Difference between revisions of "MnM Modules"

Revision as of 17:53, 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

Compute 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

'

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

get diagonal of matrix

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.

arguments:	none
attributes:	out - diagonal of the fmat
messages:	postdoc - post external doc to console out - diagonal of the fmat
inlets:	1 - incoming matrix whose diagonal will be extracted
outlets:	1 - diagonal of the fmat

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

'

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

calculate 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 - 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 - histogram vector set_n - number of bins
inlets:	1 - data fmat, fvec, jitter matrix or list
outlets:	1 - histogram vector 2 - min data value 3 - 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

'

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

'

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

Compute 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 on matrix

Output 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

Compute 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

min and max operations on fmat

Output 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

'

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

'

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

'

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

Compute 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

'

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

'

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

'

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

'

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

'

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

'

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

'

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

'

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"

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Revision as of 17:53, 3 May 2009

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@@ Line 25: / Line 25: @@
 {{Module |
-   | name=mnm.centroid
+   | name=ftm.mess
    | brief=
    | descr=
    | arguments=none
-   | attributes=mode - (1&#124;2&#124;row&#124;col) calculate over rows (default, same as 1) or columns (same as 2) for multicolumn inputs.<br>
+  | attributes=#triggerall <bool: flag> - all inlets trigger evaluation and output<br>#loadbang <bool: flag> - message box outputs at loadbang<br>#untuple <bool: flag> - single tuples are output as lists<br>
-   | messages=mode - (1&#124;2&#124;row&#124;col) calculate over rows (default, same as 1) or columns (same as 2) for multicolumn inputs.<br>
+  | messages=postdoc - post external doc to console<br>
-   | inlets=1 - input vector<br>
+  | inlets=1 - input list<br>
-   | outlets=1 - output centroids<br>2 - input sums<br>
+  | outlets=1 - message outlet<br>
+}}
+{{Module |
+  | name=mnm.biqoefs
+  | brief=Biquad coefficients
+  | descr=Calculates biquad coefficients for various filter types.
+  | arguments=none
+   | attributes=mode - filter type (default is lowpass)<br>f0 - cutoff or centre frequency (default is half Nyquist frequency)<br>unit - unit for f0: ratio to Nyquist frequency (default) or in Hertz<br>sr - sample rate for f0 in Hertz (default is 44100.)<br>q - quality/resonance (default is 1.)<br>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 - linear gain (default is 1.)<br>coefsas - output coefficients as an fmat or as a list (default is fmat)<br>out - biquad coefficients<br>
+  | messages=postdoc - post external doc to console<br>bang - bang to output coefficients<br>out - biquad coefficients<br>
+  | inlets=none
+  | 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).
+  | 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>
+  | messages=postdoc - post external doc to console<br>insize - change the inputs size and numbers<br>coefs - set the biquad coefficients<br>getstate - get the biquad state<br>clear - reset the internal state<br>out - filtered values<br>outstate - output state<br>
+   | inlets=1 - input values<br>
+   | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.delta
@@ Line 40: / Line 59: @@
    | descr=
    | arguments=1 - initialize the input size<br>2 - initialize the filter size<br>
-   | attributes=norm - normalization mode 1 (default) or 0<br>inadddel - add a delay to the delayed input<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>
-   | 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 - output delayed inputs (in phase with deltas)<br>out - output deltas<br>outstate - internal values<br>
-   | inlets=1 - input vector<br>
+   | inlets=1 - multiply matrix with given right or left operand<br>
    | outlets=1 - output delayed inputs (in phase with deltas)<br>2 - output deltas<br>3 - internal values<br>
 }}
 {{Module |
    | name=mnm.diag
-   | brief=
+   | brief=get diagonal of matrix
-   | descr=
