn Mo-Fr \n",
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"3 Innerorts Mo-Fr \n",
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"4 Außerorts Mo-Fr "
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]
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},
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"execution_count": 70,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.DataFrame({\n",
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" 'avg_speed': avg_speed,\n",
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" 'shift_behavior': shift_behavior,\n",
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" 'hard_brakes': hard_brakes,\n",
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" 'speeding': speeding,\n",
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" 'weather': weather,\n",
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" 'trip_distance': trip_distance,\n",
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" 'road_type': road_type,\n",
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" 'weekday': weekday\n",
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"})\n",
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"\n",
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"df.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f06e7c4d",
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"metadata": {},
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"source": [
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"### VIF der Abhängigkeiten prüfen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"id": "6bfb199a",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Variance Inflation Factor (VIF) für ausgewählte Variabeln:\n",
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" features VIF Factor\n",
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"0 avg_speed 2.570716\n",
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"1 speeding_manchmal 1.819868\n",
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"2 speeding_selten 1.750848\n"
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]
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}
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],
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"source": [
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"vif = pd.DataFrame()\n",
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"columns = ['avg_speed', 'speeding']\n",
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"vif_df = pd.get_dummies(df[columns], drop_first=True)\n",
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"vif_df = vif_df.astype(float)\n",
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"vif['features'] = vif_df.columns\n",
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"vif['VIF Factor'] = [variance_inflation_factor(vif_df.values, i) for i in range(vif_df.shape[1])]\n",
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"vif = vif.sort_values('VIF Factor', ascending=False).reset_index(drop=True)\n",
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"print(\"\\nVariance Inflation Factor (VIF) für ausgewählte Variabeln:\")\n",
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"print(vif)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"id": "1d3df64a",
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||
"metadata": {},
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||
"outputs": [
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{
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||
"name": "stdout",
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||
"output_type": "stream",
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"text": [
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"\n",
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"Variance Inflation Factor (VIF) für ausgewählte Variabeln:\n",
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" features VIF Factor\n",
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"0 trip_distance 1.677148\n",
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"1 road_type_Außerorts 1.342378\n",
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"2 road_type_Innerorts 1.334770\n"
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]
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}
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],
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"source": [
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"vif = pd.DataFrame()\n",
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"columns = ['road_type', 'trip_distance']\n",
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"vif_df = pd.get_dummies(df[columns], drop_first=True)\n",
