# Module util

source code

Supplementary or Auxiliary Functions for Snobfit

Most of them are the functions of the Rough MATLAB-Numpy Equivalents.

Functions

 rand(d0, d1, dn, ...) Random values in a given shape. source code

 within(low, x, high) source code

 feval(funcName, *args) source code

 find(*args, **kw) source code

 toCol(v) v is a vector source code

 toRow(m) m is a n x 1 matrix source code

 mldiv(a, b) Backslash or left matrix divide. source code

 chol(a) a is a mat source code

 svd(a) a is a mat source code

 qr(a) source code

 vector(n) Return a vector of the given length. source code

 ivector(n=0) Return a int vector of the given length. source code

 sort(x) source code

 std(x) STD(X) normalizes by (N-1) where N is the sequence length. source code

 max_(x) [Y,I] = MAX(X) returns the indices of the maximum values in vector I. source code

 min_(x) [Y,I] = MIN(X) returns the index of the minimum value in vector I. source code

 toInt(x) source code

 isEmpty(x) source code

 removeByInd(a, ind) source code

 duplicate(v, n, axis=0) source code

 dup(v, n, axis=0) source code

 dot3(a, b, c) source code

 crdot(m, v) source code

 rsort(x, w=None) Sort x in increasing order, remove multiple entries, and adapt weights w accordingly x and w must both be a row or a column default input weights are w=1 source code
 Variables eps = `2.2204460492503131e-16` inf = `inf` nan = `nan` isnan = ``
 Function Details

### rand(d0, d1, dn, ...)

source code

Random values in a given shape.

Create an array of the given shape and propagate it with random samples from a uniform distribution over [0, 1).

# Parameters

d0, d1, ..., dn : int
Shape of the output.

# Returns

out : ndarray, shape (d0, d1, ..., dn)
Random values.

random

# Notes

This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to `random`.

# Examples

```>>> np.random.rand(3,2)
array([[ 0.14022471,  0.96360618],  #random
[ 0.37601032,  0.25528411],  #random
[ 0.49313049,  0.94909878]]) #random```

### mldiv(a, b)

source code
Backslash or left matrix divide. a is the matrix division of A into B, which is roughly the same as INV(A)*B

### std(x)

source code
STD(X) normalizes by (N-1) where N is the sequence length. This makes STD(X).^2 the best unbiased estimate of the variance if X is a sample from a normal distribution.

### max_(x)

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[Y,I] = MAX(X) returns the indices of the maximum values in vector I. If the values along the first non-singleton dimension contain more than one maximal element, the index of the first one is returned.

### min_(x)

source code
[Y,I] = MIN(X) returns the index of the minimum value in vector I. If the values along the first non-singleton dimension contain more than one minimal element, the index of the first one is returned.

### rsort(x, w=None)

source code

Sort x in increasing order, remove multiple entries, and adapt weights w accordingly x and w must both be a row or a column default input weights are w=1

If w==None, the weighted empirical cdf is computed at x dof = len(x) at input is the original number of degrees of freedom

Warning: when you use this function, make sure x and w is row vector

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