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{{Module | | {{Module | | ||
− | | name= | + | | name=ftm.mess |
| brief= | | brief= | ||
| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes=mode - (1 | + | | 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=postdoc - post external doc to console<br> |
− | | inlets=1 - input | + | | inlets=1 - input list<br> |
− | | outlets=1 - | + | | 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 <'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> | ||
+ | | 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 | | {{Module | | ||
| name=mnm.delta | | name=mnm.delta | ||
Line 40: | Line 59: | ||
| descr= | | descr= | ||
| 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=norm - normalization mode 1 (default) or 0<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> | + | | 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 - | + | | 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> | | 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=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 | | arguments=none | ||
− | | attributes=out - | + | | attributes=out - diagonal of the fmat<br> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>out - diagonal of the fmat<br> |
− | | inlets=1 - incoming | + | | inlets=1 - incoming matrix whose diagonal will be extracted<br> |
| outlets=1 - diagonal of the fmat<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 | | {{Module | | ||
| name=mnm.gmmem | | name=mnm.gmmem | ||
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| descr= | | descr= | ||
| arguments=1 - number of centers to use<br> | | arguments=1 - number of centers to use<br> | ||
− | | attributes= | + | | 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> |
− | | messages= | + | | messages=postdoc - post external doc to console<br> |
| inlets=1 - fmat<br> | | inlets=1 - fmat<br> | ||
| 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=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> | | arguments=1 - number of bins<br> | ||
− | | attributes= | + | | 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> |
− | + | | messages=postdoc - post external doc to console<br>out - histogram vector<br>set_n - number of bins<br> | |
− | | inlets=1 - data fmat or | + | | inlets=1 - data fmat, fvec, jitter matrix or list <br> |
− | | outlets=1 - histogram | + | | 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|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>outind <fmat|fvec|list|jitter: indices(n, 1)> - data row indices of n <= k nearest neighbours<br> | ||
+ | | messages=postdoc - post external doc to console<br>outdist <fmat|fvec|list|jitter: distance(n, 1)> - distances to n <= k nearest neighbours<br>outind <fmat|fvec|list|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: 'info'|'raw'|'data'|'compact'|'nodes'|'profile'>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword 'profile' is given<br> | ||
+ | | inlets=1 <fmat|fvec|list|jitter: x(1, D)> - query vector to search k-nearest neighbours of<br>2 <fmat|fvec|list|jitter: data(N, D)><br>3 <fmat|fvec|list|jitter: sigma(1, D)><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 | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes= | + | | attributes=outl - L<br>outu - U<br>outpivot - pivot<br>outx - X<br>outdet - determinant<br> |
− | | messages= | + | | 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 - | + | | inlets=1 - matrix to decompose<br> |
| 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= | ||
| descr= | | descr= | ||
− | | arguments= | + | | arguments=<matrix|vector|list: mean> <matrix|vector|list: covariance> - init mean and covariance<br> |
− | | attributes=out - | + | | attributes=out - mahalanobis distance<br> |
− | | messages= | + | | 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 | + | | inlets=1 <matrix|vector|list: query vector><br>2 <matrix: mu><br>3 <matrix: sigma><br> |
| outlets=1 - mahalanobis distance<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 | | {{Module | | ||
| name=mnm.meanstd | | 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 | + | | arguments=1 <1|2|'row'|'col': mode switch> compute over rows or columns<br> |
− | | attributes= | + | | 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> |
− | | messages= | + | | 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 - | + | | inlets=1 - input matrix<br> |
− | | outlets=1 - | + | | 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 | | {{Module | | ||
| name=mnm.minmax | | 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|2|row|col) sum over rows or columns<br> | | arguments=1 - (1|2|row|col) sum over rows or columns<br> | ||
− | | attributes= | + | | 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> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>outmin - min<br>outargmin - argmin<br>outmax - max<br>outargmax - argmax<br> |
− | | inlets=1 - incoming | + | | inlets=1 - incoming matrix, vector, or list<br> |
| 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 | | |
+ | | 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 <'0'|'1': switch> - compute the standards moments for orders > 2 [1]<br>sumasfloat <'0'|'1': switch> - enable/disable float sum output [0]<br>out - moments<br>outsum - input sums<br>mode - (1|2|row|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 | | {{Module | | ||
| name=mnm.nmd | | name=mnm.nmd | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes= | + | | attributes=outh - out H<br>criteria - criteria<br>sH - sH<br>itermax - itermax<br> |
− | | messages= | + | | messages=postdoc - post external doc to console<br> |
| inlets=1 - fmat<br>2 - fmat<br>3 - <br> | | inlets=1 - fmat<br>2 - fmat<br>3 - <br> | ||
| outlets=1 - fmat<br> | | outlets=1 - fmat<br> | ||
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.nmf | | name=mnm.nmf | ||
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| descr= | | descr= | ||
| arguments=1 - number of components<br> | | arguments=1 - number of components<br> | ||
− | | attributes= | + | | 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= | + | | messages=postdoc - post external doc to console<br> |
| inlets=1 - fmat to be decomposed<br> | | inlets=1 - fmat to be decomposed<br> | ||
| outlets=1 - W<br>2 - H<br> | | outlets=1 - W<br>2 - H<br> | ||
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.obsprob | | name=mnm.obsprob | ||
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| arguments=none | | arguments=none | ||
| attributes=none | | attributes=none | ||
− | | messages= | + | | 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> | | 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> | | 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 | | {{Module | | ||
| name=mnm.qr | | name=mnm.qr | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes= | + | | attributes=outq - Q<br>outr - R<br>outx - X<br> |
− | | messages= | + | | 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> | | inlets=1 - fmat to be decomposed<br> | ||
