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# Tony Poerio adpoe

🎯
makin' the codes.
• Pittsburgh
Last active Dec 24, 2016
SVM and SIFT Nature Conservatory Kaggle Entry
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Last active Dec 24, 2016
A Quick Start Guide for Using Keras in the Nature Conservatory Kaggle
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Created Dec 16, 2016
Multivariate Gradient Descent in Python
View mv_grad_desc.py
 def multivariate_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 the weight and x_vectors W = [0 for index in range(0, len(training_examples))]
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
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 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 Jun 16, 2016
Haskell Binary Tree Traversals
View treeTraversals.hs
 -- Do Tree Traversals and Built a Visitation List for each preorder :: BinaryTree a -> [a] preorder Leaf = [] preorder (Node left root right) = root : preorder left ++ preorder right -- NOTE: Need to use the ++ so each list gets built separately and then concatenated -- after it hits bottom inorder :: BinaryTree a -> [a] inorder Leaf = []
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 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 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: