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{{Module |  
 
{{Module |  
   | name=mnm.centroid
+
   | name=ftm.mess
 
   | brief=
 
   | brief=
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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 |  
 
{{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>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>
 
   | 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 - 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>
 
   | 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
Line 62: Line 89:
 
   | descr=
 
   | descr=
 
   | arguments=1 - number of centers to use<br>
 
   | 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>
 
   | 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=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 |  
 
{{Module |  
 
   | name=mnm.lu
 
   | name=mnm.lu
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   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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>
 
   | 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=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>
 
   | 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 - (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 |  
 
{{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&#124;2&#124;row&#124;col) sum over rows or columns<br>
 
   | 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>
 
   | 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 |  
 
{{Module |  
 
   | name=mnm.nmd
 
   | name=mnm.nmd
Line 128: Line 189:
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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>
 
   | 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
Line 139: Line 199:
 
   | descr=
 
   | descr=
 
   | arguments=1 - number of components<br>
 
   | 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>
 
   | 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
Line 151: Line 210:
 
   | arguments=none
 
   | arguments=none
 
   | attributes=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>
 
   | 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
Line 161: Line 229:
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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>
 
   | 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
Line 173: Line 240:
 
   | arguments=output stats<br>
 
   | arguments=output stats<br>
 
   | attributes=norm - switch normalize<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>
 
   | 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
Line 183: Line 249:
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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>
 
   | inlets=1 - fmat<br>
 
   | outlets=1 - sub-matrix<br>
 
   | outlets=1 - sub-matrix<br>
 
}}
 
}}
 
 
{{Module |  
 
{{Module |  
 
   | name=mnm.sum
 
   | name=mnm.sum
Line 194: Line 259:
 
   | descr=
 
   | descr=
 
   | arguments=1 - (1&#124;2&#124;row&#124;col) sum over rows or columns<br>
 
   | 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>
 
   | outlets=1 - sum of fmat<br>
 
}}
 
}}
 
 
{{Module |  
 
{{Module |  
 
   | name=mnm.svd
 
   | name=mnm.svd
Line 205: Line 269:
 
   | descr=
 
   | descr=
 
   | 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>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 |  
 
{{Module |  
 
   | name=mnm.transpose
 
   | name=mnm.transpose
Line 216: Line 279:
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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 |  
 
{{Module |  
 
   | name=mnm.viterbi
 
   | name=mnm.viterbi
Line 227: Line 289:
 
   | descr=
 
   | descr=
 
   | arguments=none
 
   | 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>
 
   | 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=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 |  
 
{{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=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>
 
}}
 
}}

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 '
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