slinderman

from __future__ import division
import numpy as np
np.seterr(divide='ignore') # these warnings are usually harmless for this code
from matplotlib import pyplot as plt
import matplotlib
import os
matplotlib.rcParams['font.size'] = 8

import pyhsmm
from pyhsmm.util.text import progprint_xrange

slinderman

slinderman

# Simple demo showing weird marginal likelihood estimates
# for GP classification when using EP inference.
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt

import GPy
from GPy.kern import RBF

# Model parameters

slinderman

import numpy as np

import matplotlib.pyplot as plt
from hips.plotting.layout import create_figure, create_axis_at_location
from hips.plotting.colormaps import gradient_cmap

import seaborn as sns
color_names = ["windows blue",
               "amber",
               "crimson",

slinderman

slinderman

from __future__ import division
from builtins import range
import numpy as np
np.seterr(divide='ignore') # these warnings are usually harmless for this code
np.random.seed(0)

from matplotlib import pyplot as plt
import matplotlib
import os
matplotlib.rcParams['font.size'] = 8

slinderman

slinderman

import numpy as np
import matplotlib.pyplot as plt
from pylds.models import DefaultLDS

inputs = \
    np.array([[ 0.        ,  0.        ],
    [ 2.72785283,  7.53608657],
    [ 0.        ,  7.23201033],
    [ 2.0261219 ,  7.07866193],
    [ 2.42989525,  7.67644583],

slinderman

slinderman

"""
A collection of helper functions for optimization with JAX. 

UPDATE: This is obsolete now that `jax.scipy.optimize.minimize` is exists!
"""
import numpy as onp
import scipy.optimize
from jax import grad, jit
from jax.tree_util import tree_flatten, tree_unflatten
from jax.flatten_util import ravel_pytree