# 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.pylab as plt
from scipy import special
from .datautils import step, spiral
from .context import vfe
        xx, yy = np.meshgrid(xspaced, yspaced)
        Xplot = np.vstack((xx.flatten(), yy.flatten())).T
        return mins, maxs, xx, yy, Xplot

    def plot(m):
        col1 = '#0172B2'
        col2 = '#CC6600'
        mins, maxs, xx, yy, Xplot = gridParams()
        mf, vf = m.predict_f(Xplot)
        plt.figure()
        plt.plot(
            Xtrain[:, 0][Ytrain[:, 0] == 1],
            Xtrain[:, 1][Ytrain[:, 0] == 1],
            'o', color=col1, mew=0, alpha=0.5)
        plt.plot(
            Xtrain[:, 0][Ytrain[:, 0] == -1],
            Xtrain[:, 1][Ytrain[:, 0] == -1],
            'o', color=col2, mew=0, alpha=0.5)
        zu = m.sgp_layer.zu
        plt.plot(zu[:, 0], zu[:, 1], 'ro', mew=0, ms=4)
        plt.contour(
            xx, yy, mf.reshape(*xx.shape), [0],
            colors='k', linewidths=1.8, zorder=100)

    Xtrain = np.loadtxt(
        './examples/data/banana_X_train.txt', delimiter=',')
    Ytrain = np.loadtxt(
        './examples/data/banana_Y_train.txt', delimiter=',').reshape(-1, 1)
    Ytrain[np.where(Ytrain == 0)[0]] = -1
    M = 50

# 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_vfe_examples.py
import numpy as np
import pdb
import matplotlib.pylab as plt
from scipy import special
from .datautils import step, spiral
from .context import vfe

        xx, yy = np.meshgrid(xspaced, yspaced)
        Xplot = np.vstack((xx.flatten(), yy.flatten())).T
        return mins, maxs, xx, yy, Xplot

    def plot(m):
        col1 = '#0172B2'
        col2 = '#CC6600'
        mins, maxs, xx, yy, Xplot = gridParams()
        mf, vf = m.predict_f(Xplot)
        plt.figure()
        plt.plot(
            Xtrain[:, 0][Ytrain[:, 0] == 1],
            Xtrain[:, 1][Ytrain[:, 0] == 1],
            'o', color=col1, mew=0, alpha=0.5)
        plt.plot(
            Xtrain[:, 0][Ytrain[:, 0] == -1],
            Xtrain[:, 1][Ytrain[:, 0] == -1],
            'o', color=col2, mew=0, alpha=0.5)
        zu = m.sgp_layer.zu
        plt.plot(zu[:, 0], zu[:, 1], 'ro', mew=0, ms=4)
        plt.contour(
            xx, yy, mf.reshape(*xx.shape), [0],
            colors='k', linewidths=1.8, zorder=100)

    Xtrain = np.loadtxt(
        './examples/data/banana_X_train.txt', delimiter=',')
    Ytrain = np.loadtxt(
        './examples/data/banana_Y_train.txt', delimiter=',').reshape(-1, 1)
    Ytrain[np.where(Ytrain == 0)[0]] = -1
    M = 50

    model = vfe.SGPR(Xtrain, Ytrain, M, lik='Probit')