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schema: Rename gaussian std to stddev #13948

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Aug 27, 2020
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schema: Rename gaussian std to stddev #13948

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36 changes: 18 additions & 18 deletions common/schema/dev/stochastic.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -46,13 +46,13 @@ Expression Deterministic::ToSymbolic() const {

Gaussian::Gaussian() {}

Gaussian::Gaussian(double mean_in, double std_in)
: mean(mean_in), std(std_in) {}
Gaussian::Gaussian(double mean_in, double stddev_in)
: mean(mean_in), stddev(stddev_in) {}

Gaussian::~Gaussian() {}

double Gaussian::Sample(drake::RandomGenerator* generator) const {
std::normal_distribution<double> distribution(mean, std);
std::normal_distribution<double> distribution(mean, stddev);
return distribution(*generator);
}

Expand All @@ -61,7 +61,7 @@ double Gaussian::Mean() const {
}

Expression Gaussian::ToSymbolic() const {
std::normal_distribution<Expression> distribution(mean, std);
std::normal_distribution<Expression> distribution(mean, stddev);
return distribution();
}

Expand Down Expand Up @@ -225,25 +225,25 @@ GaussianVector<Size>::GaussianVector() {}
template <int Size>
GaussianVector<Size>::GaussianVector(
const drake::Vector<double, Size>& mean_in,
const drake::VectorX<double>& std_in)
const drake::VectorX<double>& stddev_in)
: mean(mean_in),
std(std_in) {}
stddev(stddev_in) {}

template <int Size>
GaussianVector<Size>::~GaussianVector() {}

template <int Size>
Eigen::VectorXd GaussianVector<Size>::Sample(
drake::RandomGenerator* generator) const {
if (!(std.size() == mean.size() || std.size() == 1)) {
if (!(stddev.size() == mean.size() || stddev.size() == 1)) {
throw std::logic_error(fmt::format(
"Cannot Sample() a GaussianVector distribution with "
"size {} mean but size {} dev", mean.size(), std.size()));
"size {} mean but size {} dev", mean.size(), stddev.size()));
}
Eigen::VectorXd result(mean.size());
for (int i = 0; i < mean.size(); ++i) {
const double std_i = (std.size() == 1) ? std(0) : std(i);
result(i) = Gaussian(mean(i), std_i).Sample(generator);
const double stddev_i = (stddev.size() == 1) ? stddev(0) : stddev(i);
result(i) = Gaussian(mean(i), stddev_i).Sample(generator);
}
return result;
}
Expand All @@ -255,15 +255,15 @@ Eigen::VectorXd GaussianVector<Size>::Mean() const {

