Difference between revisions of "MnM Modules"

Revision as of 16:58, 3 May 2009

(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

'

arguments:	none
attributes:	#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
messages:	postdoc - post external doc to console
inlets:	1 - input list
outlets:	1 - message outlet

mnm.biqoefs

Biquad coefficients

Calculates biquad coefficients for various filter types.

arguments:	none
attributes:	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
messages:	postdoc - post external doc to console bang - bang to output coefficients out - biquad coefficients
inlets:	none
outlets:	1 - biquad coefficients

mnm.biquad

Biquad filtering

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

arguments:	set the inputs initial size and numbers
attributes:	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
messages:	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
inlets:	1 - input values
outlets:	1 - filtered values 2 - output state

mnm.delta

Inter-frame regression.

Calculates derivative of incoming matrices or vectors.

arguments:	1 - initialize the input size 2 - initialize the filter size
attributes:	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
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 - output delayed inputs (in phase with deltas) out - output deltas outstate - internal values
inlets:	1 - multiply matrix with given right or left operand
outlets:	1 - output delayed inputs (in phase with deltas) 2 - output deltas 3 - 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 - output diagonal vector
messages:	postdoc - post external doc to console out - output diagonal vector
inlets:	1 - input matrix
outlets:	1 - output diagonal vector

mnm.dtw

Dynamic time warping.

Calculates DTW on incoming matrix or vector.

arguments:	1 - fmat to be used as right operand
attributes:	outa - reference to an external fmat to store A outb - reference to an external fmat to store B
messages:	postdoc - post external doc to console
inlets:	1 - left hand side fmat operand 2 - right hand side fmat operand
outlets:	1 - s1 2 - s2

mnm.gmmem

Expectation maximization for Gaussian mixture models

GMM EM has to be documented.

arguments:	1 - number of centers to use
attributes:	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
messages:	postdoc - post external doc to console
inlets:	1 - fmat
outlets:	1 - fmat centers 2 - fmat covariance 3 - fmat priors

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 - number of bins
attributes:	out - output 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
messages:	postdoc - post external doc to console out - output histogram vector set_n - number of bins
inlets:	1 - intput matrix or list
outlets:	1 - output histogram vector 2 - output min data value 3 - 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 - max number k of nearest neighbours to search 2 - max radius of nearest neighbours to search (0 for unlimited)
attributes:	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
messages:	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
inlets:	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)>
outlets:	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

mnm.lu

Lower-upper decomposition

Calculates LU decomposition on incoming matrix.

arguments:	none
attributes:	outl - L outu - U outpivot - pivot outx - X outdet - determinant
messages:	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
inlets:	1 - matrix to decompose
outlets:	1 - L 2 - U 3 - pivot 4 - X 5 - determinant

mnm.mahalanobis

Mahalanobis distance.

Calculates mahalanobis distance on incoming matrices or vectors.

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

mnm.mean

Mean filtering

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

arguments:	set the inputs initial size and numbers
attributes:	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
messages:	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
inlets:	1 - input values
outlets:	1 - filtered values 2 - 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
attributes:	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
messages:	postdoc - post external doc to console outmean - mean output vector or value outstd - standard deviation output vector or value
inlets:	1 - input matrix
outlets:	1 - mean output vector or value 2 - 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:	set the inputs initial size and numbers
attributes:	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
messages:	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
inlets:	1 - input values
outlets:	1 - filtered values 2 - 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) sum over rows or columns
attributes:	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
messages:	postdoc - post external doc to console outmin - min outargmin - argmin outmax - max outargmax - argmax
inlets:	1 - incoming matrix, vector, or list
outlets:	1 - min 2 - argmin 3 - max 4 - argmax

mnm.moments

Statistical moments

Calculates moments from first to specified order.

arguments:	1 <num: order> - moments maximum order [1]
attributes:	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.
messages:	postdoc - post external doc to console order <num: order> - set moments maximum order out - moments outsum - input sums
inlets:	1 - input vector
outlets:	1 - moments 2 - input sums

mnm.nmd

Non-zero matrix decomposition

Calculates NMD on incoming matrix.

