| //@ $skipModes << :lockdown if $buildType == "debug" | |
| var Statistics=new(function(){this.min=function(values){return Math.min.apply(Math,values);} | |
| this.max=function(values){return Math.max.apply(Math,values);} | |
| this.sum=function(values){return values.length?values.reduce(function(a,b){return a+b;}):0;} | |
| this.mean=function(values){return this.sum(values)/values.length;} | |
| this.weightedMean=function(valuesWithWeights){let totalWeight=0;let sum=0;for(const entry of valuesWithWeights){totalWeight+=entry.weight;sum+=entry.value*entry.weight;} | |
| return sum/totalWeight;} | |
| this.median=function(values){return values.sort(function(a,b){return a-b;})[Math.floor(values.length/2)];} | |
| this.squareSum=function(values){return values.length?values.reduce(function(sum,value){return sum+value*value;},0):0;} | |
| this.sampleStandardDeviation=function(numberOfSamples,sum,squareSum){if(numberOfSamples<2) | |
| return 0;return Math.sqrt(squareSum/(numberOfSamples-1)-sum*sum/(numberOfSamples-1)/numberOfSamples);} | |
| this.supportedConfidenceIntervalProbabilities=function(){const supportedProbabilities=[];for(const probability in tDistributionByOneSidedProbability) | |
| supportedProbabilities.push(oneSidedToTwoSidedProbability(probability).toFixed(2));return supportedProbabilities;} | |
| this.supportedOneSideTTestProbabilities=function(){return Object.keys(tDistributionByOneSidedProbability);} | |
| this.confidenceIntervalDelta=function(probability,numberOfSamples,sum,squareSum){var oneSidedProbability=twoSidedToOneSidedProbability(probability);if(!(oneSidedProbability in tDistributionByOneSidedProbability)){throw'We only support '+this.supportedConfidenceIntervalProbabilities().map(function(probability) | |
| {return probability*100+'%';}).join(', ')+' confidence intervals.';} | |
| if(numberOfSamples-2<0) | |
| return NaN;return this.minimumTForOneSidedProbability(oneSidedProbability,numberOfSamples-1)*this.sampleStandardDeviation(numberOfSamples,sum,squareSum)/Math.sqrt(numberOfSamples);} | |
| this.confidenceInterval=function(values,probability){var sum=this.sum(values);var mean=sum/values.length;var delta=this.confidenceIntervalDelta(probability||0.95,values.length,sum,this.squareSum(values));return[mean-delta,mean+delta];} | |
| this.testWelchsT=function(values1,values2,probability){return this.computeWelchsT(values1,0,values1.length,values2,0,values2.length,probability).significantlyDifferent;} | |
| function sampleMeanAndVarianceFromMultipleSamples(samples){let sum=0;let squareSum=0;let size=0;for(const sample of samples){sum+=sample.sum;squareSum+=sample.squareSum;size+=sample.sampleSize;} | |
| const mean=sum/size;const unbiasedSampleVariance=(squareSum-sum*sum/size)/(size-1);return{mean,variance:unbiasedSampleVariance,size,degreesOfFreedom:size-1,}} | |
| this.probabilityRangeForWelchsT=function(values1,values2){var result=this.computeWelchsT(values1,0,values1.length,values2,0,values2.length);return this._determinetwoSidedProbabilityBoundaryForWelchsT(result.t,result.degreesOfFreedom);} | |
| this.probabilityRangeForWelchsTForMultipleSamples=function(sampleSet1,sampleSet2){const stat1=sampleMeanAndVarianceFromMultipleSamples(sampleSet1);const stat2=sampleMeanAndVarianceFromMultipleSamples(sampleSet2);const combinedT=this._computeWelchsTFromStatistics(stat1,stat2);return this._determinetwoSidedProbabilityBoundaryForWelchsT(combinedT.t,combinedT.degreesOfFreedom)} | |
| this._determinetwoSidedProbabilityBoundaryForWelchsT=function(t,degreesOfFreedom){if(isNaN(t)||isNaN(degreesOfFreedom)) | |
| return{t:NaN,degreesOfFreedom:NaN,range:[null,null]};let lowerBound=null;let upperBound=null;for(const probability in tDistributionByOneSidedProbability){const twoSidedProbability=oneSidedToTwoSidedProbability(probability);if(t>this.minimumTForOneSidedProbability(probability,Math.round(degreesOfFreedom))) | |