template <int Size>
drake::VectorX<Expression> GaussianVector<Size>::ToSymbolic() const {
if (!(std.size() == mean.size() || std.size() == 1)) {
if (!(stddev.size() == mean.size() || stddev.size() == 1)) {
throw std::logic_error(fmt::format(
"Cannot ToSymbolic() a GaussianVector distribution with "
"size {} mean but size {} dev", mean.size(), std.size()));
"size {} mean but size {} dev", mean.size(), stddev.size()));
}
drake::VectorX<Expression> result(mean.size());
for (int i = 0; i < mean.size(); ++i) {
const double std_i = (std.size() == 1) ? std(0) : std(i);
result(i) = Gaussian(mean(i), std_i).ToSymbolic();
const double stddev_i = (stddev.size() == 1) ? stddev(0) : stddev(i);
result(i) = Gaussian(mean(i), stddev_i).ToSymbolic();
}
return result;
}
Expand Down Expand Up @@ -329,7 +329,7 @@ bool IsDeterministic(const DistributionVariant& var) {
return true;
},
[](const Gaussian& arg) -> bool {
return arg.std == 0.0;
return arg.stddev == 0.0;
},
[](const Uniform& arg) -> bool {
return arg.min == arg.max;
Expand Down Expand Up @@ -376,7 +376,7 @@ unique_ptr<DistributionVector> ToDistributionVector(
[](const Gaussian& arg) -> unique_ptr<DistributionVector> {
return std::make_unique<GaussianVector<Size>>(
Eigen::VectorXd::Constant(1, arg.mean),
Eigen::VectorXd::Constant(1, arg.std));
Eigen::VectorXd::Constant(1, arg.stddev));
},
[](const Uniform& arg) -> unique_ptr<DistributionVector> {
return std::make_unique<UniformVector<Size>>(
Expand All @@ -400,7 +400,7 @@ bool IsDeterministic(const DistributionVectorVariant<Size>& vec) {
return true;
},
[](const GaussianVector<Size>& arg) -> bool {
return arg.std.isZero(0.0);
return arg.stddev.isZero(0.0);
},
[](const UniformVector<Size>& arg) -> bool {
return arg.min == arg.max;
Expand All @@ -409,7 +409,7 @@ bool IsDeterministic(const DistributionVectorVariant<Size>& vec) {
return true;
},
[](const Gaussian& arg) -> bool {
return arg.std == 0.0;
return arg.stddev == 0.0;
},
[](const Uniform& arg) -> bool {
return arg.min == arg.max;
Expand Down
20 changes: 10 additions & 10 deletions common/schema/dev/stochastic.h
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Original file line number Diff line number Diff line change
Expand Up @@ -145,7 +145,7 @@ Whether fixed or dynamic size, you might specify stochastic variables:
thing:
value: !GaussianVector
mean: [2.1, 2.2, 2.3]
std: [1.0] # Same stddev each.
stddev: [1.0] # Same stddev each.
```

Or:
Expand All @@ -154,7 +154,7 @@ Whether fixed or dynamic size, you might specify stochastic variables:
thing:
value: !GaussianVector
mean: [2.1, 2.2, 2.3]
std: [1.0, 0.5, 0.2] # Different stddev each.
stddev: [1.0, 0.5, 0.2] # Different stddev each.
```

Or:
Expand Down Expand Up @@ -230,12 +230,12 @@ struct Deterministic final : public Distribution {
double value{};
};

/// A gaussian distribution with `mean` and `std`.
/// A gaussian distribution with `mean` and `stddev`.
struct Gaussian final : public Distribution {
DRAKE_DEFAULT_COPY_AND_MOVE_AND_ASSIGN(Gaussian)

Gaussian();
Gaussian(double mean, double std);
Gaussian(double mean, double stddev);
~Gaussian() final;

double Sample(drake::RandomGenerator*) const final;
Expand All @@ -245,11 +245,11 @@ struct Gaussian final : public Distribution {
template <typename Archive>
void Serialize(Archive* a) {
a->Visit(DRAKE_NVP(mean));
a->Visit(DRAKE_NVP(std));
a->Visit(DRAKE_NVP(stddev));
}

double mean{};
double std{};
double stddev{};
};

/// A uniform distribution with `min` inclusive and `max` exclusive.
Expand Down Expand Up @@ -371,15 +371,15 @@ struct DeterministicVector final : public DistributionVector {
drake::Vector<double, Size> value;
};

/// A gaussian distribution with vector `mean` and vector or scalar `std`.
/// A gaussian distribution with vector `mean` and vector or scalar `stddev`.
/// @tparam Size rows at compile time (max 6) or else Eigen::Dynamic.
template <int Size>
struct GaussianVector final : public DistributionVector {
DRAKE_DEFAULT_COPY_AND_MOVE_AND_ASSIGN(GaussianVector)

GaussianVector();
GaussianVector(const drake::Vector<double, Size>& mean,
const drake::VectorX<double>& std);
const drake::VectorX<double>& stddev);
~GaussianVector() final;

Eigen::VectorXd Sample(drake::RandomGenerator*) const final;
Expand All @@ -389,11 +389,11 @@ struct GaussianVector final : public DistributionVector {
template <typename Archive>
void Serialize(Archive* a) {
a->Visit(DRAKE_NVP(mean));
a->Visit(DRAKE_NVP(std));
a->Visit(DRAKE_NVP(stddev));
}

drake::Vector<double, Size> mean;
drake::VectorX<double> std;
drake::VectorX<double> stddev;
};