arguments:	none
attributes:	outh - out H criteria - criteria sH - sH itermax - itermax
messages:	postdoc - post external doc to console
inlets:	1 - fmat 2 - fmat 3 -
outlets:	1 - fmat

mnm.nmf

Non-zero matrix factorization

Calculates NMF on incoming matrix.

arguments:	1 - number of components
attributes:	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
messages:	postdoc - post external doc to console
inlets:	1 - fmat to be decomposed
outlets:	1 - W 2 - H

mnm.obsprob

Obsprob

Obsprob has to be documented.

arguments:	none
attributes:	none
messages:	postdoc - post external doc to console
inlets:	1 - ref. to observation frame : fmat [D (=feature space dim) , 1] 2 - ref. to states matrix
outlets:	1 - log(B) : fmat [K (=nb of states) , 1] 2 - test

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:	set the inputs initial size and numbers
attributes:	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
messages:	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
inlets:	1 - input values
outlets:	1 - filtered values 2 - output state

mnm.qr

Orthogonal-right decomposition

Calculates QR decomposition on incoming matrix.

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

mnm.stats

Stats

Stats has to be documented.

arguments:	output stats
attributes:	norm - switch normalize
messages:	postdoc - post external doc to console bang - output stats clear - clear histogram set - set histogram vector (fmat)
inlets:	1 - data input
outlets:	1 - average 2 - standard deviation 3 - count

mnm.submat

Sub-matrix

Copies sub-matrix outof incoming matrix.

arguments:	none
attributes:	out - sub-matrix
messages:	postdoc - post external doc to console begin - start of submatrix coordinates end - end of submatrix coordinates out - sub-matrix
inlets:	1 - fmat
outlets:	1 - sub-matrix

mnm.sum

Matrix sum

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

arguments:	1 - (1\|2\|row\|col) sum over rows or columns
attributes:	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
messages:	postdoc - post external doc to console out - sum of fmat
inlets:	1 - incoming matrix to be summed
outlets:	1 - sum of fmat

mnm.svd

Singular value decomposition

Calculates SVD decomposition on incoming matrix.

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

mnm.transpose

Transpose

Calculates transposed matrix.

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

mnm.viterbi

Viterbi algorithm

Calculates Viterbi path on incoming matrices or vector.

arguments:	none
attributes:	line - on\|off line verbose - verbose or not (0\|1) latency - maximum latency (nb of frames) get - debug : get intern values.
messages:	postdoc - post external doc to console reinit - input message reinit bang - bang to decode locpaths - get locpaths matrix
inlets:	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]
outlets:	1 - decoded best path 2 - debug

mnm.xdist2

Square of Euclidean distance

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

arguments:	matrix to be used as right operand
attributes:	swap - (yes\|no) swaps operands out - result squared distance matrix
messages:	postdoc - post external doc to console out - result squared distance matrix
inlets:	1 <matrix: left operand> 2 <matrix: right operand>
outlets:	1 - 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:	set right or (with swap enabled) left matrix multiplication operand
attributes:	out - output matrix object swap <bool: switch> swaps operands
messages:	postdoc - post external doc to console out - output matrix object
inlets:	1 - multiply matrix with given right or left operand 2 - set right or (with swap enabled) left matrix multiplication operand
outlets:	1 - output matrix object

Difference between revisions of "MnM Modules"