| lowerBound=twoSidedProbability;else if(lowerBound){upperBound=twoSidedProbability;break;}} | |
| return{t,degreesOfFreedom,range:[lowerBound,upperBound]};};this.computeWelchsT=function(values1,startIndex1,length1,values2,startIndex2,length2,probability){const stat1=sampleMeanAndVarianceForValues(values1,startIndex1,length1);const stat2=sampleMeanAndVarianceForValues(values2,startIndex2,length2);const{t,degreesOfFreedom}=this._computeWelchsTFromStatistics(stat1,stat2);const minT=this.minimumTForOneSidedProbability(twoSidedToOneSidedProbability(probability||0.8),Math.round(degreesOfFreedom));return{t,degreesOfFreedom,significantlyDifferent:t>minT};};this._computeWelchsTFromStatistics=function(stat1,stat2){const sumOfSampleVarianceOverSampleSize=stat1.variance/stat1.size+stat2.variance/stat2.size;const t=Math.abs((stat1.mean-stat2.mean)/Math.sqrt(sumOfSampleVarianceOverSampleSize)); const degreesOfFreedom=sumOfSampleVarianceOverSampleSize*sumOfSampleVarianceOverSampleSize/ | |
| (stat1.variance*stat1.variance/stat1.size/stat1.size/stat1.degreesOfFreedom | |
| +stat2.variance*stat2.variance/stat2.size/stat2.size/stat2.degreesOfFreedom);return{t,degreesOfFreedom};} | |
| this.findRangesForChangeDetectionsWithWelchsTTest=function(values,segmentations,oneSidedPossibility=0.99){if(!values.length) | |
| return[];const selectedRanges=[];const twoSidedFromOneSidedPossibility=2*oneSidedPossibility-1;for(let i=1;i+2<segmentations.length;i+=2){let found=false;const previousMean=segmentations[i].value;const currentMean=segmentations[i+1].value;console.assert(currentMean!=previousMean);const currentChangePoint=segmentations[i].seriesIndex;const start=segmentations[i-1].seriesIndex;const end=segmentations[i+2].seriesIndex;for(let leftEdge=currentChangePoint-2,rightEdge=currentChangePoint+2;leftEdge>=start&&rightEdge<=end;leftEdge--,rightEdge++){const result=this.computeWelchsT(values,leftEdge,currentChangePoint-leftEdge,values,currentChangePoint,rightEdge-currentChangePoint,twoSidedFromOneSidedPossibility);if(result.significantlyDifferent){selectedRanges.push({startIndex:leftEdge,endIndex:rightEdge-1,segmentationStartValue:previousMean,segmentationEndValue:currentMean});found=true;break;}} | |
| if(!found&&Statistics.debuggingTestingRangeNomination) | |
| console.log('Failed to find a testing range at',currentChangePoint,'changing from',previousMean,'to',currentMean);} | |
| return selectedRanges;};function sampleMeanAndVarianceForValues(values,startIndex,length){var sum=0;for(var i=0;i<length;i++) | |
| sum+=values[startIndex+i];var squareSum=0;for(var i=0;i<length;i++) | |
| squareSum+=values[startIndex+i]*values[startIndex+i];var sampleMean=sum/length;var unbiasedSampleVariance=(squareSum-sum*sum/length)/(length-1);return{mean:sampleMean,variance:unbiasedSampleVariance,size:length,degreesOfFreedom:length-1,}} | |
| this.movingAverage=function(values,backwardWindowSize,forwardWindowSize){var averages=new Array(values.length);for(var i=0;i<values.length;i++){var sum=0;var count=0;for(var j=i-backwardWindowSize;j<=i+forwardWindowSize;j++){if(j>=0&&j<values.length){sum+=values[j];count++;}} | |
| averages[i]=sum/count;} | |
| return averages;} | |
| this.cumulativeMovingAverage=function(values){var averages=new Array(values.length);var sum=0;for(var i=0;i<values.length;i++){sum+=values[i];averages[i]=sum/(i+1);} | |
| return averages;} | |
| this.exponentialMovingAverage=function(values,smoothingFactor){var averages=new Array(values.length);var movingAverage=values[0];averages[0]=movingAverage;for(var i=1;i<values.length;i++){movingAverage=smoothingFactor*values[i]+(1-smoothingFactor)*movingAverage;averages[i]=movingAverage;} | |
| return averages;} | |
| this.segmentTimeSeriesGreedyWithStudentsTTest=function(values,minLength){if(values.length<2) | |