+   | 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.
    | arguments=none
-   | attributes=out - reference to an external fmat to store the diagonal<br>
+   | attributes=out - diagonal of the fmat<br>
-   | messages=out - reference to an external fmat to store the diagonal<br>
+   | messages=postdoc - post external doc to console<br>out - diagonal of the fmat<br>
-   | inlets=1 - incoming fmat whose diagonal will be extracted<br>
+   | inlets=1 - incoming matrix whose diagonal will be extracted<br>
    | outlets=1 - diagonal of the fmat<br>
 }}
+{{Module |
+  | name=mnm.dtw
+  | brief=Dynamic time warping.
+  | descr=Calculates DTW on incoming matrix or vector.
+  | arguments=1 - fmat to be used as right operand<br>
+  | attributes=outa - reference to an external fmat to store A<br>outb - reference to an external fmat to store B<br>
+  | messages=postdoc - post external doc to console<br>
+  | inlets=1 - left hand side fmat operand<br>2 - right hand side fmat operand<br>
+  | outlets=1 - s1<br>2 - s2<br>
+}}
 {{Module |
    | name=mnm.gmmem
@@ Line 62: / Line 89: @@
    | descr=
    | arguments=1 - number of centers to use<br>
-   | attributes=ncenters - number of centers<br>criteria - criteria<br>mode - (diagonal&#124;full&#124;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 - 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>
-   | 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=1 - fmat<br>
    | outlets=1 - fmat centers<br>2 - fmat covariance<br>3 - fmat priors<br>
 }}
 {{Module |
    | name=mnm.hist
-   | brief=
+   | brief=calculate histogram of incoming matrix elements
-   | descr=
+   | 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
    | arguments=1 - 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 - 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=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 - histogram vector<br>set_n - number of bins<br>
-   | inlets=1 - data fmat or fvec <br>
+   | inlets=1 - data fmat, fvec, jitter matrix or list <br>
-   | outlets=1 - histogram fmat<br>2 - min data value<br>3 - max data value<br>
+   | outlets=1 - histogram vector<br>2 - min data value<br>3 - 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 - max number k of nearest neighbours to search<br>2 - max radius of nearest neighbours to search (0 for unlimited)<br>
+  | attributes=setdata - matrix(N, D) of data<br>setsigma - matrix(1, D) of sigma = 1/weights<br>dmode - decomposition mode: orthogonal, hyperplane, pca<br>mmode - pivot calculation mode: mean, middle, median<br>sort - sort output by distance<br>height - given tree height if positive, subtract from maxheight if negative<br>outdist <fmat&#124;fvec&#124;list&#124;jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>outind <fmat&#124;fvec&#124;list&#124;jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br>
+  | messages=postdoc - post external doc to console<br>outdist <fmat&#124;fvec&#124;list&#124;jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>outind <fmat&#124;fvec&#124;list&#124;jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br>setk - max number k of nearest neighbours to search<br>setradius - max radius of nearest neighbours to search (0 for unlimited)<br>getmeanvectors <fmat: out> - copy mean vectors for the M tree nodes to copy fmat(M, D) out<br>getsplitplanes <fmat: out> - copy vectors perpendicular to the hyperplanes splitting the M tree nodes to fmat(M, D) out<br>getprofile <dict: out> - copy profiling info to given dict and clear<br>print [<symbol: &#039;info&#039;&#124;&#039;raw&#039;&#124;&#039;data&#039;&#124;&#039;compact&#039;&#124;&#039;nodes&#039;&#124;&#039;profile&#039;>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword &#039;profile&#039; is given<br>
+  | inlets=1 <fmat&#124;fvec&#124;list&#124;jitter: x(1, D)> - query vector to search k-nearest neighbours of<br>2 <fmat&#124;fvec&#124;list&#124;jitter: data(N, D)><br>3 <fmat&#124;fvec&#124;list&#124;jitter: sigma(1, D)><br>
+  | 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
@@ Line 84: / Line 119: @@
    | descr=
    | arguments=none
-   | attributes=outx - out X<br>outpivot - out pivot<br>outu - out U<br>outl - out L<br>
+   | attributes=outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<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 fmat<br>solve - solves system with incoming fmat and decomposed fmat<br>outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<br>
-   | inlets=1 - fmat to decompose<br>
+   | inlets=1 - matrix to decompose<br>
    | outlets=1 - L<br>2 - U<br>3 - pivot<br>4 - X<br>5 - determinant<br>
 }}
 {{Module |
    | name=mnm.mahalanobis
    | brief=
    | descr=
-   | arguments=1 - fmat of mean values<br>2 - fmat of covariance<br>
+   | arguments=<matrix&#124;vector&#124;list: mean> <matrix&#124;vector&#124;list: covariance> - init mean and covariance<br>
-   | attributes=out - reference to external fmat to store result<br>
+   | attributes=out - 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 of mean values<br>set_sigma - matrix of covariance<br>out - mahalanobis distance<br>
