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Merge pull request #82 from dmorgankx/cluster_streaming
Addition of clustering fit, predict and update methods
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| \d .ml | ||
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| // Affinity propagation algorithm | ||
| /* data = data points in `value flip` format | ||
| /* df = distance function | ||
| /* dmp = damping coefficient | ||
| /* diag = similarity matrix diagonal value function | ||
| /. r > return list of clusters | ||
| clust.ap:{[data;df;dmp;diag] | ||
| // check distance function and diagonal value | ||
| if[not df in key clust.i.dd;clust.i.err.dd[]]; | ||
| // create initial table with exemplars/matches and similarity, availability and responsibility matrices | ||
| info0:clust.i.apinit["f"$data;df;diag]; | ||
| // run AP algo until there is no change in results over `0.1*count data` runs | ||
| info1:{[maxiter;info]maxiter>info`matches}[.1*count data]clust.i.apalgo[dmp]/info0; | ||
| // return list of clusters | ||
| clust.i.reindex info1`exemplars} | ||
| // Affinity Propagation | ||
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| // Initialize matrices | ||
| /* data = data points in `value flip` format | ||
| /* df = distance function | ||
| /* diag = similarity matrix diagonal value | ||
| /. r > returns a dictionary with similarity, availability and responsibility matrices | ||
| / and keys for matches and exemplars to be filled during further iterations | ||
| clust.i.apinit:{[data;df;diag] | ||
| // calculate similarity matrix values | ||
| s:@[;;:;diag raze s]'[s:clust.i.dists[data;df;data]each k;k:til n:count data 0]; | ||
| // create lists/matrices of zeros for other variables | ||
| `matches`exemplars`s`a`r!(0;0#0;s),(2;n;n)#0f} | ||
| // @kind function | ||
| // @category clust | ||
| // @fileoverview Fit affinity propagation algorithm | ||
| // @param data {float[][]} Data in matrix format, each column is an individual datapoint | ||
| // @param df {symbol} Distance function name within '.ml.clust.df' | ||
| // @param dmp {float} Damping coefficient | ||
| // @param diag {func} Function applied to the similarity matrix diagonal | ||
| // @param iter {dict} Max number of overall iterations and iterations | ||
| // without a change in clusters. (::) can be passed in which case the defaults | ||
| // of (`total`nochange!200 15) will be used | ||
| // @return {dict} Data, input variables, clusters and exemplars | ||
| // (`data`inputs`clt`exemplars) required for the predict method | ||
| clust.ap.fit:{[data;df;dmp;diag;iter] | ||
| data:clust.i.floatConversion[data]; | ||
| defaultDict:`run`total`nochange!0 200 15; | ||
| if[iter~(::);iter:()!()]; | ||
| if[99h<>type iter;'"iter must be (::) or a dictionary"]; | ||
| // update iteration dictionary with user changes | ||
| updDict:defaultDict,iter; | ||
| // cluster data using AP algo | ||
| clust.i.runap[data;df;dmp;diag;til count data 0;updDict] | ||
| } | ||
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| // Run affinity propagation algorithm | ||
| /* dmp = damping coefficient | ||
| /* info = dictionary containing exemplars and matches, similarity, availability and responsibility matrices | ||
| /. r > returns updated info | ||
| // @kind function | ||
| // @category clust | ||
| // @fileoverview Predict clusters using AP config | ||
| // @param data {float[][]} Data in matrix format, each column is an individual datapoint | ||
| // @param cfg {dict} `data`inputs`clt`exemplars returned by clust.ap.fit | ||
| // @return {long[]} List of predicted clusters | ||
| clust.ap.predict:{[data;cfg] | ||
| data:clust.i.floatConversion[data]; | ||
| if[-1~first cfg`clt; | ||
| '"'.ml.clust.ap.fit' did not converge, all clusters returned -1. Cannot predict new data."]; | ||
| // retrieve cluster centres from training data | ||
| ex:cfg[`data][;distinct cfg`exemplars]; | ||
| // predict testing data clusters | ||
| clust.i.appreddist[ex;cfg[`inputs]`df]each$[0h=type data;flip;enlist]data | ||
| } | ||
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| // Utilities | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Run affinity propagation algorithm | ||
