square root of a

    Generalizes a scalar op to tensors.

    All the inputs must have the same number of dimensions. When the
    Op is performed, for each dimension, each input's size for that
    dimension must be the same. As a special case, it can also be 1
    but only if the input's broadcastable flag is True for that
    dimension. In that case, the tensor is (virtually) replicated
    along that dimension to match the size of the others.

    The dtypes of the outputs mirror those of the scalar Op that is
    being generalized to tensors. In particular, if the calculations
    for an output are done inplace on an input, the output type must
    be the same as the corresponding input type (see the doc of
    scalar.ScalarOp to get help about controlling the output type)

    Parameters
    ----------
    scalar_op
        An instance of a subclass of scalar.ScalarOp which works uniquely
        on scalars.
    inplace_pattern
        A dictionary that maps the index of an output to the
        index of an input so the output is calculated inplace using
        the input's storage. (Just like destroymap, but without the lists.)
    nfunc_spec
        Either None or a tuple of three elements,
        (nfunc_name, nin, nout) such that getattr(numpy, nfunc_name)
        implements this operation, takes nin inputs and nout outputs.
        Note that nin cannot always be inferred from the scalar op's
        own nin field because that value is sometimes 0 (meaning a
        variable number of inputs), whereas the numpy function may
        not have varargs.

    Note
    ----
    | Elemwise(add) represents + on tensors (x + y)
    | Elemwise(add, {0 : 0}) represents the += operation (x += y)
    | Elemwise(add, {0 : 1}) represents += on the second argument (y += x)
    | Elemwise(mul)(rand(10, 5), rand(1, 5)) the second input is completed along the first dimension to match the first input
    | Elemwise(true_div)(rand(10, 5), rand(10, 1)) same but along the second dimension
    | Elemwise(int_div)(rand(1, 5), rand(10, 1)) the output has size (10, 5)
    | Elemwise(log)(rand(3, 4, 5))

elementwise [true] division (inverse of multiplication)

    scalar_op

    Note
    ----

elementwise multiplication

    scalar_op

    Note
    ----

transformed_name, transform.apply(distribution), total_size=total_size)

base e logarithm of a

    scalar_op

    Note
    ----

elementwise addition

    scalar_op

    Note
    ----

if cond then ift else iff

    scalar_op

    Note
    ----

a >= b

    scalar_op

    Note
    ----

e^`a`

    scalar_op

    Note
    ----

a > b

    scalar_op

    Note
    ----

return bound(-0.5 * tau * value**2 + 0.5 * tt.log(tau * 2. / np.pi),

elementwise power

    scalar_op

    Note
    ----

Hyper_b1_std = pm.HalfNormal('Hyper_b1_std', sd=stdGuessFactor1)

Hyper_b2_std = pm.HalfNormal('Hyper_b2_std', sd=stdGuessFactor2)

Hyper_like_std = pm.HalfNormal('Hyper_like_std', sd=0.1*minimum)

self.mean = self.median = self.mode = self.mu = mu = tt.as_tensor_variable(floatX(mu))

b0 = pm.Deterministic('b0', Hyper_b0_std * pm.Normal('b0-Tilde',mu=0, sd=1))

return bound((-tau * (value - mu)**2 + tt.log(tau / np.pi / 2.)) / 2.,

-a

    scalar_op

    Note
    ----

elementwise subtraction

    scalar_op

    Note
    ----

b1 = pm.Deterministic('b1', Hyper_b1_std * pm.Normal('b1-Tilde',mu=0, sd=1, shape=Nx1Lvl))

#section support_code_apply

int APPLY_SPECIFIC(cpu_dimshuffle)(PyArrayObject* input, PyArrayObject** res, PARAMS_TYPE* params) {
    npy_bool* input_broadcastable;
    npy_int64* new_order;
    npy_intp nd_in;
    npy_intp nd_out;
    PyArrayObject* basename;
    npy_intp* dimensions;
    npy_intp* strides;

    if (!PyArray_IS_C_CONTIGUOUS(params->input_broadcastable)) {
        PyErr_SetString(PyExc_RuntimeError, "DimShuffle: param input_broadcastable must be C-contiguous.");
        return 1;
    }
    if (!PyArray_IS_C_CONTIGUOUS(params->_new_order)) {
        PyErr_SetString(PyExc_RuntimeError, "DimShuffle: param _new_order must be C-contiguous.");
        return 1;
    }
    input_broadcastable = (npy_bool*) PyArray_DATA(params->input_broadcastable);
    new_order = (npy_int64*) PyArray_DATA(params->_new_order);
    nd_in = PyArray_SIZE(params->input_broadcastable);
    nd_out = PyArray_SIZE(params->_new_order);

