Difference between revisions of "MnM Modules"

Latest revision as of 22:27, 3 May 2009

mnm.biqoefs ... Biquad coefficients
mnm.biquad ... Biquad filtering
mnm.delta ... Inter-frame regression
mnm.diag ... Matrix diagonal
mnm.dtw ... Dynamic Time Warping
mnm.gmmem ... Expectation Maximization for Gaussian Mixture Models
mnm.hist ... Histogram
mnm.knn ... K-Nearest Neighbour search
mnm.lu ... Lower-Upper decomposition
mnm.mahalanobis ... Mahalanobis distance
mnm.mean ... Mean filtering
mnm.meanstd ... Mean and standard deviation
mnm.median ... Median filtering
mnm.minmax ... Minimum and maximum
mnm.moments ... Statistical moments
mnm.nmd ... Non-zero Matrix Decomposition
mnm.nmf ... Non-zero Matrix Factorization
mnm.obsprob ... Obsprob
mnm.onepole ... Onepole filtering
mnm.qr ... Orthogonal-Right decomposition
mnm.stats ... Stats
mnm.submat ... Sub-matrix
mnm.sum ... Matrix sum
mnm.svd ... Singular Value Decomposition
mnm.transpose ... Matrix transposition
mnm.viterbi ... Viterbi algorithm
mnm.xdist2 ... Square of Euclidean distance
mnm.xmul ... Matrix multiplication

mnm.biqoefs

Biquad coefficients

Calculates biquad coefficients for various filter types.

arguments:	none
attributes:	mode <'lp'\|'lowpass'\|'hp'\|'highpass'\|'bpcskirt'\|'resonant'\|'bpcpeak'\|'bandpass'\|'peaking'\|'peaknotch'\|'notch'\|'bs'\|'bandstop'\|'ap'\|'allpass'\|'ls'\|'lowshelf'\|'hs'\|'highshelf': type> - filter type [lowpass] f0 <num: freq> - cutoff or centre frequency (default is half Nyquist frequency) unit <'nyquist'\|'hz'\|'hertz': unit> - set unit for freq as ratio to Nyquist frequency or in hz [nyquist] sr <num: sr> - sample rate for freq in Hertz [44100] q <num: q> - quality/resonance [1] qnorm <num: 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 <num: gain> - linear gain [1] out <matrix: coeffs> - output biquad coefficients
messages:	postdoc - post external doc to console bang - output coefficients out <matrix: coeffs> - output biquad coefficients
inlets:	none
outlets:	1 <matrix: coeffs> - output biquad coefficients

mnm.biquad

Biquad filtering

Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension).

arguments:	<num: input> [<num: filter>] - init input and filter size
attributes:	mode <'df1'\|'df2t': struct> - set biquad structure [df1] dim <'col'\|'row'\|'auto'\|'stream': mode> - set the dimension on which to operate [auto] out <matrix: values> - output filtered values outstate <matrix: state> - output state
messages:	postdoc - post external doc to console insize <num: input> [<num: filter>] - init input and filter size coefs <list: coeffs> - set the biquad coefficients getstate - get the biquad state clear - reset the internal state out <matrix: values> - output filtered values outstate <matrix: state> - output state
inlets:	1 <matrix: coeffs> - input values
outlets:	1 <matrix: values> - output filtered values 2 <matrix: state> - output state

mnm.delta

Inter-frame regression

Calculates derivative of incoming matrices or vectors.

arguments:	1 <num: input> [<num: filter>] - init input size 2 <num: filter>: init filter size
attributes:	insize <num: input> - set input size filtersize <num: filter> - set filter size inadddel <num: del> - add a delay to the delayed input norm <bool: normalize> - set normalization mode ([off] outdelayed <matrix: delayed> - output delayed inputs (in phase with deltas) out <matrix: delta> - output deltas outstate <matrix: state> - internal values
messages:	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 <matrix: delayed> - output delayed inputs (in phase with deltas) out <matrix: delta> - output deltas outstate <matrix: state> - internal values
inlets:	1 <matrix: input> - multiply matrix with given right or left operand
outlets:	1 <matrix: delayed> - output delayed inputs (in phase with deltas) 2 <matrix: delta> - output deltas 3 <matrix: state> - internal values

mnm.diag

Matrix diagonal

Returns a copy of the diagonal of the incoming matrix in a vector.The length of the result is the minimum of the dimensions of the input.

arguments:	none
attributes:	out <vector: diagonal> - output diagonal vector
messages:	postdoc - post external doc to console out <vector: diagonal> - output diagonal vector
inlets:	1 <matrix: input> - input matrix
outlets:	1 <vector: diagonal> - output diagonal vector

mnm.dtw

Dynamic Time Warping

Calculates DTW of incoming matrix or vector.

arguments:	1 <matrix: op> - matrix to be used as right operand
attributes:	outa <matrix: A> - set A output matrix outb <matrix: B> - set B output matrix
messages:	postdoc - post external doc to console
inlets:	1 <matrix: left> - left hand side matrix operand 2 <matrix: right> - right hand side matrix operand
outlets:	1 <matrix: A> - A output matrix 2 <matrix: B> - B output matrix

mnm.gmmem

Expectation Maximization for Gaussian Mixture Models

GMM EM has to be documented.

arguments:	1 <num: num> - number of centers to use
attributes:	outcenters <matrix: centers> - centers output matrix outcov <matrix: covariance> - covariance output matrix outpriors <matrix: priors> - priors output matrix mode <diagonal\|full\|spherical: mode> covariance computation types criteria <num: criteria> - criteria ncenters <num: num> - number of centers
messages:	postdoc - post external doc to console
inlets:	1 <matrix: input> - matrix of values
outlets:	1 <matrix: centers> - centers output matrix 2 <matrix: covariance> - covariance output matrix 3 <matrix: priors> - priors output matrix

mnm.hist

Histogram

Calculates histogram of incoming matrix elements. The input matrix, list or vector element's occurences are counted in the given number of bins in between the min and max value

arguments:	1 <num: num> - number of bins
attributes:	out <vector: histogram> - output histogram vector bpf <matrix: histogram> - output two-column matrix with bin indices and histogram values norm <'off'\|'max'\|'sum': normalize> - normalise histogram so that max or sum is 1
messages:	postdoc - post external doc to console out <vector: histogram> - output histogram vector set_n <num: num> - number of bins
inlets:	1 <matrix: values> - intput matrix or list
outlets:	1 <vector: histogram> - output histogram vector 2 <num: min> - output min data value 3 <num: max> - output max data value

mnm.knn

K-Nearest Neighbour search

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.

