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| − | + | * mnm.biqoefs ... Biquad coefficients | |
| + | * mnm.biquad ... Biquad filtering | ||
| + | * mnm.delta ... Inter-frame regression | ||
| + | * mnm.diag ... Matrix diagonal | ||
| + | * mnm.dtw ... Dynamic Time Warping | ||
| + | * mnm.gmmem ... Expectation Maximization for Gaussian Mixture Models | ||
| + | * mnm.hist ... Histogram | ||
| + | * mnm.knn ... K-Nearest Neighbour search | ||
| + | * mnm.lu ... Lower-Upper decomposition | ||
| + | * mnm.mahalanobis ... Mahalanobis distance | ||
| + | * mnm.mean ... Mean filtering | ||
| + | * mnm.meanstd ... Mean and standard deviation | ||
| + | * mnm.median ... Median filtering | ||
| + | * mnm.minmax ... Minimum and maximum | ||
| + | * mnm.moments ... Statistical moments | ||
| + | * mnm.nmd ... Non-zero Matrix Decomposition | ||
| + | * mnm.nmf ... Non-zero Matrix Factorization | ||
| + | * mnm.obsprob ... Obsprob | ||
| + | * mnm.onepole ... Onepole filtering | ||
| + | * mnm.qr ... Orthogonal-Right decomposition | ||
| + | * mnm.stats ... Stats | ||
| + | * mnm.submat ... Sub-matrix | ||
| + | * mnm.sum ... Matrix sum | ||
| + | * mnm.svd ... Singular Value Decomposition | ||
| + | * mnm.transpose ... Matrix transposition | ||
| + | * mnm.viterbi ... Viterbi algorithm | ||
| + | * mnm.xdist2 ... Square of Euclidean distance | ||
| + | * mnm.xmul ... Matrix multiplication | ||
| − | |||
| − | |||
| − | [ | + | {{Module | |
| + | | name=mnm.biqoefs | ||
| + | | brief=Biquad coefficients | ||
| + | | descr=Calculates biquad coefficients for various filter types. | ||
| + | | arguments=none | ||
| + | | attributes=mode <'lp'|'lowpass'|'hp'|'highpass'|'bpcskirt'|'resonant'|'bpcpeak'|'bandpass'|'peaking'|'peaknotch'|'notch'|'bs'|'bandstop'|'ap'|'allpass'|'ls'|'lowshelf'|'hs'|'highshelf': type> - filter type [lowpass]<br>f0 <num: freq> - cutoff or centre frequency (default is half Nyquist frequency)<br>unit <'nyquist'|'hz'|'hertz': unit> - set unit for freq as ratio to Nyquist frequency or in hz [nyquist]<br>sr <num: sr> - sample rate for freq in Hertz [44100]<br>q <num: q> - quality/resonance [1]<br>qnorm <num: 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 <num: gain> - linear gain [1]<br>out <matrix: coeffs> - output biquad coefficients<br> | ||
| + | | messages=postdoc - post external doc to console<br>bang - output coefficients<br>out <matrix: coeffs> - output biquad coefficients<br> | ||
| + | | inlets=none | ||
| + | | outlets=1 <matrix: coeffs> - output biquad coefficients<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.biquad | ||
| + | | brief=Biquad filtering | ||
| + | | descr=Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension). | ||
| + | | arguments=<num: input> [<num: filter>] - init input and filter size<br> | ||
| + | | attributes=mode <'df1'|'df2t': struct> - set biquad structure [df1]<br>dim <'col'|'row'|'auto'|'stream': mode> - set the dimension on which to operate [auto]<br>out <matrix: values> - output filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - init input and filter size<br>coefs <list: coeffs> - set the biquad coefficients<br>getstate - get the biquad state<br>clear - reset the internal state<br>out <matrix: values> - output filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | inlets=1 <matrix: coeffs> - input values<br> | ||
| + | | outlets=1 <matrix: values> - output filtered values<br>2 <matrix: state> - output state<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.delta | ||
| + | | brief=Inter-frame regression | ||
| + | | descr=Calculates derivative of incoming matrices or vectors. | ||
| + | | arguments=1 <num: input> [<num: filter>] - init input size<br>2 <num: filter>: init filter size<br> | ||
| + | | attributes=insize <num: input> - set input size<br>filtersize <num: filter> - set filter size<br>inadddel <num: del> - add a delay to the delayed input<br>norm <bool: normalize> - set normalization mode ([off]<br>outdelayed <matrix: delayed> - output delayed inputs (in phase with deltas)<br>out <matrix: delta> - output deltas<br>outstate <matrix: state> - internal values<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 <matrix: delayed> - output delayed inputs (in phase with deltas)<br>out <matrix: delta> - output deltas<br>outstate <matrix: state> - internal values<br> | ||
