AlexanderFabisch

# Get the dataset here: http://www.gaussianprocess.org/gpml/data/
import scipy.io

# Load training set
train = scipy.io.loadmat("sarcos_inv.mat")
# Inputs  (7 joint positions, 7 joint velocities, 7 joint accelerations)
Xtrain = train["sarcos_inv"][:, :21]
# Outputs (7 joint torques)
Ytrain = train["sarcos_inv"][:, 21:]

AlexanderFabisch

from numpy import *
from pylab import *
import numpy
from sklearn.mixture import GMM
from mpl_toolkits.mplot3d import axes3d

numpy.random.seed(0)

X = arange(0, 2*numpy.pi, 0.01)
N = len(X)

AlexanderFabisch

import numpy
import pylab

steps = 100
zeros = numpy.zeros(steps)
ideal = numpy.linspace(0, 1, steps)

pylab.figure()
pylab.xlabel("Training Error")
pylab.ylabel("Test Error")

AlexanderFabisch

reset
set pm3d
set hidden3d
set isosample 100, 100
unset surface
unset xtics
unset ytics
unset ztics
unset border
unset key

AlexanderFabisch

max_hits = 3

puts "HA"
dps_ha = 1.15 * (0...max_hits).map do |i| 0.35**i end.reduce(:+)
puts sprintf("%.3f", dps_ha)
puts ""

puts "PA"
dps_pa = 1.15 * (0...max_hits).map do |i| 0.5**i end.reduce(:+)
puts sprintf("%.3f", dps_pa)

AlexanderFabisch

require 'rubygems'
require 'diablo3-api-client'

# TODO
# fury / barb attributes
# block
# edp only for dh
# efp for barbs?
# monks
# wizards

AlexanderFabisch

Comparison of Julia, Python and Octave

Overview

Julia is a new languange for technical computing. It is a mix of R, Matlab, Python and other similar languages. Its main advantage is its speed: it is just in time (JIT) compiled and almost as fast as C. Another advantage is its type inference, i.e. you do not have to specify types (although you can), but all variables are statically typed. It is a high level language that is fast as well. Here I compare the behavior to similar

AlexanderFabisch

from openann import *
import numpy

if __name__ == "__main__":
    # Create dataset
    X = numpy.array([[0, 1], [0, 0], [1, 1], [1, 0]])
    Y = numpy.array([[1], [0], [0], [1]])
    D = X.shape[1]
    F = Y.shape[1]
    N = X.shape[0]

AlexanderFabisch

import numpy

def sq_exp(x1, x2, w, sigma_f):
    #return sigma_f**2 * numpy.exp(-(numpy.abs(x1-x2)/w).sum()) # Ornstein-Uhlenbeck
    return sigma_f**2 * numpy.exp(-0.5*((x1-x2)**2/w**2).sum())

class GPR(object):
    def __init__(self, w, sigma_f, sigma_n):
        self.w = w
        self.sigma_f = sigma_f

AlexanderFabisch

Working with git

Complex git operations and commands that I always forget...

• Remove Submodule
• Subtree Merging
• Delete a Remote Branch
• Change Timestamp of the Last Commit
• Squash Commits