arguments:	1 <num: max k> - max number k of nearest neighbours to search 2 <num: max radius> - max radius of nearest neighbours to search (0 for unlimited)
attributes:	setdata <matrix: data> - matrix(N, D) of data setsigma <matrix: sigma> - matrix(1, D) of sigma = 1/weights dmode <'orthogonal'\|'hyperplane'\|'pca': mode> decomposition mode mmode <'mean'\|'middle'\|'median'> - pivot calculation mode sort <bool: switch> - sort output by distance height <num: height> - given tree height if positive, subtract from maxheight if negative outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1) outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)
messages:	postdoc - post external doc to console outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1) outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1) setk <num: max> - max number k of nearest neighbours to search setradius <num: max> - max radius of nearest neighbours to search (0 for unlimited) getmeanvectors <matrix: out> - copy mean vectors for the M tree nodes to copy matrix (M x D) out getsplitplanes <matrix: out> - copy vectors perpendicular to the hyperplanes splitting the M tree nodes to matrix (M x D) out getprofile <dict: out> - copy profiling info to given dict and clear print [<'info'\|'r\ aw'\|'data'\|'compact'\|'nodes'\|'profile': mode>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword 'profile' is given
inlets:	1 <vector: x> - query vector to search k-nearest neighbours (1 x D) 2 <matrix: data> - data input matrix (N x D) 3 <vector: sigma> - sigma input matrix (1 x D)
outlets:	1 <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1) 2 <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)

mnm.lu

Lower-Upper decomposition

Calculates LU decomposition of incoming matrix.

arguments:	none
attributes:	outl <matrix: L> - L output matrix outu <matrix: U> - U output matrix outpivot <matrix: pivot> - pivot output matrix outx <matrix: X> - X output matrix outdet <matrix: det> - determinant output matrix
messages:	postdoc - post external doc to console determinant - computes determinant of decomposed matrix solve <matrix: input> - solves system with incoming matrix and decomposed matrix outl <matrix: L> - L output matrix outu <matrix: U> - U output matrix outpivot <matrix: pivot> - pivot output matrix outx <matrix: X> - X output matrix outdet <matrix: det> - determinant output matrix
inlets:	1 <matrix: input> - matrix to decompose
outlets:	1 <matrix: L> - L output matrix 2 <matrix: U> - U output matrix 3 <matrix: pivot> - pivot output matrix 4 <matrix: X> - X output matrix 5 <matrix: det> - determinant output matrix

mnm.mahalanobis

Mahalanobis distance

Calculates mahalanobis distance of incoming matrices or vectors.

arguments:	<matrix: mean> <matrix: covariance> - init mean and covariance
attributes:	out <matrix: output> - output mahalanobis distance
messages:	postdoc - post external doc to console set_mu <matrix: mu> - matrix of mean values set_sigma <matrix: sigma> - matrix of covariance out <matrix: output> - output mahalanobis distance
inlets:	1 <vector: input> - query vector> 2 <matrix: mu> - mu input matrix 3 <matrix: sigma> - sigma input matrix
outlets:	1 <matrix: output> - output mahalanobis distance

mnm.mean

Mean filtering

Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension).

arguments:	<num: input> [<num: filter>] - init input and filter size
attributes:	filtersize <num: max> - set the maximum filter size (default is 0 for using the input size) dim <'col'\|'row'\|'auto'\|'stream': mode> - set the dimension on which to operate [auto] out <matrix: output> - filtered values outstate <matrix: state> - output state
messages:	postdoc - post external doc to console insize <num: input> [<num: filter>] - set input and filter size getstate - get the mean state clear - reset the internal state out <matrix: output> - filtered values outstate <matrix: state> - output state
inlets:	1 <matrix: input> - input values
outlets:	1 <matrix: output> - filtered values 2 <matrix: state> - output state

mnm.meanstd

Mean and standard deviation

Calculates the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector

arguments:	1 <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col]
attributes:	mode <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col] scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on] outmean <matrix: mean> - mean output vector or value outstd <matrix: stddev> - standard deviation output vector or value
messages:	postdoc - post external doc to console outmean <matrix: mean> - mean output vector or value outstd <matrix: stddev> - standard deviation output vector or value
inlets:	1 <matrix: input> - input matrix
outlets:	1 <matrix: mean> - mean output vector or value 2 <matrix: stddev> - standard deviation output vector or value

mnm.median

Median filtering

Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension).

arguments:	<num: input> [<num: filter>] - init input and filter size
attributes:	filtersize <num: filter> - set the maximum filter size (default is 0 for using the input size) dim <'col'\|'row'\|'auto'\|'stream': mode> - set the dimension on which to operate [auto] out <matrix: output> - output filtered values outstate <matrix: output> - output state
messages:	postdoc - post external doc to console insize <num: input> [<num: filter>] - set input and filter size getstate - get the median state clear - reset the internal state out <matrix: output> - output filtered values outstate <matrix: output> - output state
inlets:	1 <matrix: input> - input values
outlets:	1 <matrix: output> - output filtered values 2 <matrix: output> - output state

mnm.minmax

Minimum and maximum

Calculates the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector

arguments:	1 <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col]
attributes:	mode <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col] scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on] outmin <vector: min> - minimum output vector or value outargmin <vector: min> - minimum index output vector or value outmax <vector: max> - maximum output vector or value outargmax <vector: max> - maximum index output vector or value
messages:	postdoc - post external doc to console outmin <vector: min> - minimum output vector or value outargmin <vector: min> - minimum index output vector or value outmax <vector: max> - maximum output vector or value outargmax <vector: max> - maximum index output vector or value
inlets:	1 <matrix: input> - incoming matrix, vector, or list
outlets:	1 <vector: min> - minimum output vector or value 2 <vector: min> - minimum index output vector or value 3 <vector: max> - maximum output vector or value 4 <vector: max> - maximum index output vector or value

mnm.moments

Statistical moments

Calculates moments from first to specified order.

arguments:	1 <num: order> - moments maximum order [1]
attributes:	std <bool: switch> - compute the standards moments for orders > 2 [on] mode <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col] out <vector: moments> - output moments vector outsum <vector: moments> - output sums vector
messages:	postdoc - post external doc to console order <num: order> - set moments maximum order out <vector: moments> - output moments vector outsum <vector: moments> - output sums vector
inlets:	1 <vector: input> - input vector
outlets:	1 <vector: moments> - output moments vector 2 <vector: moments> - output sums vector

mnm.nmd

Non-zero Matrix Decomposition

Calculates NMD of incoming matrix.

arguments:	none
attributes:	outh <matrix: H> - H output matrix criteria <num: criteria> - criteria sH <num: sH> - sH itermax <num: itermax> - maximum number of iterations
messages:	postdoc - post external doc to console
inlets:	1 <matrix: input> - input matrix 2 <matrix: input> - input matrix 3 <num: L2> - L2
outlets:	1 <matrix: output> - output matrix

mnm.nmf

Non-zero Matrix Factorization

Calculates NMF of incoming matrix.

