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--- | |
# My First Slidify Deck | |
by Joao Felipe Santos | |
--- | |
### Slide 1 | |
This is an unordered list |
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<!DOCTYPE html> | |
<!-- | |
Google HTML5 slide template | |
Authors: Luke Mah?? (code) | |
Marcin Wichary (code and design) | |
Dominic Mazzoni (browser compatibility) | |
Charles Chen (ChromeVox support) |
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@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 |
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% 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 |
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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; |
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immutable Cdvec | |
val::Ptr{Cdouble} | |
len::Clong | |
end | |
a = [1.0, 2.0, 3.0] | |
b = [3.1, 4.2, 7.3] | |
Ca = Cdvec(pointer(a), length(a)) | |
Cb = Cdvec(pointer(b), length(b)) |
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!obj:pylearn2.train.Train { | |
dataset: &train !obj:research.code.pylearn2.datasets.timit.TIMIT { | |
which_set: 'train', | |
frame_length: &flen 160, | |
frames_per_example: &fpe 1, | |
samples_to_predict: &ylen 1, | |
n_next_phones: 1, | |
n_prev_phones: 1, | |
#start: 0, | |
#stop: 100, |
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using BinDeps | |
@BinDeps.setup | |
@unix_only begin | |
ecos = library_dependency("ecos",aliases=["libecos"]) | |
end | |
provides(Sources, URI("https://github.com/ifa-ethz/ecos/archive/master.zip"), | |
[ecos], os = :Unix, unpacked_dir="ecos-master") |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
import wave | |
import numpy as np | |
import scipy as sp | |
WINSIZE=8192 | |
sound='sound.wav' | |
def read_signal(filename, winsize): |
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