def _alternate_descent(self):
n, d = self.X.shape
self.centers = cluster._init_centers(self.centers, self.X,
self.n_clusters)
self.memberships = cluster._init_memberships(self.memberships,
self.centers, self.X,
self.n_clusters)
self.weights = cluster._init_weights(self.weights, self.X,
self.n_clusters)
self.memberships, self.centers = super()._alternate_descent()
for niter in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(self.centers, self.memberships, self.weights)
cst = np.min(
1 / self._memberships_hessian(self.memberships, self.centers))
print("Cst is {}".format(cst))
#print("Cst is now {}".format(cst))
new_memberships = _pfscm_prox_gradient(
self.memberships,
self._memberships_gradient,
_prox_op,
cst,
prox_arg=cst * self.memberships_gamma,
max_iter=10,
tol=self.tol,
grad_args=(self.X, self.centers))
new_centers = self._update_centers(self.X, self.n_clusters,
self.weights, new_memberships)
gain = np.linalg.norm(new_centers - self.centers)
if gain < self.tol:
break
self.centers = new_centers
self.memberships = new_memberships
return self.memberships, self.centers
def weighted_fcm(x,
n_clusters,
max_iter=300,
tol=1e-4,
centers=None,
memberships=None,
weights=None,
**kwargs):
n, d = x.shape
centers = cluster._init_centers(centers, x, n_clusters, **kwargs)
memberships = cluster._init_memberships(memberships, centers, x,
n_clusters, **kwargs)
weights = _init_weights(weights, x, n_clusters)
for i in range(max_iter):
new_memberships = cluster._update_memberships(x, n_clusters, weights,
centers, **kwargs)
new_centers = cluster._update_centers(x, n_clusters, weights,
new_memberships, **kwargs)
new_weights = _new_update_weights(x, n_clusters, new_memberships,
new_centers, **kwargs)
if np.linalg.norm(new_centers - centers) < tol:
break
memberships = new_memberships
centers = new_centers
weights = new_weights
return (memberships, centers, weights)
def _alternate_descent(self):
n, d = self.X.shape
self._init_weights()
centers = cluster._init_centers(self.centers, self.X, self.n_clusters)
memberships = cluster._init_memberships(self.memberships, centers,
self.X, self.n_clusters)
for i in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, self.weights)
new_memberships = self._update_memberships(self.X, self.weights,
centers)
new_centers = self._update_centers(self.X, self.weights,
new_memberships)
# print(new_memberships)
# print("centers")
# print(new_centers)
# print("weights")
# print(self.weights)
# print(self._compute_inertia(self.X, centers, memberships,
# self.weights, _mode="debug"))
new_weights = self._update_weights(self.X, new_memberships,
new_centers, self.weights)
# print(self._compute_inertia(self.X, centers, memberships,
# new_weights, _mode="debug"))
if np.linalg.norm(new_centers - centers) < self.tol:
break
memberships = new_memberships
centers = new_centers
self.weights = new_weights
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, self.weights)
return memberships, centers
def laplacian_weighted_fcm(x,
n_clusters,
weights,
affinity,
gamma=1.,
max_iter=300,
tol=1e-4,
centers=None,
memberships=None,
**kwargs):
n, d = x.shape
centers = cluster._init_centers(centers, x, n_clusters, **kwargs)
memberships = cluster._init_memberships(memberships, centers, x,
n_clusters, **kwargs)
for i in range(max_iter):
new_memberships = _new_laplacian_memberships(x,
n_clusters,
weights,
centers,
affinity,
memberships,
gamma=gamma,
**kwargs)
new_centers = cluster._update_centers(x, n_clusters, weights,
new_memberships, **kwargs)
if np.linalg.norm(new_centers - centers) + np.linalg.norm(
new_memberships - memberships) < tol:
break
memberships = new_memberships
centers = new_centers
return memberships, centers
def _alternate_descent(self):
n, d = self.X.shape
self.centers = cluster._init_centers(self.centers, self.X,
self.n_clusters)
self.memberships = cluster._init_memberships(self.memberships,
self.centers, self.X,
self.n_clusters)
self.weights = cluster._init_weights(self.weights, self.X,
self.n_clusters)
fcm_init = cluster.FCMeans(self.n_clusters)
fcm_init.fit(self.X)
# self.memberships, self.centers = fcm_init.memberships, fcm_init.centers
for i in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(self.centers, self.memberships, self.weights)
new_memberships = self._update_memberships(self.X, self.n_clusters,
