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[poincare/normalDistribution] Fix sigma/var typo

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This commit is contained in:
Léa Saviot
2019-08-23 17:01:35 +02:00
parent 76ed420a1d
commit f19350c763
6 changed files with 30 additions and 30 deletions
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6
poincare/include/poincare/normal_distribution.h
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@@ -7,9 +7,9 @@ namespace Poincare {
class NormalDistribution final {
public:
template<typename T> static T EvaluateAtAbscissa(T x, T mu, T sigma);
template<typename T> static T CumulativeDistributiveFunctionAtAbscissa(T x, T mu, T sigma);
template<typename T> static T CumulativeDistributiveInverseForProbability(T probability, T mu, T sigma);
template<typename T> static T EvaluateAtAbscissa(T x, T mu, T var);
template<typename T> static T CumulativeDistributiveFunctionAtAbscissa(T x, T mu, T var);
template<typename T> static T CumulativeDistributiveInverseForProbability(T probability, T mu, T var);
private:
/* For the standard normal distribution, P(X < y) > 0.9999995 for y >= 4.892 so the
* value displayed is 1. But this is dependent on the fact that we display

8
poincare/src/inv_norm.cpp
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@@ -29,16 +29,16 @@ template<typename T>
Evaluation<T> InvNormNode::templatedApproximate(Context * context, Preferences::ComplexFormat complexFormat, Preferences::AngleUnit angleUnit) const {
Evaluation<T> aEvaluation = childAtIndex(0)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> muEvaluation = childAtIndex(1)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> sigmaEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> varEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
T a = aEvaluation.toScalar();
T mu = muEvaluation.toScalar();
T sigma = sigmaEvaluation.toScalar();
T var = varEvaluation.toScalar();
if (std::isnan(a) || std::isnan(mu) || std::isnan(sigma)) {
if (std::isnan(a) || std::isnan(mu) || std::isnan(var)) {
return Complex<T>::Undefined();
}
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveInverseForProbability<T>(a, mu, sigma));
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveInverseForProbability<T>(a, mu, var));
}
Expression InvNorm::shallowReduce(ExpressionNode::ReductionContext reductionContext) {

8
poincare/src/norm_cdf.cpp
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@@ -31,16 +31,16 @@ template<typename T>
Evaluation<T> NormCDFNode::templatedApproximate(Context * context, Preferences::ComplexFormat complexFormat, Preferences::AngleUnit angleUnit) const {
Evaluation<T> aEvaluation = childAtIndex(0)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> muEvaluation = childAtIndex(1)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> sigmaEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> varEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
T a = aEvaluation.toScalar();
T mu = muEvaluation.toScalar();
T sigma = sigmaEvaluation.toScalar();
T var = varEvaluation.toScalar();
if (std::isnan(a) || std::isnan(mu) || std::isnan(sigma)) {
if (std::isnan(a) || std::isnan(mu) || std::isnan(var)) {
return Complex<T>::Undefined();
}
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(a, mu, sigma));
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(a, mu, var));
}
Expression NormCDF::shallowReduce(ExpressionNode::ReductionContext reductionContext) {

8
poincare/src/norm_cdf2.cpp
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@@ -32,20 +32,20 @@ Evaluation<T> NormCDF2Node::templatedApproximate(Context * context, Preferences:
Evaluation<T> aEvaluation = childAtIndex(0)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> bEvaluation = childAtIndex(1)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> muEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> sigmaEvaluation = childAtIndex(3)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> varEvaluation = childAtIndex(3)->approximate(T(), context, complexFormat, angleUnit);
T a = aEvaluation.toScalar();
T b = bEvaluation.toScalar();
T mu = muEvaluation.toScalar();
T sigma = sigmaEvaluation.toScalar();
T var = varEvaluation.toScalar();
if (std::isnan(a) || std::isnan(b) || std::isnan(mu) || std::isnan(sigma)) {
if (std::isnan(a) || std::isnan(b) || std::isnan(mu) || std::isnan(var)) {
return Complex<T>::Undefined();
}
if (b <= a) {
return Complex<T>::Builder((T)0.0);
}
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(b, mu, sigma) - NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(a, mu, sigma));
return Complex<T>::Builder(NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(b, mu, var) - NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(a, mu, var));
}
Expression NormCDF2::shallowReduce(ExpressionNode::ReductionContext reductionContext) {