| return[0];var segments=new Array;recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values,0,values.length,minLength,segments);segments.push(values.length);return segments;} | |
| this.debuggingSegmentation=false;this.segmentTimeSeriesByMaximizingSchwarzCriterion=function(values,segmentCountWeight,gridSize){var gridLength=gridSize||500;var totalSegmentation=[0];for(var gridCount=0;gridCount<Math.ceil(values.length/gridLength);gridCount++){var gridValues=values.slice(gridCount*gridLength,(gridCount+1)*gridLength);var segmentation=splitIntoSegmentsUntilGoodEnough(gridValues,segmentCountWeight);if(Statistics.debuggingSegmentation) | |
| console.log('grid='+gridCount,segmentation);for(var i=1;i<segmentation.length-1;i++) | |
| totalSegmentation.push(gridCount*gridLength+segmentation[i]);} | |
| if(Statistics.debuggingSegmentation) | |
| console.log('Final Segmentation',totalSegmentation);totalSegmentation.push(values.length);return totalSegmentation;} | |
| function recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values,startIndex,length,minLength,segments){var tMax=0;var argTMax=null;for(var i=1;i<length-1;i++){var firstLength=i;var secondLength=length-i;if(firstLength<minLength||secondLength<minLength) | |
| continue;var result=Statistics.computeWelchsT(values,startIndex,firstLength,values,startIndex+i,secondLength,0.9);if(result.significantlyDifferent&&result.t>tMax){tMax=result.t;argTMax=i;}} | |
| if(!tMax){segments.push(startIndex);return;} | |
| recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values,startIndex,argTMax,minLength,segments);recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values,startIndex+argTMax,length-argTMax,minLength,segments);} | |
| this.minimumTForOneSidedProbability=function(probability,degreesOfFreedom){if(degreesOfFreedom<1||isNaN(degreesOfFreedom)) | |
| return NaN;const tDistributionTableForProbability=tDistributionByOneSidedProbability[probability];if(degreesOfFreedom<=tDistributionTableForProbability.probabilityToTValue.length) | |
| return tDistributionTableForProbability.probabilityToTValue[degreesOfFreedom-1];const tValuesSortedByProbability=tDistributionTableForProbability.tValuesSortedByProbability;let low=0;let high=tValuesSortedByProbability.length;while(low<high){const mid=low+Math.floor((high-low)/2);const entry=tValuesSortedByProbability[mid];if(degreesOfFreedom<=entry.maxDF) | |
| high=mid;else | |
| low=mid+1;} | |
| return tValuesSortedByProbability[low].value;} | |
| const tDistributionByOneSidedProbability={0.9:{probabilityToTValue:[3.0777,1.8856,1.6377,1.5332,1.4759,1.4398,1.4149,1.3968,1.383,1.3722,1.3634,1.3562,1.3502,1.345,1.3406,1.3368,1.3334,1.3304,1.3277,1.3253,1.3232,1.3212,1.3195,1.3178,1.3163,1.315,1.3137,1.3125,1.3114,1.3104,1.3095,1.3086,1.3077,1.307,1.3062,1.3055,1.3049,1.3042,1.3036,1.3031,1.3025,1.302,1.3016,1.3011,1.3006,1.3002,1.2998,1.2994,1.2991,1.2987,1.2984,1.298,1.2977,1.2974,1.2971,1.2969,1.2966,1.2963,1.2961,1.2958,1.2956,1.2954,1.2951,1.2949,1.2947,1.2945,1.2943,1.2941,1.2939,1.2938,1.2936,1.2934,1.2933,1.2931,1.2929,1.2928,1.2926,1.2925,1.2924,1.2922,1.2921,1.292,1.2918,1.2917,1.2916,1.2915,1.2914,1.2912,1.2911,1.291,1.2909,1.2908,1.2907,1.2906,1.2905,1.2904,1.2903,1.2903,1.2902,1.2901],tValuesSortedByProbability:[{maxDF:101,value:1.2900},{maxDF:102,value:1.2899},{maxDF:103,value:1.2898},{maxDF:105,value:1.2897},{maxDF:106,value:1.2896},{maxDF:107,value:1.2895},{maxDF:109,value:1.2894},{maxDF:110,value:1.2893},{maxDF:112,value:1.2892},{maxDF:113,value:1.2891},{maxDF:115,value:1.2890},{maxDF:116,value:1.2889},{maxDF:118,value:1.2888},{maxDF:119,value:1.2887},{maxDF:121,value:1.2886},{maxDF:123,value:1.2885},{maxDF:125,value:1.2884},{maxDF:127,value:1.2883},{maxDF:128,value:1.2882},{maxDF:130,value:1.2881},{maxDF:132,value:1.2880},{maxDF:135,value:1.2879},