Views

Revision as of 16:58, 3 May 2009

Navigation menu

IMTR

Search

Tools

Personal tools

@@ Line 22: / Line 22: @@
 * mnm.xdist2
 * mnm.xmul
 {{Module |
@@ Line 34: / Line 33: @@
    | outlets=1 - message outlet<br>
 }}
 {{Module |
    | name=mnm.biqoefs
@@ Line 44: / Line 44: @@
    | outlets=1 - biquad coefficients<br>
 }}
 {{Module |
    | name=mnm.biquad
    | brief=Biquad filtering
-   | descr=Compute biquad filtering over vectors (rows or columns) or stream of values (of any dimension).
+   | descr=Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension).
    | arguments=set the inputs initial size and numbers<br>
    | attributes=mode <&#039;df1&#039; &#124; &#039;df2t&#039;> - set biquad structure (default &#039;df1&#039;)<br>dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).<br>out - filtered values<br>outstate - output state<br>
@@ Line 54: / Line 55: @@
    | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.delta
-   | brief=
+   | brief=Inter-frame regression.
-   | descr=
+   | descr=Calculates derivative of incoming matrices or vectors.
    | arguments=1 - initialize the input size<br>2 - initialize the filter size<br>
    | attributes=insize - set the input size<br>filtersize - set the filter size<br>inadddel - add a delay to the delayed input<br>norm - normalization mode 1 (default) or 0<br>outdelayed - output delayed inputs (in phase with deltas)<br>out - output deltas<br>outstate - internal values<br>
@@ Line 64: / Line 66: @@
    | outlets=1 - output delayed inputs (in phase with deltas)<br>2 - output deltas<br>3 - internal values<br>
 }}
 {{Module |
    | name=mnm.diag
-   | brief=get diagonal of matrix
+   | brief=Matrix diagonal
-   | descr=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.
+   | 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 - diagonal of the fmat<br>
+   | attributes=out - output diagonal vector<br>
-   | messages=postdoc - post external doc to console<br>out - diagonal of the fmat<br>
+   | messages=postdoc - post external doc to console<br>out - output diagonal vector<br>
-   | inlets=1 - incoming matrix whose diagonal will be extracted<br>
+   | inlets=1 - input matrix<br>
-   | outlets=1 - diagonal of the fmat<br>
+   | outlets=1 - output diagonal vector<br>
 }}
 {{Module |
    | name=mnm.dtw
@@ Line 84: / Line 88: @@
    | outlets=1 - s1<br>2 - s2<br>
 }}
 {{Module |
    | name=mnm.gmmem
-   | brief=
+   | brief=Expectation maximization for Gaussian mixture models
-   | descr=
+   | descr=GMM EM has to be documented.
    | arguments=1 - number of centers to use<br>
    | attributes=outcenters - reference to external fmat to store centers<br>outcov - reference to external fmat to store covariance<br>outpriors - reference to external fmat to store priors<br>mode - (diagonal&#124;full&#124;spherical) covariance computation types<br>criteria - criteria<br>ncenters - number of centers<br>
@@ Line 94: / Line 99: @@
    | outlets=1 - fmat centers<br>2 - fmat covariance<br>3 - fmat priors<br>
 }}
 {{Module |
    | name=mnm.hist
-   | brief=calculate histogram of incoming matrix elements
+   | brief=Histogram
-  | descr=The input matrix, list or vector element's occurences are counted in the given number of bins in between the min and max value
+  | 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=1 - number of bins<br>
-   | attributes=out - histogram vector<br>bpf - output two-column fmat with bin indices and histogram values<br>norm <symbol: off&#124;max&#124;sum> -- normalise histogram so that max or sum is 1<br>
+   | attributes=out - output histogram vector<br>bpf - output two-column fmat with bin indices and histogram values<br>norm <symbol: off&#124;max&#124;sum> -- normalise histogram so that max or sum is 1<br>
-   | messages=postdoc - post external doc to console<br>out - histogram vector<br>set_n - number of bins<br>
+   | messages=postdoc - post external doc to console<br>out - output histogram vector<br>set_n - number of bins<br>
-   | inlets=1 - data fmat, fvec, jitter matrix or list <br>
+   | inlets=1 - intput matrix or list <br>
-   | outlets=1 - histogram vector<br>2 - min data value<br>3 - max data value<br>
+   | outlets=1 - output histogram vector<br>2 - output min data value<br>3 - output max data value<br>
 }}
 {{Module |
    | name=mnm.knn
-   | brief=k nearest neighbour search
