sameerg07

import ast,sys

input_str = input()
input_list = ast.literal_eval(input_str)

sorting_choice = input()

# Your code goes here

print(sorted(input_list,key=lambda x: x[int(sorting_choice)-1]))

sameerg07

import numpy as np

# Theta is the vector representing coefficients (intercept, area, bedrooms)
theta = np.matrix(np.array([0,0,0])) 
alpha = 0.01
iterations = 1000


# define cost function
# takes in theta (current values of coefficients b0, b1, b2), X and y

sameerg07

# Takes in X, y, current m and c (both initialised to 0), num_iterations, learning rate
# returns gradient at current m and c for each pair of m and c

def gradient(X, y, m_current=0, c_current=0, iters=1000, learning_rate=0.01):
    N = float(len(y))
    gd_df = pd.DataFrame( columns = ['m_current', 'c_current','cost'])
    for i in range(iters):
        y_pred = (m_current * X) + c_current
        cost = sum([data**2 for data in (y-y_pred)]) / N
        m_gradient = -(2/N) * sum(X * (y - y_pred))

sameerg07

# Read the input list
import ast,sys
input_str = sys.stdin.read()
input_list = ast.literal_eval(input_str)

# Write your code here
try:
    print(list(set(sorted(input_list)))[-2])
except:
    print("not present")

sameerg07

# Read the input string
input_string = input()

# Write your code here
nl = input_string.split(" ")
for i in range(len(nl)):
    nl[i] = nl[i][0].upper() + nl[i][1:]

print(' '.join(nl))

sameerg07

n=int(input())
# 153=1^3+5^3+3^3
def armstrong(n):
    nums = [int(x)**3 for x in str(n)] ## separate the numbers and cube
    if sum(nums) == n:
        return True
    else:
        return False

print(armstrong(n))

sameerg07

n=int(input())
#write your code here
if n%2 == 0 and n!=2 and n!=0:
    print("number entered is not prime")
else:
    print("number entered is prime")

sameerg07

def fibonacci(n):
    if n == 1 or n == 2:
        return n-1
    else:
        return fibonacci(n-1) + fibonacci(n-2)

n = int(input())
i = 1
while i <= n:
    print(fibonacci(i))

sameerg07

from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import cv2
import numpy as np

sameerg07

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))