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//-->fill(3,5)
// ans  =
//    1.    3.    4.     9.     10.  
//    2.    5.    8.     11.    14.  
//    6.    7.    12.    13.    15.  

function [x] = fill(N, M)
	x = zeros(N, M)
	Z = N + M - 1
	i = 1

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//-->adiag([1 2 3 4; 5 6 7 8; 9 10 11 12],3)'
// ans  =
//    9.    6.    3.  

function [v] = adiag(x, z)
	// 2D matrix antidiagonal

	[N, M] = size(x)

	// The total diagonals count

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function [y_out, m, sig] = ssa_normalize(y)
	// http://en.wikipedia.org/wiki/Student%27s_t-statistic
	m = mean(y)
	sig = sqrt(variance(y))	// stdev(y)
	y_out = (y - m) / sig
endfunction
 
function [y_out] = ssa(y, L, I)
	[LAMBDA, U, V] = ssa_decompose(y, L)
	y_out = ssa_reconstruct(LAMBDA, U, V, I)

werediver

from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as pp


def xcorr(a, b, mode='same'):
    a = np.asarray(a)
    b = np.asarray(b)
    
    a = a - a.mean()

werediver

import base64

code="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

werediver

module WingAR where

import Data.Maybe
import Text.Printf

-- See http://en.wikipedia.org/wiki/Aspect_ratio_%28wing%29
wing_ar :: Float -> Float -> Float
wing_ar l s = l^2 / s

readMaybe s =  listToMaybe $ fmap fst $ reads s

werediver

import UIKit

class BaseSimpleDataSource<Cell: ReusableView, Collection: CollectionType
    where Collection.Index == Int,
          Collection.Generator.Element: CollectionType,
          Collection.Generator.Element.Index == Int,
          Collection.Generator.Element.Generator.Element == Cell.Model>: NSObject {

    typealias Item = Cell.Model

werediver

import UIKit
import PureLayout

extension UINib {

    func instantiateWithOwner<T: AnyObject>(owner: AnyObject?, options: [NSObject : AnyObject]? = nil, type: T.Type) -> T {
        let objs = self.instantiateWithOwner(owner, options: options)
        return objs.findFirst { $0 is T } as! T
    }

werediver

from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as pp
%matplotlib inline

def xcorr(a, b, mode='same'):
    a = np.asarray(a)
    b = np.asarray(b)
    
    a = (a - a.mean()) / np.sqrt(np.correlate(a, a))

werediver

% Force GNU Octave to interpret this file as a script
1;

function [y_out, m, sig] = ssa_normalize(y)
	% http://en.wikipedia.org/wiki/Student%27s_t-statistic
	m = mean(y);
	sig = sqrt(variance(y))	% stdev(y);
	y_out = (y - m) / sig;
endfunction