    /* check_input_nd */
    if (PyArray_NDIM(input) != nd_in) {
        PyErr_SetString(PyExc_NotImplementedError, "input nd");
        return 1;
    }

    /* clear_output */
    if (*res)
        Py_XDECREF(*res);

    /* get_base */
    if (params->inplace) {
        basename = input;
        Py_INCREF((PyObject*)basename);
    } else {
        basename =
            (PyArrayObject*)PyArray_FromAny((PyObject*)input,
                                            NULL, 0, 0, NPY_ARRAY_ALIGNED|NPY_ARRAY_ENSURECOPY, NULL);
    }

    /* shape_statements and strides_statements */
    dimensions = (npy_intp*) malloc(nd_out * sizeof(npy_intp));
    strides = (npy_intp*) malloc(nd_out * sizeof(npy_intp));
    if (dimensions == NULL || strides == NULL) {
        PyErr_NoMemory();
        free(dimensions);
        free(strides);
        return 1;
    };

    for (npy_intp i = 0; i < nd_out; ++i) {
        if (new_order[i] != -1) {
            dimensions[i] = PyArray_DIMS(basename)[new_order[i]];
            strides[i] = PyArray_DIMS(basename)[new_order[i]] == 1 ?
                            0 : PyArray_STRIDES(basename)[new_order[i]];
        } else {
            dimensions[i] = 1;
            strides[i] = 0;
        }
    }

    /* set the strides of the broadcasted dimensions.
     * This algorithm is from numpy: PyArray_Newshape() in
     * cvs/numpy/numpy/core/src/multiarraymodule.c */
    if (nd_out > 0) {
        if (strides[nd_out - 1] == 0)
            strides[nd_out - 1] = PyArray_DESCR(basename)->elsize;
        for (npy_intp i = nd_out - 2; i > -1; --i) {
            if (strides[i] == 0)
                strides[i] = strides[i + 1] * dimensions[i + 1];
        }
    }

    /* close_bracket */
    // create a new array.
    *res = (PyArrayObject*)PyArray_New(&PyArray_Type, nd_out, dimensions,
                                       PyArray_TYPE(basename), strides,
                                       PyArray_DATA(basename), PyArray_ITEMSIZE(basename),
                                       // borrow only the writable flag from the base
                                       // the NPY_OWNDATA flag will default to 0.
                                       (NPY_ARRAY_WRITEABLE * PyArray_ISWRITEABLE(basename)),
                                       NULL);

    if (*res == NULL) {
        return 1;
    }

    // recalculate flags: CONTIGUOUS, FORTRAN, ALIGNED
    PyArray_UpdateFlags(*res, NPY_ARRAY_UPDATE_ALL);

    // we are making a view in both inplace and non-inplace cases
    PyArray_SetBaseObject(*res, (PyObject*)basename);

    free(strides);
    free(dimensions);

    return 0;
}

        return 1;
    }
        return 1;
    }

        return 1;
    }

    if (*res)

    } else {
        basename =
    }

        return 1;
    };

        } else {
        }
    }

        }
    }

                                       NULL);

        return 1;
    }

    return 0;
}

b2 = pm.Deterministic('b2', Hyper_b2_std * pm.Normal('b2-Tilde',mu=0, sd=1, shape=Nx2Lvl))

M  = pm.Deterministic('M' , Hyper_M_std  * pm.Normal('M-Tilde', mu=0, sd=1 ,shape=[Nx1Lvl, Nx2Lvl]))

{
npy_float64 V%(id)s_tmp1;
V%(id)s_tmp1 = %(i1)s < -709.0 ? 0.0 : %(i1)s > 19.0 ? 1.0 : 1.0 /(1.0+exp(-%(i1)s));
%(o0)s = %(i0)s * V%(id)s_tmp1;
}

The acceptance probability does not match the target. It is 0.9914804670648745, but should be close to 0.999. Try to increase the number of tuning steps.

The acceptance probability does not match the target. It is 0.9939501044389761, but should be close to 0.999. Try to increase the number of tuning steps.

The acceptance probability does not match the target. It is 0.9948205831930729, but should be close to 0.999. Try to increase the number of tuning steps.

The acceptance probability does not match the target. It is 0.9937640064428087, but should be close to 0.999. Try to increase the number of tuning steps.