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Privacy Policy for Schedlite | |
Last Updated: [Date] | |
Welcome to [Your Company Name]'s Privacy Policy. Your privacy is of paramount importance to us. This Privacy Policy explains how we collect, use, protect, and handle your personal information when you use our services, visit our website, or interact with us via Facebook or other social media platforms. | |
1. Information We Collect | |
We may collect the following types of information: | |
Personal Identification Information: Name, email address, phone number, and any other contact details you provide when using our services or interacting with us. |
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Testing only |
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Number of times pregnant | Plasma glucose concentration a 2 hours in an oral glucose tolerance test | Diastolic blood pressure (mm Hg) | Triceps skin fold thickness (mm) | 2-Hour serum insulin (mu U/ml) | Body mass index (weight in kg/(height in m)^2) | Diabetes pedigree function | Age (years) | Class variable | |
---|---|---|---|---|---|---|---|---|---|
6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 | |
1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 | |
8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 | |
1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 | |
0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 | |
5 | 116 | 74 | 0 | 0 | 25.6 | 0.201 | 30 | 0 | |
3 | 78 | 50 | 32 | 88 | 31 | 0.248 | 26 | 1 | |
10 | 115 | 0 | 0 | 0 | 35.3 | 0.134 | 29 | 0 | |
2 | 197 | 70 | 45 | 543 | 30.5 | 0.158 | 53 | 1 |
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Number of times pregnant,Plasma glucose concentration a 2 hours in an oral glucose tolerance test,Diastolic blood pressure (mm Hg),Triceps skin fold thickness (mm),2-Hour serum insulin (mu U/ml),Body mass index (weight in kg/(height in m)^2),Diabetes pedigree function,Age (years),Class variable | |
6,148,72,35,0,33.6,0.627,50,1 | |
1,85,66,29,0,26.6,0.351,31,0 | |
8,183,64,0,0,23.3,0.672,32,1 | |
1,89,66,23,94,28.1,0.167,21,0 | |
0,137,40,35,168,43.1,2.288,33,1 | |
5,116,74,0,0,25.6,0.201,30,0 | |
3,78,50,32,88,31,0.248,26,1 | |
10,115,0,0,0,35.3,0.134,29,0 | |
2,197,70,45,543,30.5,0.158,53,1 |
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1 def decimal_to_binary(num, rems): | |
2 if num == 0: | |
3 return rems[::-1] | |
4 else: | |
5 rem = num % 2 | |
6 rems.append(rem) | |
7 return decimal_to_binary(num // 2, rems) | |
8 | |
9 assert decimal_to_binary(193, []) == [1,1,0,0,0,0,0,1] | |
10 |
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# 7. Flatten a Nested List Structure ??? | |
- what is nested list structure | |
A nested structure is when we have a structure within a similar structure. | |
Eg: List[List[1,2,3], List[4,5,6]] is a nested structure. | |
Because there are lists within a list. i.e Similiar structure within it. | |
- what is flattening | |
Flattening is converting a nested structure to a not nested structure, that is collapsing a list of lists to a list | |
Eg: Converting List[List[1,2,3], List[4,5,6]] to List[1,2,3,4,5,6] | |
- how to Flatten a Nested List Structure ??? | |
We can do that by removing the nested ness of the strucure |
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<html> | |
<head><title>DAML Visualization</title><meta charset="utf-8"></head> | |
<body> | |
<div id='viz'></div> | |
<script> | |
!function(){function n(n){return n&&(n.ownerDocument||n.document||n).documentElement}function t(n){return n&&(n.ownerDocument&&n.ownerDocument.defaultView||n.document&&n||n.defaultView)}function e(n,t){return t>n?-1:n>t?1:n>=t?0:NaN}function r(n){return null===n?NaN:+n}function u(n){return!isNaN(n)}function i(n){return{left:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)<0?r=i+1:u=i}return r},right:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)>0?u=i:r=i+1}return r}}}function a(n){return n.length}function o(n){for(var t=1;n*t%1;)t*=10;return t}function l(n,t){for(var e in t)Object.defineProperty(n.prototype,e,{value:t[e],enumerable:!1})}function c(){this._=Object.create(null)}function s(n){return(n+="")===xa||n[0]===ba?ba+n:n}function f(n){return(n+="")[0]===ba?n.slice(1):n |
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object Test { | |
trait Monad[T] { | |
def flatMap[U](f: T => Monad[U]): Monad[U] | |
} | |
def unit[T](x: T): Monad[T] = ??? | |
// Associative => m.flatMap(f).flatMap(g) == m.flatMap(x => f(x).flatMap(g)) | |
// |
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snoc :: a -> [a] -> [a] | |
snoc x xs = xs ++ [x] | |
rr :: [a] -> [a] | |
rr = foldr (snoc) [] | |
rre :: [a] -> [a] | |
rre = foldr ( \ x xs -> xs ++ [x] ) [] -- why is this wrong | |
{- | |
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>>> import json | |
>>> json_str = """{ | |
... "COU": { | |
... "Data": "[{ Title: 'Sports Footwear', image: 'http://coupontown.cloudapp.net/CouponResource/images/Fashion/shoe.jpeg', Description: 'Below Rs.999, Sparx | Fila | Lotto', DiscountDetails: '10%-30%', Category: 'Fashion', CouponId: '1', StartDate: '8/12/2015 11:00:00 AM', Expiredate: '8/30/2015 11:59:00 PM', ExpiryInDay: '18:12:59'},{ Title: 'launching MI Mobile', image: 'http://coupontown.cloudapp.net/CouponResource/images/Electronics/onida-leoTV.jpeg', Description: 'Rs. 6,999, EMI from Rs. 340, 4.7 inch HD IPS Disp...4G LTE, Dual SIM, Camera: 8MP|2MP, 64-bit, Snapdragon 410', DiscountDetails: '10% Off', Category: 'Electronics', CouponId: '2', StartDate: '8/12/2015 11:00:00 AM', Expiredate: '8/25/2015 11:59:00 PM', ExpiryInDay: '13:12:59'},{ Title: 'Laptops with best deal', image: 'http://coupontown.cloudapp.net/CouponResource/images/Electronics/hp-notebook.jpeg', Description: 'Top Brand laptops in very less price', DiscountDetails: '20%-50%', Cat |
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