Mina MinaGabriel

## Android location GPS
    public void getCurrentLocation() {
        LocationManager locationManager;
        String context = Context.LOCATION_SERVICE;
        locationManager = (LocationManager) getSystemService(context);
        Criteria crta = new Criteria();
        crta.setAccuracy(Criteria.ACCURACY_FINE);
        crta.setAltitudeRequired(false);
        crta.setBearingRequired(false);
        crta.setCostAllowed(true);
        crta.setPowerRequirement(Criteria.POWER_LOW);

## download_parse_entrezgene.py
#!/usr/bin/python

import os
from urllib2 import urlopen, URLError, HTTPError
import re

#  Change information as appropriate

# Where files will be downloaded and unzipped

## gist:19fb603a4ac82999839ba01388cf1e8e

def heapify(arr, n, i):
    largest = i  # Initialize largest as root
    l = 2 * i + 1  # left = 2*i + 1
    r = 2 * i + 2  # right = 2*i + 2
    print(arr,"largest: " + str(largest), "i: " + str(i), "l: " + str(l), "r: " + str(r), h)
    # See if left child of root exists and is
    # greater than root
    if l < n and arr[i] < arr[l]:
        largest = l

## index.html
<!DOCTYPE html>
<html>
<head>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
<script>
$(function(){

    $("button").click(function(){
      //get the value of both (username and password fields)
      var username = $("#username").val();

## iris.py
import matplotlib.pyplot as plt
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:, 0:2]
y = iris.target

plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k')
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')

## narmal_distribution_iris.py
import numpy as np
import matplotlib.pyplot as plt

from sklearn import linear_model, datasets
from scipy.special import expit
from scipy.stats import binom
from scipy import stats
import pandas as pd

plt.subplot(2, 1, 1)

## Max_likelihood_one_datapoint.py
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
from scipy.stats import norm

fig = plt.figure()
ax = fig.gca(projection='3d')

## max_likelihood_2_datapoint.py
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
from scipy.stats import norm

fig = plt.figure()
ax = fig.gca(projection='3d')

## tensorflow_mnist.py


import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'


mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()

## Kmeans.py
import sys
import numpy
import os

os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
numpy.set_printoptions(threshold=sys.maxsize)
import torch
from torch.distributions import normal
import seaborn as sns;
	public void getCurrentLocation() {
	LocationManager locationManager;
	String context = Context.LOCATION_SERVICE;
	locationManager = (LocationManager) getSystemService(context);
	Criteria crta = new Criteria();
	crta.setAccuracy(Criteria.ACCURACY_FINE);
	crta.setAltitudeRequired(false);
	crta.setBearingRequired(false);
	crta.setCostAllowed(true);
	crta.setPowerRequirement(Criteria.POWER_LOW);
	#!/usr/bin/python

	import os
	from urllib2 import urlopen, URLError, HTTPError
	import re

	# Change information as appropriate

	# Where files will be downloaded and unzipped

	def heapify(arr, n, i):
	largest = i # Initialize largest as root
	l = 2 * i + 1 # left = 2*i + 1
	r = 2 * i + 2 # right = 2*i + 2
	print(arr,"largest: " + str(largest), "i: " + str(i), "l: " + str(l), "r: " + str(r), h)
	# See if left child of root exists and is
	# greater than root
	if l < n and arr[i] < arr[l]:
	largest = l
	<!DOCTYPE html>
	<html>
	<head>
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
	<script>
	$(function(){

	$("button").click(function(){
	//get the value of both (username and password fields)
	var username = $("#username").val();
	import matplotlib.pyplot as plt
	from sklearn import datasets

	iris = datasets.load_iris()
	X = iris.data[:, 0:2]
	y = iris.target

	plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor='k')
	plt.xlabel('Sepal length')
	plt.ylabel('Sepal width')
	import numpy as np
	import matplotlib.pyplot as plt

	from sklearn import linear_model, datasets
	from scipy.special import expit
	from scipy.stats import binom
	from scipy import stats
	import pandas as pd

	plt.subplot(2, 1, 1)
	from mpl_toolkits.mplot3d import Axes3D
	import matplotlib.pyplot as plt
	from matplotlib import cm
	from matplotlib.ticker import LinearLocator, FormatStrFormatter
	import numpy as np
	from scipy.stats import norm

	fig = plt.figure()
	ax = fig.gca(projection='3d')


	import tensorflow as tf
	import os
	os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'


	mnist = tf.keras.datasets.mnist

	(x_train, y_train),(x_test, y_test) = mnist.load_data()
	import sys
	import numpy
	import os

	os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
	numpy.set_printoptions(threshold=sys.maxsize)
	import torch
	from torch.distributions import normal
	import seaborn as sns;