arguments:	1 <num: num> - number of components
attributes:	outw <matrix: W> - W output matrix outh <matrix: H> - H output matrix criteria <num: criteria> - stopping criteria rdim <num: num> - number of components itermax <num: max> - maximum number of iterations
messages:	postdoc - post external doc to console
inlets:	1 <matrix: input> - matrix to be decomposed
outlets:	1 <matrix: W> - W output matrix 2 <matrix: H> - H output matrix

mnm.obsprob

Obsprob

Obsprob has to be documented.

arguments:	none
attributes:	none
messages:	postdoc - post external doc to console
inlets:	1 <matrix: obs> - input observation frames matrix (D x 1) 2 <matrix: states> - input states matrix
outlets:	1 <matrix: prob> - output log(B) (K x 1) 2 <num> - no description

mnm.onepole

Onepole filtering

Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).

arguments:	<num: input> [<num: filter>] - init input and filter size
attributes:	f0 <num: freq> - set the onepole frequency, normalised by the Nyquist frequency (default is 1.) dim <'col'\|'row'\|'auto'\|'stream': mode> - set the dimension on which to operate [auto] mode <'lowpass'\|'highpass': mode> - filter type [lowpass] out <matrix: output> - output filtered values outstate <matrix: state> - output state
messages:	postdoc - post external doc to console getstate - get the onepole state clear - reset the internal state insize <num: input> [<num: filter>] - init input and filter size out <matrix: output> - output filtered values outstate <matrix: state> - output state
inlets:	1 <matrix: input> - input values
outlets:	1 <matrix: output> - output filtered values 2 <matrix: state> - output state

mnm.qr

Orthogonal-Right decomposition

Calculates QR decomposition of incoming matrix.

arguments:	none
attributes:	outq <matrix: Q> - Q output matrix outr <matrix: R> - R output matrix outx <matrix: X> - X output matrix
messages:	postdoc - post external doc to console solve <matrix: input> - solve system with QR decomposition outq <matrix: Q> - Q output matrix outr <matrix: R> - R output matrix outx <matrix: X> - X output matrix
inlets:	1 <matrix: input> - matrix to be decomposed
outlets:	1 <matrix: Q> - Q output matrix 2 <matrix: R> - R output matrix 3 <matrix: X> - X output matrix

mnm.stats

Stats

Stats has to be documented.

arguments:	<vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries
attributes:	norm <bool: normalize> - switch normalization [off]
messages:	postdoc - post external doc to console bang - output stats clear - clear histogram set <vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries
inlets:	1 <num: value> - data input
outlets:	1 <num: average> - output average 2 <num: stddev> - output standard deviation 3 <num: count> - output count

mnm.submat

Sub-matrix

Copies sub-matrix outof incoming matrix.

arguments:	<num: begin row> <num: begin col> <num: end row> <num: end col> - start end end indices of sub-matrix
attributes:	begin <num: row> <num: col> - start indices of sub-matrix end <num: row> <num: col> - end indices of sub-matrix out <matrix: output> - ouput sub-matrix
messages:	postdoc - post external doc to console out <matrix: output> - ouput sub-matrix
inlets:	1 <matrix: input> - input matrix
outlets:	1 <matrix: output> - ouput sub-matrix

mnm.sum

Matrix sum

Calculates sum over entire matrix, matrix rows or matrix columns.

arguments:	1 <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col]
attributes:	mode <1\|2\|'row'\|'col': mode switch> - compute over rows or columns [col] out <vector: sum> - output sum vector
messages:	postdoc - post external doc to console out <vector: sum> - output sum vector
inlets:	1 <matrix: input> - incoming matrix to be summed
outlets:	1 <vector: sum> - output sum vector

mnm.svd

Singular Value Decomposition

Calculates SVD of incoming matrix.

arguments:	1 <num: rank> - number of singular values
attributes:	mode <'manual'\|'auto': mode> - automatically eliminate negligible singular values [manual] outu <matrix: U> - U output matrix outs <matrix: S> - S output matrix outvt <matrix: V'> - V' output matrix (transposed of V)
messages:	postdoc - post external doc to console outu <matrix: U> - U output matrix outs <matrix: S> - S output matrix outvt <matrix: V'> - V' output matrix (transposed of V)
inlets:	1 <matrix: input> - matrix to be decomposed by SVD
outlets:	1 <matrix: U> - U output matrix 2 <matrix: S> - S output matrix 3 <matrix: V'> - V' output matrix (transposed of V)

mnm.transpose

Matrix transposition

Calculates transposed matrix.

arguments:	none
attributes:	out <matrix: output> - output transposed matrix
messages:	postdoc - post external doc to console out <matrix: output> - output transposed matrix
inlets:	1 <matrix: input> - matrix to be transposed
outlets:	1 <matrix: output> - output transposed matrix

mnm.viterbi

Viterbi algorithm

Calculates Viterbi path of incoming matrices or vectors.

arguments:	none
attributes:	line <bool: online> - switch online mode on/off [off] verbose <bool: verbose> - verbose or not [off] latency <num: latency> - maximum latency (nb of frames)
messages:	postdoc - post external doc to console geta - get state matrix A getpi - get state matrix Pi getpsi - get state matrix Psi locpaths - get locpaths matrix reinit - input message reinit bang - decode
inlets:	1 - messages only 2 <matrix: observations> - input observation matrix logB (T x K) 3 <matrix: pi> - input state prior distribution Pi (1 x K) 4 <matrix: A> - input state transition matrix A (K x K)
outlets:	1 <matrix: path> - output decoded best path 2 <matrix: state> - output state matrices

mnm.xdist2

Square of Euclidean distance

Calculates the square of the Euclidean distance between a vector and each line of a matrix.

arguments:	<matrix: right> - matrix to be used as right operand
attributes:	swap <bool: switch> swap operands out <matrix: output> - result squared distance matrix
messages:	postdoc - post external doc to console out <matrix: output> - result squared distance matrix
inlets:	1 <matrix: left> - input left operand> 2 <matrix: right> - input right operand>
outlets:	1 <matrix: output> - result squared distance matrix

mnm.xmul

Matrix multiplication

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.

arguments:	<matrix: op> - set right or (with swap enabled) left matrix multiplication operand
attributes:	swap <bool: switch> swap operands out <matrix: output> - output matrix
messages:	postdoc - post external doc to console out <matrix: output> - output matrix
inlets:	1 <matrix: input> - multiply matrix with given right or left operand 2 <matrix: op> - set right or (with swap enabled) left matrix multiplication operand
outlets:	1 <matrix: output> - output matrix

Difference between revisions of "MnM Modules"

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Latest revision as of 22:27, 3 May 2009

