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# Tony Poerio adpoe

🎯
makin' the codes.
• Pittsburgh
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
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
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
Created Mar 23, 2016
LCG Example
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
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:
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
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)
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);
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
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