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Y ~ W.x + epsilon | ||
Y ~ N(W.x, sigma^2) | ||
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Parameter<double> X {4.0}; // observed | ||
Parameter<double> Y {5.0}; // observed | ||
Parameter<double> W; // hidden | ||
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Model m1 = Model( // Model class defines a distribution over existing Parameters. | ||
W |= Uniform(-10, 10), // linear regression | ||
Y |= Normal(W * X, 3), // overload multiplication to build a graph from W * X | ||
); | ||
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Model m2 = Model( | ||
W |= Normal(0, 1), // ridge regression instead | ||
Y |= Normal(W * X, 3), | ||
); | ||
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m1.sample(1000); | ||
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(3*x).pdf(10) => x.pdf(10 / 3) | ||
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X.observe(3); // observe more data | ||
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// P(Y, W | X) = P(Y | W, X) P(W | X) which is doable for multiple samples, just need to | ||
// assert len(Y) == len(X) and then multiply out over all pairs of (X, Y) values. | ||
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// P(Y | X) => this is a fine distribution, but I can't talk about P(Y, X) or P(X | Y) until I put a prior on Y. | ||
// I don't have a joint distribution yet. | ||
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// Some issues: | ||
// how do we do (x ** 2).pdf(5)? This is pretty damn hard for non-bijective functions, need to integrate? | ||
// |
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#include "gtest/gtest.h" | ||
#include <autoppl/expression/model.hpp> | ||
#include <autoppl/expression/rv_tag.hpp> | ||
#include <autoppl/expression/uniform.hpp> | ||
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namespace ppl { | ||
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template <class VectorType, class IndexType> | ||
struct BracketNode | ||
{ | ||
VectorType v; | ||
IndexType i; | ||
}; | ||
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struct myvector | ||
{ | ||
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rv_tag<double> operator[](rv_tag) | ||
{ | ||
return rv | ||
} | ||
std::vector<rv_tags> v; // 3 things | ||
}; | ||
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template <class MuType, class SigType> | ||
auto normal(const MuType& mu, const SigType& sig) | ||
{ | ||
Normal<MuType, SigType>(mu, sig); | ||
} | ||
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TEST(dummy, dummy_test) | ||
{ | ||
double x_data = 2.3; // 1-sample data | ||
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std::vector<double> sampled_theta_1(100); | ||
std::vector<double> sampled_theta_2(100); | ||
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double* ptr; | ||
rv_tag<double, ...> x; | ||
rv_tag<double> theta_1(sampled_theta_1.data()); | ||
rv_tag<double> theta_2(sampled_theta_2.data()); | ||
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std::vector<rv_tag<double>> v; | ||
std::for_each(..., ... , [](){v[i].set_sample_storage(&mat.row(i));}); | ||
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x.observe(x_data); | ||
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x_1.observe(...); | ||
x_2.observe(...); | ||
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auto model = ( | ||
mu |= uniform(-10000, 10000), | ||
y |= uniform({1,2,3}) // | ||
x_1 |= normal(mu[y], 1), | ||
x_2 |= normal(mu[y], 1), | ||
); | ||
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x.observe(...); | ||
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rv_tag<double> var, mu, x; | ||
auto normal_model = ( | ||
var |= normal(0,1), | ||
mu |= normal(1,5), | ||
x |= normal(mu, var) | ||
); | ||
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std::vector<double> var_storage(1000); | ||
std::vector<double> mu_storage(1000); | ||
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var.set_storage(var_storage.data()); | ||
mu.set_storage(mu_storage.data()); | ||
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metropolis_hastings(model, 1000, 400); | ||
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auto gmm_model = ( | ||
mu |= | ||
); | ||
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std::vector<rv_tag<double>> vec(model.param_num); | ||
model.bind_storage(vec.begin(), vec.end(), ...); | ||
model.pdf(); | ||
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metropolis_hastings(model, 100); | ||
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std::vector<double> sampled_theta_1_again(1000); | ||
std::vector<double> sampled_theta_2_again(1000); | ||
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theta_1.set_storage(sampled_theta_1_again.data()); | ||
theta_2.set_storage(sampled_theta_2_again.data()); | ||
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metropolis_hastings(model, 1000); | ||
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auto model = ( | ||
w |= normal(0,1), | ||
y |= normal(w*x, 1) | ||
) | ||
metropolis_hastings(modeli) | ||
} | ||
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} |
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