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import os
import subprocess
import sys

pattern = sys.argv[1]
print(pattern)

out = subprocess.run(["ps", "aux"], stdout=subprocess.PIPE, text=True).stdout
out = str(out).split("\n")
out = list(filter(lambda x: pattern in x, out))

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import numpy as np
import tensorflow as tf

# Newton's optimization method for multivariate function in tensorflow

def cons(x):
    return tf.constant(x, dtype=tf.float32)

def compute_hessian(fn, vars):
    mat = []

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# MLP using theano
import numpy as np
import theano
import theano.tensor as T
from sklearn.datasets import load_iris

iris = load_iris()
train_x = iris.data
train_y = iris.target
nn_input_dim = train_x.shape[1]

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from __future__ import print_function
import sys
import io
import pip
import httplib2
import os
from mimetypes import MimeTypes

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# PROBLEM LINK : https://www.hackerrank.com/challenges/temperature-predictions

import numpy as np
from sklearn import ensemble

def isfloat(value):
  try:
    float(value)
    return True

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# Problem link : https://www.hackerrank.com/challenges/time-series-prediction

from sklearn import linear_model as lm
import sys
import pandas as pd
import numpy as np
import statsmodels.api as sm

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import os, filecmp

codes = {200:'success',404:'file not found',400:'error',408:'timeout'}

def compile(file,lang):
    if lang == 'java':
        class_file = file[:-4]+"class"
    elif lang == 'c':
        class_file = file[:-2]
    elif lang=='cpp':

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