ahwillia

using Optim

# low-dimensional data embedded in high-dimension
data = randn(100,5)*randn(5,100);

# a container for the parameters we fit
immutable PCA{T<:Real}
    X::Matrix{T}
    Y::Matrix{T}
end

ahwillia

@userplot DTWPlot

@recipe function f(h::DTWPlot)
    seq1, seq2 = h.args
    i1,i2 = collect(1:length(seq1)),collect(1:length(seq2))
    
    # set up the subplots
    seriestype := :line
    legend --> false
    link := :both

ahwillia

% quad %
\[ x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a} \]

% optimization %
\usepackage{amsmath}
\begin{equation*}
\begin{aligned}
& \underset{x}{\text{minimize}}
& & f_0(x) \\
& \text{subject to}

ahwillia

# Majority of credit goes to Chris Holdgraf, @choldgraf, and this StackOverflow
# post: http://stackoverflow.com/questions/5320205/matplotlib-text-dimensions

import pylab as plt
import numpy as np

def plot_equation(eq, fontsize=50, outfile=None, padding=0.1, **kwargs):
    """Plot an equation as a matplotlib figure.
    Parameters
    ----------

ahwillia

using PyPlot
using Distributions

function credible_interval(D::UnivariateDistribution; c=0.95, nx=1000)
	# Discretize over the support
	r = support(D)
	lb,ub = r.lb,r.ub

	# Histogram approximation of area under pdf
	x = linspace(lb,ub,nx)

ahwillia

# Thanks to Joe Kington
#   http://stackoverflow.com/questions/7941207/is-there-a-function-to-make-scatterplot-matrices-in-matplotlib

using PyPlot

function pairs(data)
  (nobs, nvars) = size(data)
  (fig, ax) = subplots(nvars, nvars, figsize=(8,8))
  subplots_adjust(hspace=0.05, wspace=0.05)