| 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 | ||
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| arguments=output stats<br> | | arguments=output stats<br> | ||
| attributes=norm - switch normalize<br> | | attributes=norm - switch normalize<br> | ||
− | | messages= | + | | 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> | | inlets=1 - data input<br> | ||
| 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 | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes=out - | + | | attributes=out - sub-matrix<br> |
− | | messages= | + | | 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> | | inlets=1 - fmat<br> | ||
| outlets=1 - sub-matrix<br> | | outlets=1 - sub-matrix<br> | ||
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.sum | | name=mnm.sum | ||
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| descr= | | descr= | ||
| 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= | + | | 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> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>out - sum of fmat<br> |
− | | inlets=1 - incoming | + | | inlets=1 - incoming matrix to be summed<br> |
| outlets=1 - sum of fmat<br> | | outlets=1 - sum of fmat<br> | ||
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.svd | | name=mnm.svd | ||
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| descr= | | descr= | ||
| arguments=1 - number of singular values<br> | | arguments=1 - number of singular values<br> | ||
− | | attributes=mode - (auto|manual) automatically eliminate negligible 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> |
− | | messages= | + | | 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'<br> |
− | | inlets=1 - | + | | inlets=1 - matrix to be decomposed by SVD<br> |
− | | outlets=1 - U<br>2 - S<br>3 - 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 | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes=out - | + | | attributes=out - transposed matrix<br> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>out - transposed matrix<br> |
− | | inlets=1 - | + | | inlets=1 - matrix to be transposed<br> |
− | | outlets=1 - transposed | + | | outlets=1 - transposed matrix<br> |
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.viterbi | | name=mnm.viterbi | ||
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| descr= | | descr= | ||
| arguments=none | | arguments=none | ||
− | | attributes= | + | | 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> |
− | + | | 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> | | 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> | | outlets=1 - decoded best path<br>2 - debug<br> | ||
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.xdist2 | | name=mnm.xdist2 | ||
| brief= | | brief= | ||
| descr= | | descr= | ||
− | | arguments= | + | | arguments=matrix to be used as right operand<br> |
− | | attributes=swap - (yes|no) swaps operands<br>out - | + | | attributes=swap - (yes|no) swaps operands<br>out - result squared distance matrix<br> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>out - result squared distance matrix<br> |
− | | inlets=1 | + | | inlets=1 <matrix: left operand><br>2 <matrix: right operand><br> |
− | | outlets=1 - | + | | outlets=1 - result squared distance matrix<br> |
}} | }} | ||
− | |||
{{Module | | {{Module | | ||
| name=mnm.xmul | | 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= | + | | arguments=set right or (with swap enabled) left matrix multiplication operand<br> |
− | | attributes= | + | | attributes=out - output matrix object<br>swap <bool: switch> swaps operands<br> |
− | | messages= | + | | messages=postdoc - post external doc to console<br>out - output matrix object<br> |
− | | inlets=1 - left | + | | 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 - | + | | outlets=1 - output matrix object<br> |
}} | }} |
Revision as of 16:53, 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 | ' | |||||||||||
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mnm.biqoefs | Biquad coefficients | |||||||||||
Calculates biquad coefficients for various filter types. | ||||||||||||
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mnm.biquad | Biquad filtering | |||||||||||
Compute biquad filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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mnm.delta | ' | |||||||||||
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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. | ||||||||||||
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mnm.dtw | Dynamic time warping. | |||||||||||
Calculates DTW on incoming matrix or vector. | ||||||||||||
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mnm.gmmem | ' | |||||||||||
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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 | ||||||||||||
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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. | ||||||||||||
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mnm.lu | ' | |||||||||||
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mnm.mahalanobis | ' | |||||||||||
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mnm.mean | Mean filtering | |||||||||||
Compute mean filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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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 | ||||||||||||
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mnm.median | Median filtering | |||||||||||
Compute median filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
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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 | ||||||||||||
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mnm.moments | Statistical Moments | |||||||||||
Calculates moments from first to specified order. | ||||||||||||
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mnm.nmd | ' | |||||||||||
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mnm.nmf | ' | |||||||||||
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mnm.obsprob | ' | |||||||||||
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mnm.onepole | Onepole filtering | |||||||||||
Compute 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 | ' | |||||||||||
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mnm.stats | ' | |||||||||||
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mnm.submat | ' | |||||||||||
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mnm.sum | ' | |||||||||||
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mnm.svd | ' | |||||||||||
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mnm.transpose | ' | |||||||||||
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mnm.viterbi | ' | |||||||||||
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mnm.xdist2 | ' | |||||||||||
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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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