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@@ Line 1: / Line 1: @@
-=== Reference under construction... ===
+* mnm.biqoefs ... Biquad coefficients
+* mnm.biquad ... Biquad filtering
+* mnm.delta ... Inter-frame regression
+* mnm.diag ... Matrix diagonal
+* mnm.dtw ... Dynamic Time Warping
+* mnm.gmmem ... Expectation Maximization for Gaussian Mixture Models
+* mnm.hist ... Histogram
+* mnm.knn ... K-Nearest Neighbour search
+* mnm.lu ... Lower-Upper decomposition
+* mnm.mahalanobis ... Mahalanobis distance
+* mnm.mean ... Mean filtering
+* mnm.meanstd ... Mean and standard deviation
+* mnm.median ... Median filtering
+* mnm.minmax ... Minimum and maximum
+* mnm.moments ... Statistical moments
+* mnm.nmd ... Non-zero Matrix Decomposition
+* mnm.nmf ... Non-zero Matrix Factorization
+* mnm.obsprob ... Obsprob
+* mnm.onepole ... Onepole filtering
+* mnm.qr ... Orthogonal-Right decomposition
+* mnm.stats ... Stats
+* mnm.submat ... Sub-matrix
+* mnm.sum ... Matrix sum
+* mnm.svd ... Singular Value Decomposition
+* mnm.transpose ... Matrix transposition
+* mnm.viterbi ... Viterbi algorithm
+* mnm.xdist2 ... Square of Euclidean distance
+* mnm.xmul ... Matrix multiplication
+{{Module |
+  | name=mnm.biqoefs
+  | brief=Biquad coefficients
+  | descr=Calculates biquad coefficients for various filter types.
+  | arguments=none
+  | attributes=mode <&#039;lp&#039;&#124;&#039;lowpass&#039;&#124;&#039;hp&#039;&#124;&#039;highpass&#039;&#124;&#039;bpcskirt&#039;&#124;&#039;resonant&#039;&#124;&#039;bpcpeak&#039;&#124;&#039;bandpass&#039;&#124;&#039;peaking&#039;&#124;&#039;peaknotch&#039;&#124;&#039;notch&#039;&#124;&#039;bs&#039;&#124;&#039;bandstop&#039;&#124;&#039;ap&#039;&#124;&#039;allpass&#039;&#124;&#039;ls&#039;&#124;&#039;lowshelf&#039;&#124;&#039;hs&#039;&#124;&#039;highshelf&#039;: type> - filter type [lowpass]<br>f0 <num: freq> - cutoff or centre frequency (default is half Nyquist frequency)<br>unit <&#039;nyquist&#039;&#124;&#039;hz&#039;&#124;&#039;hertz&#039;: unit> - set unit for freq as ratio to Nyquist frequency or in hz [nyquist]<br>sr <num: sr> - sample rate for freq in Hertz [44100]<br>q <num: q> - quality/resonance [1]<br>qnorm <num: 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)<br>gain <num: gain> - linear gain [1]<br>out <matrix: coeffs> - output biquad coefficients<br>
+  | messages=postdoc - post external doc to console<br>bang - output coefficients<br>out <matrix: coeffs> - output biquad coefficients<br>
+  | inlets=none
+  | outlets=1 <matrix: coeffs> - output biquad coefficients<br>
+}}
+{{Module |
+  | name=mnm.biquad
+  | brief=Biquad filtering
+  | descr=Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension).
+  | arguments=<num: input> [<num: filter>] - init input and filter size<br>
+  | attributes=mode <&#039;df1&#039;&#124;&#039;df2t&#039;: struct> - set biquad structure [df1]<br>dim <&#039;col&#039;&#124;&#039;row&#039;&#124;&#039;auto&#039;&#124;&#039;stream&#039;: mode> - set the dimension on which to operate [auto]<br>out <matrix: values> - output filtered values<br>outstate <matrix: state> - output state<br>
+  | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - init input and filter size<br>coefs <list: coeffs> - set the biquad coefficients<br>getstate - get the biquad state<br>clear - reset the internal state<br>out <matrix: values> - output filtered values<br>outstate <matrix: state> - output state<br>
+  | inlets=1 <matrix: coeffs> - input values<br>
+  | outlets=1 <matrix: values> - output filtered values<br>2 <matrix: state> - output state<br>
+}}
 {{Module |
    | name=mnm.delta
-   | brief=
+   | brief=Inter-frame regression
-   | descr=
+   | descr=Calculates derivative of incoming matrices or vectors.
-   | arguments=0 - initialize the input size<br>1 - initialize the filter size<br>
+   | arguments=1 <num: input> [<num: filter>] - init input size<br>2 <num: filter>: init filter size<br>
-   | attributes=norm - normalization mode 1 (default) or 0<br>inadddel - add a delay to the delayed input<br>
+   | attributes=insize <num: input> - set input size<br>filtersize <num: filter> - set filter size<br>inadddel <num: del> - add a delay to the delayed input<br>norm <bool: normalize> - set normalization mode ([off]<br>outdelayed <matrix: delayed> - output delayed inputs (in phase with deltas)<br>out <matrix: delta> - output deltas<br>outstate <matrix: state> - internal values<br>
-   | messages=clear - clear the memory of inputs<br>getdelay - get the input ring buffer<br>getring - get the input ring buffer<br>getnorm - get the normalization factor<br>getstate - get the internal weights vector<br>norm - normalization mode 1 (default) or 0<br>inadddel - add a delay to the delayed input<br>filtersize - set the filter size<br>insize - set the input size<br>
+   | messages=postdoc - post external doc to console<br>clear - clear the memory of inputs<br>getstate - get the internal weights vector<br>getnorm - get the normalization factor<br>getring - get the input ring buffer<br>getdelay - get the filter delay<br>outdelayed <matrix: delayed> - output delayed inputs (in phase with deltas)<br>out <matrix: delta> - output deltas<br>outstate <matrix: state> - internal values<br>
-   | inlets=0 - input vector<br>
+   | inlets=1 <matrix: input> - multiply matrix with given right or left operand<br>
-   | outlets=0 - output delayed inputs (in phase with deltas)<br>1 - output deltas<br>2 - internal values<br>
+   | outlets=1 <matrix: delayed> - output delayed inputs (in phase with deltas)<br>2 <matrix: delta> - output deltas<br>3 <matrix: state> - internal values<br>
 }}
 {{Module |
    | name=mnm.diag
-   | brief=
+   | brief=Matrix diagonal
-   | descr=
+   | descr=Returns a copy of the diagonal of the incoming matrix in a vector.The length of the result is the minimum of the dimensions of the input.
    | arguments=none
-   | attributes=out - reference to an external fmat to store the diagonal<br>
+   | attributes=out <vector: diagonal> - output diagonal vector<br>
-   | messages=out - reference to an external fmat to store the diagonal<br>
+   | messages=postdoc - post external doc to console<br>out <vector: diagonal> - output diagonal vector<br>
-   | inlets=0 - incoming fmat whose diagonal will be extracted<br>
+   | inlets=1 <matrix: input> - input matrix<br>
-   | outlets=0 - diagonal of the fmat<br>
+  | outlets=1 <vector: diagonal> - output diagonal vector<br>
+}}
+{{Module |
+  | name=mnm.dtw
+  | brief=Dynamic Time Warping
+  | descr=Calculates DTW of incoming matrix or vector.
+  | arguments=1 <matrix: op> - matrix to be used as right operand<br>
