return NaN; double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas(); double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0);
return NaN; double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas(); double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0);
return LinearTransformation.forNaN(); double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas(); if (xSumOfSquaresOfDeltas > 0.0) { if (yStats.sumOfSquaresOfDeltas() > 0.0) { return LinearTransformation.mapping(xStats.mean(), yStats.mean()) .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas); checkState(yStats.sumOfSquaresOfDeltas() > 0.0); return LinearTransformation.vertical(xStats.mean());
return NaN; double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas(); double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0);
return LinearTransformation.forNaN(); double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas(); if (xSumOfSquaresOfDeltas > 0.0) { if (yStats.sumOfSquaresOfDeltas() > 0.0) { return LinearTransformation.mapping(xStats.mean(), yStats.mean()) .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas); checkState(yStats.sumOfSquaresOfDeltas() > 0.0); return LinearTransformation.vertical(xStats.mean());
/** * Adds the given statistics to the dataset, as if the individual values used to compute the * statistics had been added directly. */ public void addAll(Stats values) { if (values.count() == 0) { return; } if (count == 0) { count = values.count(); mean = values.mean(); sumOfSquaresOfDeltas = values.sumOfSquaresOfDeltas(); min = values.min(); max = values.max(); } else { count += values.count(); if (isFinite(mean) && isFinite(values.mean())) { // This is a generalized version of the calculation in add(double) above. double delta = values.mean() - mean; mean += delta * values.count() / count; sumOfSquaresOfDeltas += values.sumOfSquaresOfDeltas() + delta * (values.mean() - mean) * values.count(); } else { mean = calculateNewMeanNonFinite(mean, values.mean()); sumOfSquaresOfDeltas = NaN; } min = Math.min(min, values.min()); max = Math.max(max, values.max()); } }
return LinearTransformation.forNaN(); double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas(); if (xSumOfSquaresOfDeltas > 0.0) { if (yStats.sumOfSquaresOfDeltas() > 0.0) { return LinearTransformation.mapping(xStats.mean(), yStats.mean()) .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas); checkState(yStats.sumOfSquaresOfDeltas() > 0.0); return LinearTransformation.vertical(xStats.mean());
/** * Adds the given statistics to the dataset, as if the individual values used to compute the * statistics had been added directly. */ public void addAll(Stats values) { if (values.count() == 0) { return; } if (count == 0) { count = values.count(); mean = values.mean(); sumOfSquaresOfDeltas = values.sumOfSquaresOfDeltas(); min = values.min(); max = values.max(); } else { count += values.count(); if (isFinite(mean) && isFinite(values.mean())) { // This is a generalized version of the calculation in add(double) above. double delta = values.mean() - mean; mean += delta * values.count() / count; sumOfSquaresOfDeltas += values.sumOfSquaresOfDeltas() + delta * (values.mean() - mean) * values.count(); } else { mean = calculateNewMeanNonFinite(mean, values.mean()); sumOfSquaresOfDeltas = NaN; } min = Math.min(min, values.min()); max = Math.max(max, values.max()); } }
/** * Adds the given statistics to the dataset, as if the individual values used to compute the * statistics had been added directly. */ public void addAll(Stats values) { if (values.count() == 0) { return; } if (count == 0) { count = values.count(); mean = values.mean(); sumOfSquaresOfDeltas = values.sumOfSquaresOfDeltas(); min = values.min(); max = values.max(); } else { count += values.count(); if (isFinite(mean) && isFinite(values.mean())) { // This is a generalized version of the calculation in add(double) above. double delta = values.mean() - mean; mean += delta * values.count() / count; sumOfSquaresOfDeltas += values.sumOfSquaresOfDeltas() + delta * (values.mean() - mean) * values.count(); } else { mean = calculateNewMeanNonFinite(mean, values.mean()); sumOfSquaresOfDeltas = NaN; } min = Math.min(min, values.min()); max = Math.max(max, values.max()); } }
return NaN; double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas(); double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0);
return NaN; double xSumOfSquaresOfDeltas = xStats().sumOfSquaresOfDeltas(); double ySumOfSquaresOfDeltas = yStats().sumOfSquaresOfDeltas(); checkState(xSumOfSquaresOfDeltas > 0.0); checkState(ySumOfSquaresOfDeltas > 0.0);
return LinearTransformation.forNaN(); double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas(); if (xSumOfSquaresOfDeltas > 0.0) { if (yStats.sumOfSquaresOfDeltas() > 0.0) { return LinearTransformation.mapping(xStats.mean(), yStats.mean()) .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas); checkState(yStats.sumOfSquaresOfDeltas() > 0.0); return LinearTransformation.vertical(xStats.mean());
return LinearTransformation.forNaN(); double xSumOfSquaresOfDeltas = xStats.sumOfSquaresOfDeltas(); if (xSumOfSquaresOfDeltas > 0.0) { if (yStats.sumOfSquaresOfDeltas() > 0.0) { return LinearTransformation.mapping(xStats.mean(), yStats.mean()) .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas); checkState(yStats.sumOfSquaresOfDeltas() > 0.0); return LinearTransformation.vertical(xStats.mean());
/** * Adds the given statistics to the dataset, as if the individual values used to compute the * statistics had been added directly. */ public void addAll(Stats values) { if (values.count() == 0) { return; } if (count == 0) { count = values.count(); mean = values.mean(); sumOfSquaresOfDeltas = values.sumOfSquaresOfDeltas(); min = values.min(); max = values.max(); } else { count += values.count(); if (isFinite(mean) && isFinite(values.mean())) { // This is a generalized version of the calculation in add(double) above. double delta = values.mean() - mean; mean += delta * values.count() / count; sumOfSquaresOfDeltas += values.sumOfSquaresOfDeltas() + delta * (values.mean() - mean) * values.count(); } else { mean = calculateNewMeanNonFinite(mean, values.mean()); sumOfSquaresOfDeltas = NaN; } min = Math.min(min, values.min()); max = Math.max(max, values.max()); } }
/** * Adds the given statistics to the dataset, as if the individual values used to compute the * statistics had been added directly. */ public void addAll(Stats values) { if (values.count() == 0) { return; } if (count == 0) { count = values.count(); mean = values.mean(); sumOfSquaresOfDeltas = values.sumOfSquaresOfDeltas(); min = values.min(); max = values.max(); } else { count += values.count(); if (isFinite(mean) && isFinite(values.mean())) { // This is a generalized version of the calculation in add(double) above. double delta = values.mean() - mean; mean += delta * values.count() / count; sumOfSquaresOfDeltas += values.sumOfSquaresOfDeltas() + delta * (values.mean() - mean) * values.count(); } else { mean = calculateNewMeanNonFinite(mean, values.mean()); sumOfSquaresOfDeltas = NaN; } min = Math.min(min, values.min()); max = Math.max(max, values.max()); } }