import inspect
def func():
	'''
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

 

from minisom import MiniSom
from warnings import filterwarnings
filterwarnings('ignore')
data = pd.read_csv('Credit_Card_Applications.csv')
X = data.iloc[:, 1:14].values
y = data.iloc[:, -1].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
X = sc.fit_transform(X)
som_grid_rows = 10
som_grid_columns = 10
iterations = 20000
sigma = 1
learning_rate = 0.5
som = MiniSom(x = som_grid_rows, y = som_grid_columns, input_len=13, sigma=sigma, learning_rate=learning_rate)

 

som.random_weights_init(X)

 

som.train_random(X, iterations)
from pylab import plot, axis, show, pcolor, colorbar, bone
bone()
pcolor(som.distance_map().T)
colorbar()
show()
bone()
pcolor(som.distance_map().T)
colorbar() 

markers = ['o', 's']                     
colors = ['r', 'g']
for i, x in enumerate(X):
    w = som.winner(x)
    plot(w[0] + 0.5,
         w[1] + 0.5,
         markers[y[i]],
         markeredgecolor = colors[y[i]],
         markerfacecolor = 'None',
         markersize = 10,
         markeredgewidth = 2)
show()
plt.show()
'''
def px():
    code=inspect.getsource(func)
    print(code)

