List of derivative() Examples

Examples of derivative()

cc.redberry.core.tensor.AbstractScalarFunction.derivative()
com.neuralnetwork.shared.functions.IActivationFunction.derivative()
Returns the output of the derivative of the activation function. @param v the input value to the activation function @return the output of the derivative of this activation function
com.neuralnetwork.shared.functions.LinearFunction.derivative()
com.neuralnetwork.shared.functions.SigmoidFunction.derivative()
com.opengamma.analytics.math.function.DoubleFunction1D.derivative()
Returns a function that calculates the first derivative. The method used is central finite difference, with $\epsilon = 10^{-5}$. Implementing classes can override this method to return a function that is the exact functional representation of the first derivative. @return A function that calculates the first derivative of this function
org.apache.commons.math.analysis.DifferentiableUnivariateRealFunction.derivative()
Returns the derivative of the function @return the derivative function
org.apache.commons.math3.analysis.function.Power.derivative()
{@inheritDoc} @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
org.apache.commons.math3.analysis.function.Sin.derivative()
{@inheritDoc} @deprecated as of 3.1, replaced by {@link #value(DerivativeStructure)}
org.apache.commons.math3.analysis.polynomials.PolynomialFunction.derivative()
Returns the derivative as a {@link UnivariateFunction}. @return the derivative function.
org.jquantlib.math.distributions.CumulativeNormalDistribution.derivative()
Computes the derivative. @param x @return gaussian.evaluate(xn) / sigma
org.jquantlib.math.distributions.NormalDistribution.derivative()
{@inheritDoc}
Calculates the first derivative of a Normal distribution at point {@latex$ x } @param x @return the first derivative of a Normal distribution at point {@latex$ x }
org.opengis.referencing.operation.MathTransform1D.derivative()
Gets the derivative of this function at a value. The derivative is the 1×1 matrix of the non-translating portion of the approximate affine map at the value. @param value The value where to evaluate the derivative. @return The derivative at the specified point. @throws TransformException if the derivative can't be evaluated at thespecified point.

Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            AbstractScalarFunction func = (AbstractScalarFunction) target;
            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        //TODO get derivative ot derivative
        return null;
    }

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Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        //TODO get derivative ot derivative
        return null;
    }

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Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            AbstractScalarFunction func = (AbstractScalarFunction) target;
            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        //TODO get derivative ot derivative
        return null;
    }

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Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        //TODO get derivative ot derivative
        return null;
    }

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Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            AbstractScalarFunction func = (AbstractScalarFunction) target;
            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        return null;
    }

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Examples of cc.redberry.core.tensor.AbstractScalarFunction.derivative()

            Tensor der = getDerivative(func.getInnerTensor(), var);
            if (der == null)
                return null;
            if (isOne(der))
                return func.derivative();
            return new Product(func.derivative(), der);
        }
        return null;
    }


    private static boolean isOne(Tensor t) {

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Examples of com.neuralnetwork.shared.functions.IActivationFunction.derivative()

     */
    private void testDerivativeValue(final double input, 
        final double expected) {
        LOGGER.debug("===== Sigmoid Derivative Test. =====");
        IActivationFunction f = new SigmoidFunction();
        double v = f.derivative(input);
        LOGGER.debug(" Input: " + input);
        LOGGER.debug(" Value: " + v);
        LOGGER.debug(" Expected: " + expected);
        assertEquals(v, expected , DELTA * Math.ulp(expected));
        LOGGER.debug("====================================");

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Examples of com.neuralnetwork.shared.functions.LinearFunction.derivative()

  public final void testLinearFunction() {
    LinearFunction f = new LinearFunction();
    assertNotNull(f);
    for (int i = 0; i < NUM_ITER; i++) {
      assertEquals(f.activate(i), i, ACCUR * Math.ulp(i));
      assertEquals(f.derivative(i), 1, ACCUR * Math.ulp(i));
    }


    f.changeFunction(FunctionType.NULL);
    
    for (int i = 0; i < NUM_ITER; i++) {

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Examples of com.neuralnetwork.shared.functions.LinearFunction.derivative()


    f.changeFunction(FunctionType.NULL);
    
    for (int i = 0; i < NUM_ITER; i++) {
      assertEquals(f.activate(i), 0, ACCUR * Math.ulp(i));
      assertEquals(f.derivative(i), 0, ACCUR * Math.ulp(i));
    }
  }


}

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Examples of com.neuralnetwork.shared.functions.SigmoidFunction.derivative()

     */
    private void testDerivativeValue(final double input, 
        final double expected) {
        LOGGER.debug("===== Sigmoid Derivative Test. =====");
        IActivationFunction f = new SigmoidFunction();
        double v = f.derivative(input);
        LOGGER.debug(" Input: " + input);
        LOGGER.debug(" Value: " + v);
        LOGGER.debug(" Expected: " + expected);
        assertEquals(v, expected , DELTA * Math.ulp(expected));
        LOGGER.debug("====================================");

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