test_class.file: deepnetts-core/src/test/java/deepnetts/net/layers/ActivationFunctionsTest.java
test_class.identifier: ActivationFunctionsTest
test_case.body: @Test
      public void testReluPrime() {
          float[] x = {-7.0f,  -6.9f,  -6.8f,  -6.7f,  -6.6f,  -6.5f,  -6.4f,  -6.3f,  -6.2f,  -6.1f,  -6.0f,  -5.9f,  -5.8f,  -5.7f,  -5.6f,  -5.5f,  -5.4f,  -5.3f,  -5.2f,  -5.1f,  -5.0f,  -4.9f,  -4.8f,  -4.7f,  -4.6f,  -4.5f,  -4.4f,  -4.3f,  -4.2f,  -4.1f,  -4.0f,  -3.9f,  -3.8f,  -3.7f,  -3.6f,  -3.5f,  -3.4f,  -3.3f,  -3.2f,  -3.1f,  -3.0f,  -2.9f,  -2.8f,  -2.7f,  -2.6f,  -2.5f,  -2.4f,  -2.3f,  -2.2f,  -2.1f,  -2.0f,  -1.9f,  -1.8f,  -1.7f,  -1.6f,  -1.5f,  -1.4f,  -1.3f,  -1.2f,  -1.1f,  -1.0f,  -0.9f,  -0.8f,  -0.7f,  -0.6f,  -0.5f,  -0.4f,  -0.3f,  -0.2f,  -0.1f,  0f,  0.1f,  0.2f,  0.3f,  0.4f,  0.5f,  0.6f,  0.7f,  0.8f,  0.9f,  1.0f,  1.1f,  1.2f,  1.3f,  1.4f,  1.5f,  1.6f,  1.7f,  1.8f,  1.9f,  2.0f,  2.1f,  2.2f,  2.3f,  2.4f,  2.5f,  2.6f,  2.7f,  2.8f,  2.9f,  3.0f,  3.1f,  3.2f,  3.3f,  3.4f,  3.5f,  3.6f,  3.7f,  3.8f,  3.9f,  4.0f,  4.1f,  4.2f,  4.3f,  4.4f,  4.5f,  4.6f,  4.7f,  4.8f,  4.9f,  5.0f,  5.1f,  5.2f,  5.3f,  5.4f,  5.5f,  5.6f,  5.7f,  5.8f,  5.9f,  6.0f,  6.1f,  6.2f,  6.3f,  6.4f,  6.5f,  6.6f,  6.7f,  6.8f,  6.9f,  7.0f};
          float[] y = {0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1 };
          
          for (int i = 0; i < x.length; i++) {
              float expResult = y[i];
              float result = ActivationFunctions.reluPrime(x[i]);
              assertEquals(expResult, result, 0);
          } 
      }
test_case.class_method_signature: ActivationFunctionsTest.testReluPrime()
test_case.full_signature: @Test public void testReluPrime()
test_case.identifier: testReluPrime
test_case.invocations: reluPrime assertEquals
test_case.modifiers: @Test public
test_case.parameters: ()
test_case.return: void
test_case.signature: void testReluPrime()
focal_class.file: deepnetts-core/src/main/java/deepnetts/net/layers/ActivationFunctions.java
focal_class.identifier: ActivationFunctions
focal_class.class_method_signature: ActivationFunctions.ActivationFunctions()
focal_class.full_signature: private  ActivationFunctions()
focal_class.identifier: ActivationFunctions
focal_class.modifiers: private
focal_class.parameters: ()
focal_class.signature:  ActivationFunctions()
focal_class.class_method_signature: ActivationFunctions.calc(final ActivationType type, final float x)
focal_class.full_signature: public static final float calc(final ActivationType type, final float x)
focal_class.identifier: calc
focal_class.modifiers: public static final
focal_class.parameters: (final ActivationType type, final float x)
focal_class.return: float
focal_class.signature: float calc(final ActivationType type, final float x)
focal_class.class_method_signature: ActivationFunctions.prime(ActivationType type, float y)
focal_class.full_signature: public static final float prime(ActivationType type, float y)
focal_class.identifier: prime
focal_class.modifiers: public static final
focal_class.parameters: (ActivationType type, float y)
focal_class.return: float
focal_class.signature: float prime(ActivationType type, float y)
focal_class.class_method_signature: ActivationFunctions.sigmoid(final float x)
focal_class.full_signature: public static final float sigmoid(final float x)
focal_class.identifier: sigmoid
focal_class.modifiers: public static final
focal_class.parameters: (final float x)
focal_class.return: float
focal_class.signature: float sigmoid(final float x)
focal_class.class_method_signature: ActivationFunctions.sigmoidPrime(final float y)
focal_class.full_signature: public static final float sigmoidPrime(final float y)
focal_class.identifier: sigmoidPrime
focal_class.modifiers: public static final
focal_class.parameters: (final float y)
focal_class.return: float
focal_class.signature: float sigmoidPrime(final float y)
focal_class.class_method_signature: ActivationFunctions.tanh(final float x)
focal_class.full_signature: public static final float tanh(final float x)
focal_class.identifier: tanh
focal_class.modifiers: public static final
focal_class.parameters: (final float x)
focal_class.return: float
focal_class.signature: float tanh(final float x)
focal_class.class_method_signature: ActivationFunctions.tanhPrime(final float y)
focal_class.full_signature: public static final float tanhPrime(final float y)
focal_class.identifier: tanhPrime
focal_class.modifiers: public static final
focal_class.parameters: (final float y)
focal_class.return: float
focal_class.signature: float tanhPrime(final float y)
focal_class.class_method_signature: ActivationFunctions.relu(final float x)
focal_class.full_signature: public static final float relu(final float x)
focal_class.identifier: relu
focal_class.modifiers: public static final
focal_class.parameters: (final float x)
focal_class.return: float
focal_class.signature: float relu(final float x)
focal_class.class_method_signature: ActivationFunctions.reluPrime(final float y)
focal_class.full_signature: public static final float reluPrime(final float y)
focal_class.identifier: reluPrime
focal_class.modifiers: public static final
focal_class.parameters: (final float y)
focal_class.return: float
focal_class.signature: float reluPrime(final float y)
focal_class.class_method_signature: ActivationFunctions.linear(final float x)
focal_class.full_signature: public static final float linear(final float x)
focal_class.identifier: linear
focal_class.modifiers: public static final
focal_class.parameters: (final float x)
focal_class.return: float
focal_class.signature: float linear(final float x)
focal_class.class_method_signature: ActivationFunctions.linearPrime(final float y)
focal_class.full_signature: public static final float linearPrime(final float y)
focal_class.identifier: linearPrime
focal_class.modifiers: public static final
focal_class.parameters: (final float y)
focal_class.return: float
focal_class.signature: float linearPrime(final float y)
focal_method.body: public static final float reluPrime(final float y) {
         return ( y > 0 ? 1 : 0);
      }
focal_method.class_method_signature: ActivationFunctions.reluPrime(final float y)
focal_method.full_signature: public static final float reluPrime(final float y)
focal_method.identifier: reluPrime
focal_method.modifiers: public static final
focal_method.parameters: (final float y)
focal_method.return: float
focal_method.signature: float reluPrime(final float y)