+  | attributes=outa <matrix: A> - set A output matrix<br>outb <matrix: B> - set B output matrix<br>
+  | messages=postdoc - post external doc to console<br>
+  | inlets=1 <matrix: left> - left hand side matrix operand<br>2 <matrix: right> - right hand side matrix operand<br>
+   | outlets=1 <matrix: A> - A output matrix<br>2 <matrix: B> - B output matrix<br>
 }}
 {{Module |
    | name=mnm.gmmem
-   | brief=
+   | brief=Expectation Maximization for Gaussian Mixture Models
-   | descr=
+   | descr=GMM EM has to be documented.
-   | arguments=0 - number of centers to use<br>
+   | arguments=1 <num: num> - number of centers to use<br>
-   | attributes=ncenters - number of centers<br>criteria - criteria<br>mode - (diagonal|full|spherical) covariance computation types<br>outpriors - reference to external fmat to store priors<br>outcov - reference to external fmat to store covariance<br>outcenters - reference to external fmat to store centers<br>
+   | attributes=outcenters <matrix: centers> - centers output matrix<br>outcov <matrix: covariance> - covariance output matrix<br>outpriors <matrix: priors> - priors output matrix<br>mode <diagonal&#124;full&#124;spherical: mode> covariance computation types<br>criteria <num: criteria> - criteria<br>ncenters <num: num> - number of centers<br>
-   | messages=outpriors - reference to external fmat to store priors<br>outcov - reference to external fmat to store covariance<br>outcenters - reference to external fmat to store centers<br>
+   | messages=postdoc - post external doc to console<br>
-   | inlets=0 - fmat<br>
+   | inlets=1 <matrix: input> - matrix of values<br>
-   | outlets=0 - fmat centers<br>1 - fmat covariance<br>2 - fmat priors<br>
+   | outlets=1 <matrix: centers> - centers output matrix<br>2 <matrix: covariance> - covariance output matrix<br>3 <matrix: priors> - priors output matrix<br>
 }}
 {{Module |
    | name=mnm.hist
-   | brief=
+   | brief=Histogram
-   | descr=
+   | descr=Calculates histogram of incoming matrix elements. The input matrix, list or vector element's occurences are counted in the given number of bins in between the min and max value
-   | arguments=0 - number of bins<br>
+   | arguments=1 <num: num> - number of bins<br>
-   | attributes=norm - values: on=max, sum: normalise histogram so that max or sum is 1<br>bpf - output two-column fmat with bin indices and histogram values<br>
+   | attributes=out <vector: histogram> - output histogram vector<br>bpf <matrix: histogram> - output two-column matrix with bin indices and histogram values<br>norm <&#039;off&#039;&#124;&#039;max&#039;&#124;&#039;sum&#039;: normalize> - normalise histogram so that max or sum is 1<br>
-   | messages=set_n - number of bins<br>norm - values: on=max, sum: normalise histogram so that max or sum is 1<br>bpf - output two-column fmat with bin indices and histogram values<br>
+   | messages=postdoc - post external doc to console<br>out <vector: histogram> - output histogram vector<br>set_n <num: num> - number of bins<br>
-   | inlets=0 - data fmat or fvec <br>
+  | inlets=1 <matrix: values> - intput matrix or list<br>
-   | outlets=0 - histogram fmat<br>1 - min data value<br>2 - max data value<br>
+  | outlets=1 <vector: histogram> - output histogram vector<br>2 <num: min> - output min data value<br>3 <num: max> - output max data value<br>
+}}
+{{Module |
+  | name=mnm.knn
+  | brief=K-Nearest Neighbour search
+  | descr=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.
+  | arguments=1 <num: max k> - max number k of nearest neighbours to search<br>2 <num: max radius> - max radius of nearest neighbours to search (0 for unlimited)<br>
+  | attributes=setdata <matrix: data> - matrix(N, D) of data<br>setsigma <matrix: sigma> - matrix(1, D) of sigma = 1/weights<br>dmode <&#039;orthogonal&#039;&#124;&#039;hyperplane&#039;&#124;&#039;pca&#039;: mode> decomposition mode<br>mmode <&#039;mean&#039;&#124;&#039;middle&#039;&#124;&#039;median&#039;> - pivot calculation mode<br>sort <bool: switch> - sort output by distance<br>height <num: height> - given tree height if positive, subtract from maxheight if negative<br>outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br>
+   | messages=postdoc - post external doc to console<br>outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br>setk <num: max> - max number k of nearest neighbours to search<br>setradius <num: max> - max radius of nearest neighbours to search (0 for unlimited)<br>getmeanvectors <matrix: out> - copy mean vectors for the M tree nodes to copy matrix (M x D) out<br>getsplitplanes <matrix: out> - copy vectors perpendicular to the hyperplanes splitting the M tree nodes to matrix (M x D) out<br>getprofile <dict: out> - copy profiling info to given dict and clear<br>print [<&#039;info&#039;&#124;&#039;r\ aw&#039;&#124;&#039;data&#039;&#124;&#039;compact&#039;&#124;&#039;nodes&#039;&#124;&#039;profile&#039;: mode>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword &#039;profile&#039; is given<br>
+   | inlets=1 <vector: x> - query vector to search k-nearest neighbours (1 x D)<br>2 <matrix: data> - data input matrix (N x D)<br>3 <vector: sigma> - sigma input matrix (1 x D)<br>
+  | outlets=1 <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>2 <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br>
 }}
 {{Module |
    | name=mnm.lu
-   | brief=
+   | brief=Lower-Upper decomposition
-   | descr=
+   | descr=Calculates LU decomposition of incoming matrix.
    | arguments=none
-   | attributes=outx - out X<br>outpivot - out pivot<br>outu - out U<br>outl - out L<br>
+   | attributes=outl <matrix: L> - L output matrix<br>outu <matrix: U> - U output matrix<br>outpivot <matrix: pivot> - pivot output matrix<br>outx <matrix: X> - X output matrix<br>outdet <matrix: det> - determinant output matrix<br>
-   | messages=outx - out X<br>outpivot - out pivot<br>outu - out U<br>outl - out L<br>solve - solves system with incoming fmat and decomposed fmat<br>determinant - computes determinant of decomposed fmat<br>
+   | messages=postdoc - post external doc to console<br>determinant - computes determinant of decomposed matrix<br>solve <matrix: input> - solves system with incoming matrix and decomposed matrix<br>outl <matrix: L> - L output matrix<br>outu <matrix: U> - U output matrix<br>outpivot <matrix: pivot> - pivot output matrix<br>outx <matrix: X> - X output matrix<br>outdet <matrix: det> - determinant output matrix<br>
-   | inlets=0 - fmat to decompose<br>
+   | inlets=1 <matrix: input> - matrix to decompose<br>
-   | outlets=0 - L<br>1 - U<br>2 - pivot<br>3 - X<br>4 - determinant<br>