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"vif_df = vif_df.astype(float)\n",
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"vif['features'] = vif_df.columns\n",
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"vif['VIF Factor'] = [variance_inflation_factor(vif_df.values, i) for i in range(vif_df.shape[1])]\n",
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"vif = vif.sort_values('VIF Factor', ascending=False).reset_index(drop=True)\n",
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"print(\"\\nVariance Inflation Factor (VIF) für ausgewählte Variabeln:\")\n",
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"print(vif)"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 73,
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||
"id": "49045ee5",
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||
"metadata": {},
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||
"outputs": [
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||
{
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"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>avg_speed</th>\n",
|
||
" <th>shift_behavior</th>\n",
|
||
" <th>hard_brakes</th>\n",
|
||
" <th>speeding</th>\n",
|
||
" <th>weather</th>\n",
|
||
" <th>trip_distance</th>\n",
|
||
" <th>road_type</th>\n",
|
||
" <th>weekday</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>64.494547</td>\n",
|
||
" <td>frueh</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>manchmal</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>30.449962</td>\n",
|
||
" <td>Innerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>44.139270</td>\n",
|
||
" <td>frueh</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>haeufig</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>11.911103</td>\n",
|
||
" <td>Autobahn</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>42.154349</td>\n",
|
||
" <td>spaet</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>selten</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>6.086055</td>\n",
|
||
" <td>Autobahn</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>20.466814</td>\n",
|
||
" <td>normal</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>haeufig</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>5.732684</td>\n",
|
||
" <td>Innerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>46.917154</td>\n",
|
||
" <td>spaet</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>selten</td>\n",
|
||
" <td>trocken</td>\n",
|
||
" <td>7.256758</td>\n",
|
||
" <td>Außerorts</td>\n",
|
||
" <td>Mo-Fr</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" avg_speed shift_behavior hard_brakes speeding weather trip_distance \\\n",
|
||
"0 64.494547 frueh 2 manchmal trocken 30.449962 \n",
|
||
"1 44.139270 frueh 3 haeufig trocken 11.911103 \n",
|
||
"2 42.154349 spaet 2 selten trocken 6.086055 \n",
|
||
"3 20.466814 normal 5 haeufig trocken 5.732684 \n",
|
||
"4 46.917154 spaet 2 selten trocken 7.256758 \n",
|
||
"\n",
|
||
" road_type weekday \n",
|
||
"0 Innerorts Mo-Fr \n",
|
||
"1 Autobahn Mo-Fr \n",
|
||
"2 Autobahn Mo-Fr \n",
|
||
"3 Innerorts Mo-Fr \n",
|
||
"4 Außerorts Mo-Fr "
|
||
]
|
||
},
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1f6ee727",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Zielvariable erzeugen"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 74,
|
||
"id": "75318d77",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C:\\Users\\yann\\AppData\\Local\\Temp\\ipykernel_7488\\2403674952.py:43: FutureWarning: \n",
|
||
"\n",
|
||
"Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
|
||
"\n",
|
||
" sns.countplot(x=target, palette='viridis')\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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qVXrmmWc0cOBAPfHEEzV+3dGjR3X8+HF5eHhU6i0vL++in9Ho0aO1detWff7555J+/M6k3W7Xvffea9asWLFCw4cPV/PmzbV06VJt3bpVmZmZGj16tM6cOVPpmDV973PmzNGf/vQnde3aVcuXL1dGRoYyMzPVr1+/KoPG+Z9LxbaSkhKdPHmyynNUfHbDhg2rdH1mzpwpwzDMaZpVqejj3LBYUxV/KXc6nResOXr0qHbu3FmpNx8fHxmGUenzu9A1kP735+HFF1/UY489ppUrV+q2225T48aNNXjwYH355ZcX7fngwYPq0aOHvv32W73wwgv65JNPlJmZaX4H7vzPpao/Z3a7vdZBsTbKy8sVERGhFStWaNKkSVq3bp22b9+ujIyMSj1u375dBw8erDK01aT32v6OrcrFfo+dq2HDhpf12gG/VqweCaDeeHp66t5779WiRYuUm5ur1157TT4+Prr77rvNGn9/f3Xs2FHPPvtslcc4/y+Ltblvmr+/vyRp8uTJF1wmu7ZLU9vt9ioX5Kj4y2ZtVfxl6OjRo2revLm5/ezZszU6ZpMmTbR9+/ZK289fiORSrkVtrvW+ffsUHR2tNm3aaMmSJbX+nJo0aaLU1NQq9/v4+FT7+nvvvVfx8fFKSkrSs88+qyVLlmjw4MEuI2VLly5V69at9fbbb7v0dqHFVWra/9KlS9WzZ08tXLjQZfuFFmKoaoGYvLw8eXh4qFGjRlW+puKzmz9//gVX96vu1hkVr68u2F3IqlWrZLPZdOutt1Z7fE9PzyoXwDj3/BUudA2k//158Pb21tSpUzV16lQdPXrUHHUbOHCgGc4vZOXKlTp16pRWrFjhstLiT7mPY13LycnRZ599pqSkJI0aNcrc/tVXX1WqXb58ua677roa3U+uKrX9HftTFRQUVPrMAVwcoQ1AvRozZoxeeuklPffcc1qzZo1iYmJcFqaIjIzUmjVrdO2119Z4Otr5LvSvvu3atVPbtm312WefKTEx8dLfxDlatWqlnTt3umz7+OOPLzhKcjEVfxl+++23ddNNN5nb33333RrdzuC2227TP//5T61atcplCtP5S7tfjmtR4cSJExoyZIjKy8uVkpLick+vmoiMjFRycrLKysrUtWvXWp/fz89PgwcP1htvvKGwsDDl5eVVmhpps9nk4eHhEsby8vKqXD2yNmw2m/nzV2Hnzp3aunWrgoODK9WvWLFCzz33nDnqdeLECb3//vvq0aOH3NzcqjxH9+7ddfXVV2vPnj2XdGPq9u3bS/px4YvaWLx4sdauXauoqCiXKXjni4yMVGJiopo0aaLWrVtf9Ljr1q3T0aNHzaBZVlamt99+W9dee22V9wgLDAxUTEyMPvvsM82bN0///e9/5eXl5fLn3tPT06yv+IzP/VwMw9CiRYtq9sZ/BlX1KP24qub5li9fXmn6dG3Uxe/Ymjpy5IjOnDnzi7utB2AFhDYA9apLly7q2LGj5s2bJ8MwKt2bbdq0aUpPT1e3bt00YcIEtWvXTmfOnNGBAwe0Zs0avfTSSxe92eu1114rT09PLVu2TO3bt1ejRo3kdDrldDr18ssvq3///urbt69iYmLUvHlz/fDDD9q7d6/+/e9/65133qnV+4mOjtaTTz6pp556SuHh4dqzZ48WLFhQ66BS4be//a3uvfdePf/883Jzc9Ptt9+u3bt36/nnn5fD4dBVV1U/y/3+++/X3Llzdf/99+vZZ59V27ZttWbNGn344YeVauv6Wpzbw969ezVx4kSdOHHCnOJ1LrvdfsH7e91zzz1atmyZ7rjjDj3yyCO6+eab5e7ursOHD2v9+vW68847XW7OXZXRo0fr7bff1kMPPaQWLVqod+/eLvsjIyO1YsUKjRs3TsOGDdOhQ4f0zDPPqFmzZjWacnchkZGReuaZZ/T0008rPDxc+/bt07Rp09S6desqQ7ebm5v69Omj+Ph4lZeXa+bMmSoqKtLUqVMveI5GjRpp/vz5GjVqlH744QcNGzZMAQEBOnbsmD777DMdO3as0kjfuVq0aKFrrrlGGRkZmjBhQqX9p0+fdpmW95///EcrV67UBx98oPDwcL300kvVXoO4uDgtX75ct956qx599FF17NhR5eXlOnjwoNLS0pSQkOASxv39/XX77bfrySeflLe3t/7+97/r888/d1n2v2vXroqMjFTHjh3l5+envXv3asmSJQoLCzP/0adDhw6SpJkzZ6p///5yc3NTx44d1adPH3l4eOjee+/VpEmTdObMGS1cuFAFBQXVvo9L0aBBA4WHh9f6e23XX3+9rr32Wv35z3+WYRhq3Lix3n//faWnp7vUZWdn6+uvv76k75dWqIvfsTVV8XN0KSuVAle8+lwFBQAMwzBeeOEFQ5IREhJS5f5jx44ZEyZMMFq3bm24u7sbjRs3Njp37mw8/vjjxsmTJw3D+N/qbc8991yVx3jrrbeM66+/3nB3d6+0Atpnn31mDB8+3AgICDDc3d2NoKAg4/bbbzdeeukls6amq0cWFxcbkyZNMoKDgw1PT08jPDzcyM7OvuDqkeevqFfVec6cOWPEx8cbAQEBRsOGDY1bbrnF2Lp1q+FwOIxHH320uktrGIZhHD582LjrrruMRo0aGT4+PsZdd91lbNmypcrV7mpyLS7U+4VIuuijZcuWZv35K0AahmGUlpYas2fPNm644QajYcOGRqNGjYzrr7/eGDt2rPHll19W+1rD+HGFwODgYEOS8fjjj1fZ54wZM4xWrVoZdrvdaN++vbFo0aILrhA6fvz4C77Xc3+2iouLjYkTJxrNmzc3GjZsaNx0003GypUrjVGjRrm854qf35kzZxpTp041WrRoYXh4eBg33nij8eGHH7qc40IrDW7cuNEYMGCA0bhxY8Pd3d1o3ry5MWDAAOOdd96pstdzPfnkk4afn59x5swZl+0VqydWPLy9vY1rrrnGGDZsmPHOO++YK4qe6/yfdcMwjJMnTxpPPPGE0a5dO8PDw8NwOBxGhw4djEcffdRlpciKa/v3v//duPbaaw13d3fj+uuvN5YtW+ZyvD//+c9Gly5dDD8/P8NutxvXXHON8eijjxrfffedy7V/8MEHjaZNmxo2m83lmr3//vvmz1Lz5s2N//f//p+5Eua5f/YutHrmhT6/8/88Sary5/F8FZ/pgQMHzG179uwx+vTpY/j4+Bh+fn7G3XffbRw8eNDlZ+yJJ55w6eNcLVu2NAYMGFBpe1V/RmryO7bi/VS1emRNfo8ZhmFER0dXWuUSQM3YDKMGSz8BACxly5Yt6t69u5YtW1ZpJUigto4cOaLWrVvrjTfeqPIefri8XnjhBcXFxenEiRMX/O5iVUJCQtS/f389//zzl7G7ulFUVCSn06m5c+fW+P5vAP6H0AYAFpeenq6tW7eqc+fO8vT01GeffaYZM2bI4XBo586dl7TqH3C+xx57TGvXrlV2dvZFp92ibhQWFmrr1q2aPHmyzp49q127dtV3S5fN1KlT9fbbb2vnzp01ul0JAFf8qQEAi/P19VVaWprmzZunEydOyN/fX/3799f06dMJbKgzTzzxhLy8vPTtt99WuUgK6t6OHTs0ZMgQdezYUa+++mp9t3NZ+fr6KikpicAGXCJG2gAAAADAwpj/AAAAAAAWRmgDAAAAAAsjtAEAAACAhfFt0J9ZeXm5jhw5Ih8fH9lstvpuBwAAAEA9MQxDJ06ckNPprHblXkLbz+zIkSOsygUAAADAdOjQIbVo0eKC+wltPzMfHx9JP34wvr6+9dwNAAAAgPpSVFSk4OBgMyNcCKHtZ1YxJdLX15fQBgAAAOCiX5tiIRIAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACysQX03AOvoMfaZ+m4BAOrUJy8/Wd8tAADwkzHSBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDC6jW0tWrVSjabrdJj/PjxkiTDMDRlyhQ5nU55enqqZ8+e2r17t8sxiouL9fDDD8vf31/e3t4aNGiQDh8+7FJTUFCg6OhoORwOORwORUdH6/jx4y41Bw8e1MCBA+Xt7S1/f39NmDBBJSUlLjW7du1SeHi4PD091bx5c02bNk2GYdT9hQEAAACA/1OvoS0zM1O5ubnmIz09XZJ09913S5JmzZqlOXPmaMGCBcrMzFRQUJD69OmjEydOmMeIi4tTSkqKkpOTtXnzZp08eVKRkZEqKysza6KiopSdna3U1FSlpqYqOztb0dHR5v6ysjINGDBAp06d0ubNm5WcnKzly5crISHBrCkqKlKfPn3kdDqVmZmp+fPna/bs2ZozZ87lvkwAAAAArmA2w0JDRXFxcfrggw/05ZdfSpKcTqfi4uL02GOPSfpxVC0wMFAzZ87U2LFjVVhYqKZNm2rJkiUaMWKEJOnIkSMKDg7WmjVr1LdvX+3du1chISHKyMhQ165dJUkZGRkKCwvT559/rnbt2mnt2rWKjIzUoUOH5HQ6JUnJycmKiYlRfn6+fH19tXDhQk2ePFlHjx6V3W6XJM2YMUPz58/X4cOHZbPZavQei4qK5HA4VFhYKF9f3zq9fj9Vj7HP1HcLAFCnPnn5yfpuAQCAC6ppNrDMd9pKSkq0dOlSjR49WjabTfv371deXp4iIiLMGrvdrvDwcG3ZskWSlJWVpdLSUpcap9Op0NBQs2br1q1yOBxmYJOkW265RQ6Hw6UmNDTUDGyS1LdvXxUXFysrK8usCQ8PNwNbRc2RI0d04MCBC76v4uJiFRUVuTwAAAAAoKYsE9pWrlyp48ePKyYmRpKUl5cnSQoMDHSpCwwMNPfl5eXJw8NDfn5+1dYEBARUOl9AQIBLzfnn8fPzk4eHR7U1Fc8raqoyffp087t0DodDwcHBF74IAAAAAHAey4S2V199Vf3793cZ7ZJUadqhYRgXnYp4fk1V9XVRUzGztLp+Jk+erMLCQvNx6NChansHAAAAgHNZIrR98803+uijj/Tggw+a24KCgiRVHsXKz883R7iCgoJUUlKigoKCamuOHj1a6ZzHjh1zqTn/PAUFBSotLa22Jj8/X1Ll0cBz2e12+fr6ujwAAAAAoKYsEdoWL16sgIAADRgwwNzWunVrBQUFmStKSj9+723jxo3q1q2bJKlz585yd3d3qcnNzVVOTo5ZExYWpsLCQm3fvt2s2bZtmwoLC11qcnJylJuba9akpaXJbrerc+fOZs2mTZtcbgOQlpYmp9OpVq1a1eHVAAAAAID/qffQVl5ersWLF2vUqFFq0KCBud1msykuLk6JiYlKSUlRTk6OYmJi5OXlpaioKEmSw+HQmDFjlJCQoHXr1mnHjh0aOXKkOnTooN69e0uS2rdvr379+ik2NlYZGRnKyMhQbGysIiMj1a5dO0lSRESEQkJCFB0drR07dmjdunWaOHGiYmNjzZGxqKgo2e12xcTEKCcnRykpKUpMTFR8fHyNV44EAAAAgNpqcPGSy+ujjz7SwYMHNXr06Er7Jk2apNOnT2vcuHEqKChQ165dlZaWJh8fH7Nm7ty5atCggYYPH67Tp0+rV69eSkpKkpubm1mzbNkyTZgwwVxlctCgQVqwYIG5383NTatXr9a4cePUvXt3eXp6KioqSrNnzzZrHA6H0tPTNX78eHXp0kV+fn6Kj49XfHz85bgsAAAAACDJYvdpuxJwnzYA+PlwnzYAgJX94u7TBgAAAACojNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZW76Ht22+/1ciRI9WkSRN5eXmpU6dOysrKMvcbhqEpU6bI6XTK09NTPXv21O7du12OUVxcrIcfflj+/v7y9vbWoEGDdPjwYZeagoICRUdHy+FwyOFwKDo6WsePH3epOXjwoAYOHChvb2/5+/trwoQJKikpcanZtWuXwsPD5enpqebNm2vatGkyDKNuLwoAAAAA/J96DW0FBQXq3r273N3dtXbtWu3Zs0fPP/+8rr76arNm1qxZmjNnjhYsWKDMzEwFBQWpT58+OnHihFkTFxenlJQUJScna/PmzTp58qQiIyNVVlZm1kRFRSk7O1upqalKTU1Vdna2oqOjzf1lZWUaMGCATp06pc2bNys5OVnLly9XQkKCWVNUVKQ+ffrI6XQqMzNT8+fP1+zZszVnzpzLe6EAAAAAXLFsRj0OE/35z3/Wv/71L33yySdV7jcMQ06nU3FxcXrsscck/TiqFhgYqJkzZ2rs2LEqLCxU06ZNtWTJEo0YMUKSdOTIEQUHB2vNmjXq27ev9u7dq5CQEGVkZKhr166SpIyMDIWFhenzzz9Xu3bttHbtWkVGRurQoUNyOp2SpOTkZMXExCg/P1++vr5auHChJk+erKNHj8put0uSZsyYofnz5+vw4cOy2WwXfc9FRUVyOBwqLCyUr6/vT76GdanH2GfquwUAqFOfvPxkfbcAAMAF1TQb1OtI26pVq9SlSxfdfffdCggI0I033qhFixaZ+/fv36+8vDxFRESY2+x2u8LDw7VlyxZJUlZWlkpLS11qnE6nQkNDzZqtW7fK4XCYgU2SbrnlFjkcDpea0NBQM7BJUt++fVVcXGxO19y6davCw8PNwFZRc+TIER04cKDK91hcXKyioiKXBwAAAADUVL2Gtv/85z9auHCh2rZtqw8//FB//OMfNWHCBL3xxhuSpLy8PElSYGCgy+sCAwPNfXl5efLw8JCfn1+1NQEBAZXOHxAQ4FJz/nn8/Pzk4eFRbU3F84qa802fPt38Hp3D4VBwcPBFrgoAAAAA/E+9hrby8nLddNNNSkxM1I033qixY8cqNjZWCxcudKk7f9qhYRgXnYp4fk1V9XVRUzG79EL9TJ48WYWFhebj0KFD1fYNAAAAAOeq19DWrFkzhYSEuGxr3769Dh48KEkKCgqSVHkUKz8/3xzhCgoKUklJiQoKCqqtOXr0aKXzHzt2zKXm/PMUFBSotLS02pr8/HxJlUcDK9jtdvn6+ro8AAAAAKCm6jW0de/eXfv27XPZ9sUXX6hly5aSpNatWysoKEjp6enm/pKSEm3cuFHdunWTJHXu3Fnu7u4uNbm5ucrJyTFrwsLCVFhYqO3bt5s127ZtU2FhoUtNTk6OcnNzzZq0tDTZ7XZ17tzZrNm0aZPLbQDS0tLkdDrVqlWrurgkAAAAAOCiXkPbo48+qoyMDCUmJuqrr77Sm2++qVdeeUXjx4+X9OOUw7i4OCUmJiolJUU5OTmKiYmRl5eXoqKiJEkOh0NjxoxRQkKC1q1bpx07dmjkyJHq0KGDevfuLenH0bt+/fopNjZWGRkZysjIUGxsrCIjI9WuXTtJUkREhEJCQhQdHa0dO3Zo3bp1mjhxomJjY83RsaioKNntdsXExCgnJ0cpKSlKTExUfHx8jVaOBAAAAIDaalCfJ//d736nlJQUTZ48WdOmTVPr1q01b9483XfffWbNpEmTdPr0aY0bN04FBQXq2rWr0tLS5OPjY9bMnTtXDRo00PDhw3X69Gn16tVLSUlJcnNzM2uWLVumCRMmmKtMDho0SAsWLDD3u7m5afXq1Ro3bpy6d+8uT09PRUVFafbs2WaNw+FQenq6xo8fry5dusjPz0/x8fGKj4+/nJcJAAAAwBWsXu/TdiXiPm0A8PPhPm0AACv7RdynDQAAAABQPUIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwsHoNbVOmTJHNZnN5BAUFmfsNw9CUKVPkdDrl6empnj17avfu3S7HKC4u1sMPPyx/f395e3tr0KBBOnz4sEtNQUGBoqOj5XA45HA4FB0drePHj7vUHDx4UAMHDpS3t7f8/f01YcIElZSUuNTs2rVL4eHh8vT0VPPmzTVt2jQZhlG3FwUAAAAAzlHvI22//e1vlZubaz527dpl7ps1a5bmzJmjBQsWKDMzU0FBQerTp49OnDhh1sTFxSklJUXJycnavHmzTp48qcjISJWVlZk1UVFRys7OVmpqqlJTU5Wdna3o6Ghzf1lZmQYMGKBTp05p8+bNSk5O1vLly5WQkGDWFBUVqU+fPnI6ncrMzNT8+fM1e/ZszZkz5zJfIQAAAABXsgb13kCDBi6jaxUMw9C8efP0+OOPa+jQoZKk119/XYGBgXrzzTc1duxYFRYW6tVXX9WSJUvUu3dvSdLSpUsVHBysjz76SH379tXevXuVmpqqjIwMde3aVZK0aNEihYWFad++fWrXrp3S0tK0Z88eHTp0SE6nU5L0/PPPKyYmRs8++6x8fX21bNkynTlzRklJSbLb7QoNDdUXX3yhOXPmKD4+Xjab7We6YgAAAACuJPU+0vbll1/K6XSqdevWuueee/Sf//xHkrR//37l5eUpIiLCrLXb7QoPD9eWLVskSVlZWSotLXWpcTqdCg0NNWu2bt0qh8NhBjZJuuWWW+RwOFxqQkNDzcAmSX379lVxcbGysrLMmvDwcNntdpeaI0eO6MCBAxd8f8XFxSoqKnJ5AAAAAEBN1Wto69q1q9544w19+OGHWrRokfLy8tStWzd9//33ysvLkyQFBga6vCYwMNDcl5eXJw8PD/n5+VVbExAQUOncAQEBLjXnn8fPz08eHh7V1lQ8r6ipyvTp083v0jkcDgUHB1d/UQAAAADgHPUa2vr376+77rpLHTp0UO/evbV69WpJP06DrHD+tEPDMC46FfH8mqrq66KmYhGS6vqZPHmyCgsLzcehQ4eq7R0AAAAAzlXv0yPP5e3trQ4dOujLL780v+d2/ihWfn6+OcIVFBSkkpISFRQUVFtz9OjRSuc6duyYS8355ykoKFBpaWm1Nfn5+ZIqjwaey263y9fX1+UBAAAAADVlqdBWXFysvXv3qlmzZmrdurWCgoKUnp5u7i8pKdHGjRvVrVs3SVLnzp3l7u7uUpObm6ucnByzJiwsTIWFhdq+fbtZs23bNhUWFrrU5OTkKDc316xJS0uT3W5X586dzZpNmza53AYgLS1NTqdTrVq1qvuLAQAAAACq59A2ceJEbdy4Ufv379e2bds0bNgwFRUVadSoUbLZbIqLi1NiYqJSUlKUk5OjmJgYeXl5KSoqSpLkcDg0ZswYJSQkaN26ddqxY4dGjhxpTreUpPbt26tfv36KjY1VRkaGMjIyFBsbq8jISLVr106SFBERoZCQEEVHR2vHjh1at26dJk6cqNjYWHNkLCoqSna7XTExMcrJyVFKSooSExNZORIAAADAZVWvS/4fPnxY9957r7777js1bdpUt9xyizIyMtSyZUtJ0qRJk3T69GmNGzdOBQUF6tq1q9LS0uTj42MeY+7cuWrQoIGGDx+u06dPq1evXkpKSpKbm5tZs2zZMk2YMMFcZXLQoEFasGCBud/NzU2rV6/WuHHj1L17d3l6eioqKkqzZ882axwOh9LT0zV+/Hh16dJFfn5+io+PV3x8/OW+TAAAAACuYDajYjUN/CyKiorkcDhUWFhoue+39Rj7TH23AAB16pOXn6zvFgAAuKCaZgNLfacNAAAAAOCK0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIU1qElRfHx8jQ84Z86cS24GAAAAAOCqRqFtx44dNTqYzWb7Sc0AAAAAAFzVKLStX7/+cvcBAAAAAKgC32kDAAAAAAur0UjbuU6dOqUZM2Zo3bp1ys/PV3l5ucv+//znP3XWHAAAAABc6Wo90vbggw/q1VdfVY8ePfTQQw/pkUcecXlcqunTp8tmsykuLs7cZhiGpkyZIqfTKU9PT/Xs2VO7d+92eV1xcbEefvhh+fv7y9vbW4MGDdLhw4ddagoKChQdHS2HwyGHw6Ho6GgdP37cpebgwYMaOHCgvL295e/vrwkTJqikpMSlZteuXQoPD5enp6eaN2+uadOmyTCMS37PAAAAAHAxtR5pW7t2rVavXq3u3bvXWROZmZl65ZVX1LFjR5fts2bN0pw5c5SUlKTrrrtOf/3rX9WnTx/t27dPPj4+kqS4uDi9//77Sk5OVpMmTZSQkKDIyEhlZWXJzc1NkhQVFaXDhw8rNTVVkvSHP/xB0dHRev/99yVJZWVlGjBggJo2barNmzfr+++/16hRo2QYhubPny9JKioqUp8+fXTbbbcpMzNTX3zxhWJiYuTt7a2EhIQ6uxYAAAAAcK5ahzY/Pz81bty4zho4efKk7rvvPi1atEh//etfze2GYWjevHl6/PHHNXToUEnS66+/rsDAQL355psaO3asCgsL9eqrr2rJkiXq3bu3JGnp0qUKDg7WRx99pL59+2rv3r1KTU1VRkaGunbtKklatGiRwsLCtG/fPrVr105paWnas2ePDh06JKfTKUl6/vnnFRMTo2effVa+vr5atmyZzpw5o6SkJNntdoWGhuqLL77QnDlzFB8fz8qZAAAAAC6LWk+PfOaZZ/TUU0/pv//9b500MH78eA0YMMAMXRX279+vvLw8RUREmNvsdrvCw8O1ZcsWSVJWVpZKS0tdapxOp0JDQ82arVu3yuFwmIFNkm655RY5HA6XmtDQUDOwSVLfvn1VXFysrKwssyY8PFx2u92l5siRIzpw4MAF319xcbGKiopcHgAAAABQUzUaabvxxhtdRpK++uorBQYGqlWrVnJ3d3ep/fe//13jkycnJ+vf//63MjMzK+3Ly8uTJAUGBrpsDwwM1DfffGPWeHh4yM/Pr1JNxevz8vIUEBBQ6fgBAQEuNeefx8/PTx4eHi41rVq1qnSein2tW7eu8j1Onz5dU6dOrXIfAAAAAFxMjULb4MGD6/zEhw4d0iOPPKK0tDQ1bNjwgnXnTzs0DOOiUxHPr6mqvi5qKhYhqa6fyZMnKz4+3nxeVFSk4ODgavsHAAAAgAo1Cm1PP/10nZ84KytL+fn56ty5s7mtrKxMmzZt0oIFC7Rv3z5JP45iNWvWzKzJz883R7iCgoJUUlKigoICl9G2/Px8devWzaw5evRopfMfO3bM5Tjbtm1z2V9QUKDS0lKXmopRt3PPI1UeDTyX3W53mVIJAAAAALVRbzfX7tWrl3bt2qXs7Gzz0aVLF913333Kzs7WNddco6CgIKWnp5uvKSkp0caNG81A1rlzZ7m7u7vU5ObmKicnx6wJCwtTYWGhtm/fbtZs27ZNhYWFLjU5OTnKzc01a9LS0mS3281QGRYWpk2bNrncBiAtLU1Op7PStEkAAAAAqCu1Xj2yrKxMc+fO1T//+U8dPHiw0r3Mfvjhhxodx8fHR6GhoS7bvL291aRJE3N7XFycEhMT1bZtW7Vt21aJiYny8vJSVFSUJMnhcGjMmDFKSEhQkyZN1LhxY02cOFEdOnQwFzZp3769+vXrp9jYWL388suSflzyPzIyUu3atZMkRUREKCQkRNHR0Xruuef0ww8/aOLEiYqNjZWvr6+kH28bMHXqVMXExOgvf/mLvvzySyUmJuqpp55i5UgAAAAAl02tR9qmTp2qOXPmaPjw4SosLFR8fLyGDh2qq666SlOmTKnT5iZNmqS4uDiNGzdOXbp00bfffqu0tDTzHm2SNHfuXA0ePFjDhw9X9+7d5eXlpffff9+8R5skLVu2TB06dFBERIQiIiLUsWNHLVmyxNzv5uam1atXq2HDhurevbuGDx+uwYMHa/bs2WaNw+FQenq6Dh8+rC5dumjcuHGKj493+b4aAAAAANQ1m1GxmkYNXXvttXrxxRc1YMAA+fj4KDs729yWkZGhN99883L1+qtQVFQkh8OhwsJCcxTPKnqMfaa+WwCAOvXJy0/WdwsAAFxQTbNBrUfa8vLy1KFDB0lSo0aNVFhYKEmKjIzU6tWrL7FdAAAAAEBVah3aWrRoYS7Y0aZNG6WlpUmSMjMzWSURAAAAAOpYrUPbkCFDtG7dOknSI488oieffFJt27bV/fffr9GjR9d5gwAAAABwJav16pEzZsww/3vYsGEKDg7Wv/71L7Vp00aDBg2q0+YAAAAA4EpX69B2vq5du6pr16510QsAAAAA4Dy1nh7p5uam2267rdL92I4ePeqyzD4AAAAA4KerdWgzDEPFxcXq0qWLcnJyKu0DAAAAANSdWoc2m82m5cuXa+DAgerWrZvee+89l30AAAAAgLpzSSNtbm5ueuGFFzR79myNGDFCf/3rXxllAwAAAIDL4CctRPKHP/xB1113nYYNG6aNGzfWVU8AAAAAgP9T65G2li1buiw40rNnT2VkZOjw4cN12hgAAAAA4BJG2vbv319pW5s2bbRjxw4dPXq0TpoCAAAAAPzokqdHlpSUKD8/X+Xl5eY2FiIBAAAAgLpV69D2xRdfaMyYMdqyZYvLdsMwZLPZVFZWVmfNAQAAAMCVrtah7YEHHlCDBg30wQcfqFmzZoyuAQAAAMBlVOvQlp2draysLF1//fWXox8AAAAAwDlqvXpkSEiIvvvuu8vRCwAAAADgPLUObTNnztSkSZO0YcMGff/99yoqKnJ5AAAAAADqTq2nR/bu3VuS1KtXL5ftLEQCAAAAAHWv1qFt/fr1F9y3Y8eOn9QMAAAAAMBVrUNbeHi4y/PCwkItW7ZM//jHP/TZZ58pLi6urnoDAAAAgCterb/TVuHjjz/WyJEj1axZM82fP1933HGHPv3007rsDQAAAACueLUaaTt8+LCSkpL02muv6dSpUxo+fLhKS0u1fPlyhYSEXK4eAQAAAOCKVeORtjvuuEMhISHas2eP5s+fryNHjmj+/PmXszcAAAAAuOLVeKQtLS1NEyZM0J/+9Ce1bdv2cvYEAAAAAPg/NR5p++STT3TixAl16dJFXbt21YIFC3Ts2LHL2RsAAAAAXPFqHNrCwsK0aNEi5ebmauzYsUpOTlbz5s1VXl6u9PR0nThx4nL2CQAAAABXpFqvHunl5aXRo0dr8+bN2rVrlxISEjRjxgwFBARo0KBBl6NHAAAAALhiXfKS/5LUrl07zZo1S4cPH9Zbb71VVz0BAAAAAP7PTwptFdzc3DR48GCtWrWqLg4HAAAAAPg/dRLaAAAAAACXB6ENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALq9fQtnDhQnXs2FG+vr7y9fVVWFiY1q5da+43DENTpkyR0+mUp6enevbsqd27d7sco7i4WA8//LD8/f3l7e2tQYMG6fDhwy41BQUFio6OlsPhkMPhUHR0tI4fP+5Sc/DgQQ0cOFDe3t7y9/fXhAkTVFJS4lKza9cuhYeHy9PTU82bN9e0adNkGEbdXhQAAAAAOEe9hrYWLVpoxowZ+vTTT/Xpp5/q9ttv15133mkGs1mzZmnOnDlasGCBMjMzFRQUpD59+ujEiRPmMeLi4pSSkqLk5GRt3rxZJ0+eVGRkpMrKysyaqKgoZWdnKzU1VampqcrOzlZ0dLS5v6ysTAMGDNCpU6e0efNmJScna/ny5UpISDBrioqK1KdPHzmdTmVmZmr+/PmaPXu25syZ8zNcKQAAAABXKpthsaGixo0b67nnntPo0aPldDoVFxenxx57TNKPo2qBgYGaOXOmxo4dq8LCQjVt2lRLlizRiBEjJElHjhxRcHCw1qxZo759+2rv3r0KCQlRRkaGunbtKknKyMhQWFiYPv/8c7Vr105r165VZGSkDh06JKfTKUlKTk5WTEyM8vPz5evrq4ULF2ry5Mk6evSo7Ha7JGnGjBmaP3++Dh8+LJvNVqP3V1RUJIfDocLCQvn6+tb15ftJeox9pr5bAIA69cnLT9Z3CwAAXFBNs4FlvtNWVlam5ORknTp1SmFhYdq/f7/y8vIUERFh1tjtdoWHh2vLli2SpKysLJWWlrrUOJ1OhYaGmjVbt26Vw+EwA5sk3XLLLXI4HC41oaGhZmCTpL59+6q4uFhZWVlmTXh4uBnYKmqOHDmiAwcOXPB9FRcXq6ioyOUBAAAAADVV76Ft165datSokex2u/74xz8qJSVFISEhysvLkyQFBga61AcGBpr78vLy5OHhIT8/v2prAgICKp03ICDApeb88/j5+cnDw6PamornFTVVmT59uvldOofDoeDg4OovCAAAAACco95DW7t27ZSdna2MjAz96U9/0qhRo7Rnzx5z//nTDg3DuOhUxPNrqqqvi5qKmaXV9TN58mQVFhaaj0OHDlXbOwAAAACcq95Dm4eHh9q0aaMuXbpo+vTpuuGGG/TCCy8oKChIUuVRrPz8fHOEKygoSCUlJSooKKi25ujRo5XOe+zYMZea889TUFCg0tLSamvy8/MlVR4NPJfdbjdXx6x4AAAAAEBN1XtoO59hGCouLlbr1q0VFBSk9PR0c19JSYk2btyobt26SZI6d+4sd3d3l5rc3Fzl5OSYNWFhYSosLNT27dvNmm3btqmwsNClJicnR7m5uWZNWlqa7Ha7OnfubNZs2rTJ5TYAaWlpcjqdatWqVd1fCAAAAABQPYe2v/zlL/rkk0904MAB7dq1S48//rg2bNig++67TzabTXFxcUpMTFRKSopycnIUExMjLy8vRUVFSZIcDofGjBmjhIQErVu3Tjt27NDIkSPVoUMH9e7dW5LUvn179evXT7GxscrIyFBGRoZiY2MVGRmpdu3aSZIiIiIUEhKi6Oho7dixQ+vWrdPEiRMVGxtrjoxFRUXJbrcrJiZGOTk5SklJUWJiouLj42u8ciQAAAAA1FaD+jz50aNHFR0drdzcXDkcDnXs2FGpqanq06ePJGnSpEk6ffq0xo0bp4KCAnXt2lVpaWny8fExjzF37lw1aNBAw4cP1+nTp9WrVy8lJSXJzc3NrFm2bJkmTJhgrjI5aNAgLViwwNzv5uam1atXa9y4cerevbs8PT0VFRWl2bNnmzUOh0Pp6ekaP368unTpIj8/P8XHxys+Pv5yXyYAAAAAVzDL3aft1477tAHAz4f7tAEArOwXd582AAAAAEBlhDYAAAAAsDBCGwAAAABYGKENAAAAACyM0AYAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAAAAsLAG9d0AAACwlojkyfXdAgDUqbR7ptd3Cz8JI20AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAur19A2ffp0/e53v5OPj48CAgI0ePBg7du3z6XGMAxNmTJFTqdTnp6e6tmzp3bv3u1SU1xcrIcfflj+/v7y9vbWoEGDdPjwYZeagoICRUdHy+FwyOFwKDo6WsePH3epOXjwoAYOHChvb2/5+/trwoQJKikpcanZtWuXwsPD5enpqebNm2vatGkyDKPuLgoAAAAAnKNeQ9vGjRs1fvx4ZWRkKD09XWfPnlVERIROnTpl1syaNUtz5szRggULlJmZqaCgIPXp00cnTpwwa+Li4pSSkqLk5GRt3rxZJ0+eVGRkpMrKysyaqKgoZWdnKzU1VampqcrOzlZ0dLS5v6ysTAMGDNCpU6e0efNmJScna/ny5UpISDBrioqK1KdPHzmdTmVmZmr+/PmaPXu25syZc5mvFAAAAIArlc2w0DDRsWPHFBAQoI0bN+rWW2+VYRhyOp2Ki4vTY489JunHUbXAwEDNnDlTY8eOVWFhoZo2baolS5ZoxIgRkqQjR44oODhYa9asUd++fbV3716FhIQoIyNDXbt2lSRlZGQoLCxMn3/+udq1a6e1a9cqMjJShw4dktPplCQlJycrJiZG+fn58vX11cKFCzV58mQdPXpUdrtdkjRjxgzNnz9fhw8fls1mu+h7LCoqksPhUGFhoXx9fS/HZbxkPcY+U98tAECd+uTlJ+u7hV+kiOTJ9d0CANSptHum13cLVappNrDUd9oKCwslSY0bN5Yk7d+/X3l5eYqIiDBr7Ha7wsPDtWXLFklSVlaWSktLXWqcTqdCQ0PNmq1bt8rhcJiBTZJuueUWORwOl5rQ0FAzsElS3759VVxcrKysLLMmPDzcDGwVNUeOHNGBAweqfE/FxcUqKipyeQAAAABATVkmtBmGofj4eP3+979XaGioJCkvL0+SFBgY6FIbGBho7svLy5OHh4f8/PyqrQkICKh0zoCAAJea88/j5+cnDw+PamsqnlfUnG/69Onm9+gcDoeCg4MvciUAAAAA4H8sE9oeeugh7dy5U2+99ValfedPOzQM46JTEc+vqaq+LmoqZpdeqJ/JkyersLDQfBw6dKjavgEAAADgXJYIbQ8//LBWrVql9evXq0WLFub2oKAgSZVHsfLz880RrqCgIJWUlKigoKDamqNHj1Y677Fjx1xqzj9PQUGBSktLq63Jz8+XVHk0sILdbpevr6/LAwAAAABqql5Dm2EYeuihh7RixQp9/PHHat26tcv+1q1bKygoSOnp6ea2kpISbdy4Ud26dZMkde7cWe7u7i41ubm5ysnJMWvCwsJUWFio7du3mzXbtm1TYWGhS01OTo5yc3PNmrS0NNntdnXu3Nms2bRpk8ttANLS0uR0OtWqVas6uioAAAAA8D/1GtrGjx+vpUuX6s0335SPj4/y8vKUl5en06dPS/pxymFcXJwSExOVkpKinJwcxcTEyMvLS1FRUZIkh8OhMWPGKCEhQevWrdOOHTs0cuRIdejQQb1795YktW/fXv369VNsbKwyMjKUkZGh2NhYRUZGql27dpKkiIgIhYSEKDo6Wjt27NC6des0ceJExcbGmqNjUVFRstvtiomJUU5OjlJSUpSYmKj4+PgarRwJAAAAALXVoD5PvnDhQklSz549XbYvXrxYMTExkqRJkybp9OnTGjdunAoKCtS1a1elpaXJx8fHrJ87d64aNGig4cOH6/Tp0+rVq5eSkpLk5uZm1ixbtkwTJkwwV5kcNGiQFixYYO53c3PT6tWrNW7cOHXv3l2enp6KiorS7NmzzRqHw6H09HSNHz9eXbp0kZ+fn+Lj4xUfH1/XlwYAAAAAJFnsPm1XAu7TBgA/H+7Tdmm4TxuAXxvu0wYAAAAAuGwIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMLqNbRt2rRJAwcOlNPplM1m08qVK132G4ahKVOmyOl0ytPTUz179tTu3btdaoqLi/Xwww/L399f3t7eGjRokA4fPuxSU1BQoOjoaDkcDjkcDkVHR+v48eMuNQcPHtTAgQPl7e0tf39/TZgwQSUlJS41u3btUnh4uDw9PdW8eXNNmzZNhmHU2fUAAAAAgPPVa2g7deqUbrjhBi1YsKDK/bNmzdKcOXO0YMECZWZmKigoSH369NGJEyfMmri4OKWkpCg5OVmbN2/WyZMnFRkZqbKyMrMmKipK2dnZSk1NVWpqqrKzsxUdHW3uLysr04ABA3Tq1Clt3rxZycnJWr58uRISEsyaoqIi9enTR06nU5mZmZo/f75mz56tOXPmXIYrAwAAAAA/alCfJ+/fv7/69+9f5T7DMDRv3jw9/vjjGjp0qCTp9ddfV2BgoN58802NHTtWhYWFevXVV7VkyRL17t1bkrR06VIFBwfro48+Ut++fbV3716lpqYqIyNDXbt2lSQtWrRIYWFh2rdvn9q1a6e0tDTt2bNHhw4dktPplCQ9//zziomJ0bPPPitfX18tW7ZMZ86cUVJSkux2u0JDQ/XFF19ozpw5io+Pl81m+xmuGAAAAIArjWW/07Z//37l5eUpIiLC3Ga32xUeHq4tW7ZIkrKyslRaWupS43Q6FRoaatZs3bpVDofDDGySdMstt8jhcLjUhIaGmoFNkvr27avi4mJlZWWZNeHh4bLb7S41R44c0YEDBy74PoqLi1VUVOTyAAAAAICasmxoy8vLkyQFBga6bA8MDDT35eXlycPDQ35+ftXWBAQEVDp+QECAS8355/Hz85OHh0e1NRXPK2qqMn36dPO7dA6HQ8HBwdW/cQAAAAA4h2VDW4Xzpx0ahnHRqYjn11RVXxc1FYuQVNfP5MmTVVhYaD4OHTpUbe8AAAAAcC7LhragoCBJlUex8vPzzRGuoKAglZSUqKCgoNqao0ePVjr+sWPHXGrOP09BQYFKS0urrcnPz5dUeTTwXHa7Xb6+vi4PAAAAAKgpy4a21q1bKygoSOnp6ea2kpISbdy4Ud26dZMkde7cWe7u7i41ubm5ysnJMWvCwsJUWFio7du3mzXbtm1TYWGhS01OTo5yc3PNmrS0NNntdnXu3Nms2bRpk8ttANLS0uR0OtWqVau6vwAAAAAAoHoObSdPnlR2drays7Ml/bj4SHZ2tg4ePCibzaa4uDglJiYqJSVFOTk5iomJkZeXl6KioiRJDodDY8aMUUJCgtatW6cdO3Zo5MiR6tChg7maZPv27dWvXz/FxsYqIyNDGRkZio2NVWRkpNq1aydJioiIUEhIiKKjo7Vjxw6tW7dOEydOVGxsrDkyFhUVJbvdrpiYGOXk5CglJUWJiYmsHAkAAADgsqrXJf8//fRT3Xbbbebz+Ph4SdKoUaOUlJSkSZMm6fTp0xo3bpwKCgrUtWtXpaWlycfHx3zN3Llz1aBBAw0fPlynT59Wr169lJSUJDc3N7Nm2bJlmjBhgrnK5KBBg1zuDefm5qbVq1dr3Lhx6t69uzw9PRUVFaXZs2ebNQ6HQ+np6Ro/fry6dOkiPz8/xcfHmz0DAAAAwOVgMypW08DPoqioSA6HQ4WFhZb7fluPsc/UdwsAUKc+efnJ+m7hFykieXJ9twAAdSrtnun13UKVapoNLPudNgAAAAAAoQ0AAAAALI3QBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQBgAAAAAWRmgDAAAAAAsjtAEAAACAhRHaAAAAAMDCCG0AAAAAYGGENgAAAACwMEIbAAAAAFgYoQ0AAAAALIzQdgn+/ve/q3Xr1mrYsKE6d+6sTz75pL5bAgAAAPArRWirpbfffltxcXF6/PHHtWPHDvXo0UP9+/fXwYMH67s1AAAAAL9ChLZamjNnjsaMGaMHH3xQ7du317x58xQcHKyFCxfWd2sAAAAAfoUa1HcDvyQlJSXKysrSn//8Z5ftERER2rJlS5WvKS4uVnFxsfm8sLBQklRUVHT5Gr1EZ0vO1HcLAFCnrPi79pfg7H+LL14EAL8gVv3/QUVfhmFUW0doq4XvvvtOZWVlCgwMdNkeGBiovLy8Kl8zffp0TZ06tdL24ODgy9IjAOB/HEmJ9d0CAMACHGPm1ncL1Tpx4oQcDscF9xPaLoHNZnN5bhhGpW0VJk+erPj4ePN5eXm5fvjhBzVp0uSCrwF+zYqKihQcHKxDhw7J19e3vtsBANQT/n8A/JgjTpw4IafTWW0doa0W/P395ebmVmlULT8/v9LoWwW73S673e6y7eqrr75cLQK/GL6+vvxPGgDA/w9wxatuhK0CC5HUgoeHhzp37qz09HSX7enp6erWrVs9dQUAAADg14yRtlqKj49XdHS0unTporCwML3yyis6ePCg/vjHP9Z3awAAAAB+hQhttTRixAh9//33mjZtmnJzcxUaGqo1a9aoZcuW9d0a8Itgt9v19NNPV5o2DAC4svD/A6DmbMbF1pcEAAAAANQbvtMGAAAAABZGaAMAAAAACyO0AQAAAICFEdoA1LtWrVpp3rx59d0GAACAJRHaAFyymJgY2Ww2zZgxw2X7ypUrZbPZanyczMxM/eEPf6jr9gAAFhITE6PBgwfXdxvALxKhDcBP0rBhQ82cOVMFBQWXfIymTZvKy8urDrsCAAD49SC0AfhJevfuraCgIE2fPv2CNVu2bNGtt94qT09PBQcHa8KECTp16pS5//zpkTabTf/4xz80ZMgQeXl5qW3btlq1atXlfBsAgJ9Ramqqfv/73+vqq69WkyZNFBkZqa+//rq+2wIsi9AG4Cdxc3NTYmKi5s+fr8OHD1fav2vXLvXt21dDhw7Vzp079fbbb2vz5s166KGHqj3u1KlTNXz4cO3cuVN33HGH7rvvPv3www+X620AAH5Gp06dUnx8vDIzM7Vu3TpdddVVGjJkiMrLy+u7NcCSCG0AfrIhQ4aoU6dOevrppyvte+655xQVFaW4uDi1bdtW3bp104svvqg33nhDZ86cueAxY2JidO+996pNmzZKTEzUqVOntH379sv5NgAAP5O77rpLQ4cOVdu2bdWpUye9+uqr2rVrl/bs2VPfrQGWRGgDUCdmzpyp119/vdL/cLOyspSUlKRGjRqZj759+6q8vFz79++/4PE6duxo/re3t7d8fHyUn59/2foHAPx8vv76a0VFRemaa66Rr6+vWrduLUk6ePBgPXcGWFOD+m4AwK/Drbfeqr59++ovf/mLYmJizO3l5eUaO3asJkyYUOk1v/nNby54PHd3d5fnNpuNaTMA8CsxcOBABQcHa9GiRXI6nSovL1doaKhKSkrquzXAkghtAOrMjBkz1KlTJ1133XXmtptuukm7d+9WmzZt6rEzAIBVfP/999q7d69efvll9ejRQ5K0efPmeu4KsDamRwKoMx06dNB9992n+fPnm9see+wxbd26VePHj1d2dra+/PJLrVq1Sg8//HA9dgoAqC9+fn5q0qSJXnnlFX311Vf6+OOPFR8fX99tAZZGaANQp5555hkZhmE+79ixozZu3Kgvv/xSPXr00I033qgnn3xSzZo1q8cuAQA/t/LycjVo0EBXXXWVkpOTlZWVpdDQUD366KN67rnn6rs9wNJsxrl/uwIAAAAug379+qlNmzZasGBBfbcC/OIw0gYAAIDLpqCgQKtXr9aGDRvUu3fv+m4H+EViIRIAAABcNqNHj1ZmZqYSEhJ055131nc7wC8S0yMBAAAAwMKYHgkAAAAAFkZoAwAAAAALI7QBAAAAgIUR2gAAAADAwghtAAAAAGBhhDYAAKphs9m0cuXKGtfHxMRo8ODBl62fC5kyZYo6depUbU3Pnj0VFxf3s/QDAKg7hDYAwBUpJiZGNptNNptN7u7uCgwMVJ8+ffTaa6+pvLzcrMvNzVX//v1/1r7qI/QBAKyL0AYAuGL169dPubm5OnDggNauXavbbrtNjzzyiCIjI3X27FlJUlBQkOx2ez13CgC4khHaAABXLLvdrqCgIDVv3lw33XST/vKXv+i9997T2rVrlZSUJKny9Mhvv/1WI0aMkJ+fn5o0aaI777xTBw4cqHTsqVOnKiAgQL6+vho7dqxKSkrMfe+++646dOggT09PNWnSRL1799apU6c0ZcoUvf7663rvvffMUcANGzZIkh577DFdd9118vLy0jXXXKMnn3xSpaWllc67ZMkStWrVSg6HQ/fcc49OnDhRl5cMAFAPCG0AAJzj9ttv1w033KAVK1ZU2vff//5Xt912mxo1aqRNmzZp8+bNatSokfr16+cSytatW6e9e/dq/fr1euutt5SSkqKpU6dK+nG65b333qvRo0dr79692rBhg4YOHSrDMDRx4kQNHz7cHAHMzc1Vt27dJEk+Pj5KSkrSnj179MILL2jRokWaO3euS39ff/21Vq5cqQ8++EAffPCBNm7cqBkzZlzGqwUA+Dk0qO8GAACwmuuvv147d+6stD05OVlXXXWV/vGPf8hms0mSFi9erKuvvlobNmxQRESEJMnDw0OvvfaavLy89Nvf/lbTpk3T//t//0/PPPOMcnNzdfbsWQ0dOlQtW7aUJHXo0ME8h6enp4qLixUUFORy7ieeeML871atWikhIUFvv/22Jk2aZG4vLy9XUlKSfHx8JEnR0dFat26dnn322Tq6MgCA+kBoAwDgPIZhmKHsXFlZWfrqq6/MUFThzJkz+vrrr83nN9xwg7y8vMznYWFhOnnypA4dOqQbbrhBvXr1UocOHdS3b19FRERo2LBh8vPzq7and999V/PmzdNXX32lkydP6uzZs/L19XWpadWqlUtvzZo1U35+fq3eOwDAepgeCQDAefbu3avWrVtX2l5eXq7OnTsrOzvb5fHFF18oKirqose12Wxyc3NTenq61q5dq5CQEM2fP1/t2rXT/v37L/i6jIwM3XPPPerfv78++OAD7dixQ48//rjLlExJcnd3r3S+c1fCBAD8MhHaAAA4x8cff6xdu3bprrvuqrTvpptu0pdffqmAgAC1adPG5eFwOMy6zz77TKdPnzafZ2RkqFGjRmrRooWkH8NU9+7dNXXqVO3YsUMeHh5KSUmR9OPUyrKyMpfz/utf/1LLli31+OOPq0uXLmrbtq2++eaby/H2AQAWRGgDAFyxiouLlZeXp2+//Vb//ve/lZiYqDvvvFORkZG6//77K9Xfd9998vf315133qlPPvlE+/fv18aNG/XII4/o8OHDZl1JSYnGjBmjPXv2aO3atXr66af10EMP6aqrrtK2bduUmJioTz/9VAcPHtSKFSt07NgxtW/fXtKPUxx37typffv26bvvvlNpaanatGmjgwcPKjk5WV9//bVefPFFM+QBAH79+E4bAOCKlZqaqmbNmqlBgwby8/PTDTfcoBdffFGjRo3SVVdV/ndNLy8vbdq0SY899piGDh2qEydOqHnz5urVq5fL98t69eqltm3b6tZbb1VxcbHuueceTZkyRZLk6+urTZs2ad68eSoqKlLLli31/PPPmzfwjo2N1YYNG9SlSxedPHlS69ev15133qlHH31UDz30kIqLizVgwAA9+eST5jEBAL9uNsMwjPpuAgAAAABQNaZHAgAAAICFEdoAAAAAwMIIbQAAAABgYYQ2AAAAALAwQhsAAAAAWBihDQAAAAAsjNAGAAAAABZGaAMAAAAACyO0AQAAAICFEdoAAAAAwMIIbQAAAABgYf8f8UmzWaToYHoAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"shift_dummies = pd.get_dummies(shift_behavior, prefix='shift')\n",
|
||
"speeding_dummies = pd.get_dummies(speeding, prefix='speeding')\n",
|
||
"weather_dummies = pd.get_dummies(weather, prefix='weather')\n",
|
||
"road_dummies = pd.get_dummies(road_type, prefix='road')\n",
|
||
"weekday_dummies = pd.get_dummies(weekday, prefix='weekday')\n",
|
||
"\n",
|
||
"X = pd.DataFrame({\n",
|
||
" 'avg_speed': avg_speed,\n",
|
||
" 'hard_brakes': hard_brakes,\n",
|
||
" 'trip_distance': trip_distance\n",
|
||
"})\n",
|
||
"X = pd.concat([X, shift_dummies, speeding_dummies, weather_dummies, road_dummies, weekday_dummies], axis=1)\n",
|
||
"\n",
|
||
"# Betas reduziert, damit die Zielvariable nicht zu häufig 1 ist\n",
|
||
"betas = np.array([\n",
|
||
" 0.015, # avg_speed\n",
|
||
" 0.1, # hard_brakes\n",
|
||
" 0., # trip_distance\n",
|
||
" 0.05, 0.075, # shift_dummies\n",
|
||
" 0.05, 0.075, # speeding_dummies\n",
|
||
" 0., 0., # weather_dummies\n",
|
||
" 0., 0., # road_dummies\n",
|
||
" 0., 0. # weekday_dummies\n",
|
||
"])\n",
|
||
"\n",
|
||
"# Sicherstellen, dass die Dimension passt\n",
|
||
"X_model = X.iloc[:, :len(betas)]\n",
|
||
"\n",
|
||
"X_model = X_model.astype(float)\n",
|
||
"rauschen = np.random.normal(0, 0.2, size=N)\n",
|
||
"\n",
|
||
"# Linearkombination + Rauschen + Offset (damit die Zielvariable nicht zu häufig 1 ist)\n",
|
||
"lin_comb = X_model.values @ betas + rauschen - 2\n",
|
||
"\n",
|
||
"# Logistische Funktion → Wahrscheinlichkeiten\n",
|
||
"pi = 1 / (1 + np.exp(-lin_comb))\n",
|
||
"\n",
|
||
"# Zielvariable simulieren\n",
|
||
"target = np.random.binomial(1, pi)\n",
|
||
"\n",
|
||
"# Visualisierung der Verteilung der Zielvariable\n",
|
||
"plt.figure(figsize=(10, 6))\n",
|
||
"sns.countplot(x=target, palette='viridis')\n",
|
||
"plt.title('Verteilung der Zielvariable (Diebstahl: ja/nein)')\n",
|
||
"plt.xlabel('Diebstahl')\n",
|
||
"plt.ylabel('Anzahl')\n",
|
||
"plt.xticks([0, 1], ['Nein', 'Ja'])\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "825cc812",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Verteilungen der Variablen\n",
|
||
"\n",
|
||