self.weights,
self.centers,
self.memberships)
new_centers = self._update_centers(self.X, self.n_clusters,
self.weights, new_memberships)
new_weights = self._update_weights(self.X, self.n_clusters,
new_memberships, new_centers)
if np.linalg.norm(new_centers - self.centers) < self.tol:
break
self.memberships = new_memberships
self.centers = new_centers
self.weights = new_weights
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(self.centers, self.memberships, self.weights)
self._fcm_init = fcm_init
return self.memberships, self.centers, self.weights
def _alternate_descent(self):
n, d = self.X.shape
self._init_weights()
n_clusters = self.n_clusters
centers = initialization.max_min_method(self.X, n_clusters)
memberships = cluster._init_memberships(self.memberships, centers,
self.X, n_clusters)
eta = _init_eta(self.X, centers, self.beta)
weights = self.weights
for i in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, weights)
new_memberships = self._update_memberships(self.X, n_clusters,
weights, centers, eta)
new_centers = self._update_centers(self.X, n_clusters, weights,
new_memberships)
if np.linalg.norm(new_centers - centers) < self.tol:
break
n_clusters, new_memberships, new_centers, new_weights = self._update_clusters(
self.X, new_memberships, new_centers, weights)
eta = self._update_eta(self.X, new_memberships, new_centers)
memberships = new_memberships
centers = new_centers
weights = new_weights
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, self.weights)
self.eta = eta
return memberships, centers
def _alternate_descent(self):
n, d = self.X.shape
self.centers = cluster._init_centers(self.centers, self.X,
self.n_clusters)
self.memberships = cluster._init_memberships(self.memberships,
self.centers, self.X,
self.n_clusters)
self.weights = cluster._init_weights(self.weights, self.X,
self.n_clusters)
self.memberships, self.centers = cluster.FCMeans._alternate_descent(
self)
for niter in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(self.centers, self.memberships, self.weights)
memberships_cst = np.min(1 / self._memberships_hessian(
self.memberships, self.centers, self.weights))
new_memberships = _pfscm_prox_gradient(
self.memberships,
self._memberships_gradient,
_prox_op,
memberships_cst,
prox_arg=memberships_cst * self.memberships_gamma,
max_iter=10,
tol=self.tol,
grad_args=(self.X, self.centers, self.weights))
#print(np.sum(new_memberships))
new_centers = self._update_centers(self.X, self.n_clusters,
self.weights, new_memberships)
weights_cst = np.min(1 / self._weights_hessian(
new_memberships, new_centers, n, d, self.n_clusters))
new_weights = _pfscm_prox_gradient(
self.weights,
self._weights_gradient,
_sparsity_prox_op,
weights_cst,
prox_arg=weights_cst * self.weights_gamma,
max_iter=10,
tol=self.tol,
grad_args=(self.X, new_centers, new_memberships))
gain = np.linalg.norm(new_centers - self.centers)
#print(self.centers)
#print(gain)
if gain < self.tol:
break
self.weights = new_weights
self.centers = new_centers
self.memberships = new_memberships
return self.memberships, self.centers, self.weights
def _alternate_descent(self):
n, d = self.X.shape
centers = cluster._init_centers(self.centers, self.X, self.n_clusters)
memberships = cluster._init_memberships(self.memberships, centers,
self.X, self.n_clusters)
weights = cluster._init_weights(self.weights, self.X, self.n_clusters)
for i in range(self.max_iter):
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, weights)
new_memberships = self._update_memberships(self.X, self.n_clusters,
weights, centers)
new_centers = self._update_centers(self.X, self.n_clusters,
weights, new_memberships)
new_weights = self._update_weights(self.X, self.n_clusters,
new_memberships, new_centers)
if np.linalg.norm(new_centers - centers) < self.tol:
break
memberships = new_memberships
centers = new_centers
weights = new_weights
if self.verbose & Verbosity.COST_FUNCTION:
self._log_cost(centers, memberships, weights)
return memberships, centers, weights
def _init_A(self):
memberships = cluster._init_memberships(None, None, self.X,
self.n_archetypes)
return memberships.T # different conventions between NMF and FCM
def _init_W(self):
if self.W is None:
memberships = cluster._init_memberships(None, self.H, self.X,
self.n_components)
self.W = memberships.T
return self.W