+   | outlets=1 <matrix: L> - L output matrix<br>2 <matrix: U> - U output matrix<br>3 <matrix: pivot> - pivot output matrix<br>4 <matrix: X> - X output matrix<br>5 <matrix: det> - determinant output matrix<br>
 }}
 {{Module |
    | name=mnm.mahalanobis
-   | brief=
+   | brief=Mahalanobis distance
-   | descr=
+   | descr=Calculates mahalanobis distance of incoming matrices or vectors.
-   | arguments=0 - fmat of mean values<br>1 - fmat of covariance<br>
+   | arguments=<matrix: mean> <matrix: covariance> - init mean and covariance<br>
-   | attributes=out - reference to external fmat to store result<br>
+   | attributes=out <matrix: output> - output mahalanobis distance<br>
-   | messages=out - reference to external fmat to store result<br>set_shift_weight - set shift weight per target (in) line<br>set_shift_weight - set shift weight per target (in) line<br>set_shift - set shift vector per database (mu) line<br>set_shift - set shift vector per database (mu) line<br>
+   | messages=postdoc - post external doc to console<br>set_mu <matrix: mu> - matrix of mean values<br>set_sigma <matrix: sigma> - matrix of covariance<br>out <matrix: output> - output mahalanobis distance<br>
-   | inlets=0 - vector<br>1 - mu<br>2 - sigma<br>
+  | inlets=1 <vector: input> - query vector><br>2 <matrix: mu> - mu input matrix<br>3 <matrix: sigma> - sigma input matrix<br>
-   | outlets=0 - mahalanobis distance<br>
+  | outlets=1 <matrix: output> - output mahalanobis distance<br>
+}}
+{{Module |
+  | name=mnm.mean
+  | brief=Mean filtering
+  | descr=Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension).
+  | arguments=<num: input> [<num: filter>] - init input and filter size<br>
+  | attributes=filtersize <num: max> - set the maximum filter size (default is 0 for using the input size)<br>dim <&#039;col&#039;&#124;&#039;row&#039;&#124;&#039;auto&#039;&#124;&#039;stream&#039;: mode> - set the dimension on which to operate [auto]<br>out <matrix: output> - filtered values<br>outstate <matrix: state> - output state<br>
+   | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - set input and filter size<br>getstate - get the mean state<br>clear - reset the internal state<br>out <matrix: output> - filtered values<br>outstate <matrix: state> - output state<br>
+  | inlets=1 <matrix: input> - input values<br>
+   | outlets=1 <matrix: output> - filtered values<br>2 <matrix: state> - output state<br>
 }}
 {{Module |
    | name=mnm.meanstd
-   | brief=
+   | brief=Mean and standard deviation
-   | descr=
+   | descr=Calculates the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector
-   | arguments=0 - (1|2|row|col) sum over rows or columns<br>
+   | arguments=1 <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row|col|1|2) computes over rows or columns<br>outstd - reference to an external fmat to store the standard deviation<br>outmean - reference to an external fmat to store the mean<br>
+   | attributes=mode <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmean <matrix: mean> - mean output vector or value<br>outstd <matrix: stddev> - standard deviation output vector or value<br>
-   | messages=outstd - reference to an external fmat to store the standard deviation<br>outmean - reference to an external fmat to store the mean<br>
+  | messages=postdoc - post external doc to console<br>outmean <matrix: mean> - mean output vector or value<br>outstd <matrix: stddev> - standard deviation output vector or value<br>
-   | inlets=0 - incoming fmat<br>
+  | inlets=1 <matrix: input> - input matrix<br>
-   | outlets=0 - mean<br>1 - standard deviation<br>
+  | outlets=1 <matrix: mean> - mean output vector or value<br>2 <matrix: stddev> - standard deviation output vector or value<br>
+}}
+{{Module |
+  | name=mnm.median
+  | brief=Median filtering
+  | descr=Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension).
+  | arguments=<num: input> [<num: filter>] - init input and filter size<br>
+  | attributes=filtersize <num: filter> - set the maximum filter size (default is 0 for using the input size)<br>dim <&#039;col&#039;&#124;&#039;row&#039;&#124;&#039;auto&#039;&#124;&#039;stream&#039;: mode> - set the dimension on which to operate [auto]<br>out <matrix: output> - output filtered values<br>outstate <matrix: output> - output state<br>
+   | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - set input and filter size<br>getstate - get the median state<br>clear - reset the internal state<br>out <matrix: output> - output filtered values<br>outstate <matrix: output> - output state<br>
+   | inlets=1 <matrix: input> - input values<br>
+   | outlets=1 <matrix: output> - output filtered values<br>2 <matrix: output> - output state<br>
 }}
 {{Module |
    | name=mnm.minmax
-   | brief=
+   | brief=Minimum and maximum
-   | descr=
+   | descr=Calculates the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector
-   | arguments=0 - (1|2|row|col) sum over rows or columns<br>
+   | arguments=1 <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row|col|1|2) computes over rows or columns<br>outargmax - reference to fmat to store the argmax<br>outmax - reference to fmat to store the max<br>outargmin - reference to fmat to store the argmin<br>outmin - reference to fmat to store the min<br>
+   | attributes=mode <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmin <vector: min> - minimum output vector or value<br>outargmin <vector: min> - minimum index output vector or value<br>outmax <vector: max> - maximum output vector or value<br>outargmax <vector: max> - maximum index output vector or value<br>
-   | messages=outargmax - reference to fmat to store the argmax<br>outmax - reference to fmat to store the max<br>outargmin - reference to fmat to store the argmin<br>outmin - reference to fmat to store the min<br>
+  | messages=postdoc - post external doc to console<br>outmin <vector: min> - minimum output vector or value<br>outargmin <vector: min> - minimum index output vector or value<br>outmax <vector: max> - maximum output vector or value<br>outargmax <vector: max> - maximum index output vector or value<br>
-   | inlets=0 - incoming fmat<br>
+  | inlets=1 <matrix: input> - incoming matrix, vector, or list<br>
-   | outlets=0 - min<br>1 - argmin<br>2 - max<br>3 - argmax<br>
+  | outlets=1 <vector: min> - minimum output vector or value<br>2 <vector: min> - minimum index output vector or value<br>3 <vector: max> - maximum output vector or value<br>4 <vector: max> - maximum index output vector or value<br>
+}}
+{{Module |
+  | name=mnm.moments
+  | brief=Statistical moments
+  | descr=Calculates moments from first to specified order.
