(Reference under construction)
- mnm.centroid
- mnm.delta
- mnm.diag
- mnm.gmmem
- mnm.hist
- mnm.lu
- mnm.mahalanobis
- mnm.meanstd
- mnm.minmax
- mnm.nmd
- mnm.nmf
- mnm.obsprob
- mnm.qr
- mnm.stats
- mnm.submat
- mnm.sum
- mnm.svd
- mnm.transpose
- mnm.viterbi
- mnm.xdist2
- mnm.xmul
| ftm.mess
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'
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| arguments:
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none
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| attributes:
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#triggerall <bool: flag> - all inlets trigger evaluation and output
#loadbang <bool: flag> - message box outputs at loadbang
#untuple <bool: flag> - single tuples are output as lists
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - input list
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| outlets:
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1 - message outlet
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| mnm.biqoefs
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Biquad coefficients
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Calculates biquad coefficients for various filter types.
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| arguments:
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none
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| attributes:
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mode - filter type (default is lowpass)
f0 - cutoff or centre frequency (default is half Nyquist frequency)
unit - unit for f0: ratio to Nyquist frequency (default) or in Hertz
sr - sample rate for f0 in Hertz (default is 44100.)
q - quality/resonance (default is 1.)
qnorm - if qnorm = 1, divide q by 1./sqrt(2.) so as to get a monotonic filter response with q = 1. (instead of 1./sqrt(2.), which is the default with qnorm = 0)
gain - linear gain (default is 1.)
coefsas - output coefficients as an fmat or as a list (default is fmat)
out - biquad coefficients
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| messages:
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postdoc - post external doc to console
bang - bang to output coefficients
out - biquad coefficients
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| inlets:
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none
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| outlets:
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1 - biquad coefficients
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| mnm.biquad
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Biquad filtering
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Compute biquad filtering over vectors (rows or columns) or stream of values (of any dimension).
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| arguments:
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set the inputs initial size and numbers
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| attributes:
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mode <'df1' | 'df2t'> - set biquad structure (default 'df1')
dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).
out - filtered values
outstate - output state
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| messages:
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postdoc - post external doc to console
insize - change the inputs size and numbers
coefs - set the biquad coefficients
getstate - get the biquad state
clear - reset the internal state
out - filtered values
outstate - output state
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| inlets:
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1 - input values
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| outlets:
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1 - filtered values
2 - output state
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| mnm.delta
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'
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| arguments:
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1 - initialize the input size
2 - initialize the filter size
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| attributes:
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insize - set the input size
filtersize - set the filter size
inadddel - add a delay to the delayed input
norm - normalization mode 1 (default) or 0
outdelayed - output delayed inputs (in phase with deltas)
out - output deltas
outstate - internal values
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| messages:
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postdoc - post external doc to console
clear - clear the memory of inputs
getstate - get the internal weights vector
getnorm - get the normalization factor
getring - get the input ring buffer
getdelay - get the filter delay
outdelayed - output delayed inputs (in phase with deltas)
out - output deltas
outstate - internal values
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| inlets:
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1 - multiply matrix with given right or left operand
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| outlets:
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1 - output delayed inputs (in phase with deltas)
2 - output deltas
3 - internal values
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| mnm.diag
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get diagonal of matrix
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returns a copy of the diagonal of the incoming matrix in a row vector.The length of the result is the minimum of the dimensions of the input.
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| arguments:
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none
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| attributes:
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out - diagonal of the fmat
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| messages:
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postdoc - post external doc to console
out - diagonal of the fmat
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| inlets:
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1 - incoming matrix whose diagonal will be extracted
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| outlets:
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1 - diagonal of the fmat
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| mnm.dtw
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Dynamic time warping.
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Calculates DTW on incoming matrix or vector.
