Tejas tejancsu

## gist:2922fb91989e4a32e19a508e3a505a95
package practice

/**
  * @author tejsudha
  */
// To execute Scala code, please define an object named Solution that extends App

// open this pic:
// http://www.meeplemountain.com/wp-content/uploads/2017/05/scoring-example-1.jpg

## Sammu script
import sys
import os
from optparse import OptionParser


def create_crif(input_filename=None, output_filename=None):
    if input_filename and output_filename:
        if os.path.exists(input_filename):
            input_f = open(input_filename, "r")
            # f = sys.stdout

## linear_classifier
import json
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn import mixture
from mpl_toolkits.mplot3d import Axes3D
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

def load_tmdb_movies(path):

## proj_code
import json
import pandas as pd

def load_tmdb_movies(path):
    df = pd.read_csv(path)
    df['release_date'] = pd.to_datetime(df['release_date']).apply(lambda x: x.date())
    json_columns = ['genres', 'keywords', 'production_countries', 'production_companies', 'spoken_languages']
    for column in json_columns:
        df[column] = df[column].apply(json.loads)
    return df

## Popular.scala
import java.io.PrintWriter
import scala.collection.mutable
import scala.collection.mutable.ListBuffer

object Solution {

    def popular(num: Int, requests: Array[String]): Array[String] = {
        val dateMap = new mutable.HashMap[String, mutable.HashMap[String, mutable.HashSet[String]]]

        requests.foreach { request =>

## Low priority show

    case class MyShow[T](f: T => String) extends AnyVal

    trait LowPriorityShow {
        implicit def showAnything[T] = MyShow[T](_.toString)

    }

    object MyShow extends LowPriorityShow {
        implicit val showDateTime = MyShow[DateTime](ISODateTimeFormat.dateTime().withZone(DateTimeZone.forID("America/New_York")).print _)

## mpanel_chinmay
# def matrix_sum(arr, i1, j1, i2, j2)
#   sum = 0
#   (i1..(i2-1)).each do |i|
#     (j1..(j2-1)).each do |j|
#       sum += arr[i][j]
#     end
#   end
#   return sum
# end

## checkr_sol

def print_spiral(n)
  arr = Array.new(n)
  visited = Array.new(n)
  (0..(n-1)).each do |i|
    visited[i] = Array.new(n, false)
    arr[i] = Array.new(n)
  end

  i = (n-1)/2

## daily_send
["309ccc71-9a29-3c44-e3c7-9c585a54c052", "20e5030b-5624-9bf9-3a36-e0b21fb96280", "bd587e52-96cb-9194-574d-b3dfebea0cad", "abe524e0-c24b-faa3-046b-cd74422090c2", "1fe9ce0b-d5e9-6bec-968c-651e16f5a03e", "e34affae-cfda-cec7-4122-eaf368db7205", "09136de3-651b-6ebf-ef05-bb02c91e2da3", "b754a956-ec4c-a081-01c6-85e160f50750", "ef9bf7f6-717c-4890-12f1-191a1e97649c", "05f0f35e-5cef-2ba1-a31a-3142db2ce0ad", "40f531de-ea24-4751-8bdb-c3b2cf978aab", "f81e1a5f-30e0-fff9-01bb-aba21a4b358c", "c8464f85-5161-f4a5-a519-0a14d4125f88", "df62e416-4de7-43fa-8eb7-a181b0ea6224", "2c10ea1a-15a3-7c28-3ae3-93a9fc6e5dd9", "f3dfeb0a-40a8-cf97-60db-7f8b5a6332ad", "26b092f7-3cc3-5360-266b-70c659393cf1", "3e3df53e-1609-eaa7-8d40-c2bfc1c44a4c", "cd992614-e8a9-1e79-efff-09ca6d957a97", "547813fe-1a01-8f1d-5118-a55c0b002ce0", "455917de-a5e6-11e2-8883-0025906a929e", "38d97059-6709-cb96-0ac3-c87c74fa8c8c", "ce1dd24a-292b-11e2-935a-00259061289e", "9199da6e-43ae-37bc-7b64-a2028f38d2bd", "28e1aa50-bd7b-ab51-6851-0c823cf7de20", "bcb2670a-75bb-687b-91b

## gist:ada541906fb53e327181

@sym_arr = ['/', '*', '+', '-']

class Node
  attr_accessor :val, :next, :prev
end

def createLinkedList(exp_arr)
  prev = nil
  head = nil
	package practice

	/**
	* @author tejsudha
	*/
	// To execute Scala code, please define an object named Solution that extends App

	// open this pic:
	// http://www.meeplemountain.com/wp-content/uploads/2017/05/scoring-example-1.jpg
	import sys
	import os
	from optparse import OptionParser


	def create_crif(input_filename=None, output_filename=None):
	if input_filename and output_filename:
	if os.path.exists(input_filename):
	input_f = open(input_filename, "r")
	# f = sys.stdout
	import json
	import pandas as pd
	import numpy as np
	from sklearn.decomposition import PCA
	from sklearn import mixture
	from mpl_toolkits.mplot3d import Axes3D
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import confusion_matrix

	def load_tmdb_movies(path):
	import java.io.PrintWriter
	import scala.collection.mutable
	import scala.collection.mutable.ListBuffer

	object Solution {

	def popular(num: Int, requests: Array[String]): Array[String] = {
	val dateMap = new mutable.HashMap[String, mutable.HashMap[String, mutable.HashSet[String]]]

	requests.foreach { request =>

	case class MyShow[T](f: T => String) extends AnyVal

	trait LowPriorityShow {
	implicit def showAnything[T] = MyShow[T](_.toString)

	}

	object MyShow extends LowPriorityShow {
	implicit val showDateTime = MyShow[DateTime](ISODateTimeFormat.dateTime().withZone(DateTimeZone.forID("America/New_York")).print _)
	# def matrix_sum(arr, i1, j1, i2, j2)
	# sum = 0
	# (i1..(i2-1)).each do \|i\|
	# (j1..(j2-1)).each do \|j\|
	# sum += arr[i][j]
	# end
	# end
	# return sum
	# end

	def print_spiral(n)
	arr = Array.new(n)
	visited = Array.new(n)
	(0..(n-1)).each do \|i\|
	visited[i] = Array.new(n, false)
	arr[i] = Array.new(n)
	end

	i = (n-1)/2

	@sym_arr = ['/', '*', '+', '-']

	class Node
	attr_accessor :val, :next, :prev
	end

	def createLinkedList(exp_arr)
	prev = nil
	head = nil