-   | inlets=1 - vector<br>2 - mu<br>3 - sigma<br>
+   | inlets=1 <matrix&#124;vector&#124;list: query vector><br>2 <matrix: mu><br>3 <matrix: sigma><br>
    | 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).
+  | 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>
+  | messages=postdoc - post external doc to console<br>insize - change the inputs size and numbers<br>getstate - get the mean state<br>clear - reset the internal state<br>out - filtered values<br>outstate - output state<br>
+  | inlets=1 - input values<br>
+  | outlets=1 - filtered values<br>2 - output state<br>
+}}
 {{Module |
    | name=mnm.meanstd
-   | brief=
+   | brief=mean and standard deviation on matrix
-   | descr=
+   | descr=Output 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;row&#124;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<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row&#124;col&#124;1&#124;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 [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>
-   | 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 - mean output vector or value<br>outstd - standard deviation output vector or value<br>
-   | inlets=1 - incoming fmat<br>
+  | inlets=1 - input matrix<br>
-   | outlets=1 - mean<br>2 - standard deviation<br>
+  | 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).
+  | 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>
+   | messages=postdoc - post external doc to console<br>insize - change the inputs size and numbers<br>getstate - get the median state<br>clear - reset the internal state<br>out - filtered values<br>outstate - output state<br>
+   | inlets=1 - input values<br>
+   | outlets=1 - filtered values<br>2 - output state<br>
 }}
 {{Module |
    | name=mnm.minmax
-   | brief=
+   | brief=min and max operations on fmat
-   | descr=
+   | 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
    | arguments=1 - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row&#124;col&#124;1&#124;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;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>
-   | 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 - min<br>outargmin - argmin<br>outmax - max<br>outargmax - argmax<br>
-   | inlets=1 - incoming fmat<br>
+   | inlets=1 - incoming matrix, vector, or list<br>
    | outlets=1 - min<br>2 - argmin<br>3 - max<br>4 - argmax<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 <&#039;0&#039;&#124;&#039;1&#039;: switch> - compute the standards moments for orders > 2 [1]<br>sumasfloat <&#039;0&#039;&#124;&#039;1&#039;: switch> - enable/disable float sum output [0]<br>out - moments<br>outsum - input sums<br>mode - (1&#124;2&#124;row&#124;col) calculate over rows (same as 1) or columns (default, same as 2) for multicolumn inputs.<br>
+  | messages=postdoc - post external doc to console<br>order <num: order> - set moments maximum order<br>out - moments<br>outsum - input sums<br>
+  | inlets=1 - input vector<br>
+  | outlets=1 - moments<br>2 - input sums<br>
+}}
 {{Module |
    | name=mnm.nmd
@@ Line 128: / Line 189: @@
    | descr=
    | arguments=none
-   | attributes=itermax - itermax<br>sH - sH<br>criteria - criteria<br>outh - out H<br>
+   | attributes=outh - out H<br>criteria - criteria<br>sH - sH<br>itermax - itermax<br>
-   | messages=outh - out H<br>
+   | messages=postdoc - post external doc to console<br>
    | inlets=1 - fmat<br>2 - fmat<br>3 - <br>
    | outlets=1 - fmat<br>
 }}
 {{Module |
    | name=mnm.nmf
@@ Line 139: / Line 199: @@
    | descr=
    | arguments=1 - 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 - 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>
-   | 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=1 - fmat to be decomposed<br>
    | outlets=1 - W<br>2 - H<br>
 }}
 {{Module |
    | name=mnm.obsprob
@@ Line 151: / Line 210: @@
    | arguments=none
    | attributes=none
-   | messages=none
+   | messages=postdoc - post external doc to console<br>
    | inlets=1 - ref. to observation frame : fmat [D (=feature space dim) , 1]<br>2 - ref. to states matrix<br>
    | 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).
+  | 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>
+  | messages=postdoc - post external doc to console<br>getstate - get the onepole state<br>clear - reset the internal state<br>out - filtered values<br>outstate - output state<br>insize - change the inputs size and numbers<br>
+  | inlets=1 - input values<br>
+  | outlets=1 - filtered values<br>2 - output state<br>
+}}
 {{Module |
    | name=mnm.qr
@@ Line 161: / Line 229: @@
    | descr=
    | 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 - Q<br>outr - R<br>outx - X<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 - solve system with QR decomposition<br>outq - Q<br>outr - R<br>outx - X<br>
    | inlets=1 - fmat to be decomposed<br>
    | outlets=1 - Q<br>2 - R<br>3 - X<br>
 }}
 {{Module |
    | name=mnm.stats
@@ Line 173: / Line 240: @@
    | arguments=output stats<br>
    | attributes=norm - switch normalize<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 - set histogram vector (fmat)<br>
