def add(self, x, y, update=True):
assert x.shape[0] == y.shape[0]
assert x.shape[1] == self.d
assert y.shape[1] == 1
x = np.asmatrix(x)
y = np.asmatrix(y)
if x.shape[0] > 0:
self.x = np.concatenate((self.x, x))
self.y = np.concatenate((self.y, y))
if update:
self._update()
def permutation_matrix(dim):
'''
Generate haar permuation matrix.
'''
I = M.eye(dim, dim)
return M.concatenate((I[::2][:],
I[1::2][:]))
def threedize_phi_angles(data, view, cameraposor, laserpos, lasertheta):
"""Call threedize on each element of data. Data is a list of pairs whose first
element is phi and whose second element is an array of x--y pairs. phi is
combined with laserpos and lasertheta to create laserposor."""
per_angles = [threedize(xys, view,
cameraposor,
Posor(laserpos, lasertheta, phi, 0))
for (phi, xys) in data]
return np.concatenate(per_angles)
def grid(self, ngrid=30):
x1 = np.linspace(self.lim['x1'][0], self.lim['x1'][1], ngrid)
x2 = np.linspace(self.lim['x2'][0], self.lim['x2'][1], ngrid)
(x1, x2) = np.meshgrid(x1, x2)
x1r = x1.reshape((-1, 1));
x2r = x2.reshape((-1, 1));
x = np.concatenate((x1r, x2r), 1)
if self.type == 'fun':
y = self.fun.eval(x)
else:
y = self.model.sample(x)
y = y.reshape((ngrid, ngrid))
return (x1, x2, y)
def grid(self, ngrid=30):
x1 = np.linspace(self.lim['x1'][0], self.lim['x1'][1], ngrid)
x2 = np.linspace(self.lim['x2'][0], self.lim['x2'][1], ngrid)
(x1, x2) = np.meshgrid(x1, x2)
x1r = x1.reshape((-1, 1))
x2r = x2.reshape((-1, 1))
x = np.concatenate((x1r, x2r), 1)
if self.type == 'fun':
y = self.fun.eval(x)
else:
y = self.model.sample(x)
y = y.reshape((ngrid, ngrid))
return (x1, x2, y)
def fit(self, X): self.X = X if self.ivalues is None: self._set_ivalues() converged = False iteration = 0 d = self.X.shape[1] while not converged and iteration < self.max_iterations: priors = concatenate([self._prior(component) for component in range(self.components)], axis=1) set_trace() new_priors = np.asarray([np.sum(priors[:,component])/self.X.shape[0] for component in range(self.components)]) new_mus = np.asarray([np.sum(priors[:,[component]] * self.X, axis=0)/np.sum(priors[:,component]) for component in range(self.components)]) new_sigmas = concatenate([sum(np.reshape((np.dot((self.X[[n],:] - self.mus[[component],:]).transpose(), (self.X[[n],:] - self.mus[[component],:]))*priors[n,component]),(d,d,1)) for n in range(X.shape[0])) for component in range(self.components)],axis=2) # converged = np.abs(self._log_likelihood(self.X, new_mus, new_sigmas, new_priors) - self._log_likelihood(self.X, self.mus, self.sigmas, self.priors)) < self.tolerance self.priors = new_priors self.mus = new_mus self.sigmas = new_sigmas iteration+=1
def add(self, x, y, update=True):
x = np.asmatrix(x)
y = np.asmatrix(y)
if x.shape[1] != self.d:
if x.shape[0] == self.d:
x = x.T
else:
raise Exception('Invalid train-set (x) dimension -- '
'expected d = ' + str(self.d) + '.')
if y.shape[1] != 1:
if y.shape[0] == 1:
y = y.T
else:
raise Exception('Invalid train-set (y) dimension -- '
'expected d = 1.')
if y.shape[0] != x.shape[0]:
raise Exception('Incompatible train-set (x, y) lengths.')
x = np.asmatrix(x)
y = np.asmatrix(y)
if x.shape[0] > 0:
self.x = np.concatenate((self.x, x))
self.y = np.concatenate((self.y, y))
if update:
self._update()
def permutation_matrix(dim):
'''
Generate haar permuation matrix.
'''
I = M.eye(dim, dim)
return M.concatenate((I[::2][:], I[1::2][:]))