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| arguments:
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1 - fmat to be used as right operand
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| attributes:
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outa - reference to an external fmat to store A
outb - reference to an external fmat to store B
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - left hand side fmat operand
2 - right hand side fmat operand
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| outlets:
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1 - s1
2 - s2
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| mnm.gmmem
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'
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| arguments:
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1 - number of centers to use
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| attributes:
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outcenters - reference to external fmat to store centers
outcov - reference to external fmat to store covariance
outpriors - reference to external fmat to store priors
mode - (diagonal|full|spherical) covariance computation types
criteria - criteria
ncenters - number of centers
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - fmat
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| outlets:
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1 - fmat centers
2 - fmat covariance
3 - fmat priors
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| mnm.hist
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calculate histogram of incoming matrix elements
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The input matrix, list or vector element's occurences are counted in the given number of bins in between the min and max value
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| arguments:
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1 - number of bins
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| attributes:
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out - histogram vector
bpf - output two-column fmat with bin indices and histogram values
norm <symbol: off|max|sum> -- normalise histogram so that max or sum is 1
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| messages:
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postdoc - post external doc to console
out - histogram vector
set_n - number of bins
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| inlets:
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1 - data fmat, fvec, jitter matrix or list
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| outlets:
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1 - histogram vector
2 - min data value
3 - max data value
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| mnm.knn
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k nearest neighbour search
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Find the k nearest neighbours and their distances to the query point in multi-dimensional data using an efficient multidimensional search tree with logarithmic time complexity.
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| arguments:
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1 - max number k of nearest neighbours to search
2 - max radius of nearest neighbours to search (0 for unlimited)
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| attributes:
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setdata - matrix(N, D) of data
setsigma - matrix(1, D) of sigma = 1/weights
dmode - decomposition mode: orthogonal, hyperplane, pca
mmode - pivot calculation mode: mean, middle, median
sort - sort output by distance
height - given tree height if positive, subtract from maxheight if negative
outdist <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours
outind <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours
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| messages:
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postdoc - post external doc to console
outdist <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours
outind <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours
setk - max number k of nearest neighbours to search
setradius - max radius of nearest neighbours to search (0 for unlimited)
getmeanvectors <fmat: out> - copy mean vectors for the M tree nodes to copy fmat(M, D) out
getsplitplanes <fmat: out> - copy vectors perpendicular to the hyperplanes splitting the M tree nodes to fmat(M, D) out
getprofile <dict: out> - copy profiling info to given dict and clear
print [<symbol: 'info'|'raw'|'data'|'compact'|'nodes'|'profile'>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword 'profile' is given
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| inlets:
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1 <fmat|fvec|list|jitter: x(1, D)> - query vector to search k-nearest neighbours of
2 <fmat|fvec|list|jitter: data(N, D)>
3 <fmat|fvec|list|jitter: sigma(1, D)>
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| outlets:
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1 <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours
2 <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours
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| mnm.lu
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'
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| arguments:
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none
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| attributes:
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outl - L
outu - U
outpivot - pivot
outx - X
outdet - determinant
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| messages:
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postdoc - post external doc to console
determinant - computes determinant of decomposed fmat
solve - solves system with incoming fmat and decomposed fmat
outl - L
outu - U
outpivot - pivot
outx - X
outdet - determinant
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| inlets:
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1 - matrix to decompose
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| outlets:
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1 - L
2 - U
3 - pivot
4 - X
5 - determinant
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| mnm.mahalanobis
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'
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| arguments:
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<matrix|vector|list: mean> <matrix|vector|list: covariance> - init mean and covariance
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| attributes:
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out - mahalanobis distance
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| messages:
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postdoc - post external doc to console
set_mu - matrix of mean values
set_sigma - matrix of covariance
out - mahalanobis distance
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| inlets:
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1 <matrix|vector|list: query vector>
2 <matrix: mu>
3 <matrix: sigma>
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| outlets:
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1 - mahalanobis distance
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| mnm.mean
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Mean filtering
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Compute mean filtering over vectors (rows or columns) or stream of values (of any dimension).
