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Merge pull request #39 from ChristianeHofer/FractionalAirlineLogs
Fractional airline estimation extension
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...re/src/main/java/jdplus/highfreq/base/core/extendedairline/ExtendedAirlineEstimation.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,155 +1,164 @@ | ||
| /* | ||
| * To change this license header, choose License Headers in Project Properties. | ||
| * To change this template file, choose Tools | Templates | ||
| * and open the template in the editor. | ||
| */ | ||
| package jdplus.highfreq.base.core.extendedairline; | ||
|
|
||
| import jdplus.toolkit.base.api.data.DoubleSeq; | ||
| import jdplus.toolkit.base.api.data.DoubleSeqCursor; | ||
| import jdplus.highfreq.base.api.ExtendedAirline; | ||
| import jdplus.toolkit.base.core.stats.likelihood.LikelihoodStatistics; | ||
| import jdplus.toolkit.base.api.math.matrices.Matrix; | ||
| import jdplus.toolkit.base.api.modelling.OutlierDescriptor; | ||
| import jdplus.toolkit.base.api.information.GenericExplorable; | ||
|
|
||
| /** | ||
| * Low-level results. Should be refined | ||
| * | ||
| * @author palatej | ||
| */ | ||
| @lombok.Value | ||
| @lombok.Builder | ||
| public class ExtendedAirlineEstimation implements GenericExplorable { | ||
|
|
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| double[] y; | ||
| Matrix x; //user-def reg var, outlier | ||
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| ExtendedAirline model; | ||
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| OutlierDescriptor[] outliers; | ||
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| DoubleSeq coefficients; // user-def-var,outlier | ||
| Matrix coefficientsCovariance; | ||
|
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| private DoubleSeq parameters, score; | ||
| private Matrix parametersCovariance; | ||
|
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| LikelihoodStatistics likelihood; | ||
| DoubleSeq residuals; | ||
|
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| public double[] linearized() { | ||
|
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| double[] l = y.clone(); | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
| for (int j = 0; j < x.getColumnsCount(); ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] -= a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
| return l; | ||
| } | ||
|
|
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| public double[] component_userdef_reg_variables() { | ||
|
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| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
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| for (int j = 0; j < x.getColumnsCount() - outliers.length; ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
|
|
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| return l; | ||
| } | ||
|
|
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| public double[] component_outliers() { | ||
|
|
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| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
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| for (int i = 1; i < x.getColumnsCount() - outliers.length+1; i++) { | ||
| acur.getAndNext(); | ||
| } | ||
|
|
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| for (int j = x.getColumnsCount() - outliers.length; j < x.getColumnsCount(); ++j) { | ||
|
|
||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
|
|
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| return l; | ||
| } | ||
|
|
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| public double[] component_ao() { | ||
| return component_outlier("AO"); | ||
| } | ||
|
|
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| public double[] component_wo() { | ||
| return component_outlier("WO"); | ||
| } | ||
|
|
||
| public double[] component_ls() { | ||
| return component_outlier("LS"); | ||
| } | ||
|
|
||
| /** | ||
| * @return sum (coefficients*regression variable) if ao | ||
| * | ||
| */ | ||
| private double[] component_outlier(String outlierTyp) { | ||
|
|
||
| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
|
||
| for (int i = 1; i < x.getColumnsCount() - outliers.length+1; i++) { | ||
| acur.getAndNext(); | ||
| } | ||
|
|
||
| for (int j = x.getColumnsCount() - outliers.length; j < x.getColumnsCount(); ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (outlierTyp.equalsIgnoreCase(outliers[j - x.getColumnsCount() + outliers.length].getCode())) { | ||
|
|
||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
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| return l; | ||
| } | ||
|
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||
|
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| public double[] tstats() { | ||
| double[] t = coefficients.toArray(); | ||
