ririw

import pymc3 as pm
from pymc3.distributions.dist_math import bound, logpow, factln
from pymc3.distributions import draw_values, generate_samples
import theano.tensor as tt
import numpy as np
import scipy.stats.distributions

class ZTP(pm.Discrete):
    def __init__(self, mu, *args, **kwargs):
        super().__init__(*args, **kwargs)

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# An implementation of "Machine Learning on Sequential Data Using a Recurrent Weighted Average" using pytorch 
# https://arxiv.org/pdf/1703.01253.pdf
#
# 
# This is a RNN (recurrent neural network) type that uses a weighted average of values seen in the past, rather
# than a separate running state.
# 
# Check the test code at the bottom for an example of usage, where you can compare it's performance
# against LSTM and GRU, at a classification task from the paper. It handily beats both the LSTM and 
# GRU :)

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# Thanks Fast.AI
# Based on their apache-2.0 licensed script
#   https://github.com/fastai/courses/blob/master/setup/install-gpu.sh
# 

set -e
# This script is designed to work with ubuntu 16.04 LTS
# ensure system is updated and has basic build tools
sudo apt-get update
sudo apt-get --assume-yes upgrade

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#include "Arduino.h"
#include <Servo.h>

Servo myservo; 
void setup()
{
    myservo.attach(0);
    Serial.begin(9600);
}
void loop()

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## THIS IS WRONG, it'll return the wrong shape. I'll need
## to tweak it to make it work
#class Repeat(lasagne.layers.Layer):
#    def __init__(self, incoming, n, **kwargs):
#        super(Repeat, self).__init__(incoming, **kwargs)
#        self.n = n
#
#    def get_output_shape_for(self, input_shape):
#        return tuple([self.n] + list(input_shape))
#

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ghc -O2 -rtsopts -threaded -prof -fprof-auto -fforce-recomp reader.hs
 
time ./reader +RTS -K1G -sstderr -pa -A3M 

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ghc -O2 -rtsopts -threaded -prof -fprof-auto -fforce-recomp reader.hs  

time ./reader +RTS -K1G -sstderr -pa -A3M 

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def new_folder(folder_name, content_type, parentID, key=None, read_only=False, allow_children=True):
  if key is None:
    key = folder_name

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interface Functor {
	fmap: (any) => any;
}

interface Monad extends Functor {
	bind: (any) => Monad;
}

interface Maybe extends Monad {
}