8
poincare/src/norm_pdf.cpp
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@@ -31,16 +31,16 @@ template<typename T>
Evaluation<T> NormPDFNode::templatedApproximate(Context * context, Preferences::ComplexFormat complexFormat, Preferences::AngleUnit angleUnit) const {
Evaluation<T> xEvaluation = childAtIndex(0)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> muEvaluation = childAtIndex(1)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> sigmaEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
Evaluation<T> varEvaluation = childAtIndex(2)->approximate(T(), context, complexFormat, angleUnit);
T x = xEvaluation.toScalar();
T mu = muEvaluation.toScalar();
T sigma = sigmaEvaluation.toScalar();
T var = varEvaluation.toScalar();
if (std::isnan(x) || std::isnan(mu) || std::isnan(sigma)) {
if (std::isnan(x) || std::isnan(mu) || std::isnan(var)) {
return Complex<T>::Undefined();
}
return Complex<T>::Builder(NormalDistribution::EvaluateAtAbscissa(x, mu, sigma));
return Complex<T>::Builder(NormalDistribution::EvaluateAtAbscissa(x, mu, var));
}
Expression NormPDF::shallowReduce(ExpressionNode::ReductionContext reductionContext) {

22
poincare/src/normal_distribution.cpp
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@@ -7,29 +7,29 @@
namespace Poincare {
template<typename T>
T NormalDistribution::EvaluateAtAbscissa(T x, T mu, T sigma) {
assert(!std::isnan(x) && !std::isnan(mu) && !std::isnan(sigma));
if (sigma == (T)0.0) {
T NormalDistribution::EvaluateAtAbscissa(T x, T mu, T var) {
assert(!std::isnan(x) && !std::isnan(mu) && !std::isnan(var));
if (var == (T)0.0) {
return NAN;
}
const float xMinusMuOverSigma = (x - mu)/sigma;
return ((T)1.0)/(std::fabs(sigma) * std::sqrt(((T)2.0) * M_PI)) * std::exp(-((T)0.5) * xMinusMuOverSigma * xMinusMuOverSigma);
const float xMinusMuOverVar = (x - mu)/var;
return ((T)1.0)/(std::fabs(var) * std::sqrt(((T)2.0) * M_PI)) * std::exp(-((T)0.5) * xMinusMuOverVar * xMinusMuOverVar);
}
template<typename T>
T NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(T x, T mu, T sigma) {
if (sigma == (T)0.0) {
T NormalDistribution::CumulativeDistributiveFunctionAtAbscissa(T x, T mu, T var) {
if (var == (T)0.0) {
return NAN;
}
return StandardNormalCumulativeDistributiveFunctionAtAbscissa<T>((x-mu)/std::fabs(sigma));
return StandardNormalCumulativeDistributiveFunctionAtAbscissa<T>((x-mu)/std::fabs(var));
}
template<typename T>
T NormalDistribution::CumulativeDistributiveInverseForProbability(T probability, T mu, T sigma) {
if (sigma == (T)0.0) {
T NormalDistribution::CumulativeDistributiveInverseForProbability(T probability, T mu, T var) {
if (var == (T)0.0) {
return NAN;
}
return StandardNormalCumulativeDistributiveInverseForProbability(probability) * std::fabs(sigma) + mu;
return StandardNormalCumulativeDistributiveInverseForProbability(probability) * std::fabs(var) + mu;
}
template<typename T>