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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

class MLP(nn.Module):

    def __init__(self, input_size, feature_categories):
        super(MLP, self).__init__()
        self.feature_categories = feature_categories

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import torch
import torch.nn as nn
from torch.autograd import Variable

affine = nn.Linear(10, 10)
# A linear mapping to a random vector... just for quick demo purposes
x = Variable(torch.randn(100, 10))
y = Variable(torch.randn(100, 10))

weird_loss = torch.mean(torch.exp(affine.weight))

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import numpy as np
import copy

# NOTE: a = 1 is a(-), a = 0 is a(+)
gamma = 0.9
action_space = 2
state_space = 4
eps = 1e-9

# Action conditioned reward function

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import numpy as np
import os

class WSJ():
    """ Load the WSJ speech dataset
        
        Ensure WSJ_PATH is path to directory containing 
        all data files (.npy) provided on Kaggle.
        
        Example usage:

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#!/bin/bash

cd ~

apt-get install git

# setup dotfiles
rm -rf .vim*
git clone https://github.com/rbrigden/dotfiles.git .dotfiles
ln -s .dotfiles/.vimrc .vimrc

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import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import random

class TenArmedBandit(object):
    
    def __init__(self):
        self.action_space = 10
        self.q_true = np.random.randn(self.action_space)  

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from __future__ import division, print_function, absolute_import

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=False)

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HW0 of 11-364

Objective

The original assignment set forth instructed students to implement a feedforward artificial neural network (ANN) in a relatively low level language or framework. Although higher level scripting languages such as Python and Lua have wrapped heavily optimized libraries that perform the same functions, the goal of this assignment is to truly understand the theoretical underpinnings of feedforward neural networks by writing the routines from scratch (almost).

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#!/bin/python
# author: Ryan Brigden

# The "brute" in brute force
# Question: Given a (large) list of words and a list of top-level domains (TLDs)
# from the Internet Assigned Numbers Authority (IANA), such as ".com" and ".net",
# find all of the possible "singleton" domains that can be registered with words
# from the word list. A singleton domain is defined as a sensical word (ie from
# the word list) whose suffix is a legitimate TLD (ie from the TLD list). You are
# given a function (is_available) that checks whether a given domain name is

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