| // @param data {float[][]} Data in matrix format, each column is an individual datapoint | ||
| // @param df {symbol} Distance function name within '.ml.clust.df' | ||
| // @param dmp {float} Damping coefficient | ||
| // @param diag {func} Function applied to the similarity matrix diagonal | ||
| // @param idxs {long[]} List of indicies to find distances for | ||
| // @param iter {dict} Max number of overall iterations and iterations | ||
| // without a change in clusters. (::) can be passed in where the defaults | ||
| // of (`total`nochange!200 15) will be used | ||
| // @return {long[]} List of clusters | ||
| clust.i.runap:{[data;df;dmp;diag;idxs;iter] | ||
| // check negative euclidean distance has been given | ||
| if[df<>`nege2dist;clust.i.err.ap[]]; | ||
| // calculate distances, availability and responsibility | ||
| info0:clust.i.apinit[data;df;diag;idxs]; | ||
| // initialize exemplar matrix and convergence boolean | ||
| info0,:`emat`conv`iter!((count data 0;iter`nochange)#0b;0b;iter); | ||
| // run ap algo until maximum number of iterations completed or convergence | ||
| info1:clust.i.apstop clust.i.apalgo[dmp]/info0; | ||
| // return data, inputs, clusters and exemplars | ||
| inputs:`df`dmp`diag`iter!(df;dmp;diag;iter); | ||
| exemplars:info1`exemplars; | ||
| clt:$[info1`conv;clust.i.reindex exemplars;count[data 0]#-1]; | ||
| `data`inputs`clt`exemplars!(data;inputs;clt;exemplars) | ||
| } | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Initialize matrices | ||
| // @param data {float[][]} Data in matrix format, each column is an individual datapoint | ||
| // @param df {symbol} Distance function name within '.ml.clust.df' | ||
| // @param diag {func} Function applied to the similarity matrix diagonal | ||
| // @param idxs {long[]} List of point indices | ||
| // @return {dict} Similarity, availability and responsibility matrices | ||
| // and keys for matches and exemplars to be filled during further iterations | ||
| clust.i.apinit:{[data;df;diag;idxs] | ||
| // calculate similarity matrix values | ||
| s:clust.i.dists[data;df;data]each idxs; | ||
| // update diagonal | ||
| s:@[;;:;diag raze s]'[s;k:til n:count data 0]; | ||
| // create lists/matrices of zeros for other variables | ||
| `matches`exemplars`s`a`r!(0;0#0;s),(2;n;n)#0f | ||
| } | ||
|
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Run affinity propagation algorithm | ||
| // @param dmp {float} Damping coefficient | ||
| // @param info {dict} Similarity, availability, responsibility, exemplars, | ||
| // matches, iter dictionary, no_conv boolean and iter dict | ||
| // @return {dict} Updated inputs | ||
| clust.i.apalgo:{[dmp;info] | ||
| // update responsibility matrix | ||
| info[`r]:clust.i.updr[dmp;info]; | ||
| // update availability matrix | ||
| info[`a]:clust.i.upda[dmp;info]; | ||
| // find new exemplars | ||
| ex:imax each sum info`a`r; | ||
| // return updated `info` with new exemplars/matches | ||
| update exemplars:ex,matches:?[exemplars~ex;matches+1;0]from info} | ||
| // update responsibility matrix | ||
| info[`r]:clust.i.updr[dmp;info]; | ||
| // update availability matrix | ||
| info[`a]:clust.i.upda[dmp;info]; | ||
| // find new exemplars | ||
| ex:imax each sum info`a`r; | ||
| // update `info` with new exemplars/matches | ||
| info:update exemplars:ex,matches:?[exemplars~ex;matches+1;0]from info; | ||
| // update iter dictionary | ||
| .[clust.i.apconv info;(`iter;`run);+[1]] | ||
| } | ||
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| // Update responsibility matrix | ||
| /* dmp = damping coefficient | ||
| /* info = dictionary containing exemplars and matches, similarity, availability and responsibility matrices | ||
| /. r > returns updated responsibility matrix | ||
| // @kind function | ||
| // @category private | ||
| // @fileoverview Check affinity propagation algorithm for convergence | ||
| // @param info {dict} Similarity, availability, responsibility, exemplars, | ||
| // matches, iter dictionary, no_conv boolean and iter dict | ||
| // @return {dict} Updated info dictionary | ||
| clust.i.apconv:{[info] | ||
| // iteration dictionary | ||
| iter:info`iter; | ||
