# Path: examples/context.py

# 
# Path: examples/datautils.py
# def step(x):
# 	y = x.copy()
# 	y[y < 0.0] = 0.0
# 	y[y > 0.0] = 1.0
# 	return y + 0.05*np.random.randn(x.shape[0], 1)
# 
# def spiral(no_samples, noise=0.0):
# 	def genSpiral(N, delta):
# 		X = np.zeros((N, 2))
# 		for i in range(N):
# 			r = i * 5.0 / N
# 			t = 1.75 * i / N * 2.0 * np.pi + delta
# 			x = r * np.sin(t) + (2*np.random.rand() - 1) * noise
# 			y = r * np.cos(t) + (2*np.random.rand() - 1) * noise
# 			X[i, :] = np.array([x, y])
# 		return X
# 
# 	N = int(no_samples / 2.0)
# 	pos_x = genSpiral(N, 0)
# 	neg_x = genSpiral(N, np.pi)
# 	x = np.vstack((pos_x, neg_x))
# 	y = np.ones((2*N, 1))
# 	y[N:] = -1
# 	return x, y


import numpy as np
import pdb
import matplotlib.pylab as plt
from scipy import special
from .context import aep
from .datautils import step, spiral
    Y = step(X)
    # plt.plot(X, Y, 'kx', mew=2)

    def plot(m):
        xx = np.linspace(-3, 3, 100)[:, None]
        mean, var = m.predict_f(xx)
        zu = m.sgp_layers[0].zu
        mean_u, var_u = m.predict_f(zu)
        plt.figure()
        plt.plot(X, Y, 'kx', mew=2)
        plt.plot(xx, mean, 'b', lw=2)
        plt.fill_between(
            xx[:, 0],
            mean[:, 0] - 2 * np.sqrt(var[:, 0]),
            mean[:, 0] + 2 * np.sqrt(var[:, 0]),
            color='blue', alpha=0.2)
        plt.errorbar(zu, mean_u, yerr=2 * np.sqrt(var_u), fmt='ro')

        no_samples = 20
        xx = np.linspace(-3, 3, 500)[:, None]
        f_samples = m.sample_f(xx, no_samples)
        for i in range(no_samples):
            plt.plot(xx, f_samples[:, :, i], linewidth=0.5, alpha=0.5)

        plt.xlim(-3, 3)

    # inference
    print "create model and optimize ..."
    M = 20
    hidden_size = [3, 2]

# Path: examples/context.py

# 
# Path: examples/datautils.py
# def step(x):
# 	y = x.copy()
# 	y[y < 0.0] = 0.0
# 	y[y > 0.0] = 1.0
# 	return y + 0.05*np.random.randn(x.shape[0], 1)
# 
# def spiral(no_samples, noise=0.0):
# 	def genSpiral(N, delta):
# 		X = np.zeros((N, 2))
# 		for i in range(N):
# 			r = i * 5.0 / N
# 			t = 1.75 * i / N * 2.0 * np.pi + delta
# 			x = r * np.sin(t) + (2*np.random.rand() - 1) * noise
# 			y = r * np.cos(t) + (2*np.random.rand() - 1) * noise
# 			X[i, :] = np.array([x, y])
# 		return X
# 
# 	N = int(no_samples / 2.0)
# 	pos_x = genSpiral(N, 0)
# 	neg_x = genSpiral(N, np.pi)
# 	x = np.vstack((pos_x, neg_x))
# 	y = np.ones((2*N, 1))
# 	y[N:] = -1
# 	return x, y


# Path: examples/dgprh_aep_examples.py
import numpy as np
import pdb
import matplotlib.pylab as plt
from scipy import special
from .context import aep
from .datautils import step, spiral

    Y = step(X)
    # plt.plot(X, Y, 'kx', mew=2)

    def plot(m):
        xx = np.linspace(-3, 3, 100)[:, None]
        mean, var = m.predict_f(xx)
        zu = m.sgp_layers[0].zu
        mean_u, var_u = m.predict_f(zu)
        plt.figure()
        plt.plot(X, Y, 'kx', mew=2)
        plt.plot(xx, mean, 'b', lw=2)
        plt.fill_between(
            xx[:, 0],
            mean[:, 0] - 2 * np.sqrt(var[:, 0]),
            mean[:, 0] + 2 * np.sqrt(var[:, 0]),
            color='blue', alpha=0.2)
        plt.errorbar(zu, mean_u, yerr=2 * np.sqrt(var_u), fmt='ro')

        no_samples = 20
        xx = np.linspace(-3, 3, 500)[:, None]
        f_samples = m.sample_f(xx, no_samples)
        for i in range(no_samples):
            plt.plot(xx, f_samples[:, :, i], linewidth=0.5, alpha=0.5)

        plt.xlim(-3, 3)

    # inference
    print "create model and optimize ..."
    M = 20
    hidden_size = [3, 2]

    model = aep.SDGPR_H(X, Y, M, hidden_size, lik='Gaussian')