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function [cmpImg] = svdCompress(img,k)

  % Obtains SVD
  [u,s,v] = svd(img);

  % Vectorized sum of k rank one matrices
  cmpImg = u(:,1:k)*s(1:k,1:k)*v’(1:k,:);

end;

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function [Q,R]=BandQR(A, p)
    [n,n]=size(A);
    R=A; %Start with R=A
    Q=eye(n); %Set Q as the identity matrix
   
    
    for k=1:n-1

        x = zeros(p+1,1);
        j=min(n, k+p-1);

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from chainer.links import caffe

func = caffe.CaffeFunction("../bvlc_alexnet.caffemodel")

# construct a correctly sized chainer variable 
x = chainer.Variable(...)

# outputs is a list of layers to extract and it will return
# each layer seperatly
fc7, = func(inputs={"data":x}, outputs=["fc7"])

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library(pROC)

df = read.csv("...")

## 75% of the sample size
set.seed(123)

smp_size <- floor(0.75 * nrow(df))
train_ind <- sample(seq_len(nrow(df)), size = smp_size)

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from __future__ import division

from tqdm import *

import chainer
from chainer import functions as F
from chainer import links as L

import numpy as np

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from itertools import product
from itertools import permutations
from random import sample

cards = filter(
    lambda _: all(
        [
            # look at all unique values of each property and see if there is 1 or 3
            len(set([a[i][1] for a in _])) in {1,3} for i in range(4)
        ]

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\usepackage{listings}
\usepackage[numbered,framed]{matlab-prettifier}
\usepackage{color}
\usepackage{geometry}

\geometry{verbose,tmargin=1.0in,bmargin=1.0in,lmargin=0.5in,rmargin=0.5in}

\definecolor{dkgreen}{rgb}{0,0.6,0}
\definecolor{gray}{rgb}{0.5,0.5,0.5}
\definecolor{mauve}{rgb}{0.58,0,0.82}

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package learning

object LinearAlgebra {
  
  class Row(r: Double*) extends Seq[Double] {
    val row: List[Double] = r.toList
    val length = row.length
    
    def * (that: Row): Double = {
      require(that.length == length, "Sizes don't align")