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| arguments:
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set the inputs initial size and numbers
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| attributes:
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filtersize - set the maximum filter size (default is 0 for using the input size)
dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).
outtype - set the output type: fmat, float or auto (default, matches the input type).
out - filtered values
outstate - output state
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| messages:
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postdoc - post external doc to console
insize - change the inputs size and numbers
getstate - get the mean state
clear - reset the internal state
out - filtered values
outstate - output state
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| inlets:
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1 - input values
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| outlets:
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1 - filtered values
2 - output state
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| mnm.meanstd
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mean and standard deviation on matrix
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Output the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector
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| arguments:
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1 <1|2|'row'|'col': mode switch> compute over rows or columns
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| attributes:
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mode <1|2|'row'|'col': mode switch> compute over rows or columns [rows]
scalar <bool: switch> output a simple float value (instead of 1 x 1 matrix) for scalar results [on]
outmean - mean output vector or value
outstd - standard deviation output vector or value
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| messages:
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postdoc - post external doc to console
outmean - mean output vector or value
outstd - standard deviation output vector or value
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| inlets:
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1 - input matrix
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| outlets:
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1 - mean output vector or value
2 - standard deviation output vector or value
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| mnm.median
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Median filtering
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Compute median filtering over vectors (rows or columns) or stream of values (of any dimension).
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| arguments:
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set the inputs initial size and numbers
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| attributes:
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filtersize - set the maximum filter size (default is 0 for using the input size)
dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).
outtype - set the output type: fmat, float or auto (default, matches the input type).
out - filtered values
outstate - output state
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| messages:
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postdoc - post external doc to console
insize - change the inputs size and numbers
getstate - get the median state
clear - reset the internal state
out - filtered values
outstate - output state
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| inlets:
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1 - input values
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| outlets:
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1 - filtered values
2 - output state
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| mnm.minmax
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min and max operations on fmat
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Output the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector
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| arguments:
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1 - (1|2|row|col) sum over rows or columns
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| attributes:
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mode - (1|2|row|col) sum over rows or columns
scalar <bool: switch> output a simple float value (instead of 1 x 1 matrix) for scalar results [on]
outmin - min
outargmin - argmin
outmax - max
outargmax - argmax
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| messages:
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postdoc - post external doc to console
outmin - min
outargmin - argmin
outmax - max
outargmax - argmax
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| inlets:
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1 - incoming matrix, vector, or list
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| outlets:
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1 - min
2 - argmin
3 - max
4 - argmax
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| mnm.moments
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Statistical Moments
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Calculates moments from first to specified order.
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| arguments:
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1 <num: order> - moments maximum order [1]
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| attributes:
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std <'0'|'1': switch> - compute the standards moments for orders > 2 [1]
sumasfloat <'0'|'1': switch> - enable/disable float sum output [0]
out - moments
outsum - input sums
mode - (1|2|row|col) calculate over rows (same as 1) or columns (default, same as 2) for multicolumn inputs.
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| messages:
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postdoc - post external doc to console
order <num: order> - set moments maximum order
out - moments
outsum - input sums
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| inlets:
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1 - input vector
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| outlets:
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1 - moments
2 - input sums
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| mnm.nmd
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'
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| arguments:
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none
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| attributes:
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outh - out H
criteria - criteria
sH - sH
itermax - itermax
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - fmat
2 - fmat
3 -
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| outlets:
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1 - fmat
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| mnm.nmf
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'
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| arguments:
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1 - number of components
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| attributes:
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outw - reference to external fmat to store W
outh - reference to external fmat to store H
criteria - (float) stopping criteria
rdim - number of components
itermax - maximum number of iterations
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - fmat to be decomposed
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| outlets:
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1 - W
2 - H
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| mnm.obsprob
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'
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| arguments:
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none
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| attributes:
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none
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| messages:
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postdoc - post external doc to console
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| inlets:
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1 - ref. to observation frame : fmat [D (=feature space dim) , 1]
2 - ref. to states matrix
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| outlets:
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1 - log(B) : fmat [K (=nb of states) , 1]
2 - test
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| mnm.onepole
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Onepole filtering
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Compute onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).