+  | arguments=1 <num: order> - moments maximum order [1]<br>
+  | attributes=std <bool: switch> - compute the standards moments for orders > 2 [on]<br>mode <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>out <vector: moments> - output moments vector<br>outsum <vector: moments> - output sums vector<br>
+   | messages=postdoc - post external doc to console<br>order <num: order> - set moments maximum order<br>out <vector: moments> - output moments vector<br>outsum <vector: moments> - output sums vector<br>
+   | inlets=1 <vector: input> - input vector<br>
+   | outlets=1 <vector: moments> - output moments vector<br>2 <vector: moments> - output sums vector<br>
 }}
 {{Module |
    | name=mnm.nmd
-   | brief=
+   | brief=Non-zero Matrix Decomposition
-   | descr=
+   | descr=Calculates NMD of incoming matrix.
    | arguments=none
-   | attributes=itermax - itermax<br>sH - sH<br>criteria - criteria<br>outh - out H<br>
+   | attributes=outh <matrix: H> - H output matrix<br>criteria <num: criteria> - criteria<br>sH <num: sH> - sH<br>itermax <num: itermax> - maximum number of iterations<br>
-   | messages=outh - out H<br>
+   | messages=postdoc - post external doc to console<br>
-   | inlets=0 - fmat<br>1 - fmat<br>2 - <br>
+   | inlets=1 <matrix: input> - input matrix<br>2 <matrix: input> - input matrix<br>3 <num: L2> - L2<br>
-   | outlets=0 - fmat<br>
+   | outlets=1 <matrix: output> - output matrix<br>
 }}
 {{Module |
    | name=mnm.nmf
-   | brief=
+   | brief=Non-zero Matrix Factorization
-   | descr=
+   | descr=Calculates NMF of incoming matrix.
-   | arguments=0 - number of components<br>
+   | arguments=1 <num: num> - number of components<br>
-   | attributes=itermax - maximum number of iterations<br>rdim - number of components<br>criteria - (float) stopping criteria<br>outh - reference to external fmat to store H<br>outw - reference to external fmat to store W<br>
+   | attributes=outw <matrix: W> - W output matrix<br>outh <matrix: H> - H output matrix<br>criteria <num: criteria> - stopping criteria<br>rdim <num: num> - number of components<br>itermax <num: max> - maximum number of iterations<br>
-   | messages=outh - reference to external fmat to store H<br>outw - reference to external fmat to store W<br>
+  | messages=postdoc - post external doc to console<br>
-   | inlets=0 - fmat to be decomposed<br>
+  | inlets=1 <matrix: input> - matrix to be decomposed<br>
-   | outlets=0 - W<br>1 - H<br>
+  | outlets=1 <matrix: W> - W output matrix<br>2 <matrix: H> - H output matrix<br>
+}}
+{{Module |
+  | name=mnm.obsprob
+  | brief=Obsprob
+  | descr=Obsprob has to be documented.
+  | arguments=none
+  | attributes=none
+   | messages=postdoc - post external doc to console<br>
+  | inlets=1 <matrix: obs> - input observation frames matrix (D x 1)<br>2 <matrix: states> - input states matrix<br>
+  | outlets=1 <matrix: prob> - output log(B) (K x 1)<br>2 <num> - no description<br>
+}}
+{{Module |
+  | name=mnm.onepole
+  | brief=Onepole filtering
+  | descr=Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).
+  | arguments=<num: input> [<num: filter>] - init input and filter size<br>
+  | attributes=f0 <num: freq> - set the onepole frequency, normalised by the Nyquist frequency (default is 1.)<br>dim <&#039;col&#039;&#124;&#039;row&#039;&#124;&#039;auto&#039;&#124;&#039;stream&#039;: mode> - set the dimension on which to operate [auto]<br>mode <&#039;lowpass&#039;&#124;&#039;highpass&#039;: mode> - filter type [lowpass]<br>out <matrix: output> - output filtered values<br>outstate <matrix: state> - output state<br>
+  | messages=postdoc - post external doc to console<br>getstate - get the onepole state<br>clear - reset the internal state<br>insize <num: input> [<num: filter>] - init input and filter size<br>out <matrix: output> - output filtered values<br>outstate <matrix: state> - output state<br>
+   | inlets=1 <matrix: input> - input values<br>
+   | outlets=1 <matrix: output> - output filtered values<br>2 <matrix: state> - output state<br>
 }}
 {{Module |
    | name=mnm.qr
-   | brief=
+   | brief=Orthogonal-Right decomposition
-   | descr=
+   | descr=Calculates QR decomposition of incoming matrix.
    | arguments=none
-   | attributes=outx - reference to external fmat to store X<br>outr - reference to external fmat to store R<br>outq - reference to external fmat to store Q<br>
+   | attributes=outq <matrix: Q> - Q output matrix<br>outr <matrix: R> - R output matrix<br>outx <matrix: X> - X output matrix<br>
-   | messages=outx - reference to external fmat to store X<br>outr - reference to external fmat to store R<br>outq - reference to external fmat to store Q<br>solve - solve system with QR decomposition<br>
+   | messages=postdoc - post external doc to console<br>solve <matrix: input> - solve system with QR decomposition<br>outq <matrix: Q> - Q output matrix<br>outr <matrix: R> - R output matrix<br>outx <matrix: X> - X output matrix<br>
-   | inlets=0 - fmat to be decomposed<br>
+   | inlets=1 <matrix: input> - matrix to be decomposed<br>
-   | outlets=0 - Q<br>1 - R<br>2 - X<br>
+   | outlets=1 <matrix: Q> - Q output matrix<br>2 <matrix: R> - R output matrix<br>3 <matrix: X> - X output matrix<br>
 }}
 {{Module |
    | name=mnm.stats
-   | brief=
+   | brief=Stats
-   | descr=
+   | descr=Stats has to be documented.
-   | arguments=output stats<br>
+   | arguments=<vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries<br>
-   | attributes=norm - switch normalize<br>
+   | attributes=norm <bool: normalize> - switch normalization [off]<br>
-   | messages=set - set histogram vector (fmat)<br>clear - clear histogram<br>bang - output stats<br>norm - switch normalize<br>
+   | messages=postdoc - post external doc to console<br>bang - output stats<br>clear - clear histogram<br>set <vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries<br>
-   | inlets=0 - data input<br>
+   | inlets=1 <num: value> - data input<br>
-   | outlets=0 - average<br>1 - standard deviation<br>2 - count<br>
+   | outlets=1 <num: average> - output average<br>2 <num: stddev> - output standard deviation<br>3 <num: count> - output count<br>
 }}
 {{Module |
    | name=mnm.submat
-   | brief=
+   | brief=Sub-matrix
-   | descr=
+   | descr=Copies sub-matrix outof incoming matrix.
-   | arguments=none
+   | arguments=<num: begin row> <num: begin col> <num: end row> <num: end col> - start end end indices of sub-matrix<br>
-   | attributes=out - reference to external fmat to store the sub-matrix<br>
+   | attributes=begin <num: row> <num: col> - start indices of sub-matrix<br>end <num: row> <num: col> - end indices of sub-matrix<br>out <matrix: output> - ouput sub-matrix<br>
