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MKE Muslims August 25th, 2020

Ultimate Frisbee

Rules

1. At time of pull (kickoff) each team must be behind the first blue cone before your end-zone.
2. The marker (person guarding thrower) should count loud enough to 10 Mississippi (Fully enunciate) for nearby players to hear.  Count ends at "10" and not "10 Mississippi" (15.A.3). Anyone who isn’t guarding the thrower should not count on behalf of the marker. If a player who catches the frisbee down field and there is no marker within 10 feet of them, then no count should be started till the marker reaches 10 feet of them (3.Q.6).
3. Any contact to thrower before frisbee is released is a foul and should restart play. If throw is released, then it is fair game to block pass.
4. Marker should give 3 feet of space to thrower.  If the thrower pivots and hits marker or hit

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Regular Expression Engine Comparison Chart

Many different applications claim to support regular expressions. But what does that even mean?

Well there are lots of different regular expression engines, and they all have different feature sets and different time-space efficiencies.

The information here is just copied from: http://regular-expressions.mobi/refflavors.html

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System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

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function solve() {
    const limit = 15000;
    for (x = 1; x < limit; x++) {
        for (y = 1; y < limit; y++) {
            for (z = 1; z < limit; z++) {
                if (x*x*z + x*y*y + y*z*z === 17*x*y*z)
                    return { x, y, z };
            }
        }
    }

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{
    "swagger": "2.0",
    "info": {
        "description": "A RESTful API that provides aggegrate information on my private Google Calendars.",
        "version": "1.0.0",
        "title": "Google Calendar Analytics API",
        "license": {
            "name": "MIT",
            "url": "https://choosealicense.com/licenses/mit/"
        }

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import * as express from 'express';
import * as bodyParser from 'body-parser';
import * as swaggerUi from 'swagger-ui-express';
import * as swaggerDoc from '~/../swagger.json';
import routes from './routes';

const app = express();
app.use(bodyParser.json());
app.use(bodyParser.urlencoded({extended: false}));
app.use('/docs', swaggerUi.serve, swaggerUi.setup(swaggerDoc));

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function [model] = svmTrain(X, Y, C, kernelFunction, ...
                            tol, max_passes)
%SVMTRAIN Trains an SVM classifier using a simplified version of the SMO 
%algorithm. 
%   [model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an
%   SVM classifier and returns trained model. X is the matrix of training 
%   examples.  Each row is a training example, and the jth column holds the 
%   jth feature.  Y is a column matrix containing 1 for positive examples 
%   and 0 for negative examples.  C is the standard SVM regularization 
%   parameter.  tol is a tolerance value used for determining equality of 

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function pred = svmPredict(model, X)
%SVMPREDICT returns a vector of predictions using a trained SVM model
%(svmTrain). 
%   pred = SVMPREDICT(model, X) returns a vector of predictions using a 
%   trained SVM model (svmTrain). X is a mxn matrix where there each 
%   example is a row. model is a svm model returned from svmTrain.
%   predictions pred is a m x 1 column of predictions of {0, 1} values.
%

% Check if we are getting a column vector, if so, then assume that we only