| + | | inlets=1 <matrix: input> - multiply matrix with given right or left operand<br> | ||
| + | | outlets=1 <matrix: delayed> - output delayed inputs (in phase with deltas)<br>2 <matrix: delta> - output deltas<br>3 <matrix: state> - internal values<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.diag | ||
| + | | brief=Matrix diagonal | ||
| + | | 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 | ||
| + | | attributes=out <vector: diagonal> - output diagonal vector<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <vector: diagonal> - output diagonal vector<br> | ||
| + | | inlets=1 <matrix: input> - input matrix<br> | ||
| + | | outlets=1 <vector: diagonal> - output diagonal vector<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.dtw | ||
| + | | brief=Dynamic Time Warping | ||
| + | | descr=Calculates DTW of incoming matrix or vector. | ||
| + | | arguments=1 <matrix: op> - matrix to be used as right operand<br> | ||
| + | | attributes=outa <matrix: A> - set A output matrix<br>outb <matrix: B> - set B output matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br> | ||
| + | | inlets=1 <matrix: left> - left hand side matrix operand<br>2 <matrix: right> - right hand side matrix operand<br> | ||
| + | | outlets=1 <matrix: A> - A output matrix<br>2 <matrix: B> - B output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.gmmem | ||
| + | | brief=Expectation Maximization for Gaussian Mixture Models | ||
| + | | descr=GMM EM has to be documented. | ||
| + | | arguments=1 <num: num> - number of centers to use<br> | ||
| + | | attributes=outcenters <matrix: centers> - centers output matrix<br>outcov <matrix: covariance> - covariance output matrix<br>outpriors <matrix: priors> - priors output matrix<br>mode <diagonal|full|spherical: mode> covariance computation types<br>criteria <num: criteria> - criteria<br>ncenters <num: num> - number of centers<br> | ||
| + | | messages=postdoc - post external doc to console<br> | ||
| + | | inlets=1 <matrix: input> - matrix of values<br> | ||
| + | | outlets=1 <matrix: centers> - centers output matrix<br>2 <matrix: covariance> - covariance output matrix<br>3 <matrix: priors> - priors output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.hist | ||
| + | | 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 <num: num> - number of bins<br> | ||
| + | | attributes=out <vector: histogram> - output histogram vector<br>bpf <matrix: histogram> - output two-column matrix with bin indices and histogram values<br>norm <'off'|'max'|'sum': normalize> - normalise histogram so that max or sum is 1<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <vector: histogram> - output histogram vector<br>set_n <num: num> - number of bins<br> | ||
| + | | inlets=1 <matrix: values> - intput matrix or list<br> | ||
| + | | outlets=1 <vector: histogram> - output histogram vector<br>2 <num: min> - output min data value<br>3 <num: max> - output 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 <num: max k> - max number k of nearest neighbours to search<br>2 <num: max radius> - max radius of nearest neighbours to search (0 for unlimited)<br> | ||
| + | | attributes=setdata <matrix: data> - matrix(N, D) of data<br>setsigma <matrix: sigma> - matrix(1, D) of sigma = 1/weights<br>dmode <'orthogonal'|'hyperplane'|'pca': mode> decomposition mode<br>mmode <'mean'|'middle'|'median'> - pivot calculation mode<br>sort <bool: switch> - sort output by distance<br>height <num: height> - given tree height if positive, subtract from maxheight if negative<br>outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br> | ||
