SalemAmeen

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

SalemAmeen

import numpy as np
from matplotlib import pylab as plt
#from mpltools import style # uncomment for prettier plots
#style.use(['ggplot'])

'''
function definitions
'''
# generate all bernoulli rewards ahead of time
def generate_bernoulli_bandit_data(num_samples,K):

SalemAmeen

#!/usr/bin/env bash
# Test script for checking if Cuda and Drivers correctly installed on Ubuntu 14.04, by Roelof Pieters (@graphific)
# BSD License

if [ "$(whoami)" == "root" ]; then
  echo "running as root, please run as user you want to have stuff installed as"
  exit 1
fi
###################################
#   Ubuntu 14.04 Install script for:

SalemAmeen

from numpy import *
from scipy.stats import beta
import random


class BetaBandit(object):
    def __init__(self, num_options=2, prior=None):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options

SalemAmeen

--[[ A Confusion Matrix class

Example:

    conf = optim.ConfusionMatrix( {'cat','dog','person'} )   -- new matrix
    conf:zero()                                              -- reset matrix
    for i = 1,N do
        conf:add( neuralnet:forward(sample), label )         -- accumulate errors
    end
    print(conf)                                              -- print matrix

SalemAmeen

require 'nn'

-- you just need to provide the linear module you want to convert,
-- and the dimensions of the field of view of the linear layer
function convertLinear2Conv1x1(linmodule,in_size)
   local s_in = linmodule.weight:size(2)/(in_size[1]*in_size[2])
   local s_out = linmodule.weight:size(1)
   local convmodule = nn.SpatialConvolutionMM(s_in,s_out,in_size[1],in_size[2],1,1)
   convmodule.weight:copy(linmodule.weight)
   convmodule.bias:copy(linmodule.bias)

SalemAmeen

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):