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makin' the codes.

Tony Poerio adpoe

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makin' the codes.
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@adpoe
adpoe / sim_queue.py
Created Mar 14, 2016
A Queue Class for Python Simulation
View sim_queue.py
##########################
#### CHECK-IN QUEUES #####
##########################
class CheckInQueue:
""" Class used to model a check-in line Queue
"""
def __init__(self):
self.queue = q.Queue()
self.customers_added = 0
@adpoe
adpoe / simulation_server.py
Created Mar 14, 2016
Sample Serve for Python Simulation
View simulation_server.py
###########################
##### AIRPORT SERVERS #####
###########################
class CheckInServer:
""" Class used to model a server at the Check-in terminal
"""
def __init__(self):
""" Initialize the class variables
"""
self.service_time = 0.0
@adpoe
adpoe / carry-on.py
Created Mar 14, 2016
Geometric Distribution for Carry On Items in Airport Simulation
View carry-on.py
def gen_number_of_carry_on_items__for_COMMUTER_passenger():
"""
Number of bags a passenger carries is determined using a GEOMETRIC DISTIBUTION
BERNOULLI TRIAL with success bias %p = chance of passenger bringing bags
Bernoulli with 80% chance
P = 0.80 for international
:return: Number of bags a commuter passenger has carried on
"""
# Count number of iterations until a success
View lcg_example.py
def generate_lcg( num_iterations ):
"""
LCG - generates as many random numbers as requested by user, using a Linear Congruential Generator
LCG uses the formula: X_(i+1) = (aX_i + c) mod m
:param num_iterations: int - the number of random numbers requested
:return: void
"""
# Initialize variables
@adpoe
adpoe / std_normal.py
Created Mar 25, 2016
standard normal random variates
View std_normal.py
# PROCEDURE, From ROSS: Simulation (5th Edition) Page 78
# Step 1: Generate Y1, an exponential random variable with rate 1
Y1 = gen_exponential_distro_rand_variable()
# Step 2: Generate Y2, an exponential random variable with rate 2
Y2 = gen_exponential_distro_rand_variable()
# Step 3: If Y2 - (Y1 - 1)^2/2 > 0, set Y = Y2 - (Y1 - 1)^2/2, and go to Step 4 (accept)
#         Otherwise, go to Step 1 (reject)
subtraction_value = ( math.pow( ( Y1 - 1 ), 2 ) ) / 2
critical_value = Y2 - subtraction_value
if critical_value > 0:
@adpoe
adpoe / quicksort.hs
Created Jun 16, 2016
Very simple Quicksort Implementation from "Learn You a Haskell For Great Good!"
View quicksort.hs
quicksort :: (Ord a) => [a] -> [a]
quicksort [] = []
quicksort (x:xs) =
let smallerSorted = quicksort [a | a <- xs, a <= x]
biggerSorted = quicksort [a | a <- xs, a > x]
in smallerSorted ++ [x] ++ biggerSorted
@adpoe
adpoe / binaryTree.hs
Created Jun 16, 2016
Binary Tree in Haskell
View binaryTree.hs
data BinaryTree a =
Leaf
| Node (BinaryTree a) a (BinaryTree a)
deriving (Eq, Ord, Show)
@adpoe
adpoe / energy.m
Created Sep 27, 2016
Energy Function - Matlab
View energy.m
function [ energy_matrix ] = energy_image( image_matrix_input )
%ENERGY_IMAGE Computes the energy at each pixel in a matrix nxmx3 matrix
% Outputs a 2D-matrix containing energy equation outputs, of datatype DBL
% convert image to grayscale first
G = rgb2gray(image_matrix_input);
% convert to double
G2 = im2double(G);
@adpoe
adpoe / flappy_states.py
Created Dec 15, 2016
State Representations for a Flappy Bird AI
View flappy_states.py
# first value in state tuple
height_category = 0
dist_to_pipe_bottom = pipe_bottom - bird.y
if dist_to_pipe_bottom < 8: # very close
height_category = 0
elif dist_to_pipe_bottom < 20: # close
height_category = 1
elif dist_to_pipe_bottom < 125: #mid
height_category = 2
elif dist_to_pipe_bottom < 250: # far
@adpoe
adpoe / uni_gd.py
Created Dec 16, 2016
Univariate Gradient Descent, in Python
View uni_gd.py
def gradient_descent(training_examples, alpha=0.01):
"""
Apply gradient descent on the training examples to learn a line that fits through the examples
:param examples: set of all examples in (x,y) format
:param alpha = learning rate
:return:
"""
# initialize w0 and w1 to some small value, here just using 0 for simplicity
w0 = 0
w1 = 0