+   | 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 - max number k of nearest neighbours to search<br>2 - max radius of nearest neighbours to search (0 for unlimited)<br>
@@ Line 114: / Line 121: @@
    | outlets=1 <fmat&#124;fvec&#124;list&#124;jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>2 <fmat&#124;fvec&#124;list&#124;jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br>
 }}
 {{Module |
    | name=mnm.lu
-   | brief=
+   | brief=Lower-upper decomposition
-   | descr=
+   | descr=Calculates LU decomposition on incoming matrix.
    | arguments=none
    | attributes=outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<br>
@@ Line 124: / Line 132: @@
    | outlets=1 - L<br>2 - U<br>3 - pivot<br>4 - X<br>5 - determinant<br>
 }}
 {{Module |
    | name=mnm.mahalanobis
-   | brief=
+   | brief=Mahalanobis distance.
-   | descr=
+   | descr=Calculates mahalanobis distance on incoming matrices or vectors.
    | arguments=<matrix&#124;vector&#124;list: mean> <matrix&#124;vector&#124;list: covariance> - init mean and covariance<br>
    | attributes=out - mahalanobis distance<br>
@@ Line 134: / Line 143: @@
    | outlets=1 - mahalanobis distance<br>
 }}
 {{Module |
    | name=mnm.mean
    | brief=Mean filtering
-   | descr=Compute mean filtering over vectors (rows or columns) or stream of values (of any dimension).
+   | descr=Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension).
    | arguments=set the inputs initial size and numbers<br>
    | attributes=filtersize - set the maximum filter size (default is 0 for using the input size)<br>dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).<br>outtype - set the output type: fmat, float or auto (default, matches the input type).<br>out - filtered values<br>outstate - output state<br>
@@ Line 144: / Line 154: @@
    | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.meanstd
-   | brief=mean and standard deviation on matrix
+   | brief=Mean and standard deviation
-   | descr=Output the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector
+   | descr=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&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> compute over rows or columns<br>
    | attributes=mode <1&#124;2&#124;&#039;row&#039;&#124;&#039;col&#039;: mode switch> compute over rows or columns [rows]<br>scalar <bool: switch> output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmean - mean output vector or value<br>outstd - standard deviation output vector or value<br>
@@ Line 154: / Line 165: @@
    | outlets=1 - mean output vector or value<br>2 - standard deviation output vector or value<br>
 }}
 {{Module |
    | name=mnm.median
    | brief=Median filtering
-   | descr=Compute median filtering over vectors (rows or columns) or stream of values (of any dimension).
+   | descr=Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension).
    | arguments=set the inputs initial size and numbers<br>
    | attributes=filtersize - set the maximum filter size (default is 0 for using the input size)<br>dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).<br>outtype - set the output type: fmat, float or auto (default, matches the input type).<br>out - filtered values<br>outstate - output state<br>
@@ Line 164: / Line 176: @@
    | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.minmax
-   | brief=min and max operations on fmat
+   | brief=Minimum and maximum
-   | descr=Output the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector
+   | 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=1 - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>
    | attributes=mode - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>scalar <bool: switch> output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmin - min<br>outargmin - argmin<br>outmax - max<br>outargmax - argmax<br>
@@ Line 174: / Line 187: @@
    | outlets=1 - min<br>2 - argmin<br>3 - max<br>4 - argmax<br>
 }}
 {{Module |
    | name=mnm.moments
-   | brief=Statistical Moments
+   | brief=Statistical moments
    | descr=Calculates moments from first to specified order.
    | arguments=1 <num: order> - moments maximum order [1]<br>
@@ Line 184: / Line 198: @@
    | outlets=1 - moments<br>2 - input sums<br>
 }}
 {{Module |
    | name=mnm.nmd
-   | brief=
+   | brief=Non-zero matrix decomposition
-   | descr=
+   | descr=Calculates NMD on incoming matrix.
    | arguments=none
    | attributes=outh - out H<br>criteria - criteria<br>sH - sH<br>itermax - itermax<br>
