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Vector api tweaks.
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@ -29,7 +29,7 @@
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<description>
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</description>
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</method>
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<method name="add_mlpp_vector">
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<method name="append_mlpp_vector">
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<return type="void" />
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<argument index="0" name="other" type="float" />
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<description>
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@ -65,7 +65,7 @@ void MLPPGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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Ref<MLPPVector> y_hat = model_set_test_discriminator(discriminator_input_set);
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Ref<MLPPVector> output_set = alg.zerovecnv(_n);
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Ref<MLPPVector> output_set_real = alg.onevecnv(_n);
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output_set->add_mlpp_vector(output_set_real); // Fake + real output scores.
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output_set->append_mlpp_vector(output_set_real); // Fake + real output scores.
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ComputeDiscriminatorGradientsResult dgrads = compute_discriminator_gradients(y_hat, _output_set);
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@ -36,7 +36,7 @@ void MLPPVector::push_back(real_t p_elem) {
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_data[_size - 1] = p_elem;
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}
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void MLPPVector::add_mlpp_vector(const Ref<MLPPVector> &p_other) {
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void MLPPVector::append_mlpp_vector(const Ref<MLPPVector> &p_other) {
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ERR_FAIL_COND(!p_other.is_valid());
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int other_size = p_other->size();
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@ -899,7 +899,7 @@ void MLPPVector::absb(const Ref<MLPPVector> &a) {
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}
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}
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Ref<MLPPVector> MLPPVector::zero_vec(int n) const {
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Ref<MLPPVector> MLPPVector::vecn_zero(int n) const {
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Ref<MLPPVector> vec;
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vec.instance();
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@ -908,7 +908,7 @@ Ref<MLPPVector> MLPPVector::zero_vec(int n) const {
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return vec;
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}
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Ref<MLPPVector> MLPPVector::one_vec(int n) const {
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Ref<MLPPVector> MLPPVector::vecn_one(int n) const {
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Ref<MLPPVector> vec;
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vec.instance();
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@ -917,7 +917,7 @@ Ref<MLPPVector> MLPPVector::one_vec(int n) const {
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return vec;
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}
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Ref<MLPPVector> MLPPVector::full_vec(int n, int k) const {
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Ref<MLPPVector> MLPPVector::vecn_full(int n, int k) const {
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Ref<MLPPVector> vec;
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vec.instance();
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@ -1360,7 +1360,7 @@ void MLPPVector::_bind_methods() {
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ADD_PROPERTY(PropertyInfo(Variant::POOL_REAL_ARRAY, "data"), "set_data", "get_data");
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ClassDB::bind_method(D_METHOD("push_back", "elem"), &MLPPVector::push_back);
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ClassDB::bind_method(D_METHOD("add_mlpp_vector", "other"), &MLPPVector::push_back);
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ClassDB::bind_method(D_METHOD("append_mlpp_vector", "other"), &MLPPVector::append_mlpp_vector);
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ClassDB::bind_method(D_METHOD("remove", "index"), &MLPPVector::remove);
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ClassDB::bind_method(D_METHOD("remove_unordered", "index"), &MLPPVector::remove_unordered);
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ClassDB::bind_method(D_METHOD("erase", "val"), &MLPPVector::erase);
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@ -1450,9 +1450,9 @@ void MLPPVector::_bind_methods() {
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ClassDB::bind_method(D_METHOD("absn"), &MLPPVector::absn);
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ClassDB::bind_method(D_METHOD("absb", "a"), &MLPPVector::absb);
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ClassDB::bind_method(D_METHOD("zero_vec", "n"), &MLPPVector::zero_vec);
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ClassDB::bind_method(D_METHOD("one_vec", "n"), &MLPPVector::one_vec);
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ClassDB::bind_method(D_METHOD("full_vec", "n", "k"), &MLPPVector::full_vec);
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ClassDB::bind_method(D_METHOD("vecn_zero", "n"), &MLPPVector::vecn_zero);
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ClassDB::bind_method(D_METHOD("vecn_one", "n"), &MLPPVector::vecn_one);
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ClassDB::bind_method(D_METHOD("vecn_full", "n", "k"), &MLPPVector::vecn_full);
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ClassDB::bind_method(D_METHOD("sin"), &MLPPVector::sin);
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ClassDB::bind_method(D_METHOD("sinn"), &MLPPVector::sinn);
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@ -33,7 +33,7 @@ public:
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}
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void push_back(real_t p_elem);
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void add_mlpp_vector(const Ref<MLPPVector> &p_other);
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void append_mlpp_vector(const Ref<MLPPVector> &p_other);
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void remove(real_t p_index);
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@ -177,9 +177,9 @@ public:
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Ref<MLPPVector> absn() const;
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void absb(const Ref<MLPPVector> &a);
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Ref<MLPPVector> zero_vec(int n) const;
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Ref<MLPPVector> one_vec(int n) const;
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Ref<MLPPVector> full_vec(int n, int k) const;
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Ref<MLPPVector> vecn_zero(int n) const;
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Ref<MLPPVector> vecn_one(int n) const;
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Ref<MLPPVector> vecn_full(int n, int k) const;
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void sin();
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Ref<MLPPVector> sinn() const;
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@ -71,7 +71,7 @@ void MLPPWGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ly_hat = model_set_test_discriminator(discriminator_input_set);
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loutput_set = alg.scalar_multiplynv(-1, alg.onevecnv(_n)); // WGAN changes y_i = 1 and y_i = 0 to y_i = 1 and y_i = -1
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Ref<MLPPVector> output_set_real = alg.onevecnv(_n);
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loutput_set->add_mlpp_vector(output_set_real); // Fake + real output scores.
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loutput_set->append_mlpp_vector(output_set_real); // Fake + real output scores.
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DiscriminatorGradientResult discriminator_gradient_results = compute_discriminator_gradients(ly_hat, loutput_set);
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Vector<Ref<MLPPMatrix>> cumulative_discriminator_hidden_layer_w_grad = discriminator_gradient_results.cumulative_hidden_layer_w_grad;
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