    | inlets=1 - data input<br>
    | outlets=1 - average<br>2 - standard deviation<br>3 - count<br>
 }}
 {{Module |
    | name=mnm.submat
@@ Line 183: / Line 249: @@
    | descr=
    | arguments=none
-   | attributes=out - reference to external fmat to store the sub-matrix<br>
+   | attributes=out - 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>begin - start of submatrix coordinates<br>end - end of submatrix coordinates<br>out - sub-matrix<br>
    | inlets=1 - fmat<br>
    | outlets=1 - sub-matrix<br>
 }}
 {{Module |
    | name=mnm.sum
@@ Line 194: / Line 259: @@
    | descr=
    | arguments=1 - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>
-   | attributes=type - (fmat) always output an fmat even for scalar results<br>mode - (row&#124;col&#124;1&#124;2) perform sum over rows or columns<br>out - reference to external fmat to store the sum<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>
-   | messages=out - reference to external fmat to store the sum<br>
+   | messages=postdoc - post external doc to console<br>out - sum of fmat<br>
-   | inlets=1 - incoming fmat<br>
+   | inlets=1 - incoming matrix to be summed<br>
    | outlets=1 - sum of fmat<br>
 }}
 {{Module |
    | name=mnm.svd
@@ Line 205: / Line 269: @@
    | descr=
    | arguments=1 - number of singular values<br>
-   | attributes=mode - (auto&#124;manual) automatically eliminate negligible singular values<br>outvt - reference to external fmat to store V&#039;<br>outs - reference to external fmat to store S<br>outu - reference to external fmat to store U<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>
-   | messages=outvt - reference to external fmat to store V&#039;<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 - output matrix for U<br>outs - output matrix for S<br>outvt - output matrix for V&#039;<br>
-   | inlets=1 - fmat to be decomposed by SVD<br>
+   | inlets=1 - matrix to be decomposed by SVD<br>
-   | outlets=1 - U<br>2 - S<br>3 - V&#039;<br>
+   | outlets=1 - output matrix for U<br>2 - output matrix for S<br>3 - output matrix for V&#039;<br>
 }}
 {{Module |
    | name=mnm.transpose
@@ Line 216: / Line 279: @@
    | descr=
    | arguments=none
-   | attributes=out - reference to external fmat to store the transposition<br>
+   | attributes=out - transposed matrix<br>
-   | messages=out - reference to external fmat to store the transposition<br>
+   | messages=postdoc - post external doc to console<br>out - transposed matrix<br>
-   | inlets=1 - fmat to be transposed<br>
+   | inlets=1 - matrix to be transposed<br>
-   | outlets=1 - transposed fmat<br>
+   | outlets=1 - transposed matrix<br>
 }}
 {{Module |
    | name=mnm.viterbi
@@ Line 227: / Line 289: @@
    | descr=
    | arguments=none
-   | attributes=get - debug : get intern values.<br>latency - maximum latency (nb of frames)<br>verbose - verbose or not (0&#124;1)<br>line - on&#124;off line<br>
+   | 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>
-  | messages=get - debug : get intern values.<br>locpaths - get locpaths matrix<br>latency - maximum latency (nb of frames)<br>verbose - verbose or not (0&#124;1)<br>bang - bang to decode<br>reinit - input message reinit<br>
+  | messages=postdoc - post external doc to console<br>reinit - input message reinit<br>bang - bang to decode<br>locpaths - get locpaths matrix<br>
    | inlets=1 - bang to decode, or reinit message to reset PSIs and DELTAs<br>2 - observation matrix logB[T,K]<br>3 - state prior distribution Pi[1,K]<br>4 - state transition matrix A[K,K]<br>
    | outlets=1 - decoded best path<br>2 - debug<br>
 }}
 {{Module |
    | name=mnm.xdist2
    | brief=
    | descr=
-   | arguments=1 - fmat to be used as right operand<br>
+   | arguments=matrix to be used as right operand<br>
-   | attributes=swap - (yes&#124;no) swaps operands<br>out - reference to external fmat to store the result<br>
+   | attributes=swap - (yes&#124;no) swaps operands<br>out - 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 - result squared distance matrix<br>
-   | inlets=1 - left operand<br>2 - right operand<br>
+   | inlets=1 <matrix: left operand><br>2 <matrix: right operand><br>
-   | outlets=1 - resulting fmat<br>
+   | outlets=1 - 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=1 - fmat to be used as right operand<br>
+   | arguments=set right or (with swap enabled) left matrix multiplication operand<br>
-   | attributes=swap - (yes&#124;no) swaps operands<br>out - reference to an external fmat to store the result<br>
+   | attributes=out - output matrix object<br>swap <bool: switch> swaps operands<br>
-   | messages=out - reference to an external fmat to store the result<br>
+   | messages=postdoc - post external doc to console<br>out - output matrix object<br>
-   | inlets=1 - left hand side fmat operand<br>2 - right hand side fmat operand<br>
+   | inlets=1 - multiply matrix with given right or left operand<br>2 - set right or (with swap enabled) left matrix multiplication operand<br>
-   | outlets=1 - fmat result<br>
+   | outlets=1 - output matrix object<br>
 }}