@@ Line 194: / Line 209: @@
    | outlets=1 - fmat<br>
 }}
 {{Module |
    | name=mnm.nmf
-   | brief=
+   | brief=Non-zero matrix factorization
-   | descr=
+   | descr=Calculates NMF on incoming matrix.
    | arguments=1 - number of components<br>
    | attributes=outw - reference to external fmat to store W<br>outh - reference to external fmat to store H<br>criteria - (float) stopping criteria<br>rdim - number of components<br>itermax - maximum number of iterations<br>
@@ Line 204: / Line 220: @@
    | outlets=1 - W<br>2 - H<br>
 }}
 {{Module |
    | name=mnm.obsprob
-   | brief=
+   | brief=Obsprob
-   | descr=
+   | descr=Obsprob has to be documented.
    | arguments=none
    | attributes=none
@@ Line 214: / Line 231: @@
    | outlets=1 - log(B) : fmat [K (=nb of states) , 1]<br>2 - test<br>
 }}
 {{Module |
    | name=mnm.onepole
    | brief=Onepole filtering
-   | descr=Compute onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).
+   | descr=Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).
    | arguments=set the inputs initial size and numbers<br>
    | attributes=f0 - set the onepole f0, normalised by the Nyquist frequency (default is 1.)<br>dim - set the dimension on which to operate: col, row, auto (default) or stream (element by element).<br>mode - filter type (feault is lowpass).<br>out - filtered values<br>outstate - output state<br>
@@ Line 224: / Line 242: @@
    | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.qr
-   | brief=
+   | brief=Orthogonal-right decomposition
-   | descr=
+   | descr=Calculates QR decomposition on incoming matrix.
    | arguments=none
    | attributes=outq - Q<br>outr - R<br>outx - X<br>
@@ Line 234: / Line 253: @@
    | outlets=1 - Q<br>2 - R<br>3 - X<br>
 }}
 {{Module |
    | name=mnm.stats
-   | brief=
+   | brief=Stats
-   | descr=
+   | descr=Stats has to be documented.
    | arguments=output stats<br>
    | attributes=norm - switch normalize<br>
@@ Line 244: / Line 264: @@
    | outlets=1 - average<br>2 - standard deviation<br>3 - count<br>
 }}
 {{Module |
    | name=mnm.submat
-   | brief=
+   | brief=Sub-matrix
-   | descr=
+   | descr=Copies sub-matrix outof incoming matrix.
    | arguments=none
    | attributes=out - sub-matrix<br>
@@ Line 254: / Line 275: @@
    | outlets=1 - sub-matrix<br>
 }}
 {{Module |
    | name=mnm.sum
-   | brief=
+   | brief=Matrix sum
-   | descr=
+   | descr=Calculates sum over entire matrix, matrix rows or matrix columns.
    | arguments=1 - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>
    | attributes=out - sum of fmat<br>mode - &#039;row&#039;&#124;&#039;col&#039;&#124;1&#124;2 -- perform sum over rows or columns<br>type <symbol: &#039;float&#039;&#124;&#039;fmat&#039;> -- always output a matrix even for scalar results<br>
@@ Line 264: / Line 286: @@
    | outlets=1 - sum of fmat<br>
 }}
 {{Module |
    | name=mnm.svd
-   | brief=
+   | brief=Singular value decomposition
-   | descr=
+   | descr=Calculates SVD decomposition on incoming matrix.
    | arguments=1 - number of singular values<br>
    | attributes=mode - (auto&#124;manual) automatically eliminate negligible singular values<br>outu - output matrix for U<br>outs - output matrix for S<br>outvt - output matrix for V&#039;<br>
@@ Line 274: / Line 297: @@
    | outlets=1 - output matrix for U<br>2 - output matrix for S<br>3 - output matrix for V&#039;<br>
 }}
 {{Module |
    | name=mnm.transpose
-   | brief=
+   | brief=Transpose
-   | descr=
+   | descr=Calculates transposed matrix.
    | arguments=none
    | attributes=out - transposed matrix<br>
@@ Line 284: / Line 308: @@
    | outlets=1 - transposed matrix<br>
 }}
 {{Module |
    | name=mnm.viterbi
-   | brief=
+   | brief=Viterbi algorithm
-   | descr=
+   | descr=Calculates Viterbi path on incoming matrices or vector.
    | arguments=none
    | attributes=line - on&#124;off line<br>verbose - verbose or not (0&#124;1)<br>latency - maximum latency (nb of frames)<br>get - debug : get intern values.<br>
@@ Line 294: / Line 319: @@
    | outlets=1 - decoded best path<br>2 - debug<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=matrix to be used as right operand<br>
    | attributes=swap - (yes&#124;no) swaps operands<br>out - result squared distance matrix<br>
@@ Line 304: / Line 330: @@
    | outlets=1 - result squared distance matrix<br>
 }}
 {{Module |
    | name=mnm.xmul