rohanp

class Node:
    def __init__(self, val, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def levelOrderWrong1(root):
    curr_level = [root]
    levels = []

rohanp

#cython: wraparound=False, boundscheck=False, cdivision=True, overflowcheck=False
#cython: profile=False, nonecheck=False, cdivision_warnings=True

import numpy as np
cimport numpy as np
from libc.math import sqrt, exp
from sklearn.utils.extmath import fast_dot
from sklearn.metrics.pairwise import pairwise_distances

def diffusion_map(X, n_components=2, metric="euclidean"):

rohanp

import random
import sys 
from time import sleep
from getpass import getpass

def main():
	n_games = int(sys.argv[1])
	print("You have 100 tanks do distrubute between 5 territories.")
	print("Enter the number of tanks to put in each territory.")
	print("You may -1 for the territory to auto-select number needed to add to 100.")

rohanp

import numpy as np

def algorithm(cities):
	best_order = []
	best_length = float('inf')

	for i_start, start in enumerate(cities):
		order = [i_start]
		length = 0

rohanp

cdef DTYPE_t getEpsilon(int xi, DTYPE_t[:,:] RMSD, DTYPE_t[:] epsilonList, DTYPE_t[:] derivativeEpsilonList, int N):
	cdef DTYPE_t e,k
	cdef int i,j,x,y,neighborsLength
	cdef DTYPE_t[:,:] eigenValues=np.zeros((len(derivativeEpsilonList),N), dtype=DTYPE)
	cdef DTYPE_t[:,:] neighborsMatrix
	cdef DTYPE_t[:] A

	for i,e in enumerate(derivativeEpsilonList):
		neighbors=[]
		for j in range(N):

rohanp

"""
The code was modified so that you dont have to import any libraries or use external files :)
"""

import numpy as np
import sys
#import Bio.PDB
#from scipy import linalg
from time import clock