| + | | messages=postdoc - post external doc to console<br>outdist <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>outind <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br>setk <num: max> - max number k of nearest neighbours to search<br>setradius <num: max> - max radius of nearest neighbours to search (0 for unlimited)<br>getmeanvectors <matrix: out> - copy mean vectors for the M tree nodes to copy matrix (M x D) out<br>getsplitplanes <matrix: out> - copy vectors perpendicular to the hyperplanes splitting the M tree nodes to matrix (M x D) out<br>getprofile <dict: out> - copy profiling info to given dict and clear<br>print [<'info'|'r\ aw'|'data'|'compact'|'nodes'|'profile': mode>] - print tree info of varying detail (default: nodes), print and clear profiling info if keyword 'profile' is given<br> | ||
| + | | inlets=1 <vector: x> - query vector to search k-nearest neighbours (1 x D)<br>2 <matrix: data> - data input matrix (N x D)<br>3 <vector: sigma> - sigma input matrix (1 x D)<br> | ||
| + | | outlets=1 <vector: distance> - output vector of distances to n <= k nearest neighbours (n x 1)<br>2 <vector: indices> - data row indices of n <= k nearest neighbours (n x 1)<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.lu | ||
| + | | brief=Lower-Upper decomposition | ||
| + | | descr=Calculates LU decomposition of incoming matrix. | ||
| + | | arguments=none | ||
| + | | attributes=outl <matrix: L> - L output matrix<br>outu <matrix: U> - U output matrix<br>outpivot <matrix: pivot> - pivot output matrix<br>outx <matrix: X> - X output matrix<br>outdet <matrix: det> - determinant output matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>determinant - computes determinant of decomposed matrix<br>solve <matrix: input> - solves system with incoming matrix and decomposed matrix<br>outl <matrix: L> - L output matrix<br>outu <matrix: U> - U output matrix<br>outpivot <matrix: pivot> - pivot output matrix<br>outx <matrix: X> - X output matrix<br>outdet <matrix: det> - determinant output matrix<br> | ||
| + | | inlets=1 <matrix: input> - matrix to decompose<br> | ||
| + | | outlets=1 <matrix: L> - L output matrix<br>2 <matrix: U> - U output matrix<br>3 <matrix: pivot> - pivot output matrix<br>4 <matrix: X> - X output matrix<br>5 <matrix: det> - determinant output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.mahalanobis | ||
| + | | brief=Mahalanobis distance | ||
| + | | descr=Calculates mahalanobis distance of incoming matrices or vectors. | ||
| + | | arguments=<matrix: mean> <matrix: covariance> - init mean and covariance<br> | ||
| + | | attributes=out <matrix: output> - output mahalanobis distance<br> | ||
| + | | messages=postdoc - post external doc to console<br>set_mu <matrix: mu> - matrix of mean values<br>set_sigma <matrix: sigma> - matrix of covariance<br>out <matrix: output> - output mahalanobis distance<br> | ||
| + | | inlets=1 <vector: input> - query vector><br>2 <matrix: mu> - mu input matrix<br>3 <matrix: sigma> - sigma input matrix<br> | ||
| + | | outlets=1 <matrix: output> - output mahalanobis distance<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.mean | ||
| + | | brief=Mean filtering | ||
| + | | descr=Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension). | ||
| + | | arguments=<num: input> [<num: filter>] - init input and filter size<br> | ||
| + | | attributes=filtersize <num: max> - set the maximum filter size (default is 0 for using the input size)<br>dim <'col'|'row'|'auto'|'stream': mode> - set the dimension on which to operate [auto]<br>out <matrix: output> - filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - set input and filter size<br>getstate - get the mean state<br>clear - reset the internal state<br>out <matrix: output> - filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | inlets=1 <matrix: input> - input values<br> | ||
| + | | outlets=1 <matrix: output> - filtered values<br>2 <matrix: state> - output state<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.meanstd | ||
| + | | brief=Mean and standard deviation | ||
| + | | 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 [col]<br> | ||
