# Import LabelEncoder
from sklearn import preprocessing

#Generating the Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB

# Assign features and encoding labels
weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny',
'Rainy','Sunny','Overcast','Overcast','Rainy']
humidity=['High','High','High','Medium','Low','Low','Low','Medium','Low','Medium','Medium','Medium','High','Medium']

batfirst=['No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No']

# Creating labelEncoder
le = preprocessing.LabelEncoder()
# Converting string labels into numbers.
weather_encoded=le.fit_transform(weather)
hum_encoded=le.fit_transform(humidity)
label=le.fit_transform(batfirst)
print(weather_encoded,hum_encoded,label)


#Combining weather and humidity in a single tuple as features
features=list(zip(weather_encoded,hum_encoded))

#Create a Gaussian Classifier
model = GaussianNB()
model.fit(features,label) #Train the model using training set.

print("Enter Weather and Humidtity conditions : ")
w,h=map(int, input().split())

#Predict Output
predicted= model.predict([[w,h]]) # ''' For Weather : 0:Overcast, 2:Sunny , 1:Rainy ''' For Humidity : 0:High, 2:Medium, 1:low

print(predicted) # --> [1] that means yes, the player should bat first and [0] that means No, player should bowl first.