{maxDF:137,value:1.2878},{maxDF:139,value:1.2877},{maxDF:141,value:1.2876},{maxDF:144,value:1.2875},{maxDF:146,value:1.2874},{maxDF:149,value:1.2873},{maxDF:151,value:1.2872},{maxDF:154,value:1.2871},{maxDF:157,value:1.2870},{maxDF:160,value:1.2869},{maxDF:163,value:1.2868},{maxDF:166,value:1.2867},{maxDF:170,value:1.2866},{maxDF:173,value:1.2865},{maxDF:177,value:1.2864},{maxDF:180,value:1.2863},{maxDF:184,value:1.2862},{maxDF:188,value:1.2861},{maxDF:193,value:1.2860},{maxDF:197,value:1.2859},{maxDF:202,value:1.2858},{maxDF:207,value:1.2857},{maxDF:212,value:1.2856},{maxDF:217,value:1.2855},{maxDF:223,value:1.2854},{maxDF:229,value:1.2853},{maxDF:235,value:1.2852},{maxDF:242,value:1.2851},{maxDF:249,value:1.2850},{maxDF:257,value:1.2849},{maxDF:265,value:1.2848},{maxDF:273,value:1.2847},{maxDF:283,value:1.2846},{maxDF:292,value:1.2845},{maxDF:303,value:1.2844},{maxDF:314,value:1.2843},{maxDF:326,value:1.2842},{maxDF:339,value:1.2841},{maxDF:353,value:1.2840},{maxDF:369,value:1.2839},{maxDF:385,value:1.2838},{maxDF:404,value:1.2837},{maxDF:424,value:1.2836},{maxDF:446,value:1.2835},{maxDF:471,value:1.2834},{maxDF:499,value:1.2833},{maxDF:530,value:1.2832},{maxDF:565,value:1.2831},{maxDF:606,value:1.2830},{maxDF:652,value:1.2829},{maxDF:707,value:1.2828},{maxDF:771,value:1.2827},{maxDF:848,value:1.2826},{maxDF:942,value:1.2825},{maxDF:1060,value:1.2824},{maxDF:1212,value:1.2823},{maxDF:1415,value:1.2822},{maxDF:1699,value:1.2821},{maxDF:2125,value:1.2820},{maxDF:2837,value:1.2819},{maxDF:4266,value:1.2818},{maxDF:8601,value:1.2817},{maxDF:Infinity,value:1.2816}]},0.95:{probabilityToTValue:[6.3138,2.92,2.3534,2.1318,2.015,1.9432,1.8946,1.8595,1.8331,1.8125,1.7959,1.7823,1.7709,1.7613,1.7531,1.7459,1.7396,1.7341,1.7291,1.7247,1.7207,1.7171,1.7139,1.7109,1.7081,1.7056,1.7033,1.7011,1.6991,1.6973,1.6955,1.6939,1.6924,1.6909,1.6896,1.6883,1.6871,1.686,1.6849,1.6839,1.6829,1.682,1.6811,1.6802,1.6794,1.6787,1.6779,1.6772,1.6766,1.6759,1.6753,1.6747,1.6741,1.6736,1.673,1.6725,1.672,1.6716,1.6711,1.6706,1.6702,1.6698,1.6694,1.669,1.6686,1.6683,1.6679,1.6676,1.6672,1.6669,1.6666,1.6663,1.666,1.6657,1.6654,1.6652,1.6649,1.6646,1.6644,1.6641,1.6639,1.6636,1.6634,1.6632,1.663,1.6628,1.6626,1.6624,1.6622,1.662,1.6618,1.6616,1.6614,1.6612,1.6611,1.6609,1.6607,1.6606,1.6604,1.6602,1.6601,1.6599,1.6598,1.6596,1.6595,1.6594,1.6592,1.6591,1.659,1.6588,1.6587,1.6586,1.6585,1.6583,1.6582,1.6581,1.658,1.6579,1.6578,1.6577],tValuesSortedByProbability:[{maxDF:121,value:1.6575},{maxDF:122,value:1.6574},{maxDF:123,value:1.6573},{maxDF:124,value:1.6572},{maxDF:125,value:1.6571},{maxDF:126,value:1.6570},{maxDF:127,value:1.6569},{maxDF:129,value:1.6568},{maxDF:130,value:1.6567},{maxDF:131,value:1.6566},{maxDF:132,value:1.6565},{maxDF:133,value:1.6564},{maxDF:134,value:1.6563},{maxDF:135,value:1.6562},{maxDF:137,value:1.6561},{maxDF:138,value:1.6560},{maxDF:139,value:1.6559},{maxDF:140,value:1.6558},{maxDF:142,value:1.6557},{maxDF:143,value:1.6556},{maxDF:144,value:1.6555},{maxDF:146,value:1.6554},{maxDF:147,value:1.6553},{maxDF:148,value:1.6552},{maxDF:150,value:1.6551},{maxDF:151,value:1.6550},{maxDF:153,value:1.6549},{maxDF:154,value:1.6548},{maxDF:156,value:1.6547},{maxDF:158,value:1.6546},{maxDF:159,value:1.6545},{maxDF:161,value:1.6544},{maxDF:163,value:1.6543},{maxDF:164,value:1.6542},{maxDF:166,value:1.6541},{maxDF:168,value:1.6540},{maxDF:170,value:1.6539},{maxDF:172,value:1.6538},{maxDF:174,value:1.6537},{maxDF:176,value:1.6536},{maxDF:178,value:1.6535},{maxDF:180,value:1.6534},{maxDF:182,value:1.6533},{maxDF:184,value:1.6532},{maxDF:186,value:1.6531},{maxDF:189,value:1.6530},{maxDF:191,value:1.6529},{maxDF:193,value:1.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oneSidedToTwoSidedProbability(probability){return 2*probability-1;} | |
| function twoSidedToOneSidedProbability(probability){return(1-(1-probability)/2);} | |