| + | | attributes=mode <1|2|'row'|'col': mode switch> - compute over rows or columns [col]<br>scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmean <matrix: mean> - mean output vector or value<br>outstd <matrix: stddev> - standard deviation output vector or value<br> | ||
| + | | messages=postdoc - post external doc to console<br>outmean <matrix: mean> - mean output vector or value<br>outstd <matrix: stddev> - standard deviation output vector or value<br> | ||
| + | | inlets=1 <matrix: input> - input matrix<br> | ||
| + | | outlets=1 <matrix: mean> - mean output vector or value<br>2 <matrix: stddev> - standard deviation output vector or value<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.median | ||
| + | | brief=Median filtering | ||
| + | | descr=Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension). | ||
| + | | arguments=<num: input> [<num: filter>] - init input and filter size<br> | ||
| + | | attributes=filtersize <num: filter> - set the maximum filter size (default is 0 for using the input size)<br>dim <'col'|'row'|'auto'|'stream': mode> - set the dimension on which to operate [auto]<br>out <matrix: output> - output filtered values<br>outstate <matrix: output> - output state<br> | ||
| + | | messages=postdoc - post external doc to console<br>insize <num: input> [<num: filter>] - set input and filter size<br>getstate - get the median state<br>clear - reset the internal state<br>out <matrix: output> - output filtered values<br>outstate <matrix: output> - output state<br> | ||
| + | | inlets=1 <matrix: input> - input values<br> | ||
| + | | outlets=1 <matrix: output> - output filtered values<br>2 <matrix: output> - output state<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.minmax | ||
| + | | brief=Minimum and maximum | ||
| + | | 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': mode switch> - compute over rows or columns [col]<br> | ||
| + | | attributes=mode <1|2|'row'|'col': mode switch> - compute over rows or columns [col]<br>scalar <bool: switch> - output a simple float value (instead of 1 x 1 matrix) for scalar results [on]<br>outmin <vector: min> - minimum output vector or value<br>outargmin <vector: min> - minimum index output vector or value<br>outmax <vector: max> - maximum output vector or value<br>outargmax <vector: max> - maximum index output vector or value<br> | ||
| + | | messages=postdoc - post external doc to console<br>outmin <vector: min> - minimum output vector or value<br>outargmin <vector: min> - minimum index output vector or value<br>outmax <vector: max> - maximum output vector or value<br>outargmax <vector: max> - maximum index output vector or value<br> | ||
| + | | inlets=1 <matrix: input> - incoming matrix, vector, or list<br> | ||
| + | | outlets=1 <vector: min> - minimum output vector or value<br>2 <vector: min> - minimum index output vector or value<br>3 <vector: max> - maximum output vector or value<br>4 <vector: max> - maximum index output vector or value<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 <bool: switch> - compute the standards moments for orders > 2 [on]<br>mode <1|2|'row'|'col': mode switch> - compute over rows or columns [col]<br>out <vector: moments> - output moments vector<br>outsum <vector: moments> - output sums vector<br> | ||
| + | | messages=postdoc - post external doc to console<br>order <num: order> - set moments maximum order<br>out <vector: moments> - output moments vector<br>outsum <vector: moments> - output sums vector<br> | ||
| + | | inlets=1 <vector: input> - input vector<br> | ||
| + | | outlets=1 <vector: moments> - output moments vector<br>2 <vector: moments> - output sums vector<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.nmd | ||
| + | | brief=Non-zero Matrix Decomposition | ||
| + | | descr=Calculates NMD of incoming matrix. | ||
| + | | arguments=none | ||
| + | | attributes=outh <matrix: H> - H output matrix<br>criteria <num: criteria> - criteria<br>sH <num: sH> - sH<br>itermax <num: itermax> - maximum number of iterations<br> | ||
| + | | messages=postdoc - post external doc to console<br> | ||