/// A uniform distribution with vector `min` inclusive and vector `max`
Expand Down
18 changes: 9 additions & 9 deletions common/schema/dev/test/stochastic_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -33,20 +33,20 @@ struct DistributionStruct {

const char* all_variants = R"""(
vec: [ !Deterministic { value: 5.0 },
!Gaussian { mean: 2.0, std: 4.0 },
!Gaussian { mean: 2.0, stddev: 4.0 },
!Uniform { min: 1.0, max: 5.0 },
!UniformDiscrete { values: [1, 1.5, 2] },
3.2 ]
)""";

const char* floats = "vec: [5.0, 6.1, 7.2]";

void CheckGaussianSymbolic(const Expression& e, double mean, double std) {
void CheckGaussianSymbolic(const Expression& e, double mean, double stddev) {
const Variables vars{e.GetVariables()};
ASSERT_EQ(vars.size(), 1);
const Variable& v{*(vars.begin())};
EXPECT_EQ(v.get_type(), Variable::Type::RANDOM_GAUSSIAN);
EXPECT_PRED2(ExprEqual, e, mean + std * v);
EXPECT_PRED2(ExprEqual, e, mean + stddev * v);
}

void CheckUniformSymbolic(const Expression& e, double min, double max) {
Expand Down Expand Up @@ -82,9 +82,9 @@ GTEST_TEST(StochasticTest, ScalarTest) {

const Gaussian& g = std::get<Gaussian>(variants.vec[1]);
EXPECT_EQ(g.mean, 2.);
EXPECT_EQ(g.std, 4.);
EXPECT_EQ(g.stddev, 4.);
EXPECT_EQ(g.Mean(), 2.);
CheckGaussianSymbolic(g.ToSymbolic(), g.mean, g.std);
CheckGaussianSymbolic(g.ToSymbolic(), g.mean, g.stddev);
EXPECT_FALSE(IsDeterministic(variants.vec[1]));
EXPECT_THROW(GetDeterministicValue(variants.vec[1]),
std::logic_error);
Expand Down Expand Up @@ -140,7 +140,7 @@ GTEST_TEST(StochasticTest, ScalarTest) {
VectorX<Expression> symbolic_vec = ToSymbolic(variants.vec);
ASSERT_EQ(symbolic_vec.size(), 5);
EXPECT_PRED2(ExprEqual, symbolic_vec(0), 5.0);
CheckGaussianSymbolic(symbolic_vec(1), g.mean, g.std);
CheckGaussianSymbolic(symbolic_vec(1), g.mean, g.stddev);
CheckUniformSymbolic(symbolic_vec(2), u.min, u.max);
CheckUniformDiscreteSymbolic(symbolic_vec(3), ub.values);
EXPECT_PRED2(ExprEqual, symbolic_vec(4), 3.2);
Expand Down Expand Up @@ -178,11 +178,11 @@ struct DistributionVectorStruct {
const char* vector_variants = R"""(
vector: [1., 2., 3.]
deterministic: !DeterministicVector { value: [3., 4., 5.] }
gaussian1: !GaussianVector { mean: [1.1, 1.2, 1.3], std: [0.1, 0.2, 0.3] }
gaussian2: !GaussianVector { mean: [2.1, 2.2, 2.3, 2.4], std: [1.0] }
gaussian1: !GaussianVector { mean: [1.1, 1.2, 1.3], stddev: [0.1, 0.2, 0.3] }
gaussian2: !GaussianVector { mean: [2.1, 2.2, 2.3, 2.4], stddev: [1.0] }
uniform: !UniformVector { min: [10, 20], max: [11, 22] }
deterministic_scalar: !Deterministic { value: 19.5 }
gaussian_scalar: !Gaussian { mean: 5, std: 1 }
gaussian_scalar: !Gaussian { mean: 5, stddev: 1 }
uniform_scalar: !Uniform { min: 1, max: 2 }
)""";

Expand Down