| function splitIntoSegmentsUntilGoodEnough(values,BirgeAndMassartC){if(values.length<2) | |
| return[0,values.length];var matrix=new SampleVarianceUpperTriangularMatrix(values);var SchwarzCriterionBeta=Math.log1p(values.length-1)/values.length;BirgeAndMassartC=BirgeAndMassartC||2.5;var BirgeAndMassartPenalization=function(segmentCount){return segmentCount*(1+BirgeAndMassartC*Math.log1p(values.length/segmentCount-1));} | |
| var segmentation;var minTotalCost=Infinity;var maxK=Math.min(50,values.length);for(var k=1;k<maxK;k++){var start=Date.now();var result=findOptimalSegmentation(values,matrix,k);var cost=result.cost/values.length;var penalty=SchwarzCriterionBeta*BirgeAndMassartPenalization(k);if(cost+penalty<minTotalCost){minTotalCost=cost+penalty;segmentation=result.segmentation;}else | |
| maxK=Math.min(maxK,k+3);if(Statistics.debuggingSegmentation) | |
| console.log('splitIntoSegmentsUntilGoodEnough',k,Date.now()-start,cost+penalty);} | |
| return segmentation;} | |
| function allocateCostUpperTriangularForSegmentation(values,segmentCount) | |
| {var cost=new Array(values.length);for(var segmentEnd=0;segmentEnd<values.length;segmentEnd++) | |
| cost[segmentEnd]=new Float32Array(segmentCount+1);return cost;} | |
| function allocatePreviousNodeForSegmentation(values,segmentCount) | |
| {var previousNode=new Array(values.length);for(var i=0;i<values.length;i++) | |
| previousNode[i]=new Array(segmentCount+1);return previousNode;} | |
| function findOptimalSegmentationInternal(cost,previousNode,values,costMatrix,segmentCount) | |
| {cost[0]=new Float32Array([0]);previousNode[0]=[-1];for(var segmentStart=0;segmentStart<values.length;segmentStart++){var costOfOptimalSegmentationThatEndAtCurrentStart=cost[segmentStart];for(var k=0;k<segmentCount;k++){var noSegmentationOfLenghtKEndsAtCurrentStart=previousNode[segmentStart][k]===undefined;if(noSegmentationOfLenghtKEndsAtCurrentStart) | |
| continue;for(var segmentEnd=segmentStart+1;segmentEnd<values.length;segmentEnd++){var costOfOptimalSegmentationOfLengthK=costOfOptimalSegmentationThatEndAtCurrentStart[k];var costOfCurrentSegment=costMatrix.costBetween(segmentStart,segmentEnd);var totalCost=costOfOptimalSegmentationOfLengthK+costOfCurrentSegment;if(previousNode[segmentEnd][k+1]===undefined||totalCost<cost[segmentEnd][k+1]){cost[segmentEnd][k+1]=totalCost;previousNode[segmentEnd][k+1]=segmentStart;}}}}} | |
| function findOptimalSegmentation(values,costMatrix,segmentCount){var cost=allocateCostUpperTriangularForSegmentation(values,segmentCount);var previousNode=allocatePreviousNodeForSegmentation(values,segmentCount);findOptimalSegmentationInternal(cost,previousNode,values,costMatrix,segmentCount);if(Statistics.debuggingSegmentation){console.log('findOptimalSegmentation with',segmentCount,'segments');for(var end=0;end<values.length;end++){for(var k=0;k<=segmentCount;k++){var start=previousNode[end][k];if(start===undefined) | |
| continue;console.log(`C(segment=[${start}, ${end + 1}], segmentCount=${k})=${cost[end][k]}`);}}} | |
| var segmentEnd=values.length-1;var segmentation=new Array(segmentCount+1);segmentation[segmentCount]=values.length;for(var k=segmentCount;k>0;k--){segmentEnd=previousNode[segmentEnd][k];segmentation[k-1]=segmentEnd;} | |
| var costOfOptimalSegmentation=cost[values.length-1][segmentCount];if(Statistics.debuggingSegmentation) | |
| console.log('Optimal segmentation:',segmentation,'with cost =',costOfOptimalSegmentation);return{segmentation:segmentation,cost:costOfOptimalSegmentation};} | |
