Merge remote-tracking branch 'origin/master'

libnd4j/include/ops/declarable/generic/random/exponential.cpp

@@ -27,29 +27,8 @@
 namespace sd {
     namespace ops {
         CUSTOM_OP_IMPL(random_exponential, 1, 1, true, 1, 0) {
-            // uniform distribution
+            // random generator for distribution
             auto rng = block.randomGenerator();
-
-            // FIXME: to be implemented
-            /*
-            if (rng == nullptr)
-                return Status::THROW("RNG is null, aborting...");
-
-            auto x = INPUT_VARIABLE(0);
-            auto z = OUTPUT_VARIABLE(0);
-
-            if (block.width() == 1)
-                functions::random::RandomFunction<T>::template execTransform<randomOps::ExponentialDistribution<T>>(block.getRNG(), z->getBuffer(), z->getShapeInfo(), block.getTArguments()->data());
-            else {
-                auto y = INPUT_VARIABLE(1);
-                REQUIRE_TRUE(y->isSameShape(z), 0, "ExponentialDistribution: Y shape should be equal to Z shape");
-
-                functions::random::RandomFunction<T>::template execTransform<randomOps::ExponentialDistribution<T>>(block.getRNG(), y->getBuffer(), y->getShapeInfo(), z->getBuffer(), z->getShapeInfo(), block.getTArguments()->data());
-            }
-
-            STORE_RESULT(*z);
-*/
-
             auto z = OUTPUT_VARIABLE(0);
             auto lambda = T_ARG(0);
 

libnd4j/include/ops/random_ops.h

@@ -119,8 +119,8 @@ namespace randomOps {
 
         random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) {
             T lambda = extraParams[0];
-            T x = helper->relativeT<T>(idx); //, T(0.f) , max);
+            T x = helper->relativeT<T>(idx,  sd::DataTypeUtils::min<T>(), T(1.f) - sd::DataTypeUtils::template min<T>()); // x from (0, 1) without bounds
-            T xVal = -sd::math::nd4j_log<T,T>(T(1.f) - x);
+            T xVal = -sd::math::nd4j_log<T,T>(x);
 
             return xVal <= (T)0.f ? (T)0.f : xVal / lambda; //pow<T, T, T>((T) M_E, -(lambda * x));
         }
@@ -270,7 +270,7 @@ namespace randomOps {
 
         random_def T op(Nd4jLong idx, Nd4jLong length, sd::graph::RandomGenerator *helper, T *extraParams) {
             T lambda = extraParams[0];
-            T x = helper->relativeT(idx, sd::DataTypeUtils::template min<T>(), (T)1.f);
+            T x = helper->relativeT(idx, sd::DataTypeUtils::template min<T>(), (T)1.f - sd::DataTypeUtils::template min<T>());
             return -sd::math::nd4j_log<T, T>((T)1.f - x) / lambda;
         }
 

libnd4j/tests_cpu/layers_tests/RNGTests.cpp

@@ -790,6 +790,75 @@ TEST_F(RNGTests, Test_ExponentialDistribution_1_SGA) {
     
 }
 
+TEST_F(RNGTests, Test_ExponentialDistribution_2_SGA) {
+    auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
+    auto exp0 = NDArrayFactory::create<float>('c', {10, 10});
+    RandomGenerator oc(2716049175077475646L, -6182841917129177862L);
+
+    sd::ops::random_exponential op;
+    RandomLauncher::fillExponential(x.getContext(), oc, &exp0, 2.f);
+    auto result = op.evaluate({&x}, {1.f}, {0});
+    ASSERT_EQ(Status::OK(), result.status());
+
+    auto z = result.at(0);
+    ASSERT_TRUE(exp0.isSameShape(z));
+    ASSERT_FALSE(exp0.equalsTo(z));
+    //
+//    z->printBuffer("\nExponential2+");
+    auto mean = z->reduceNumber(reduce::Mean);
+    auto variance = z->varianceNumber(variance::SummaryStatsVariance, false);
+    mean.printBuffer("Mean for exponential with param 1.0 (1 exp) is");
+    variance.printBuffer("Variance for exponential with param 1. (1 exp) is");
+    ASSERT_FALSE(nexp0->equalsTo(z));
+    ASSERT_FALSE(nexp1->equalsTo(z));
+    ASSERT_FALSE(nexp2->equalsTo(z));
+    mean = exp0.reduceNumber(reduce::Mean);
+    variance = exp0.varianceNumber(variance::SummaryStatsVariance, false);
+    mean.printBuffer("Mean for exponential with param 2.0 (1/2 exp) is");
+    variance.printBuffer("Variance for exponential with param 2. (1/2 exp) is");
+}
+
+TEST_F(RNGTests, Test_ExponentialDistribution_3_SGA) {
+    auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {1000, 1000});
+    auto exp0 = NDArrayFactory::create<double>('c', {1000, 1000});
+    RandomGenerator oc(2716049175077475646L, -6182841917129177862L);
+    auto expMean = NDArrayFactory::create<double>(0.5f);
+    auto expVar = NDArrayFactory::create<double>(0.25f);
+    sd::ops::random_exponential op;
+    RandomLauncher::fillExponential(exp0.getContext(), oc, &exp0, 2.f);
+
+    auto result = op.evaluate({&x}, {1.});
+    ASSERT_EQ(Status::OK(), result.status());
+
+    auto z = result.at(0);
+    //ASSERT_TRUE(exp0.isSameShape(z));
+    //ASSERT_FALSE(exp0.equalsTo(z));
+    //
+//    z->printBuffer("\nExponential2+");
+   auto mean = z->reduceNumber(reduce::Mean);
+   auto variance = z->varianceNumber(variance::SummaryStatsVariance, false);
+   mean.printBuffer("Mean");
+   variance.printBuffer("Variance");
+    ASSERT_NEAR(mean.e<double>(0), 1.f, 1.e-2f);
+    ASSERT_NEAR(variance.e<double>(0), 1.f, 1.e-2f);
+//    mean.printBuffer("Mean for exponential with param 1.0 (1 exp) is");
+//    variance.printBuffer("Variance for exponential with param 1. (1 exp) is");
+//    ASSERT_FALSE(nexp0->equalsTo(z));
+//    ASSERT_FALSE(nexp1->equalsTo(z));
+//    ASSERT_FALSE(nexp2->equalsTo(z));
+    mean = exp0.reduceNumber(reduce::Mean);
+    variance = exp0.varianceNumber(variance::SummaryStatsVariance, false);
+    mean.printBuffer("Mean for exponential with param 2.0 (1/2 exp) is");
+    variance.printBuffer("Variance for exponential with param 2. (1/4 exp) is");
+    ASSERT_TRUE(mean.equalsTo(expMean, 1.e-3));
+    ASSERT_TRUE(variance.equalsTo(expVar, 1.e-3));
+    RandomLauncher::fillExponential(exp0.getContext(), oc, &exp0, 1.f);
+    mean = exp0.reduceNumber(reduce::Mean);
+    variance = exp0.varianceNumber(variance::SummaryStatsVariance, false);
+    mean.printBuffer("Mean for exponential with param 1.0 (1 exp) is");
+    variance.printBuffer("Variance for exponential with param 1.0 (1 exp) is");
+}
+
 TEST_F(RNGTests, Test_ExponentialDistribution_2) {
     auto x = NDArrayFactory::create<Nd4jLong>('c', {2}, {10, 10});
     auto y = NDArrayFactory::create<float>('c', {10, 10});