| if (t == null) { | ||
| return null; | ||
| } | ||
| DoubleSeqCursor v = coefficientsCovariance.diagonal().cursor(); | ||
| for (int i = 0; i < t.length; ++i) { | ||
| t[i] /= Math.sqrt(v.getAndNext()); | ||
| } | ||
| return t; | ||
| } | ||
|
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||
| public int getNx() { | ||
| return coefficients == null ? 0 : coefficients.length(); | ||
| } | ||
| } | ||
| /* | ||
| * To change this license header, choose License Headers in Project Properties. | ||
| * To change this template file, choose Tools | Templates | ||
| * and open the template in the editor. | ||
| */ | ||
| package jdplus.highfreq.base.core.extendedairline; | ||
|
|
||
| import jdplus.toolkit.base.api.data.DoubleSeq; | ||
| import jdplus.toolkit.base.api.data.DoubleSeqCursor; | ||
| import jdplus.highfreq.base.api.ExtendedAirline; | ||
| import jdplus.toolkit.base.core.stats.likelihood.LikelihoodStatistics; | ||
| import jdplus.toolkit.base.api.math.matrices.Matrix; | ||
| import jdplus.toolkit.base.api.modelling.OutlierDescriptor; | ||
| import jdplus.toolkit.base.api.information.GenericExplorable; | ||
|
|
||
| /** | ||
| * Low-level results. Should be refined | ||
| * | ||
| * @author palatej | ||
| */ | ||
| @lombok.Value | ||
| @lombok.Builder | ||
| public class ExtendedAirlineEstimation implements GenericExplorable { | ||
|
|
||
| double[] y; | ||
| Matrix x; //user-def reg var, outlier | ||
| boolean log; //true if logs are taken from y | ||
| ExtendedAirline model; | ||
| int[] missing;// positions where missing values are replaced, null based | ||
|
|
||
| OutlierDescriptor[] outliers; | ||
|
|
||
| DoubleSeq coefficients; // user-def-var,outlier | ||
| Matrix coefficientsCovariance; | ||
|
|
||
| private DoubleSeq parameters, score; | ||
| private Matrix parametersCovariance; | ||
|
|
||
| LikelihoodStatistics likelihood; | ||
| DoubleSeq residuals; | ||
|
|
||
| private double[] exp(double[] a) { | ||
| DoubleSeq exp = DoubleSeq.of(a).exp(); | ||
| return exp.toArray(); | ||
| } | ||
|
|
||
| public double[] getY() { | ||
| return log ? exp(y) : y; | ||
| } | ||
|
|
||
| public double[] linearized() { | ||
|
|
||
| double[] l = y.clone(); | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
| for (int j = 0; j < x.getColumnsCount(); ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] -= a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
| return l; | ||
| } | ||
|
|
||
| public double[] component_userdef_reg_variables() { | ||
|
|
||
| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
|
||
| for (int j = 0; j < x.getColumnsCount() - outliers.length; ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
|
|
||
| return log ? exp(l) : l; | ||
| } | ||
|
|
||
| public double[] component_outliers() { | ||
|
|
||
| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
|
||
| for (int i = 1; i < x.getColumnsCount() - outliers.length + 1; i++) { | ||
| acur.getAndNext(); | ||
| } | ||
|
|
||
| for (int j = x.getColumnsCount() - outliers.length; j < x.getColumnsCount(); ++j) { | ||
|
|
||
| double a = acur.getAndNext(); | ||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
|
|
||
| return log ? exp(l) : l; | ||
| } | ||
|
|
||
| public double[] component_ao() { | ||
| return component_outlier("AO"); | ||
| } | ||
|
|
||
| public double[] component_wo() { | ||
| return component_outlier("WO"); | ||
| } | ||
|
|
||
| public double[] component_ls() { | ||
| return component_outlier("LS"); | ||
| } | ||
|
|
||
| /** | ||
| * @return sum (coefficients*regression variable) if ao | ||
| * | ||
| */ | ||
| private double[] component_outlier(String outlierTyp) { | ||
|
|
||
| double[] l = new double[y.length]; | ||
| DoubleSeqCursor acur = coefficients.cursor(); | ||
|
|
||
| for (int i = 1; i < x.getColumnsCount() - outliers.length + 1; i++) { | ||
| acur.getAndNext(); | ||
| } | ||
|
|
||
| for (int j = x.getColumnsCount() - outliers.length; j < x.getColumnsCount(); ++j) { | ||
| double a = acur.getAndNext(); | ||
| if (outlierTyp.equalsIgnoreCase(outliers[j - x.getColumnsCount() + outliers.length].getCode())) { | ||
|
|
||
| if (a != 0) { | ||
| DoubleSeqCursor cursor = x.column(j).cursor(); | ||
| for (int k = 0; k < l.length; ++k) { | ||
| l[k] += a * cursor.getAndNext(); | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
|
||
| return log ? exp(l) : l; | ||
| } | ||
|
|
||
| public double[] tstats() { | ||
| double[] t = coefficients.toArray(); | ||
| if (t == null) { | ||
| return null; | ||
| } | ||
| DoubleSeqCursor v = coefficientsCovariance.diagonal().cursor(); | ||
| for (int i = 0; i < t.length; ++i) { | ||
| t[i] /= Math.sqrt(v.getAndNext()); | ||
| } | ||
| return t; | ||
| } | ||
|
|
||
| public int getNx() { | ||
| return coefficients == null ? 0 : coefficients.length(); | ||
| } | ||
| } |
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