예제 #1
0
    def mom_call(self, k, dist):
        "Moment generator call backend wrapper"
        assert len(k) == len(dist)
        graph = self.graph
        self.dist, dist_ = dist, self.dist

        graph.add_node(dist, key=k)
        if hasattr(dist, "_mom"):
            if dist.advance:
                out = dist._mom(k, self)
            else:
                out = np.empty(k.shape[1:])
                prm = self.D.build()
                prm.update(self.K.build())
                out[:] = dist._mom(k, **prm)
        else:
            out = mom(dist, k, **self.meta)
        graph.add_node(dist, val=out)

        self.dist = dist_
        return np.array(out)
예제 #2
0
파일: graph.py 프로젝트: apetcho/chaospy
    def mom_call(self, k, dist):
        "Moment generator call backend wrapper"
        assert len(k) == len(dist)
        graph = self.graph
        self.dist, dist_ = dist, self.dist

        graph.add_node(dist, key=k)
        if hasattr(dist, "_mom"):
            if dist.advance:
                out = dist._mom(k, self)
            else:
                out = np.empty(k.shape[1:])
                prm = self.D.build()
                prm.update(self.K.build())
                out[:] = dist._mom(k, **prm)
        else:
            out = mom(dist, k, **self.meta)
        graph.add_node(dist, val=out)

        self.dist = dist_
        return np.array(out)
예제 #3
0
파일: backend.py 프로젝트: simetenn/chaospy
    def mom(self, K, **kws):
        """Raw statistical moments.

Creates non-centralized raw moments from the random variable. If
analytical options can not be utilized, Monte Carlo integration
will be used.

Parameters
----------
K : array_like
    Index of the raw moments. k.shape must be compatible with
    distribution shape.
    Sampling scheme when performing Monte Carlo
rule : str
    rule for estimating the moment if the analytical method
    fails.

    Key Monte Carlo schemes
    --- ------------------------
    "H" Halton sequence
    "K" Korobov set
    "L" Latin hypercube sampling
    "M" Hammersley sequence
    "R" (Pseudo-)Random sampling
    "S" Sobol sequence

    Key Quadrature schemes
    --- ------------------------
    "C" Clenshaw-Curtis
    "Q" Gaussian quadrature
    "E" Gauss-Legendre

composit : int, array_like optional
    If provided, composit quadrature will be used.
    Ignored in the case if gaussian=True.

    If int provided, determines number of even domain splits
    If array of ints, determines number of even domain splits along
        each axis
    If array of arrays/floats, determines location of splits

antithetic : array_like, optional
    List of bool. Represents the axes to mirror using antithetic
    variable during MCI.

Returns
-------
out : ndarray
    Shapes are related through the identity
    k.shape==dist.shape+k.shape
        """
        K = np.array(K, dtype=int)
        shape = K.shape
        dim = len(self)

        if dim > 1:
            shape = shape[1:]

        size = K.size / dim
        K = K.reshape(dim, size)

        try:
            out, G = self.G.run(K, "mom", **kws)

        except NotImplementedError:
            out = mom(self, K, **kws)

        return out.reshape(shape)
예제 #4
0
파일: backend.py 프로젝트: JC1024/chaospy
    def mom(self, K, **kws):
        """Raw statistical moments.

Creates non-centralized raw moments from the random variable. If
analytical options can not be utilized, Monte Carlo integration
will be used.

Parameters
----------
K : array_like
    Index of the raw moments. k.shape must be compatible with
    distribution shape.
    Sampling scheme when performing Monte Carlo
rule : str
    rule for estimating the moment if the analytical method
    fails.

    Key Monte Carlo schemes
    --- ------------------------
    "H" Halton sequence
    "K" Korobov set
    "L" Latin hypercube sampling
    "M" Hammersley sequence
    "R" (Pseudo-)Random sampling
    "S" Sobol sequence

    Key Quadrature schemes
    --- ------------------------
    "C" Clenshaw-Curtis
    "Q" Gaussian quadrature
    "E" Gauss-Legendre

composit : int, array_like optional
    If provided, composit quadrature will be used.
    Ignored in the case if gaussian=True.

    If int provided, determines number of even domain splits
    If array of ints, determines number of even domain splits along
        each axis
    If array of arrays/floats, determines location of splits

antithetic : array_like, optional
    List of bool. Represents the axes to mirror using antithetic
    variable during MCI.

Returns
-------
out : ndarray
    Shapes are related through the identity
    k.shape==dist.shape+k.shape
        """
        K = np.array(K, dtype=int)
        shape = K.shape
        dim = len(self)
        if dim>1:
            shape = shape[1:]
        size = K.size/dim
        K = K.reshape(dim, size)

        try:
            out, G = self.G.run(K, "mom", **kws)
        except NotImplementedError:
            out = mom(self, K, **kws)
        return out.reshape(shape)