| + | | inlets=1 <matrix: input> - input matrix<br>2 <matrix: input> - input matrix<br>3 <num: L2> - L2<br> | ||
| + | | outlets=1 <matrix: output> - output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.nmf | ||
| + | | brief=Non-zero Matrix Factorization | ||
| + | | descr=Calculates NMF of incoming matrix. | ||
| + | | arguments=1 <num: num> - number of components<br> | ||
| + | | attributes=outw <matrix: W> - W output matrix<br>outh <matrix: H> - H output matrix<br>criteria <num: criteria> - stopping criteria<br>rdim <num: num> - number of components<br>itermax <num: max> - maximum number of iterations<br> | ||
| + | | messages=postdoc - post external doc to console<br> | ||
| + | | inlets=1 <matrix: input> - matrix to be decomposed<br> | ||
| + | | outlets=1 <matrix: W> - W output matrix<br>2 <matrix: H> - H output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.obsprob | ||
| + | | brief=Obsprob | ||
| + | | descr=Obsprob has to be documented. | ||
| + | | arguments=none | ||
| + | | attributes=none | ||
| + | | messages=postdoc - post external doc to console<br> | ||
| + | | inlets=1 <matrix: obs> - input observation frames matrix (D x 1)<br>2 <matrix: states> - input states matrix<br> | ||
| + | | outlets=1 <matrix: prob> - output log(B) (K x 1)<br>2 <num> - no description<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.onepole | ||
| + | | brief=Onepole filtering | ||
| + | | descr=Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension). | ||
| + | | arguments=<num: input> [<num: filter>] - init input and filter size<br> | ||
| + | | attributes=f0 <num: freq> - set the onepole frequency, normalised by the Nyquist frequency (default is 1.)<br>dim <'col'|'row'|'auto'|'stream': mode> - set the dimension on which to operate [auto]<br>mode <'lowpass'|'highpass': mode> - filter type [lowpass]<br>out <matrix: output> - output filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | messages=postdoc - post external doc to console<br>getstate - get the onepole state<br>clear - reset the internal state<br>insize <num: input> [<num: filter>] - init input and filter size<br>out <matrix: output> - output filtered values<br>outstate <matrix: state> - output state<br> | ||
| + | | inlets=1 <matrix: input> - input values<br> | ||
| + | | outlets=1 <matrix: output> - output filtered values<br>2 <matrix: state> - output state<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.qr | ||
| + | | brief=Orthogonal-Right decomposition | ||
| + | | descr=Calculates QR decomposition of incoming matrix. | ||
| + | | arguments=none | ||
| + | | attributes=outq <matrix: Q> - Q output matrix<br>outr <matrix: R> - R output matrix<br>outx <matrix: X> - X output matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>solve <matrix: input> - solve system with QR decomposition<br>outq <matrix: Q> - Q output matrix<br>outr <matrix: R> - R output matrix<br>outx <matrix: X> - X output matrix<br> | ||
| + | | inlets=1 <matrix: input> - matrix to be decomposed<br> | ||
| + | | outlets=1 <matrix: Q> - Q output matrix<br>2 <matrix: R> - R output matrix<br>3 <matrix: X> - X output matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.stats | ||
| + | | brief=Stats | ||
| + | | descr=Stats has to be documented. | ||
| + | | arguments=<vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries<br> | ||
| + | | attributes=norm <bool: normalize> - switch normalization [off]<br> | ||
| + | | messages=postdoc - post external doc to console<br>bang - output stats<br>clear - clear histogram<br>set <vector: histogram> [<num: low> <num: high>] - set histogram vector and boundaries<br> | ||
| + | | inlets=1 <num: value> - data input<br> | ||
| + | | outlets=1 <num: average> - output average<br>2 <num: stddev> - output standard deviation<br>3 <num: count> - output count<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.submat | ||
| + | | brief=Sub-matrix | ||
| + | | descr=Copies sub-matrix outof incoming matrix. | ||
| + | | arguments=<num: begin row> <num: begin col> <num: end row> <num: end col> - start end end indices of sub-matrix<br> | ||
