def sample(self, N):
"""Sample N samples from the model. If N, n are both specified,
then generate N samples, but only keep n of them"""
N = int(N)
# Get a random permutation of N elements
perm = permutation(N)
# Sample the number of samples from each view
cnts = multinomial(N, self.weights)
# Generate Xs
xs = multivariate_normal(self.mean, self.sigma, N)
ys = zeros((N, ))
cnt_ = 0
for i in xrange(self.k):
cnt = cnts[i]
# Generate a bunch of points for each mean
beta = self.betas.T[i]
perm_ = perm[cnt_:cnt_ + cnt]
ys[perm_] = xs[perm_].dot(beta)
cnt_ += cnt
return xs, ys
def sample(self, N, n = -1):
"""Sample N samples from the model. If N, n are both specified,
then generate N samples, but only keep n of them"""
if n <= 0:
n = N
shape = (n, self.d)
#X = self._allocate_samples("X", shape)
X = zeros(shape)
# Get a random permutation of N elements
perm = permutation(N)
# Sample the number of samples from each view
cnts = multinomial(N, self.weights)
cnt_ = 0
for i in xrange(self.k):
cnt = cnts[i]
# Generate a bunch of points for each mean
mean, sigma = self.means.T[ i ], self.sigmas[ i ]
perm_ = perm[cnt_ : cnt_ + cnt]
X[perm_] = multivariate_normal(mean, sigma, cnt)
cnt_ += cnt
#X.flush()
return X
def sample(self, N):
"""Sample N samples from the model. If N, n are both specified,
then generate N samples, but only keep n of them"""
N = int(N)
# Get a random permutation of N elements
perm = permutation(N)
# Sample the number of samples from each view
cnts = multinomial(N, self.weights)
# Generate Xs
xs = multivariate_normal(self.mean, self.sigma, N)
ys = zeros((N,))
cnt_ = 0
for i in xrange(self.k):
cnt = cnts[i]
# Generate a bunch of points for each mean
beta = self.betas.T[i]
perm_ = perm[cnt_ : cnt_ + cnt]
ys[perm_] = xs[perm_].dot(beta)
cnt_ += cnt
return xs, ys
def sample(self, N):
"""Sample N samples from the model. If N, n are both specified,
then generate N samples, but only keep n of them"""
shape = (N, self.d, 3)
X = zeros(shape)
# Get a random permutation of N elements
perm = permutation(N)
# Sample the number of samples from each view
cnts = multinomial(N, self.weights)
cnt_ = 0
for i in xrange(self.k):
cnt = cnts[i]
# Generate a bunch of points for each mean
mean = self.means.T[i]
perm_ = perm[cnt_ : cnt_ + cnt]
X[perm_,:,0] = multinomial(1, mean, size=int(cnt))
X[perm_,:,1] = multinomial(1, mean, size=int(cnt))
X[perm_,:,2] = multinomial(1, mean, size=int(cnt))
cnt_ += cnt
return X
