From ftm
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* mnm.xdist2 | * mnm.xdist2 | ||
* mnm.xmul | * mnm.xmul | ||
− | |||
{{Module | | {{Module | | ||
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| outlets=1 - message outlet<br> | | outlets=1 - message outlet<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.biqoefs | | name=mnm.biqoefs | ||
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| outlets=1 - biquad coefficients<br> | | outlets=1 - biquad coefficients<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.biquad | | name=mnm.biquad | ||
| brief=Biquad filtering | | brief=Biquad filtering | ||
− | | descr= | + | | 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> | | arguments=set the inputs initial size and numbers<br> | ||
| attributes=mode <'df1' | 'df2t'> - set biquad structure (default 'df1')<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> | | attributes=mode <'df1' | 'df2t'> - set biquad structure (default 'df1')<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> | ||
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| outlets=1 - filtered values<br>2 - output state<br> | | outlets=1 - filtered values<br>2 - output state<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.delta | | 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> | | 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> | | 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> | ||
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| outlets=1 - output delayed inputs (in phase with deltas)<br>2 - output deltas<br>3 - internal values<br> | | outlets=1 - output delayed inputs (in phase with deltas)<br>2 - output deltas<br>3 - internal values<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.diag | | name=mnm.diag | ||
− | | brief= | + | | brief=Matrix diagonal |
− | | descr= | + | | descr=Returns a copy of the diagonal of the incoming matrix in a vector.The length of the result is the minimum of the dimensions of the input. |
| arguments=none | | arguments=none | ||
− | | attributes=out - diagonal | + | | attributes=out - output diagonal vector<br> |
− | | messages=postdoc - post external doc to console<br>out - diagonal | + | | messages=postdoc - post external doc to console<br>out - output diagonal vector<br> |
− | | inlets=1 - | + | | inlets=1 - input matrix<br> |
− | | outlets=1 - diagonal | + | | outlets=1 - output diagonal vector<br> |
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.dtw | | name=mnm.dtw | ||
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| outlets=1 - s1<br>2 - s2<br> | | outlets=1 - s1<br>2 - s2<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.gmmem | | 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> | | 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|full|spherical) covariance computation types<br>criteria - criteria<br>ncenters - number of 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|full|spherical) covariance computation types<br>criteria - criteria<br>ncenters - number of centers<br> | ||
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| outlets=1 - fmat centers<br>2 - fmat covariance<br>3 - fmat priors<br> | | outlets=1 - fmat centers<br>2 - fmat covariance<br>3 - fmat priors<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.hist | | name=mnm.hist | ||
− | | brief= | + | | brief=Histogram |
− | + | | 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> | | 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|max|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|max|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 - | + | | 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 | | {{Module | | ||
| name=mnm.knn | | name=mnm.knn | ||
− | | brief= | + | | 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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| outlets=1 <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>2 <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br> | | outlets=1 <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>2 <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.lu | | name=mnm.lu | ||
− | | brief= | + | | brief=Lower-upper decomposition |
− | | descr= | + | | descr=Calculates LU decomposition on 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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| outlets=1 - L<br>2 - U<br>3 - pivot<br>4 - X<br>5 - determinant<br> | | outlets=1 - L<br>2 - U<br>3 - pivot<br>4 - X<br>5 - determinant<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.mahalanobis | | name=mnm.mahalanobis | ||
− | | brief= | + | | brief=Mahalanobis distance. |
− | | descr= | + | | descr=Calculates mahalanobis distance on incoming matrices or vectors. |
| arguments=<matrix|vector|list: mean> <matrix|vector|list: covariance> - init mean and covariance<br> | | arguments=<matrix|vector|list: mean> <matrix|vector|list: covariance> - init mean and covariance<br> | ||
| attributes=out - mahalanobis distance<br> | | attributes=out - mahalanobis distance<br> | ||
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| outlets=1 - mahalanobis distance<br> | | outlets=1 - mahalanobis distance<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.mean | | name=mnm.mean | ||
| brief=Mean filtering | | brief=Mean filtering | ||
− | | descr= | + | | 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> | | 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> | | 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> | ||
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| outlets=1 - filtered values<br>2 - output state<br> | | outlets=1 - filtered values<br>2 - output state<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.meanstd | | name=mnm.meanstd | ||
− | | brief= | + | | brief=Mean and standard deviation |
− | | descr= | + | | descr=Calculates the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector |
| arguments=1 <1|2|'row'|'col': mode switch> compute over rows or columns<br> | | arguments=1 <1|2|'row'|'col': mode switch> compute over rows or columns<br> | ||
| attributes=mode <1|2|'row'|'col': 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> | | attributes=mode <1|2|'row'|'col': 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> | ||