| + | | attributes=begin <num: row> <num: col> - start indices of sub-matrix<br>end <num: row> <num: col> - end indices of sub-matrix<br>out <matrix: output> - ouput sub-matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <matrix: output> - ouput sub-matrix<br> | ||
| + | | inlets=1 <matrix: input> - input matrix<br> | ||
| + | | outlets=1 <matrix: output> - ouput sub-matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.sum | ||
| + | | brief=Matrix sum | ||
| + | | descr=Calculates sum over entire matrix, matrix rows or matrix columns. | ||
| + | | arguments=1 <1|2|'row'|'col': mode switch> - compute over rows or columns [col]<br> | ||
| + | | attributes=mode <1|2|'row'|'col': mode switch> - compute over rows or columns [col]<br>out <vector: sum> - output sum vector<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <vector: sum> - output sum vector<br> | ||
| + | | inlets=1 <matrix: input> - incoming matrix to be summed<br> | ||
| + | | outlets=1 <vector: sum> - output sum vector<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.svd | ||
| + | | brief=Singular Value Decomposition | ||
| + | | descr=Calculates SVD of incoming matrix. | ||
| + | | arguments=1 <num: rank> - number of singular values<br> | ||
| + | | attributes=mode <'manual'|'auto': mode> - automatically eliminate negligible singular values [manual]<br>outu <matrix: U> - U output matrix<br>outs <matrix: S> - S output matrix<br>outvt <matrix: V'> - V' output matrix (transposed of V)<br> | ||
| + | | messages=postdoc - post external doc to console<br>outu <matrix: U> - U output matrix<br>outs <matrix: S> - S output matrix<br>outvt <matrix: V'> - V' output matrix (transposed of V)<br> | ||
| + | | inlets=1 <matrix: input> - matrix to be decomposed by SVD<br> | ||
| + | | outlets=1 <matrix: U> - U output matrix<br>2 <matrix: S> - S output matrix<br>3 <matrix: V'> - V' output matrix (transposed of V)<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.transpose | ||
| + | | brief=Matrix transposition | ||
| + | | descr=Calculates transposed matrix. | ||
| + | | arguments=none | ||
| + | | attributes=out <matrix: output> - output transposed matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <matrix: output> - output transposed matrix<br> | ||
| + | | inlets=1 <matrix: input> - matrix to be transposed<br> | ||
| + | | outlets=1 <matrix: output> - output transposed matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.viterbi | ||
| + | | brief=Viterbi algorithm | ||
| + | | descr=Calculates Viterbi path of incoming matrices or vectors. | ||
| + | | arguments=none | ||
| + | | attributes=line <bool: online> - switch online mode on/off [off]<br>verbose <bool: verbose> - verbose or not [off]<br>latency <num: latency> - maximum latency (nb of frames)<br> | ||
| + | | messages=postdoc - post external doc to console<br>geta - get state matrix A<br>getpi - get state matrix Pi<br>getpsi - get state matrix Psi<br>locpaths - get locpaths matrix<br>reinit - input message reinit<br>bang - decode<br> | ||
| + | | inlets=1 - messages only<br>2 <matrix: observations> - input observation matrix logB (T x K)<br>3 <matrix: pi> - input state prior distribution Pi (1 x K)<br>4 <matrix: A> - input state transition matrix A (K x K)<br> | ||
| + | | outlets=1 <matrix: path> - output decoded best path<br>2 <matrix: state> - output state matrices<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.xdist2 | ||
| + | | brief=Square of Euclidean distance | ||
| + | | descr=Calculates the square of the Euclidean distance between a vector and each line of a matrix. | ||
| + | | arguments=<matrix: right> - matrix to be used as right operand<br> | ||
| + | | attributes=swap <bool: switch> swap operands<br>out <matrix: output> - result squared distance matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <matrix: output> - result squared distance matrix<br> | ||
| + | | inlets=1 <matrix: left> - input left operand><br>2 <matrix: right> - input right operand><br> | ||
