(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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'
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arguments:
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none
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attributes:
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#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
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messages:
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postdoc - post external doc to console
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inlets:
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1 - input list
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outlets:
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1 - message outlet
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mnm.biqoefs
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Biquad coefficients
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Calculates biquad coefficients for various filter types.
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arguments:
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none
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attributes:
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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
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messages:
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postdoc - post external doc to console bang - bang to output coefficients out - biquad coefficients
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inlets:
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none
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outlets:
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1 - biquad coefficients
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mnm.biquad
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Biquad filtering
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Calculates biquad filtering over vectors (rows or columns) or stream of values (of any dimension).
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arguments:
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set the inputs initial size and numbers
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attributes:
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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
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messages:
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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
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inlets:
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1 - input values
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outlets:
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1 - filtered values 2 - output state
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mnm.delta
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Inter-frame regression.
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Calculates derivative of incoming matrices or vectors.
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arguments:
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1 - initialize the input size 2 - initialize the filter size
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attributes:
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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
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messages:
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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
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inlets:
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1 - multiply matrix with given right or left operand
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outlets:
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1 - output delayed inputs (in phase with deltas) 2 - output deltas 3 - internal values
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mnm.diag
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Matrix diagonal
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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.
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arguments:
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none
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attributes:
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out - output diagonal vector
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messages:
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postdoc - post external doc to console out - output diagonal vector
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inlets:
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1 - input matrix
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outlets:
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1 - output diagonal vector
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mnm.dtw
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Dynamic time warping.
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Calculates DTW on incoming matrix or vector.
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arguments:
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1 - fmat to be used as right operand
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attributes:
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outa - reference to an external fmat to store A outb - reference to an external fmat to store B
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messages:
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postdoc - post external doc to console
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inlets:
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1 - left hand side fmat operand 2 - right hand side fmat operand
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outlets:
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1 - s1 2 - s2
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mnm.gmmem
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Expectation maximization for Gaussian mixture models
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GMM EM has to be documented.
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arguments:
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1 - number of centers to use
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attributes:
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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
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messages:
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postdoc - post external doc to console
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inlets:
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1 - fmat
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outlets:
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1 - fmat centers 2 - fmat covariance 3 - fmat priors
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mnm.hist
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Histogram
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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
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arguments:
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1 - number of bins
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attributes:
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out - output histogram vector bpf - output two-column fmat with bin indices and histogram values norm <symbol: off|max|sum> -- normalise histogram so that max or sum is 1
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messages:
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postdoc - post external doc to console out - output histogram vector set_n - number of bins
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inlets:
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1 - intput matrix or list
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outlets:
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1 - output histogram vector 2 - output min data value 3 - output max data value
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mnm.knn
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K nearest neighbour search
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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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arguments:
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1 - max number k of nearest neighbours to search 2 - max radius of nearest neighbours to search (0 for unlimited)
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attributes:
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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
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messages:
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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
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inlets:
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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)>
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outlets:
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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
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mnm.lu
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Lower-upper decomposition
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Calculates LU decomposition on incoming matrix.
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arguments:
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none
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attributes:
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outl - L outu - U outpivot - pivot outx - X outdet - determinant
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messages:
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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
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inlets:
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1 - matrix to decompose
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outlets:
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1 - L 2 - U 3 - pivot 4 - X 5 - determinant
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mnm.mahalanobis
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Mahalanobis distance.
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Calculates mahalanobis distance on incoming matrices or vectors.
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arguments:
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<matrix|vector|list: mean> <matrix|vector|list: covariance> - init mean and covariance
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attributes:
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out - mahalanobis distance
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messages:
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postdoc - post external doc to console set_mu - matrix of mean values set_sigma - matrix of covariance out - mahalanobis distance
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inlets:
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1 <matrix|vector|list: query vector> 2 <matrix: mu> 3 <matrix: sigma>
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outlets:
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1 - mahalanobis distance
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mnm.mean
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Mean filtering
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Calculates mean filtering over vectors (rows or columns) or stream of values (of any dimension).
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arguments:
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set the inputs initial size and numbers
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attributes:
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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
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messages:
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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
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inlets:
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1 - input values
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outlets:
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1 - filtered values 2 - output state
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mnm.meanstd
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Mean and standard deviation
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Calculates the arithmetic mean and standard deviation of each column or row (depending on 'mode' argument) as one row or column vector
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arguments:
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1 <1|2|'row'|'col': mode switch> compute over rows or columns
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attributes:
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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
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messages:
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postdoc - post external doc to console outmean - mean output vector or value outstd - standard deviation output vector or value
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inlets:
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1 - input matrix
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outlets:
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1 - mean output vector or value 2 - standard deviation output vector or value
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mnm.median
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Median filtering
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Calculates median filtering over vectors (rows or columns) or stream of values (of any dimension).
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arguments:
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set the inputs initial size and numbers
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attributes:
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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
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messages:
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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
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inlets:
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1 - input values
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outlets:
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1 - filtered values 2 - output state
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mnm.minmax
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Minimum and maximum
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Calculates the min, index of min, max, index of max of each column or row (depending on argument) as one row or column vector
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arguments:
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1 - (1|2|row|col) sum over rows or columns
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attributes:
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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
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messages:
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postdoc - post external doc to console outmin - min outargmin - argmin outmax - max outargmax - argmax
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inlets:
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1 - incoming matrix, vector, or list
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outlets:
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1 - min 2 - argmin 3 - max 4 - argmax
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mnm.moments
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Statistical moments
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Calculates moments from first to specified order.
