from Markov import Markov
from collections import Counter
city_weather = {
'New York': 'rainy',
'Chicago': 'snowy',
'Seattle': 'rainy',
'Boston': 'hailing',
'Miami': 'windy',
'Los Angeles': 'cloudy',
'San Francisco': 'windy'
}
print(
"The number of occurrences of each weather condition over the 100 trials for each city"
)
print("----------------------------------")
predictions = {}
for city, weather in city_weather.items():
weather_today = Markov(day_zero_weather=weather)
weather_today.load_data(file_path='./weather.csv')
ans = Counter(weather_today.get_weather_for_day(day=7, trials=100))
predictions[city] = max(ans.items(), key=lambda x: x[1])[0]
print("{}: {}".format(city, dict(ans)))
print()
print("Most likely weather in seven days")
print("----------------------------------")
for city, weather in predictions.items():
print("{}: {}".format(city, weather))
'Chicago': 'snowy',
'Seattle': 'rainy',
'Boston': 'hailing',
'Miami': 'windy',
'Los Angeles': 'cloudy',
'San Francisco': 'windy'
}
weather_types = ['sunny', 'cloudy', 'rainy', 'snowy', 'windy', 'hailing']
forecasts = []
for key, value in city_weather.items():
weather_today = Markov(value)
weather_today.load_data()
city_forecast = weather_today.get_weather_for_day(7, 100)
forecasts.append(np.array(city_forecast))
city_forecast_dicts = []
for key, city_forecast in zip(city_weather.keys(), forecasts):
city_forecast_dict = {}
# weather_type_occurances = []
for weather_type in weather_types:
occurance = np.count_nonzero(city_forecast == weather_type)
# weather_type_occurances.append(occurances)
city_forecast_dict.update({weather_type: occurance})
city_forecast_dicts.append(city_forecast_dict)
print(key, ':', city_forecast_dict)
print('\nMost likely weather in seven days')
