fp = open("faithful.txt")
data = []
for line in fp.readlines():
x, y = line.split()
data.append([float(x), float(y)])
data = npa(data)
pl.scatter(data[:, 0], data[:, 1])
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:
if False:
fp = open("faithful.txt")
data = []
for line in fp.readlines():
x,y = line.split()
data.append([float(x),float(y)])
data = npa(data)
pl.scatter(data[:,0],data[:,1])
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