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| outlets=1 - mean output vector or value<br>2 - standard deviation output vector or value<br> | | outlets=1 - mean output vector or value<br>2 - standard deviation output vector or value<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.median | | name=mnm.median | ||
| brief=Median filtering | | brief=Median filtering | ||
− | | descr= | + | | 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> | | 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> | | 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> | ||
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| outlets=1 - filtered values<br>2 - output state<br> | | outlets=1 - filtered values<br>2 - output state<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.minmax | | name=mnm.minmax | ||
− | | brief= | + | | brief=Minimum and maximum |
− | | descr= | + | | descr=Calculates the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector |
| arguments=1 - (1|2|row|col) sum over rows or columns<br> | | arguments=1 - (1|2|row|col) sum over rows or columns<br> | ||
| attributes=mode - (1|2|row|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> | | attributes=mode - (1|2|row|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> | ||
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| outlets=1 - min<br>2 - argmin<br>3 - max<br>4 - argmax<br> | | outlets=1 - min<br>2 - argmin<br>3 - max<br>4 - argmax<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.moments | | name=mnm.moments | ||
− | | brief=Statistical | + | | brief=Statistical moments |
| descr=Calculates moments from first to specified order. | | descr=Calculates moments from first to specified order. | ||
| arguments=1 <num: order> - moments maximum order [1]<br> | | arguments=1 <num: order> - moments maximum order [1]<br> | ||
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| outlets=1 - moments<br>2 - input sums<br> | | outlets=1 - moments<br>2 - input sums<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.nmd | | name=mnm.nmd | ||
− | | brief= | + | | brief=Non-zero matrix decomposition |
− | | descr= | + | | descr=Calculates NMD on 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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| outlets=1 - fmat<br> | | outlets=1 - fmat<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.nmf | | name=mnm.nmf | ||
− | | brief= | + | | brief=Non-zero matrix factorization |
− | | descr= | + | | descr=Calculates NMF on 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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| outlets=1 - W<br>2 - H<br> | | outlets=1 - W<br>2 - H<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.obsprob | | name=mnm.obsprob | ||
− | | brief= | + | | brief=Obsprob |
− | | descr= | + | | descr=Obsprob has to be documented. |
| arguments=none | | arguments=none | ||
| attributes=none | | attributes=none | ||
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| outlets=1 - log(B) : fmat [K (=nb of states) , 1]<br>2 - test<br> | | outlets=1 - log(B) : fmat [K (=nb of states) , 1]<br>2 - test<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.onepole | | name=mnm.onepole | ||
| brief=Onepole filtering | | brief=Onepole filtering | ||
− | | descr= | + | | 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> | | 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> | | 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> | ||
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| outlets=1 - filtered values<br>2 - output state<br> | | outlets=1 - filtered values<br>2 - output state<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.qr | | name=mnm.qr | ||
− | | brief= | + | | brief=Orthogonal-right decomposition |
− | | descr= | + | | descr=Calculates QR decomposition on 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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| outlets=1 - Q<br>2 - R<br>3 - X<br> | | outlets=1 - Q<br>2 - R<br>3 - X<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.stats | | name=mnm.stats | ||
− | | brief= | + | | brief=Stats |
− | | descr= | + | | descr=Stats has to be documented. |
| arguments=output stats<br> | | arguments=output stats<br> | ||
| attributes=norm - switch normalize<br> | | attributes=norm - switch normalize<br> | ||
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| outlets=1 - average<br>2 - standard deviation<br>3 - count<br> | | outlets=1 - average<br>2 - standard deviation<br>3 - count<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.submat | | name=mnm.submat | ||
− | | brief= | + | | brief=Sub-matrix |
− | | descr= | + | | descr=Copies sub-matrix outof incoming matrix. |
| arguments=none | | arguments=none | ||
| attributes=out - sub-matrix<br> | | attributes=out - sub-matrix<br> | ||
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| outlets=1 - sub-matrix<br> | | outlets=1 - sub-matrix<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.sum | | name=mnm.sum | ||
− | | brief= | + | | brief=Matrix sum |
− | | descr= | + | | descr=Calculates sum over entire matrix, matrix rows or matrix columns. |
| arguments=1 - (1|2|row|col) sum over rows or columns<br> | | arguments=1 - (1|2|row|col) sum over rows or columns<br> | ||
| attributes=out - sum of fmat<br>mode - 'row'|'col'|1|2 -- perform sum over rows or columns<br>type <symbol: 'float'|'fmat'> -- always output a matrix even for scalar results<br> | | attributes=out - sum of fmat<br>mode - 'row'|'col'|1|2 -- perform sum over rows or columns<br>type <symbol: 'float'|'fmat'> -- always output a matrix even for scalar results<br> | ||
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| outlets=1 - sum of fmat<br> | | outlets=1 - sum of fmat<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.svd | | name=mnm.svd | ||
− | | brief= | + | | brief=Singular value decomposition |
− | | descr= | + | | descr=Calculates SVD decomposition on incoming matrix. |
| arguments=1 - number of singular values<br> | | arguments=1 - number of singular values<br> | ||
