João Felipe Santos jfsantos

## gist:c0f3f4cd5c76dfc5f1ba8310a821c2d5
Agility

&{template:default} {{name=@{selected|character_name}}}{{Agility roll=[[1d20 + @{selected|agility_mod} + [[?{# Boons|0} - ?{# Banes|0}]]d6k1]]}}

Intellect

&{template:default} {{name=@{selected|character_name}}}{{Intellect roll=[[1d20 + @{selected|intellect_mod} + [[?{# Boons|0} - ?{# Banes|0}]]d6k1]]}}

Perception

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

## model_issues.py
from __future__ import print_function

from keras.models import Model, Sequential
from keras.layers import Input, Dense, TimeDistributed
from keras.layers.core import Reshape, Flatten, Dropout, TimeDistributedDense
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D
from keras.layers.recurrent import LSTM
from keras.optimizers import Adam

## juliabench.jl
function groupby{T}(fn::Function, seq::Array{T})
    dict = Dict{Any, Array{T}}();
    for item in seq
        key = fn(item);
        if !haskey(dict, key)
            dict[key] = [];
        end
        push!(dict[key], item);
    end
    return dict;

## gist:0c3b19fb23ba680dbf3e
model = nn.Sequential()

lstm = nn.Sequencer(
  nn.Sequential()
  :add(nn.LSTM(nFeatures,nHidden))
  :add(nn.Dropout())
  :add(nn.LSTM(nHidden,nHidden))
)

lstm:remember('neither') -- force model to call forget at each call to forward

## istft.lua
signal = require "signal"
complex = require "signal.complex"

function istft(X, win, hop)
    local x = torch.zeros((X:size(1)-1)*hop + win)
    framesamp = X:size(2)
    hopsamp = hop
    for n=1,X:size(1) do
        i = 1 + (n-1)*hopsamp
        print(i, i + framesamp - 1)

## gist:5091135
% shepardtones.m - plays the shepard tones
% based on an example I found here: http://hebb.mit.edu/courses/9.29/2003/athena/auditory/matlab/shepardtones.m

% adjustable parameters
max_time = 2; % time for each tone
max_repeat = 3; % number of loops
A = 440;     % "base frequency"
num_harm = 7; % number of harmonics used for each tone
sigmasq = 1; % variance of the amplitude for each partial

## error.log
creating an lstm with 2 layers
setting forget gate biases to 1 in LSTM layer 1
setting forget gate biases to 1 in LSTM layer 2
number of parameters in the model: 240321
cloning rnn
cloning criterion
/Users/jfsantos/torch/install/bin/luajit: /Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: bad argument #1 to 'set' (expecting number or Tensor or Storage at /tmp/luarocks_torch-scm-1-8315/torch7/generic/Tensor.c:1089)
stack traceback:
	[C]: in function 'set'
	/Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: in function 'func'

## presentation.bib
@article{Lord1994,
abstract = {Describes the Autism Diagnostic Interview-Revised (ADI-R), a revision of the Autism Diagnostic Interview, a semistructured, investigator-based interview for caregivers of children and adults for whom autism or pervasive developmental disorders is a possible diagnosis. The revised interview has been reorganized, shortened, modified to be appropriate for children with mental ages from about 18 months into adulthood and linked to ICD-10 and DSM-IV criteria. Psychometric data are presented for a sample of preschool children.},
author = {Lord, C and Rutter, M and {Le Couteur}, A},
issn = {0162-3257},
journal = {Journal of autism and developmental disorders},
keywords = {Algorithms,Autistic Disorder,Autistic Disorder: classification,Autistic Disorder: diagnosis,Autistic Disorder: psychology,Caregivers,Caregivers: psychology,Child,Female,Humans,Intellectual Disability,Intellectual Disability: classification,Intellectual Disability: diagnosis,Intellectual Disability: psychology,Intervie

## index.html
<!DOCTYPE html>
	<!--
	Google HTML5 slide template

Authors: Luke Mah?? (code)
Marcin Wichary (code and design)

Dominic Mazzoni (browser compatibility)
Charles Chen (ChromeVox support)
	Agility