-   | messages=out - reference to external fmat to store the sub-matrix<br>end - end of submatrix coordinates<br>begin - start of submatrix coordinates<br>
+   | messages=postdoc - post external doc to console<br>out <matrix: output> - ouput sub-matrix<br>
-   | inlets=0 - fmat<br>
+   | inlets=1 <matrix: input> - input matrix<br>
-   | outlets=0 - sub-matrix<br>
+   | outlets=1 <matrix: output> - ouput sub-matrix<br>
 }}
 {{Module |
    | name=mnm.sum
-   | brief=
+   | brief=Matrix sum
-   | descr=
+   | descr=Calculates sum over entire matrix, matrix rows or matrix columns.
-   | arguments=0 - (1|2|row|col) sum over rows or columns<br>
+   | arguments=1 <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row|col|1|2) perform sum over rows or columns<br>out - reference to external fmat to store the sum<br>
+   | attributes=mode <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> - compute over rows or columns [col]<br>out <vector: sum> - output sum vector<br>
-   | messages=out - reference to external fmat to store the sum<br>
+   | messages=postdoc - post external doc to console<br>out <vector: sum> - output sum vector<br>
-   | inlets=0 - incoming fmat<br>
+   | inlets=1 <matrix: input> - incoming matrix to be summed<br>
-   | outlets=0 - sum of fmat<br>
+   | outlets=1 <vector: sum> - output sum vector<br>
 }}
 {{Module |
    | name=mnm.svd
-   | brief=
+   | brief=Singular Value Decomposition
-   | descr=
+   | descr=Calculates SVD of incoming matrix.
-   | arguments=0 - number of singular values<br>
+   | arguments=1 <num: rank> - number of singular values<br>
-   | attributes=mode - (auto|manual) automatically eliminate negligible singular values<br>outvt - reference to external fmat to store V'<br>outs - reference to external fmat to store S<br>outu - reference to external fmat to store U<br>
+   | attributes=mode <&#039;manual&#039;&#124;&#039;auto&#039;: mode> - automatically eliminate negligible singular values [manual]<br>outu <matrix: U> - U output matrix<br>outs <matrix: S> - S output matrix<br>outvt <matrix: V&#039;> - V&#039; output matrix (transposed of V)<br>
-   | messages=outvt - reference to external fmat to store V'<br>outs - reference to external fmat to store S<br>outu - reference to external fmat to store U<br>
+   | messages=postdoc - post external doc to console<br>outu <matrix: U> - U output matrix<br>outs <matrix: S> - S output matrix<br>outvt <matrix: V&#039;> - V&#039; output matrix (transposed of V)<br>
-   | inlets=0 - fmat to be decomposed by SVD<br>
+   | inlets=1 <matrix: input> - matrix to be decomposed by SVD<br>
-   | outlets=0 - U<br>1 - S<br>2 - V'<br>
+   | outlets=1 <matrix: U> - U output matrix<br>2 <matrix: S> - S output matrix<br>3 <matrix: V&#039;> - V&#039; output matrix (transposed of V)<br>
 }}
 {{Module |
    | name=mnm.transpose
-   | brief=
+   | brief=Matrix transposition
-   | descr=
+   | descr=Calculates transposed matrix.
+  | arguments=none
+  | attributes=out <matrix: output> - output transposed matrix<br>
+  | messages=postdoc - post external doc to console<br>out <matrix: output> - output transposed matrix<br>
+  | inlets=1 <matrix: input> - matrix to be transposed<br>
+  | outlets=1 <matrix: output> - output transposed matrix<br>
+}}
+{{Module |
+  | name=mnm.viterbi
+  | brief=Viterbi algorithm
+  | descr=Calculates Viterbi path of incoming matrices or vectors.
    | arguments=none
-   | attributes=out - reference to external fmat to store the transposition<br>
+   | attributes=line <bool: online> - switch online mode on/off [off]<br>verbose <bool: verbose> - verbose or not [off]<br>latency <num: latency> - maximum latency (nb of frames)<br>
-   | messages=out - reference to external fmat to store the transposition<br>
+   | messages=postdoc - post external doc to console<br>geta - get state matrix A<br>getpi - get state matrix Pi<br>getpsi - get state matrix Psi<br>locpaths - get locpaths matrix<br>reinit - input message reinit<br>bang - decode<br>
-   | inlets=0 - fmat to be transposed<br>
+   | inlets=1 - messages only<br>2 <matrix: observations> - input observation matrix logB (T x K)<br>3 <matrix: pi> - input state prior distribution Pi (1 x K)<br>4 <matrix: A> - input state transition matrix A (K x K)<br>
-   | outlets=0 - transposed fmat<br>
+   | outlets=1 <matrix: path> - output decoded best path<br>2 <matrix: state> - output state matrices<br>
 }}
 {{Module |
    | name=mnm.xdist2
-   | brief=
+   | brief=Square of Euclidean distance
-   | descr=
+   | descr=Calculates the square of the Euclidean distance between a vector and each line of a matrix.
-   | arguments=0 - fmat to be used as right operand<br>
+   | arguments=<matrix: right> - matrix to be used as right operand<br>
-   | attributes=swap - (yes|no) swaps operands<br>out - reference to external fmat to store the result<br>
+   | attributes=swap <bool: switch> swap operands<br>out <matrix: output> - result squared distance matrix<br>
-   | messages=out - reference to external fmat to store the result<br>
+   | messages=postdoc - post external doc to console<br>out <matrix: output> - result squared distance matrix<br>
-   | inlets=0 - left operand<br>1 - right operand<br>
+   | inlets=1 <matrix: left> - input left operand><br>2 <matrix: right> - input right operand><br>
-   | outlets=0 - resulting fmat<br>
+   | outlets=1 <matrix: output> - result squared distance matrix<br>
 }}
 {{Module |
    | name=mnm.xmul
-   | brief=
+   | brief=Matrix multiplication
-   | descr=
+   | descr=Calculates matrix multiplication as in out = left x right.<br>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.
-   | arguments=0 - fmat to be used as right operand<br>
+   | arguments=<matrix: op> - set right or (with swap enabled) left matrix multiplication operand<br>
-   | attributes=swap - (yes|no) swaps operands<br>out - reference to an external fmat to store the result<br>
+   | attributes=swap <bool: switch> swap operands<br>out <matrix: output> - output matrix<br>
-   | messages=out - reference to an external fmat to store the result<br>
+   | messages=postdoc - post external doc to console<br>out <matrix: output> - output matrix<br>
-   | inlets=0 - left hand side fmat operand<br>1 - right hand side fmat operand<br>
+   | inlets=1 <matrix: input> - multiply matrix with given right or left operand<br>2 <matrix: op> - set right or (with swap enabled) left matrix multiplication operand<br>
-   | outlets=0 - fmat result<br>
+   | outlets=1 <matrix: output> - output matrix<br>
 }}