| attributes=mode - (auto|manual) automatically eliminate negligible singular values<br>outu - output matrix for U<br>outs - output matrix for S<br>outvt - output matrix for V'<br> | | attributes=mode - (auto|manual) automatically eliminate negligible singular values<br>outu - output matrix for U<br>outs - output matrix for S<br>outvt - output matrix for V'<br> | ||
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| outlets=1 - output matrix for U<br>2 - output matrix for S<br>3 - output matrix for V'<br> | | outlets=1 - output matrix for U<br>2 - output matrix for S<br>3 - output matrix for V'<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.transpose | | name=mnm.transpose | ||
− | | brief= | + | | brief=Transpose |
− | | descr= | + | | descr=Calculates transposed matrix. |
| arguments=none | | arguments=none | ||
| attributes=out - transposed matrix<br> | | attributes=out - transposed matrix<br> | ||
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| outlets=1 - transposed matrix<br> | | outlets=1 - transposed matrix<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.viterbi | | name=mnm.viterbi | ||
− | | brief= | + | | brief=Viterbi algorithm |
− | | descr= | + | | descr=Calculates Viterbi path on incoming matrices or vector. |
| arguments=none | | arguments=none | ||
| attributes=line - on|off line<br>verbose - verbose or not (0|1)<br>latency - maximum latency (nb of frames)<br>get - debug : get intern values.<br> | | attributes=line - on|off line<br>verbose - verbose or not (0|1)<br>latency - maximum latency (nb of frames)<br>get - debug : get intern values.<br> | ||
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| outlets=1 - decoded best path<br>2 - debug<br> | | outlets=1 - decoded best path<br>2 - debug<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.xdist2 | | 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> | | arguments=matrix to be used as right operand<br> | ||
| attributes=swap - (yes|no) swaps operands<br>out - result squared distance matrix<br> | | attributes=swap - (yes|no) swaps operands<br>out - result squared distance matrix<br> | ||
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| outlets=1 - result squared distance matrix<br> | | outlets=1 - result squared distance matrix<br> | ||
}} | }} | ||
+ | |||
{{Module | | {{Module | | ||
| name=mnm.xmul | | name=mnm.xmul |
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 | ' | |||||||||||
|
mnm.biqoefs | Biquad coefficients | |||||||||||
Calculates biquad coefficients for various filter types. | ||||||||||||
|
mnm.biquad | Biquad filtering | |||||||||||
Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
|
mnm.delta | Inter-frame regression. | |||||||||||
Calculates derivative of incoming matrices or vectors. | ||||||||||||
|
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. | ||||||||||||
|
mnm.dtw | Dynamic time warping. | |||||||||||
Calculates DTW on incoming matrix or vector. | ||||||||||||
|
mnm.gmmem | Expectation maximization for Gaussian mixture models | |||||||||||
GMM EM has to be documented. | ||||||||||||
|
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 | ||||||||||||
|
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. | ||||||||||||
|
mnm.lu | Lower-upper decomposition | |||||||||||
Calculates LU decomposition on incoming matrix. | ||||||||||||
|
mnm.mahalanobis | Mahalanobis distance. | |||||||||||
Calculates mahalanobis distance on incoming matrices or vectors. | ||||||||||||
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mnm.mean | Mean filtering | |||||||||||
Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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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 | ||||||||||||
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mnm.median | Median filtering | |||||||||||
Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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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 | ||||||||||||
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mnm.moments | Statistical moments | |||||||||||
Calculates moments from first to specified order. | ||||||||||||
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mnm.nmd | Non-zero matrix decomposition | |||||||||||
Calculates NMD on incoming matrix. | ||||||||||||
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mnm.nmf | Non-zero matrix factorization | |||||||||||
Calculates NMF on incoming matrix. | ||||||||||||
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mnm.obsprob | Obsprob | |||||||||||
Obsprob has to be documented. | ||||||||||||
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mnm.onepole | Onepole filtering | |||||||||||
Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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mnm.qr | Orthogonal-right decomposition | |||||||||||
Calculates QR decomposition on incoming matrix. | ||||||||||||
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mnm.stats | Stats | |||||||||||
Stats has to be documented. | ||||||||||||
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mnm.submat | Sub-matrix | |||||||||||
Copies sub-matrix outof incoming matrix. | ||||||||||||
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mnm.sum | Matrix sum | |||||||||||
Calculates sum over entire matrix, matrix rows or matrix columns. | ||||||||||||
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mnm.svd | Singular value decomposition | |||||||||||
Calculates SVD decomposition on incoming matrix. | ||||||||||||
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mnm.transpose | Transpose | |||||||||||
Calculates transposed matrix. | ||||||||||||
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mnm.viterbi | Viterbi algorithm | |||||||||||
Calculates Viterbi path on incoming matrices or vector. | ||||||||||||
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mnm.xdist2 | Square of Euclidean distance | |||||||||||
Calculates the square of the Euclidean distance between a vector and each line of a matrix. | ||||||||||||
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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. | ||||||||||||
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