| // exemplar matrix | ||
| emat:info`emat; | ||
| // existing exemplars | ||
| ediag:0<sum clust.i.diag each info`a`r; | ||
| emat[;iter[`run]mod iter`nochange]:ediag; | ||
| // check for convergence | ||
| if[iter[`nochange]<=iter`run; | ||
| unconv:count[info`s]<>sum(se=iter`nochange)+0=se:sum each emat; | ||
| conv:$[(iter[`total]=iter`run)|not[unconv]&sum[ediag]>0;1b;0b]]; | ||
| // return updated info | ||
| info,`emat`conv!(emat;conv) | ||
| } | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Retrieve diagonal from a square matrix | ||
| // @param m {any[][]} Square matrix | ||
| // @return {any[]} Matrix diagonal | ||
| clust.i.diag:{[m] | ||
| {x y}'[m;til count m] | ||
| } | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Update responsibility matrix | ||
| // @param dmp {float} Damping coefficient | ||
| // @param info {dict} Similarity, availability, responsibility, exemplars, | ||
| // matches, iter dictionary, no_conv boolean and iter dict | ||
| // @return {float[][]} Updated responsibility matrix | ||
| clust.i.updr:{[dmp;info] | ||
| // create matrix with every points max responsibility - diagonal becomes -inf, current max is becomes second max | ||
| mx:{[x;i]@[count[x]#mx;j;:;]max@[x;i,j:x?mx:max x;:;-0w]}'[sum info`s`a;til count info`r]; | ||
| // calculate new responsibility | ||
| (dmp*info`r)+(1-dmp)*info[`s]-mx} | ||
| // create matrix with every points max responsibility | ||
| // diagonal becomes -inf, current max is becomes second max | ||
| mxresp:{[x;i]@[count[x]#mx;j;:;]max@[x;i,j:x?mx:max x;:;-0w]}; | ||
| mx:mxresp'[sum info`s`a;til count info`r]; | ||
| // calculate new responsibility | ||
| (dmp*info`r)+(1-dmp)*info[`s]-mx | ||
| } | ||
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| // Update availability matrix | ||
| /* dmp = damping coefficient | ||
| /* info = dictionary containing exemplars and matches, similarity, availability and responsibility matrices | ||
| /. r > returns updated availability matrix | ||
| // @kind function | ||
| // @category private | ||
| // @fileoverview Update availability matrix | ||
| // @param dmp {float} Damping coefficient | ||
| // @param info {dict} Similarity, availability, responsibility, exemplars, | ||
| // matches, iter dictionary, no_conv boolean and iter dict | ||
| // @return {float[][]} Returns updated availability matrix | ||
| clust.i.upda:{[dmp;info] | ||
| // sum values in positive availability matrix | ||
| s:sum@[;;:;0f]'[pv:0|info`r;k:til n:count info`a]; | ||
| // create a matrix using the negative values produced by the availability sum + responsibility diagonal - positive availability values | ||
| a:@[;;:;]'[0&(s+info[`r]@'k)-/:pv;k;s]; | ||
| // calculate new availability | ||
| (dmp*info`a)+a*1-dmp} | ||
| // sum values in positive availability matrix | ||
| s:sum@[;;:;0f]'[pv:0|info`r;k:til n:count info`a]; | ||
| // create a matrix using the negative values produced by the availability sum | ||
| // + responsibility diagonal - positive availability values | ||
| a:@[;;:;]'[0&(s+info[`r]@'k)-/:pv;k;s]; | ||
| // calculate new availability | ||
| (dmp*info`a)+a*1-dmp | ||
| } | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Stopping condition for affinity propagation algorithm | ||
| // @param info {dict} Similarity, availability, responsibility, exemplars, | ||
| // matches, iter dictionary, no_conv boolean and iter dict | ||
| // @return {bool} Indicates whether to continue or stop running AP (1/0b) | ||
| clust.i.apstop:{[info] | ||
| (info[`iter;`total]>info[`iter]`run)¬ 1b~info`conv | ||
| } | ||
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| // @kind function | ||
| // @category private | ||
| // @fileoverview Predict clusters using AP training exemplars | ||
| // @param ex {float[][]} Training cluster centres in matrix format, | ||
| // each column is an individual datapoint | ||
| // @param df {symbol} Distance function name within '.ml.clust.df' | ||
| // @param pt {float[]} Current data point | ||
| // @return {long[]} Predicted clusters | ||
| clust.i.appreddist:{[ex;df;pt] | ||
| d?max d:clust.i.dists[ex;df;pt]each til count ex 0 | ||
| } |
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