	&{template:default} {{name=@{selected\|character_name}}}{{Agility roll=[[1d20 + @{selected\|agility_mod} + [[?{# Boons\|0} - ?{# Banes\|0}]]d6k1]]}}

	Intellect

	&{template:default} {{name=@{selected\|character_name}}}{{Intellect roll=[[1d20 + @{selected\|intellect_mod} + [[?{# Boons\|0} - ?{# Banes\|0}]]d6k1]]}}

	Perception
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console
	from __future__ import print_function

	from keras.models import Model, Sequential
	from keras.layers import Input, Dense, TimeDistributed
	from keras.layers.core import Reshape, Flatten, Dropout, TimeDistributedDense
	from keras.layers.advanced_activations import LeakyReLU
	from keras.layers.normalization import BatchNormalization
	from keras.layers.convolutional import Convolution2D
	from keras.layers.recurrent import LSTM
	from keras.optimizers import Adam
	function groupby{T}(fn::Function, seq::Array{T})
	dict = Dict{Any, Array{T}}();
	for item in seq
	key = fn(item);
	if !haskey(dict, key)
	dict[key] = [];
	end
	push!(dict[key], item);
	end
	return dict;
	model = nn.Sequential()

	lstm = nn.Sequencer(
	nn.Sequential()
	:add(nn.LSTM(nFeatures,nHidden))
	:add(nn.Dropout())
	:add(nn.LSTM(nHidden,nHidden))
	)

	lstm:remember('neither') -- force model to call forget at each call to forward
	signal = require "signal"
	complex = require "signal.complex"

	function istft(X, win, hop)
	local x = torch.zeros((X:size(1)-1)*hop + win)
	framesamp = X:size(2)
	hopsamp = hop
	for n=1,X:size(1) do
	i = 1 + (n-1)*hopsamp
	print(i, i + framesamp - 1)
	% shepardtones.m - plays the shepard tones
	% based on an example I found here: http://hebb.mit.edu/courses/9.29/2003/athena/auditory/matlab/shepardtones.m

	% adjustable parameters
	max_time = 2; % time for each tone
	max_repeat = 3; % number of loops
	A = 440; % "base frequency"
	num_harm = 7; % number of harmonics used for each tone
	sigmasq = 1; % variance of the amplitude for each partial
	creating an lstm with 2 layers
	setting forget gate biases to 1 in LSTM layer 1
	setting forget gate biases to 1 in LSTM layer 2
	number of parameters in the model: 240321
	cloning rnn
	cloning criterion
	/Users/jfsantos/torch/install/bin/luajit: /Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: bad argument #1 to 'set' (expecting number or Tensor or Storage at /tmp/luarocks_torch-scm-1-8315/torch7/generic/Tensor.c:1089)
	stack traceback:
	[C]: in function 'set'
	/Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: in function 'func'
	@article{Lord1994,
	abstract = {Describes the Autism Diagnostic Interview-Revised (ADI-R), a revision of the Autism Diagnostic Interview, a semistructured, investigator-based interview for caregivers of children and adults for whom autism or pervasive developmental disorders is a possible diagnosis. The revised interview has been reorganized, shortened, modified to be appropriate for children with mental ages from about 18 months into adulthood and linked to ICD-10 and DSM-IV criteria. Psychometric data are presented for a sample of preschool children.},
	author = {Lord, C and Rutter, M and {Le Couteur}, A},
	issn = {0162-3257},
	journal = {Journal of autism and developmental disorders},
	keywords = {Algorithms,Autistic Disorder,Autistic Disorder: classification,Autistic Disorder: diagnosis,Autistic Disorder: psychology,Caregivers,Caregivers: psychology,Child,Female,Humans,Intellectual Disability,Intellectual Disability: classification,Intellectual Disability: diagnosis,Intellectual Disability: psychology,Intervie
	<!DOCTYPE html>
	<!--
	Google HTML5 slide template

	Authors: Luke Mah?? (code)
	Marcin Wichary (code and design)

	Dominic Mazzoni (browser compatibility)
	Charles Chen (ChromeVox support)