from Markov import Markov
city_weather = {
'New York': 'rainy',
'Chicago': 'snowy',
'Seattle': 'rainy',
'Boston': 'hailing',
'Miami': 'windy',
'Los Angeles': 'cloudy',
'San Francisco': 'windy'
}
city_weather_prediction = {}
for city in city_weather:
weather = Markov(day_zero_weather=city_weather[city])
weather.load_data(file_path='./weather.csv')
prediction = weather._simulate_weather_for_day(7, trials=100)
city_weather_prediction[city] = prediction
for city in city_weather_prediction:
print(city, ':', city_weather_prediction[city])
print('\nMost likely weather in seven days\n---------------------------------')
for city in city_weather_prediction:
weather_guess = max(city_weather_prediction[city],
key=city_weather_prediction[city].get)
print(city, ':', weather_guess)
#!/bin/usr/env python3
from Markov import Markov
weather_today = Markov()
weather_today.load_data()
print(weather_today.get_prob('sunny', 'cloudy'))
