# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
data = pd.read_csv("../input/ml-lab/FindS.csv")
print(data)

h = np.array(data)[:,:-1]
t = np.array(data)[:,-1]

h = ["*","*"]

def training_example(H,t):
    for z,x in list(enumerate(H)):
        if t[z]=="Yes":
            for i in range(len(x)):
                if h[i]=="*" and x[i]:
                    h[i]=x[i]
                elif h[i]!= x[i] and h[i]!="?":
                        h[i]="?"
                        
    return h

                        