View svm_and_sift.ipynb
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View keras_for_nature_conservatory_kaggle.ipynb
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View mv_grad_desc.py
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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[0][0]))] |
View uni_gd.py
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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 |
View flappy_states.py
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# 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 |
View energy.m
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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); |
View treeTraversals.hs
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-- 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 = [] |
View binaryTree.hs
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data BinaryTree a = | |
Leaf | |
| Node (BinaryTree a) a (BinaryTree a) | |
deriving (Eq, Ord, Show) |
View quicksort.hs
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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 |
View std_normal.py
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# 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: |
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