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Statistical Models

  • fd_cauchy~ fd_cauchy~ generates the same Cauchy function as fd_cauchy and stores it into an array via DSP, for use by fd_windows; it has no outlets.
  • ml.svm ml.svm implements a support vector machine for classifying feature vectors.
  • ml.softmax ml.softmax is a softmax classifier that trains on labeled feature vectors added with add and classifies an input feature vector using map.
  • gendyn~ The gendyn~ object implements Dynamic Stochastic Synthesis, generating waveforms where each point's amplitude and frequency evolve via a random walk.
  • ml.randforest ml.randforest implements Random Forest classification, training a model from labeled feature vectors and mapping input feature vectors to output values.
  • median The median object calculates the median value of a given list of numbers.
  • fluid.skmeans fluid.skmeans implements the K-means clustering algorithm with cosine similarity.
  • fd_miller-gauss fd_miller-gauss generates a two-dimensional Gaussian distribution, outputting the x and y distribution values through its two outlets.
  • fluid.kmeans The fluid.kmeans object implements the K-means clustering algorithm, used to group data points from a fluid.dataset into a specified number of clusters.
  • fd_logistic fd_logistic computes Xenakis’ logistic distribution, described by π⁻¹ × (x × (1 − x))⁻¹/².
  • ml.adaboost ml.adaboost performs classification using Adaptive Boosting, training a model from labeled feature vectors added with add and mapping input feature vectors to output values with map.
  • ml.mindist ml.mindist implements minimum-distance classification, training a model from labeled feature vectors added as lists whose first value is the class ID.
  • fluid.nmfmorph~ The fluid.nmfmorph~ object performs cross-synthesis between two audio sources using Nonnegative Matrix Factorization (NMF) and Optimal Transport (OT).
  • ml.knn ml.knn implements K-nearest-neighbour classification, mapping an input feature vector to output value(s).
  • prob The prob object generates a weighted series of random numbers based on a first-order Markov chain.
  • ml.hmmc ml.hmmc implements a continuous Hidden Markov Model for classifying input feature-vector time series.
  • markov The markov object generates sequences based on learned patterns, creating Markov chains of any order from floats, symbols, or lists.
  • fluid.knnregressor The fluid.knnregressor object performs K-Nearest Neighbours regression, enabling the prediction of outputs by finding the weighted average of neighboring data points within datasets.
  • fluid.mds The fluid.mds object performs Multidimensional Scaling (MDS) for dimensionality reduction on a fluid.dataset.
  • commonality The commonality object calculates the 'Pitch Commonality' between three or more specified pitches.
  • fluid.bufnmfcross fluid.bufnmfcross reconstructs a target sound by using components learned from a source sound through Non-negative Matrix Factorization (NMF).
  • fluid.bufstats The fluid.bufstats object calculates various statistical descriptors (e.g., mean, standard deviation, skewness, kurtosis, pitch) from segments within an audio buffer.
  • fd_poisson~ fd_poisson~ performs the same function as fd_poisson, but stores the result into an array via DSP.
  • histo The histo object records the frequency of received positive integers, creating a histogram.
  • ml.gmm ml.gmm is a Gaussian Mixture Model classifier that trains on labeled feature vectors and maps input feature vectors to classification output.
  • fluid.bufspectralshape The fluid.bufspectralshape object computes seven spectral shape descriptors (centroid, spread, skewness, kurtosis, rolloff, flatness, crest) from an audio source.
  • ml.linreg ml.linreg implements linear regression, training a model from labeled feature vectors added with add and generating output values for an input feature vector with map.
  • ml.logreg ml.logreg implements logistic regression for classifying feature vectors.
  • fd_cauchy fd_cauchy computes the Cauchy distribution, using the formula t/(π(t²+x²)).
  • smooth_triple smooth_triple performs triple exponential smoothing on control-rate float data, producing outputs for the smoothed signal, periodicity, and trend.
  • fd_logistic~ fd_logistic~ is the same as fd_logistic, but stores its output into an array using DSP, and is used by fd_windows.
  • brown The brown object generates pseudo-random numbers following a bounded Brownian motion, producing a control signal that takes small, random steps within a defined range.
  • fluid.stats The fluid.stats object calculates the rolling mean and sample standard deviation for multichannel control inputs over a specified window.
  • ml.dtree ml.dtree implements decision-tree classification, training a model from labeled feature vectors added with add and generating results for new vectors with map.
  • fluid.standardize The fluid.standardize object rescales a FluidDataSet by transforming its data to have a mean of 0 and a standard deviation of 1 for each dimension.
  • anal The anal object calculates and stores transition probabilities between sequences of numbers, primarily for implementing Markov Chains.
  • fluid.robustscale The fluid.robustscale object applies robust scaling to fluid.dataset objects.
  • smooth_double smooth_double performs double exponential smoothing (DES) on float control values, producing two float outputs.
  • fd_poisson fd_poisson computes the Poisson expression P(k) = λ^k / k! × e^-λ for an input x value.
  • fluid.pca fluid.pca performs Principal Component Analysis (PCA) on a fluid.dataset to reduce dimensionality, such as transforming 13 MFCC values into 2.
  • kalman The kalman object implements a simple, linear, one-dimensional Kalman filter.
  • fluid.normalize The fluid.normalize object normalizes a fluid.dataset or a single data point.