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fibs = {0: 0, 1: 1} | |
def fib(n): | |
if n in fibs: return fibs[n] | |
if n % 2 == 0: | |
fibs[n] = ((2 * fib((n / 2) - 1)) + fib(n / 2)) * fib(n / 2) | |
else: | |
fibs[n] = (fib((n - 1) / 2) ** 2) + (fib((n + 1) / 2) ** 2) | |
return fibs[n] | |
fib(10000000) |
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//FightCode can only understand your robot | |
//if its class is called Robot | |
var Robot = function(robot) { | |
}; | |
Robot.prototype.onIdle = function(ev) { | |
var robot = ev.robot; | |
robot.ahead(Math.random(100)*100); |
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var robots = new Array(); | |
//FightCode can only understand your robot | |
//if its class is called Robot | |
var Robot = function(robot) { | |
}; | |
Robot.prototype.start = function( ev ){ |
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function Robot(robot) {} | |
var direcao = 1; | |
var distancia = 2050; | |
var rotate_angulo = 360; | |
var encontrou = 0; | |
var target = 1; | |
// well, we need to do something... | |
// whenever our robot is idle, this method gets called. |
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var Robot = function(robot) { | |
robot.clone(); | |
}; | |
Robot.prototype.onIdle = function(ev) { | |
ev.robot.back(30); | |
robot.rotateCannon(180); | |
}; |
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var x_target = -1; | |
var y_target = -1; | |
var target_valid = false; | |
var idle_counter = 0; | |
var time_frame = 0; | |
var last_reverse = -10000; | |
var last_turn = -10000; | |
var at_wall = false; | |
var clockwise = true; |
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<link rel="import" href="../components/polymer/polymer.html"> | |
<polymer-element name="my-element"> | |
<template> | |
<style> | |
:host { | |
position: absolute; | |
width: 100%; | |
height: 100%; |
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We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
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Data;D1;D2;D3;D4;D5;D6;Arrecadacao_Total;Sena;Rateio_Sena;Quina;Rateio_Quina;Quadra;Rateio_Quadra;Acumulado;Valor_Acumulado | |
11/03/1996;4;5;30;33;41;52;0;0;0;17;39158.92;2016;330.21;SIM;1714650.23 | |
18/03/1996;9;37;39;41;43;49;0;1;2307162.23;65;14424.02;4488;208.91;NÃO;0 | |
25/03/1996;10;11;29;30;36;47;0;2;391192.51;62;10515.93;4261;153.01;NÃO;0 | |
01/04/1996;1;5;6;27;42;59;0;0;0;39;15322.24;3311;180.48;SIM;717080.75 | |
08/04/1996;1;2;6;16;19;46;0;0;0;98;5318.10;5399;96.53;SIM;1342488.85 | |
15/04/1996;7;13;19;22;40;47;0;0;0;109;7214.66;7147;110.03;SIM;2286166.33 | |
22/04/1996;3;5;20;21;38;56;0;0;0;100;8746.05;5736;152.48;SIM;3335692.28 | |
29/04/1996;4;17;37;38;47;53;0;0;0;60;16084.11;5262;183.4;SIM;4493748.19 | |
06/05/1996;8;43;54;55;56;60;0;0;0;17;60043.79;2175;469.31;SIM;5718641.49 |
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from sklearn import svm | |
X = mega_results[['D1','D2','D3','D4','D5','D6']] | |
y = mega_results['Sena'] | |
clf = svm.SVC() | |
clf.fit(X, y) | |
import random |
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