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arguments:
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1 <num: order> - moments maximum order [1]
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attributes:
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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.
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messages:
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postdoc - post external doc to console order <num: order> - set moments maximum order out - moments outsum - input sums
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inlets:
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1 - input vector
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outlets:
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1 - moments 2 - input sums
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mnm.nmd
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Non-zero matrix decomposition
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Calculates NMD on incoming matrix.
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arguments:
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none
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attributes:
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outh - out H criteria - criteria sH - sH itermax - itermax
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messages:
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postdoc - post external doc to console
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inlets:
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1 - fmat 2 - fmat 3 -
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outlets:
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1 - fmat
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mnm.nmf
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Non-zero matrix factorization
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Calculates NMF on incoming matrix.
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arguments:
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1 - number of components
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attributes:
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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
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messages:
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postdoc - post external doc to console
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inlets:
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1 - fmat to be decomposed
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outlets:
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1 - W 2 - H
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mnm.obsprob
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Obsprob
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Obsprob has to be documented.
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arguments:
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none
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attributes:
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none
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messages:
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postdoc - post external doc to console
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inlets:
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1 - ref. to observation frame : fmat [D (=feature space dim) , 1] 2 - ref. to states matrix
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outlets:
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1 - log(B) : fmat [K (=nb of states) , 1] 2 - test
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mnm.onepole
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Onepole filtering
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Calculates onepole filtering (low-pass or high-pass) over vectors (rows or columns) or stream of values (of any dimension).
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arguments:
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set the inputs initial size and numbers
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attributes:
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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
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messages:
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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
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inlets:
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1 - input values
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outlets:
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1 - filtered values 2 - output state
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mnm.qr
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Orthogonal-right decomposition
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Calculates QR decomposition on incoming matrix.
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arguments:
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none
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attributes:
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outq - Q outr - R outx - X
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messages:
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postdoc - post external doc to console solve - solve system with QR decomposition outq - Q outr - R outx - X
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inlets:
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1 - fmat to be decomposed
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outlets:
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1 - Q 2 - R 3 - X
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mnm.stats
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Stats
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Stats has to be documented.
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arguments:
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output stats
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attributes:
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norm - switch normalize
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messages:
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postdoc - post external doc to console bang - output stats clear - clear histogram set - set histogram vector (fmat)
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inlets:
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1 - data input
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outlets:
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1 - average 2 - standard deviation 3 - count
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mnm.submat
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Sub-matrix
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Copies sub-matrix outof incoming matrix.
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arguments:
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none
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attributes:
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out - sub-matrix
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messages:
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postdoc - post external doc to console begin - start of submatrix coordinates end - end of submatrix coordinates out - sub-matrix
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inlets:
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1 - fmat
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outlets:
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1 - sub-matrix
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mnm.sum
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Matrix sum
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Calculates sum over entire matrix, matrix rows or matrix columns.
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arguments:
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1 - (1|2|row|col) sum over rows or columns
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attributes:
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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
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messages:
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postdoc - post external doc to console out - sum of fmat
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inlets:
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1 - incoming matrix to be summed
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outlets:
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1 - sum of fmat
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mnm.svd
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Singular value decomposition
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Calculates SVD decomposition on incoming matrix.
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arguments:
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1 - number of singular values
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attributes:
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mode - (auto|manual) automatically eliminate negligible singular values outu - output matrix for U outs - output matrix for S outvt - output matrix for V'
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messages:
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postdoc - post external doc to console outu - output matrix for U outs - output matrix for S outvt - output matrix for V'
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inlets:
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1 - matrix to be decomposed by SVD
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outlets:
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1 - output matrix for U 2 - output matrix for S 3 - output matrix for V'
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mnm.transpose
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Transpose
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Calculates transposed matrix.
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arguments:
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none
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attributes:
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out - transposed matrix
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messages:
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postdoc - post external doc to console out - transposed matrix
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inlets:
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1 - matrix to be transposed
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outlets:
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1 - transposed matrix
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mnm.viterbi
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Viterbi algorithm
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Calculates Viterbi path on incoming matrices or vector.
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arguments:
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none
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attributes:
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line - on|off line verbose - verbose or not (0|1) latency - maximum latency (nb of frames) get - debug : get intern values.
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messages:
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postdoc - post external doc to console reinit - input message reinit bang - bang to decode locpaths - get locpaths matrix
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inlets:
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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]
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outlets:
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1 - decoded best path 2 - debug
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mnm.xdist2
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Square of Euclidean distance
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Calculates the square of the Euclidean distance between a vector and each line of a matrix.
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arguments:
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matrix to be used as right operand
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attributes:
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swap - (yes|no) swaps operands out - result squared distance matrix
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messages:
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postdoc - post external doc to console out - result squared distance matrix
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inlets:
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1 <matrix: left operand> 2 <matrix: right operand>
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outlets:
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1 - result squared distance matrix
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mnm.xmul
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Matrix multiplication
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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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arguments:
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set right or (with swap enabled) left matrix multiplication operand
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attributes:
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out - output matrix object swap <bool: switch> swaps operands
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messages:
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postdoc - post external doc to console out - output matrix object
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inlets:
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1 - multiply matrix with given right or left operand 2 - set right or (with swap enabled) left matrix multiplication operand
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outlets:
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1 - output matrix object
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