"- **Durchschnittsgeschwindigkeit (`avg_speed`)**: normalverteilt mit Mittelwert 47 km/h und Standardabweichung 10, begrenzt auf den Bereich [10, 130].\n",
|
||
"- **Schaltverhalten (`shift_behavior`)**: ordinal skaliert mit 3 Kategorien, gezogen aus einer Multinomialverteilung (Wahrscheinlichkeiten: 40% früh, 40% normal, 20% spät).\n",
|
||
"- **Harte Bremsmanöver (`hard_brakes`)**: metrisch, modelliert mit einer Poisson-Verteilung (λ = 2).\n",
|
||
"- **Geschwindigkeitsüberschreitungen (`speeding`)**: ordinal skaliert mit 3 Stufen, abhängig von `avg_speed` über eine Softmax-basierte Wahrscheinlichkeitsverteilung.\n",
|
||
"- **Wetterbedingungen (`weather`)**: nominal skaliert, zufällig verteilt mit 75% trocken, 20% nass und 5% winterlich.\n",
|
||
"- **Fahrstrecke (`trip_distance`)**: lognormal verteilt mit μ = ln(12) und σ = 0.7.\n",
|
||
"- **Straßentyp (`road_type`)**: abhängig von `trip_distance`, mit kategorialer Softmax-Verteilung (Innerorts, Außerorts, Autobahn).\n",
|
||
"- **Wochentag (`weekday`)**: nominal mit festgelegter Verteilung (70% Werktage, je 15% Samstag und Sonntag).\n",
|
||
"\n",
|
||
"### Modellierte Abhängigkeiten\n",
|
||
"\n",
|
||
"- Die Variablen `speeding` und `road_type` wurden nicht rein zufällig erzeugt, sondern basieren auf skalierter Eingangsvariablen (`avg_speed` bzw. `trip_distance`) und einem gewichteten Softmax-Verfahren. Dies erlaubt realistischere Zusammenhänge.\n",
|
||
"\n",
|
||
"### Zielvariable und 𝑝ᵢ-Werte\n",
|
||
"\n",
|
||
"Die Zielvariable, die eine Diebstahlwahrscheinlichkeit beschreibt, wurde aus diesen Features modelliert. Die 𝑝ᵢ-Werte repräsentieren dabei individuelle Wahrscheinlichkeiten für Fahrerwechsel und wurden anhand von Beta-Verteilungen erzeugt.\n",
|
||
"\n",
|
||
"### Wahl und Begründung der Beta-Werte\n",
|
||
"\n",
|
||
"Die Beta-Gewichte wurden bewusst so gewählt, dass sie realistische Zusammenhänge zwischen den Merkmalen und dem Auftreten eines möglichen Diebstahls modellieren, ohne die Zielvariable künstlich zu verzerren. Das Ziel war ein realistischer, aber nicht übermäßig häufiger positiver Klassenanteil.\n",
|
||
"\n",
|
||
"- **`avg_speed (0.015)`**: Ein leicht positiver Zusammenhang, da ungewöhnliche Geschwindigkeit (insbesondere zu hohe) auf atypisches Fahrverhalten hindeuten kann.\n",
|
||
"- **`hard_brakes (0.1)`**: Ein höherer Beta-Wert, da aggressive Bremsmanöver oft stark mit ungewohnten Fahrstilen korrelieren.\n",
|
||
"- **`trip_distance (0)`**: Kein Einfluss, da die reine Strecke keine Aussagekraft hat.\n",
|
||
"- **`shift_behavior` (0.05, 0.075)**: Personen, die spät schalten, zeigen eher sportliches oder ineffizientes Verhalten – potenziell abweichend vom üblichen Fahrerprofil.\n",
|
||
"- **`speeding` (0.05, 0.075)**: Häufige Geschwindigkeitsüberschreitungen deuten ebenfalls auf untypisches Fahrverhalten hin.\n",
|
||
"- **`weather` (0, 0)**: Wetterbedingungen spielen keine Rolle.\n",
|
||
"- **`road_type` (0, 0)**: Verschiedene Straßentypen implizieren ebenfalls kein Fahrerprofil\n",
|
||
"- **`weekday` (0, 0)**: Ebenfalls unbedeutend für den Fahrstil.\n",
|
||
"\n",
|
||
"Alle Beta-Werte wurden bewusst reduziert (unterhalb von 0.1), um eine Übergewichtung einzelner Faktoren zu vermeiden und die Zielvariable `Diebstahl` in einem realistischen Bereich (ca. 10–12 %) zu halten. Eine stärkere Gewichtung hätte zu einer zu häufigen Klassifikation als Diebstahl geführt.\n",
|
||
"\n",
|
||
"```visualisierung\n",
|
||
"Die grafische Darstellung zeigt, dass rund 11 % der Fahrten als potenzielle Diebstähle klassifiziert wurden – ein realistischer Wert, der ein geeignetes Trainingsverhältnis für ein Klassifikationsmodell ermöglicht.\n",
|
||
"```\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "adbc166b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Simulation der Perspektive des Data Scientist"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f8abd5f5",
|
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"metadata": {},
|
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"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "hft_ml",
|
||
"language": "python",
|
||
"name": "python3"
|
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},
|
||
"language_info": {
|
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"codemirror_mode": {
|
||
"name": "ipython",
|
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"version": 3
|
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},
|
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"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
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"version": "3.11.10"
|
||
}
|
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|
||
"nbformat": 4,
|
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"nbformat_minor": 5
|
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|