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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) |
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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() |
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import base64 | |
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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 |
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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 |
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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 | |
} |
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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)) |
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% 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 |
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