| function SampleVarianceUpperTriangularMatrix(values){var costMatrix=new Array(values.length-1);for(var i=0;i<values.length-1;i++){var remainingValueCount=values.length-i-1;costMatrix[i]=new Float32Array(remainingValueCount);var sum=values[i];var squareSum=sum*sum;costMatrix[i][0]=0;for(var j=i+1;j<values.length;j++){var currentValue=values[j];sum+=currentValue;squareSum+=currentValue*currentValue;var sampleSize=j-i+1;var stdev=Statistics.sampleStandardDeviation(sampleSize,sum,squareSum);costMatrix[i][j-i-1]=stdev>0?sampleSize*Math.log(stdev*stdev):0;}} | |
| this.costMatrix=costMatrix;} | |
| SampleVarianceUpperTriangularMatrix.prototype.costBetween=function(from,to){if(from>=this.costMatrix.length||from==to) | |
| return 0;return this.costMatrix[from][to-from-1];}})();if(typeof module!='undefined') | |
| module.exports=Statistics; | |
| function main() { | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([580.69999999999,579.5,587.4,581.8,586.7,586.65,590.75,582.7,586.65000000001,587,582.6,586.5,587.4,585.4,582.69999999999,581.7,581.9,582.4,583.3,586.55,582.25,580.3,580.6,583,586.4,575.35,577.05000000001,576.05,575.35,578.35,578.85,585.35,579.05,577.8,579.6,581.3,583.1,579.25,578.3,581.4,572.15,576.55,581.95,572.70000000001,581.2,574.35,579.9,565.65,566.49999999999,566.69999999999,566.10000000001,567.1,564.6,563.6,559.4,562.8,566.1,558.9,565.6,563.7,562.65,564.9,565.55,561.05,564.9,566.94999999999,566.05,545.25,549.1,551.5,546.85,544.45,549.7,544.5,543.85,544.35,543.2,508.3,506.65,517.65,515.40000000001,509.1,510.55,511.1,509.45,507.8,510.25,509.15,508.79999999999,514.95,510.55,506.89999999999,507.8,515.1,509.65,505.75,509.5,510.45,504.70000000001,512.3,510.35,508.15,509,503.6,508.85,510.25,509.95,513.05,513.9,511.5,508.3,510.54999999999,515.35,510.25,509.75000000001,511.45,510.15,514.5,513.3,504.3,514.45,512.85,513.6,516.45,517.05,515.9,514.4,512.7,516.1,514.6,518.3,518.8,520.1,517.9,530.45,537.8,531.7,532.45,520.05,517.4,520.60000000001,520.6,515.25,514.05,514,516.5,515.75,514.75,510.05,516.1,515.45,515.7,525.4,517.75,513.1,516.20000000001,515,514.6,515.65,510.95,517.3,519.8,514.55,518.5,520.85,520.05,520.34999999999,513.4,516,516.45,511.04999999999,518.3,517.5,520.7,517.75,516.2,515.84999999999,516.4,525.45,519.9,519.65,516.6,515.3,517.95,520.4,516.95,519.95,521,517.8,520.45,523.6], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([523.65,520,519.95,519,516.45,520.95,520.1,521.65,523.5,520,521.25,516.05,515.1,515.85,516.05,518.85,522.45,518.6,516.05,519.75,521.9,523.5,515.45,517.35,509.75,514.4,518.35,517.9,520,519.15,515.6,516.95,514.25,523.55,517.3,524.80000000001,522.8,522.49999999999,514.55,515.25,513.35,516.45,520.09999999999,515.35,517.15,514.45,517.95,517.35000000001,520.85,522.15,520.7,515.2,518.55,524.15,517.05,518.34999999999,518.35,518.35,516.3,519.7,518.2,517.75000000001,521.15,524.35,528.6,522.55,520.35,519.75,522.4,519.69999999999,522.55,514.4,518.6,517.94999999999,520.95,521,520.9,517.15,518.45,519.1,516.1,515.25000000001,519,516.4,517.05,517.3,514.6,520.4,521.95,520.55,524.95], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([64.51349817345,64.531006645215,63.661771671568,63.867946245268,63.89748966976,64.204788416579,63.67465095761,64.215521020728,63.599157735961,63.957379903817,64.343991932988,63.774363513245,63.609091197581,63.583319993527,63.759321564516,63.918960374907,63.801120882983,63.731877440644,64.085232338106,63.649029420884,64.316655791177,64.644287073335,64.686805343296,63.986208253878,64.594819277397,64.268309445212,64.64587668872,64.622419831832,64.57572989236,64.068294107484,64.534343593812,63.784702524715,64.064073217604,64.303600945907,64.570670200758,64.557177229554,64.141383426806,64.660265289,64.360582907909,64.489737605779,64.812313354582,64.636389293423,64.357981876299,64.641258671465,63.692908491999,64.405135623421,64.085437912312,64.710599853047,64.466287035576,64.733763028513,64.879629883608,64.845232597169,64.920221640441,64.340715345564,64.211750160018,64.143907911907,64.151107531319,64.848467422927,64.495195697021,64.540217976647,64.447066229215,64.453007982943,64.203102696704,64.413908