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| arguments:
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set the inputs initial size and numbers
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| attributes:
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f0 - set the onepole f0, normalised by the Nyquist frequency (default is 1.)
dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).
mode - filter type (feault is lowpass).
out - filtered values
outstate - output state
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| messages:
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postdoc - post external doc to console
getstate - get the onepole state
clear - reset the internal state
out - filtered values
outstate - output state
insize - change the inputs size and numbers
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| inlets:
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1 - input values
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| outlets:
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1 - filtered values
2 - output state
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| mnm.qr
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'
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| arguments:
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none
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| attributes:
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outq - Q
outr - R
outx - X
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| messages:
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postdoc - post external doc to console
solve - solve system with QR decomposition
outq - Q
outr - R
outx - X
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| inlets:
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1 - fmat to be decomposed
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| outlets:
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1 - Q
2 - R
3 - X
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| mnm.stats
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'
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| arguments:
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output stats
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| attributes:
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norm - switch normalize
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| messages:
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postdoc - post external doc to console
bang - output stats
clear - clear histogram
set - set histogram vector (fmat)
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| inlets:
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1 - data input
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| outlets:
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1 - average
2 - standard deviation
3 - count
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| mnm.submat
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'
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| arguments:
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none
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| attributes:
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out - sub-matrix
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| messages:
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postdoc - post external doc to console
begin - start of submatrix coordinates
end - end of submatrix coordinates
out - sub-matrix
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| inlets:
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1 - fmat
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| outlets:
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1 - sub-matrix
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| mnm.sum
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'
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| arguments:
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1 - (1|2|row|col) sum over rows or columns
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| attributes:
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out - sum of fmat
mode - 'row'|'col'|1|2 -- perform sum over rows or columns
type <symbol: 'float'|'fmat'> -- always output a matrix even for scalar results
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| messages:
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postdoc - post external doc to console
out - sum of fmat
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| inlets:
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1 - incoming matrix to be summed
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| outlets:
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1 - sum of fmat
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| mnm.svd
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'
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| arguments:
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1 - number of singular values
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| attributes:
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mode - (auto|manual) automatically eliminate negligible singular values
outu - output matrix for U
outs - output matrix for S
outvt - output matrix for V'
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| messages:
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postdoc - post external doc to console
outu - output matrix for U
outs - output matrix for S
outvt - output matrix for V'
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| inlets:
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1 - matrix to be decomposed by SVD
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| outlets:
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1 - output matrix for U
2 - output matrix for S
3 - output matrix for V'
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| mnm.transpose
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'
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| arguments:
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none
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| attributes:
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out - transposed matrix
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| messages:
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postdoc - post external doc to console
out - transposed matrix
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| inlets:
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1 - matrix to be transposed
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| outlets:
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1 - transposed matrix
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| mnm.viterbi
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'
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| arguments:
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none
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| attributes:
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line - on|off line
verbose - verbose or not (0|1)
latency - maximum latency (nb of frames)
get - debug : get intern values.
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| messages:
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postdoc - post external doc to console
reinit - input message reinit
bang - bang to decode
locpaths - get locpaths matrix
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| inlets:
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1 - bang to decode, or reinit message to reset PSIs and DELTAs
2 - observation matrix logB[T,K]
3 - state prior distribution Pi[1,K]
4 - state transition matrix A[K,K]
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| outlets:
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1 - decoded best path
2 - debug
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| mnm.xdist2
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'
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| arguments:
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matrix to be used as right operand
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| attributes:
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swap - (yes|no) swaps operands
out - result squared distance matrix
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| messages:
|
postdoc - post external doc to console
out - result squared distance matrix
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| inlets:
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1 <matrix: left operand>
2 <matrix: right operand>
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| outlets:
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1 - result squared distance matrix
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| mnm.xmul
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Matrix multiplication
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Calculates matrix multiplication as in out = left x right.
The left and right operands of the matrix multiplication are given by the respective inlets unless the swap option is enabled.The dimensions of the resulting output matrix are corresponding to the minimum dimensions of the two operators.
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| arguments:
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set right or (with swap enabled) left matrix multiplication operand
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| attributes:
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out - output matrix object
swap <bool: switch> swaps operands
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| messages:
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postdoc - post external doc to console
out - output matrix object
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| inlets:
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1 - multiply matrix with given right or left operand
2 - set right or (with swap enabled) left matrix multiplication operand
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| outlets:
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1 - output matrix object
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