# Path: examples/context.py



import matplotlib
import numpy as np
import pdb
import matplotlib.pylab as plt
from scipy import special
from .context import vfe, compute_kernel, compute_psi_weave

    def forward_prop_random_thru_post_mm(self, model, mx, vx, mu, Su):
        Kuu_noiseless = compute_kernel(
            2 * model.ls, 2 * model.sf, model.zu, model.zu)
        Kuu = Kuu_noiseless + np.diag(jitter * np.ones((self.M, )))
        # TODO: remove inv
        Kuuinv = np.linalg.inv(Kuu)
        A = np.dot(Kuuinv, mu)
        Smm = Su + np.outer(mu, mu)
        B_sto = np.dot(Kuuinv, np.dot(Smm, Kuuinv)) - Kuuinv
        psi0 = np.exp(2.0 * model.sf)
        psi1, psi2 = compute_psi_weave(
            2 * model.ls, 2 * model.sf, mx, vx, model.zu)
        mout = np.einsum('nm,md->nd', psi1, A)
        Bpsi2 = np.einsum('ab,nab->n', B_sto, psi2)[:, np.newaxis]
        vout = psi0 + Bpsi2 - mout**2
        return mout, vout


class IndepSGPR(object):

    def __init__(self, X_train, Y_train, M):
        self.N_train = Y_train.shape[0]
        self.Dout = Y_train.shape[1]
        self.Din = Y_train.shape[1]
        self.M = M
        models = []
        for i in range(self.Dout):
            X_train_i = X_train
            y_train_i = Y_train[:, [i]]

# Path: examples/context.py



# Path: examples/gpr_autoreg_example.py
import matplotlib
import numpy as np
import pdb
import matplotlib.pylab as plt
from scipy import special
from .context import vfe, compute_kernel, compute_psi_weave


    def forward_prop_random_thru_post_mm(self, model, mx, vx, mu, Su):
        Kuu_noiseless = compute_kernel(
            2 * model.ls, 2 * model.sf, model.zu, model.zu)
        Kuu = Kuu_noiseless + np.diag(jitter * np.ones((self.M, )))
        # TODO: remove inv
        Kuuinv = np.linalg.inv(Kuu)
        A = np.dot(Kuuinv, mu)
        Smm = Su + np.outer(mu, mu)
        B_sto = np.dot(Kuuinv, np.dot(Smm, Kuuinv)) - Kuuinv
        psi0 = np.exp(2.0 * model.sf)
        psi1, psi2 = compute_psi_weave(
            2 * model.ls, 2 * model.sf, mx, vx, model.zu)
        mout = np.einsum('nm,md->nd', psi1, A)
        Bpsi2 = np.einsum('ab,nab->n', B_sto, psi2)[:, np.newaxis]
        vout = psi0 + Bpsi2 - mout**2
        return mout, vout


class IndepSGPR(object):

    def __init__(self, X_train, Y_train, M):
        self.N_train = Y_train.shape[0]
        self.Dout = Y_train.shape[1]
        self.Din = Y_train.shape[1]
        self.M = M
        models = []
        for i in range(self.Dout):
            X_train_i = X_train
            y_train_i = Y_train[:, [i]]

            model_i = vfe.SGPR(X_train_i, y_train_i, M)