theSage21

wget https://drive.google.com/file/d/1M8kRWh-pt-CiSXtXSMX68YTwB7ibCB4C/view?usp=sharing -O small.zip
unzip small.zip
rm -rf uploads
cp -r Anganwadi-upload uploads
cd uploads
mv 'pdf/activity bank' 'Activity Bank'
mv 'pdf/activity book' 'Activity Book'
mv 'pdf/Child Assesment Card' 'Child Assessment Card'
mv 'pdf/curriculam' 'Curriculum'
mv 'pdf/syllabus' 'Syllabus'

theSage21

{
    "wrappers": {
        "python3": "ulimit -m {memory_limit} && ulimit -t {time_limit} && python3 '{code}' < '{input}'",
        "python2": "ulimit -m {memory_limit} && ulimit -t {time_limit} && python2 '{code}' < '{input}'",
        "perl5": "ulimit -m {memory_limit} && ulimit -t {time_limit} && perl '{code}' <'{input}'",
        "lua": "ulimit -m {memory_limit} && ulimit -t {time_limit} && lua '{code}' <'{input}'",
        "g++": "g++ '{code}' -o '{code}.compiled' && ulimit -m {memory_limit} && ulimit -t {time_limit} && '{code}.compiled' <'{input}'",
        "java(use 'class Solution')": "mkdir 'dir_{code}' && mv '{code}' 'dir_{code}/Solution.java' && cd 'dir_{code}' && javac Solution.java && ulimit -m {memory_limit} && ulimit -t {time_limit} && java Solution < '{input}' && cd .. ; rm -rf 'dir_{code}'",
        "gcc": "gcc '{code}' -o '{code}.compiled' && ulimit -m {memory_limit} && ulimit -t {time_limit} && '{code}.compiled' <'{input}'"
    },

theSage21

from scipy.misc import imread, imsave
import numpy as np
import os

# Place images of same size in folder called images.
# name them in the order you want them to appear in.
# run script.
# view file all.png

images = list([os.path.join('images', i)

theSage21

var script = document.createElement("script");
script.setAttribute("src", "http://localhost:8000/static/bot.js");
document.body.appendChild(script);

theSage21

from keras import layers as L
from keras import models as M

inputs = L.Input((4, ))
output = L.Dense(3)(inputs)
model = M.Model(inputs, outputs)
model.compile('sgd', 'mse')

import numpy as np

theSage21

import tensorflow as tf
import numpy as np


x = np.array([[0, 0],
              [0, 1],
              [1, 0],
              [1, 1]]*100)
y = np.array([0, 1, 1, 0]*100).reshape((-1, 1))

theSage21

theSage21

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
import pickle
from collections import deque
import multiprocessing as mp
import editdistance
import os

theSage21

# AUTHOR: Arjoonn Sharma
# LICENSE: MIT
# Recommended Reading before tinkering with code:
# https://en.wikipedia.org/wiki/Heaviside_step_function
# https://en.wikipedia.org/wiki/Perceptron

def dot_product(vector1, vector2):
    "Returns the dot product between two vectors"
    # assertions allow us to make sure some things are true before
    # we proceede. Here dot product is only defined for vectors

theSage21

import os
import time
import cv2
import sys

print('python script.py harr.xml 1')
print(sys.argv)
script, casc, nodisplay = sys.argv
nodisplay = (nodisplay == '0')