492481,64.285390273677,64.097705594706,64.311962014655,65.823375840566,66.257915902228,66.189144995213,66.293359223946,66.143383869982,65.673146465646,66.234325193836,66.531932372833,66.459315751532,66.118392388876,67.861479360044,68.18875901579,67.186045545002,67.449322607509,67.784494192078,67.568623288211,67.491327882741,67.296394920925,67.908652312264,67.68850862222,67.87215323525,67.68159791417,67.309535031433,67.825211053167,67.796674857738,67.673239606497,67.273275173449,67.785604121115,67.604832689909,67.802238783249,68.060068049983,68.215388038961,67.739689671757,68.283492963354,67.95023409358,68.32158149145,67.859474872227,67.745678149743,67.683696150831,67.641060035625,68.062517243366,67.77577507809,67.591310302292,68.03153051715,67.508351040937,67.308835812774,67.687294266175,67.675792730459,67.632005343429,68.062513319659,67.838469917107,67.867243596587,68.308768436951,67.985008714381,68.288808484589,68.136285090447,68.052453195786,68.081642326447,67.973655519714,68.363748638548,67.59223674148,67.459943518148,67.311654393723,67.361311348715,67.261057166106,67.605342639933,67.511163410812,67.199900103853,66.871712654312,66.882879783886,66.626419103873,67.288809021099,67.265741784841,67.122390709591,67.259216200296,67.90984706412,67.895882948346,67.851737412488,67.318536472751,68.069884004975,68.010452506905,68.112727626574,67.735638207935,67.72357563057,67.684926049522,66.808079604127,67.622565299641,67.587619569521,67.67955099175,68.28295389598,68.251363203321,67.945609952005,68.151131594218,67.267007561669,67.502025526139,68.224928048878,68.06147145547,67.971554249987,67.344843875373,67.066671489369,67.909813488066,67.884450885488,68.014195332488,68.134246150401,66.976062466155,67.229851506121,67.211457419844,67.874026338275,68.050772715164,67.89008059593,67.73009379905,66.884105476311,67.315430191661,67.305882850731,67.612565292886,67.594111476957,67.377554093459,67.636943486005,67.771692930933,67.716325971708,67.472753284344,67.568353751699,66.488022922399,66.648651451673], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([66.771452345775,67.322744528389,67.340598413137,67.433369577215,67.476058185342,66.645045185389,66.659459231276,66.742062372772,66.579428064484,66.795363742505,66.786793401721,66.576012166033,67.268312562092,67.110793015633,67.141213298777,67.058578265842,66.961532228462,67.39617543905,67.280846887692,66.777509606657,66.500111571776,66.218542504731,67.496358006452,67.469636282665,67.666683374831,67.54851950964,66.693756897026,67.086398817754,66.832106017453,66.630604222009,66.881107681829,66.858990495972,66.962084893533,67.239233293659,67.298136993936,67.272109351169,66.377635177057,66.215516523439,67.380023683621,67.465027293956,67.267536735204,67.301055809545,66.846117877425,67.233087281612,67.373434158581,67.451136691427,66.837176447448,66.74851619274,66.584597174477,66.398474679315,66.553795942527,67.460909388772,67.189582221517,67.059979527805,67.465098750276,67.486136338397,67.299805837744,67.339086967469,67.501225163962,67.249339187525,67.273216949896,67.449117394915,66.480531613892,67.659355897727,66.623686524613,66.950987116943,66.748292275872,67.021253383184,66.791751078778,67.653373967706,67.869088415657,67.7409274308,67.254452360493,67.740531938892,67.136732856325,67.152731163248,67.724959172279,67.292022283343,67.124827517772,67.134264766268,67.108803475727,67.085857586198,66.968894783784,67.393742661972,67.090533192851,67.12725146099,67.961422878014,66.928922772453,66.824292344399,66.661940895074,66.585764991843], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([946.6,946.35,965.9,964.9,950.35,946.85,961.2,946.3,967.3,952.8,944.6,963.35,957.4,964.5,954.85,958.9,959.95,959.55,944.95,957.15,941.7,934.95,935.5,954,936.75,956.25,936.5,938.75,943.05,949.8,938.45,957.7,954.15,949.75,939.65,941.15,941.45,941.5,938.3,938.