# 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/context.py



import numpy as np
import pdb
import matplotlib
import matplotlib.pylab as plt
from scipy import special
from .datautils import step, spiral
from .context import aep
        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')
        plt.xlim(-0.1, 1.1)

    # inference
    print "create model and optimize ..."
    M = 20
    model = aep.SGPR(X, Y, M, lik='Gaussian')
    model.optimise(method='L-BFGS-B', alpha=0.1, maxiter=50000)
    plot(model)
    plt.show()


def run_banana():

    def gridParams():
        mins = [-3.25, -2.85]
        maxs = [3.65, 3.4]
        nGrid = 50
        xspaced = np.linspace(mins[0], maxs[0], nGrid)
        yspaced = np.linspace(mins[1], maxs[1], nGrid)
        xx, yy = np.meshgrid(xspaced, yspaced)
        Xplot = np.vstack((xx.flatten(), yy.flatten())).T

# 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/context.py



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

        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')
        plt.xlim(-0.1, 1.1)

    # inference
    print "create model and optimize ..."
    M = 20
    model = aep.SGPR(X, Y, M, lik='Gaussian')
    model.optimise(method='L-BFGS-B', alpha=0.1, maxiter=50000)
    plot(model)
    plt.show()


def run_banana():

    def gridParams():
        mins = [-3.25, -2.85]
        maxs = [3.65, 3.4]
        nGrid = 50
        xspaced = np.linspace(mins[0], maxs[0], nGrid)
        yspaced = np.linspace(mins[1], maxs[1], nGrid)
        xx, yy = np.meshgrid(xspaced, yspaced)
        Xplot = np.vstack((xx.flatten(), yy.flatten())).T

        return mins, maxs, xx, yy, Xplot