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{{Module |  
 
{{Module |  
 
   | name=mnm.delta
 
   | name=mnm.delta
−
   | brief=Inter-frame regression.
+
   | brief=Inter-frame regression
 
   | descr=Calculates derivative of incoming matrices or vectors.
 
   | descr=Calculates derivative of incoming matrices or vectors.
 
   | arguments=1 - initialize the input size<br>2 - initialize the filter size<br>
 
   | arguments=1 - initialize the input size<br>2 - initialize the filter size<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.dtw
 
   | name=mnm.dtw
−
   | brief=Dynamic time warping.
+
   | brief=Dynamic Time Warping
−
   | descr=Calculates DTW on incoming matrix or vector.
+
   | descr=Calculates DTW of incoming matrix or vector.
 
   | arguments=1 - fmat to be used as right operand<br>
 
   | 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>
 
   | attributes=outa - reference to an external fmat to store A<br>outb - reference to an external fmat to store B<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.gmmem
 
   | name=mnm.gmmem
−
   | brief=Expectation maximization for Gaussian mixture models
+
   | brief=Expectation Maximization for Gaussian Mixture Models
 
   | descr=GMM EM has to be documented.
 
   | descr=GMM EM has to be documented.
 
   | arguments=1 - number of centers to use<br>
 
   | arguments=1 - number of centers to use<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.knn
 
   | 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.
 
   | 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>
 
   | arguments=1 - max number k of nearest neighbours to search<br>2 - max radius of nearest neighbours to search (0 for unlimited)<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.lu
 
   | name=mnm.lu
−
   | brief=Lower-upper decomposition
+
   | brief=Lower-Upper decomposition
−
   | descr=Calculates LU decomposition on incoming matrix.
+
   | descr=Calculates LU decomposition of incoming matrix.
 
   | arguments=none
 
   | arguments=none
 
   | attributes=outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<br>
 
   | attributes=outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.mahalanobis
 
   | name=mnm.mahalanobis
−
   | brief=Mahalanobis distance.
+
   | brief=Mahalanobis distance
−
   | descr=Calculates mahalanobis distance on incoming matrices or vectors.
+
   | descr=Calculates mahalanobis distance of incoming matrices or vectors.
 
   | arguments=<matrix&#124;vector&#124;list: mean> <matrix&#124;vector&#124;list: covariance> - init mean and covariance<br>
 
   | arguments=<matrix&#124;vector&#124;list: mean> <matrix&#124;vector&#124;list: covariance> - init mean and covariance<br>
 
   | attributes=out - mahalanobis distance<br>
 
   | attributes=out - mahalanobis distance<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.nmd
 
   | name=mnm.nmd
−
   | brief=Non-zero matrix decomposition
+
   | brief=Non-zero Matrix Decomposition
−
   | descr=Calculates NMD on incoming matrix.
+
   | descr=Calculates NMD of incoming matrix.
 
   | arguments=none
 
   | arguments=none
 
   | attributes=outh - out H<br>criteria - criteria<br>sH - sH<br>itermax - itermax<br>
 
   | attributes=outh - out H<br>criteria - criteria<br>sH - sH<br>itermax - itermax<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.nmf
 
   | name=mnm.nmf
−
   | brief=Non-zero matrix factorization
+
   | brief=Non-zero Matrix Factorization
−
   | descr=Calculates NMF on incoming matrix.
+
   | descr=Calculates NMF of incoming matrix.
 
   | arguments=1 - number of components<br>
 
   | 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>
 
   | 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>
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{{Module |  
 
{{Module |  
 
   | name=mnm.qr
 
   | name=mnm.qr
−
   | brief=Orthogonal-right decomposition
+
   | brief=Orthogonal-Right decomposition
−
   | descr=Calculates QR decomposition on incoming matrix.
+
   | descr=Calculates QR decomposition of incoming matrix.
 
   | arguments=none
 
   | arguments=none
 
   | attributes=outq - Q<br>outr - R<br>outx - X<br>
 
   | attributes=outq - Q<br>outr - R<br>outx - X<br>
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{{Module |  
 
{{Module |  
 
   | name=mnm.svd
 
   | name=mnm.svd
−
   | brief=Singular value decomposition
+
   | brief=Singular Value Decomposition
−
   | descr=Calculates SVD decomposition on incoming matrix.
+
   | descr=Calculates SVD of incoming matrix.
 
   | arguments=1 - number of singular values<br>
 
   | 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>
 
   | 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>
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   | name=mnm.viterbi
 
   | name=mnm.viterbi
 
   | brief=Viterbi algorithm
 
   | brief=Viterbi algorithm
−
   | descr=Calculates Viterbi path on incoming matrices or vector.
+
   | descr=Calculates Viterbi path of incoming matrices or vector.
 
   | arguments=none
 
   | 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>
 
   | 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>

Revision as of 17:08, 3 May 2009

  • mnm.biqoefs
  • mnm.biquad
  • mnm.delta
  • mnm.diag
  • mnm.dtw
  • mnm.gmmem
  • mnm.hist
  • mnm.knn
  • mnm.lu
  • mnm.mahalanobis
  • mnm.mean
  • mnm.meanstd
  • mnm.median
  • mnm.minmax
  • mnm.moments
  • mnm.nmd
  • mnm.nmf
  • mnm.obsprob
  • mnm.onepole
  • mnm.qr
  • mnm.stats
  • mnm.submat
  • mnm.sum
  • mnm.svd
  • mnm.transpose
  • mnm.viterbi
  • mnm.xdist2
  • mnm.xmul


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 of 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 of 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 of 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 of 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 of 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 of 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 of 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 of 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
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