6,945.6,938.8,936.2,938,961.65,943.05,956.1,955.9,956.7,945.8,942.85,947.65,941.75,950,954.25,958.2,960.4,948.8,950.4,952.3,954.1,952.75,950.85,950.7,955,955.65,955.05,909.9,896.6,890.45,892.1,898.8,911.25,899,894.4,891.9,897.4,891.4,886.35,899.4,900.6,889.25,889.1,898.9,905.65,896.25,898.35,896.6,901.6,900,892.35,886.7,899.25,900.5,895.15,897,882.8,882.2,879.05,884.4,882.95,894.9,876.4,889.1,896.45,892.65,893,883.3,891.65,898.45,884.35,896.6,905.6,895.85,898.65,897.1,875.5,877.5,877.85,881.8,878.5,881.85,883.2,878.95,878.45,884.05,882.9,895.8,898.45,901.4,896.7,901.85,895.6,902.15,889.25,890.6,905.75,909.85,903.75,903.4,903.35,905.25,883.75,885.7,886.44999999999,900.7,883.65,881.1,882.15,883.25,886.15,887.15,903.95,899.4,904.35,901.15,878.35,878.75,881.4,881.55,906.2,901.95,877.75,881.45,880.85,889.95,908.55,880,876.4,877.6,879,905.45,900,899.7,887.75,881.8,882.45,883.25,901.4,902,898.4,901.05,901.4,907.35,886.25,883.65,883.4,895.5,897.1,921.85,918.75], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([919,897,897.05,899.85,898.8,920.7,922.15,922.15,919.35,920.2,919.3,926.05,899.8,901.65,904.05,902.45,903.15,901.6,903.6,919.8,926.85,925.05,897.2,895.85,895.45,894.75,914.65,899.7,904.6,919.8,918,912.9,915.6,898.25,894.9,894.95,918.75,924.4,895.2,893.7,898.25,893.1,906.65,899.3,894.25,895.3,913.85,920.85,923.75,918.2,913.45,898.55,898.8,899.95,896.4,898.05,902.2,899.85,901.6,899.5,894.95,895.65,914.6,885.9,908.6,906.7,910.55,900.45,905,884.3,882.2,887.9,897.35,887.69999999999,902,900.95,883.25,895.75,902.9,897.95,902.85,902.55,906,904.1,902.4,904.4,881.2,904.05,900.1,909.7,906.95], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([1045.25,1045.2,1062.3,1060.65,1056.4,1049.35,1062.7,1050.05,1068.8,1058.45,1047.9,1070.45,1065.05,1062.85,1062.75,1061.1,1060.15,1064.45,1048.65,1058.75,1050.6,1034.25,1034.1,1052.95,1036.1,1058.2,1041.4,1041.75,1041,1064.3,1038.85,1064.25,1057.3,1050.15,1042.55,1050,1043.45,1039.75,1048.4,1045.3,1045.45,1048.85,1048.05,1045.45,1067.15,1051.55,1057.05,1057.2,1058.25,1051.4,1048.25,1044.05,1047.9,1059.05,1059.8,1061.55,1064.85,1054.9,1053.7,1057.25,1052.85,1057.95,1060.05,1055.65,1053.85,1065.25,1062.75,1017.75,999.85,999.9,1003.65,1001.75,1016.7,999.5,997.4,1000,1001.85,1001.35,992.3,1012.15,1009.3,993.55,994,1005.35,1011.2,1000.2,1005.55,1000.35,1007.75,1008.3,1004.4,998.75,1002.35,1013.6,1004.75,1011.05,995.7,992.15,991.7,992.25,991.4,1002.65,988.3,1008.8,1001.4,999.1,1002.75,990.85,1000.1,1007.45,992.3,1003.7,1010.85,1007,1003.85,1000.1,985.3,984.5,986.45,989.45,985.6,987.15,989.95,983.30000000001,985.15,989.75,986.55,1007.8,1008.55,1009.5,1007.35,1006.7,1004.9,1007.35,991.7,994.9,1013.2,1013.7,1008.25,1007.35,1013.6,1011.1,988.55,992.65,988.95,1008.85,988.75,991.9,991.95,990.65,996.8,992.1,1012,1007.05,1004.8,1008.4,981.85,984.5,987.5,986.55,1017.7,1008.5,984.1,989.55,988.15,997.85,1014,991.5,987.45,985.85,984.25,1015,1012.2,1014.25,990.6,983.1,991,989.9,1013.8,1006.35,1010.7,1011.45,1007.3,1017.05,994.85,989.65,986.25,1008.85,1000.8,1029.65,1030.6], 2.5, 500); | |
| Statistics.segmentTimeSeriesByMaximizingSchwarzCriterion([1022.6,1001.35,1003.25,999.95,1006.3,1029.9,1027.55,1024.4,1026.4,1027.1,1021.15,1030.25,1010.7,1010.6,1008.85,1005.75,1008.25,1004.85,1006.2,1026.05,1027.5,1025.85,1005.5,1000.8,999.85,1002.75,1015.85,1001.2,1008.95,1020,1024.5,1022.3,1020.15,1002.05,1001.15,1005,1030.35,1031.95,1005.1,1000.65,1006.25,1004.35,1011.15,1009.75,1014.05,1004.4,1022.1,1024.9,1028.6,1022,1022.3,1004.05,1003.8,1006.3,1008.5,1002.85,1013.2,1004.2,1004.85,1006.6,1009.9,1007.85,1024.05,989.4,1017,1015.35,1013.6,1011,1013.6,991.15,989.65,994.95,1004,993.45,1010.45,1015.4,987.55,1007.5,1014.9,1008.4,1015.85,1018.1,1016.8,1009.4,1005.75,1007,984.95,1011.6,1013,1021.6,1013.9], 2.5, 500); | |
| } | |
| for (var i = 0; i < 10; ++i) | |
| main(); |