x = Normal(2,
mu=np.array([0.1, 0.7]),
sigma=np.array([[0.6, 0.4], [0.4, 0.6]]))
s = x.simulate()
draw2dnormal(x)
pl.scatter(s[:, 0], s[:, 1])
pl.show()
print(s)
if False:
x = Normal(2,
mu=np.array([0.1, 0.7]),
sigma=np.array([[0.6, 0.4], [0.4, 0.6]]))
#draw2dnormal(x,show=True)
print(x)
new = x.condition([0], 0.1)
print(new)
if False:
from randcov import gencov
import numpy.random as npr
import numpy.linalg as la
S = gencov(5)
mu = npr.randn(5)
x = Normal(5, mu=mu, sigma=S)
newx = x.condition([0, 1], np.array([0.1, 0.3]))
print(newx)
x = Normal(2, data=data)
draw2dnormal(x,show=True,axes=pl.gca())
if True:
x = Normal(2,mu = np.array([0.1,0.7]), sigma = np.array([[ 0.6, 0.4], [ 0.4, 0.6]]))
s = x.simulate()
draw2dnormal(x)
pl.scatter(s[:,0],s[:,1])
pl.show()
print s
if False:
x = Normal(2,mu = np.array([0.1,0.7]), sigma = np.array([[ 0.6, 0.4], [ 0.4, 0.6]]))
#draw2dnormal(x,show=True)
print x
new = x.condition([0],0.1)
print new
if False:
from randcov import gencov
import numpy.random as npr
import numpy.linalg as la
S = gencov(5)
mu = npr.randn(5)
x = Normal(5,mu = mu, sigma = S)
newx = x.condition([0,1],np.array([0.1,0.3]))
print newx
