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)

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

# 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

% 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

@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

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

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

ahwillia

import numpy as np
import tensorflow as tf

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R)
C_true = np.random.randn(R,N)

ahwillia

import tensorflow as tf # works on version 1.0.0
import numpy as np
from tqdm import trange

# create fake data (low-rank matrix X)
A = np.random.randn(100, 3).astype(np.float32)
B = np.random.randn(3, 100).astype(np.float32)
X = np.dot(A, B)

# create tensorflow variables to predict low-rank decomposition

ahwillia

import numpy as np
import tensorflow as tf
from tqdm import tqdm

# N, size of matrix. R, rank of data
N = 100
R = 5

# generate data
W_true = np.random.randn(N,R).astype(np.float32)