import numpy as np
from sklearn import datasets
iris = datasets.load_iris()

# 4 features in np array - 150 rows

case = 1        # change cases to see variation

if case == 1:   # first feature deleted
    iris.data = np.delete(iris.data,0, 1)

if case == 2:   # first 2 features deleted
    iris.data = np.delete(iris.data,0, 1)
    iris.data = np.delete(iris.data,0, 1)

if case == 3:   # first 3 features deleted (1 feature left)
    iris.data = np.delete(iris.data,0, 1)
    iris.data = np.delete(iris.data,0, 1)
    iris.data = np.delete(iris.data,0, 1)

if case == 4:   # only second feature deleted from np array
    iris.data = np.delete(iris.data,1, 1)

if case == 5:   # only third feature deleted from np array
    iris.data = np.delete(iris.data,2, 1)

if case == 6:   # only last feature deleted from np array
    iris.data = np.delete(iris.data,3, 1)

# print iris.data
# exit()

from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
pred = gnb.fit(iris.data, iris.target).predict(iris.data)
# pred = gnb.fit(iris.data, iris.target).predict(test_data)

from sklearn.metrics import accuracy_score
print accuracy_score(iris.target, pred)