| + | | outlets=1 <matrix: output> - result squared distance matrix<br> | ||
| + | }} | ||
| + | |||
| + | {{Module | | ||
| + | | name=mnm.xmul | ||
| + | | brief=Matrix multiplication | ||
| + | | 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=<matrix: op> - set right or (with swap enabled) left matrix multiplication operand<br> | ||
| + | | attributes=swap <bool: switch> swap operands<br>out <matrix: output> - output matrix<br> | ||
| + | | messages=postdoc - post external doc to console<br>out <matrix: output> - output matrix<br> | ||
| + | | inlets=1 <matrix: input> - multiply matrix with given right or left operand<br>2 <matrix: op> - set right or (with swap enabled) left matrix multiplication operand<br> | ||
| + | | outlets=1 <matrix: output> - output matrix<br> | ||
| + | }} | ||
Latest revision as of 22:27, 3 May 2009
- mnm.biqoefs ... Biquad coefficients
- mnm.biquad ... Biquad filtering
- mnm.delta ... Inter-frame regression
- mnm.diag ... Matrix diagonal
- mnm.dtw ... Dynamic Time Warping
- mnm.gmmem ... Expectation Maximization for Gaussian Mixture Models
- mnm.hist ... Histogram
- mnm.knn ... K-Nearest Neighbour search
- mnm.lu ... Lower-Upper decomposition
- mnm.mahalanobis ... Mahalanobis distance
- mnm.mean ... Mean filtering
- mnm.meanstd ... Mean and standard deviation
- mnm.median ... Median filtering
- mnm.minmax ... Minimum and maximum
- mnm.moments ... Statistical moments
- mnm.nmd ... Non-zero Matrix Decomposition
- mnm.nmf ... Non-zero Matrix Factorization
- mnm.obsprob ... Obsprob
- mnm.onepole ... Onepole filtering
- mnm.qr ... Orthogonal-Right decomposition
- mnm.stats ... Stats
- mnm.submat ... Sub-matrix
- mnm.sum ... Matrix sum
- mnm.svd ... Singular Value Decomposition
- mnm.transpose ... Matrix transposition
- mnm.viterbi ... Viterbi algorithm
- mnm.xdist2 ... Square of Euclidean distance
- mnm.xmul ... Matrix multiplication
| 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 of 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 of incoming matrix. | ||||||||||||
|
| mnm.mahalanobis | Mahalanobis distance | |||||||||||
| Calculates mahalanobis distance of incoming matrices or vectors. | ||||||||||||
|
| mnm.mean | Mean filtering | |||||||||||
| Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
|
| 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 | ||||||||||||
|
| mnm.median | Median filtering | |||||||||||
| Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
|
| 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 | ||||||||||||
|
| mnm.moments | Statistical moments | |||||||||||
| Calculates moments from first to specified order. | ||||||||||||
|
| mnm.nmd | Non-zero Matrix Decomposition | |||||||||||
| Calculates NMD of incoming matrix. | ||||||||||||
|
| mnm.nmf | Non-zero Matrix Factorization | |||||||||||
| Calculates NMF of incoming matrix. | ||||||||||||
|
| mnm.obsprob | Obsprob | |||||||||||
| Obsprob has to be documented. | ||||||||||||
|
| mnm.onepole | Onepole filtering | |||||||||||
| Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension). | ||||||||||||
|
| mnm.qr | Orthogonal-Right decomposition | |||||||||||
| Calculates QR decomposition of incoming matrix. | ||||||||||||
|
| mnm.stats | Stats | |||||||||||
| Stats has to be documented. | ||||||||||||
|
| mnm.submat | Sub-matrix | |||||||||||
| Copies sub-matrix outof incoming matrix. | ||||||||||||
|
| mnm.sum | Matrix sum | |||||||||||
| Calculates sum over entire matrix, matrix rows or matrix columns. | ||||||||||||
|
| mnm.svd | Singular Value Decomposition | |||||||||||
| Calculates SVD of incoming matrix. | ||||||||||||
|
| mnm.transpose | Matrix transposition | |||||||||||
| Calculates transposed matrix. | ||||||||||||
|
| mnm.viterbi | Viterbi algorithm | |||||||||||
| Calculates Viterbi path of incoming matrices or vectors. | ||||||||||||
|
| mnm.xdist2 | Square of Euclidean distance | |||||||||||
| Calculates the square of the Euclidean distance between a vector and each line of a 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. | ||||||||||||
|