allanino

FROM allanino/nupic

# Clone Cerebro repository 
RUN git clone https://github.com/numenta/nupic.cerebro.git /usr/local/src/nupic.cerebro

# Install dependencies
# Install Mongo
RUN \
    apt-get install -y libevent-dev;\
    apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 7F0CEB10;\

allanino

First of all, the major change was in the way to use the anomaly scores. Clearly we can see from the monitor's plots that anomalies do occur, but we don't use it in a pratical manner. The threshold line at 0.6 anomaly is there for visual purposes only, as it would catch too many events if we used it as an actual threshold. So a way to look at it is to seek patterns within the anomalies scores. The first thing I though was to use the frequency of anomalies beyond the threshold as a metric, as it is more probable that something is wrong if we get a lot of anomalous patterns in a short period of time. But the key here is the word probable, so why not use a probability distribution to estimate the likelihood of something really anomalous? The interesting thing is that the people at Grok also realized that the raw anomaly score is not a very good metric, as we can see in this excelent talk by Grok's engineer Subutai. As they released

allanino

FROM ubuntu:14.04

RUN apt-get update

# Install curl (for downloading pip) and other dependencies
RUN apt-get install -y curl clang cmake git python2.7-dev python2.7 python-numpy

# Install pip
RUN curl --silent --show-error --retry 5 https://bootstrap.pypa.io/get-pip.py | sudo python2.7

allanino

git fetch origin pull/1234/head:pr-1234

Here: 1234 is the PR number. pr-1234 is your local branch name.

allanino

def character(i)
    return i < 10 ? (i + 48).chr : (i + 87).chr
end

def generate(n)
    i1 = n % 36
    i3 = n/(36*36)
    i2 = n/36 - i3*36
    return "#{character(i3)}#{character(i2)}#{character(i1)}"
end

allanino

#include "stdio.h"

// Compute the solution x to x*n % m == 1 using the Generalized Euclidean Agorithm
int inverse(int n, int m){
	int t0 = 0, t1 = 1;
	int s0 = 1, s1 = 0;
	int r = m - 1; // Just to get started
	int a = m;
	int b = n;
	int q, s, t;

allanino

#!/bin/bash

wget -qO- http://ipecho.net/plain ; echo

allanino

"""
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):

allanino

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
from keras.utils import np_utils

# Create a random matrix with 1000 rows (data points) and 15 columns (features)
train_rows = 1000
X_train = np.random.rand(train_rows, 15)

allanino

#!/bin/bash

# Run this
docker run -it -p 8888:8888 b.gcr.io/tensorflow/tensorflow-full /run_jupyter.sh --notebook-dir=/tensorflow/tensorflow/tools/docker/notebooks/

# Go to http://localhost:8888