def f(self, beta):
"""Function value.
"""
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
return self.l * (maths.norm1(beta_) - self.c)
def feasible(self, beta):
"""Feasibility of the constraint.
From the interface "Constraint".
"""
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
return maths.norm1(beta_) <= self.c
def feasible(self, beta):
"""Feasibility of the constraint.
From the interface "Constraint".
"""
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
return maths.norm1(beta_) <= self.c
def f(self, beta):
"""Function value.
"""
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
if maths.norm(beta_) ** 2.0 > self.l2:
return consts.FLOAT_INF
return self.l1 * maths.norm1(beta_)
def f(self, beta):
""" Function value.
"""
if self.l < consts.TOLERANCE:
return 0.0
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
return self.l * (maths.norm1(beta_) - self.c)
def feasible(self, beta):
"""Feasibility of the constraint.
From the interface "Constraint".
Parameters
----------
beta : Numpy array. The variable to check for feasibility.
"""
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
return maths.norm1(beta_) <= self.c
def f(self, beta):
""" Function value.
"""
if self.l < consts.TOLERANCE and self.g < consts.TOLERANCE:
return 0.0
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
A = self.A()
return maths.norm1(A[0].dot(beta_)) + \
np.sum(np.sqrt(A[1].dot(beta_) ** 2.0 +
A[2].dot(beta_) ** 2.0 +
A[3].dot(beta_) ** 2.0))
def f(self, beta):
""" Function value.
"""
if self.l < consts.TOLERANCE and self.g < consts.TOLERANCE:
return 0.0
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
# lambda and gamma are in A.
A = self.A()
abeta_tv = A[1].dot(beta_) ** 2
for k in range(2, len(A)):
abeta_tv += A[k].dot(beta_) ** 2
return maths.norm1(A[0].dot(beta_)) + np.sum(np.sqrt(abeta_tv))
def f(self, beta):
""" Function value.
"""
if self.l < consts.TOLERANCE and self.g < consts.TOLERANCE:
return 0.0
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
# lambda and gamma are in A.
A = self.A()
abeta_tv = A[1].dot(beta_) ** 2.0
for k in xrange(2, len(A)):
abeta_tv += A[k].dot(beta_) ** 2
return maths.norm1(A[0].dot(beta_)) + np.sum(np.sqrt(abeta_tv))
def _f(self, Xbeta_y, y, beta):
n = y.shape[0]
if self.mean:
d = 2.0 * n
else:
d = 2.0
f = (1.0 / d) * np.sum(Xbeta_y ** 2.0)
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
f += self.l * maths.norm1(beta_)
return f
def _f(self, Xbeta_y, y, beta):
n = y.shape[0]
if self.mean:
d = 2.0 * n
else:
d = 2.0
f = (1.0 / d) * np.sum(Xbeta_y**2.0)
if self.penalty_start > 0:
beta_ = beta[self.penalty_start:, :]
else:
beta_ = beta
f += self.l * maths.norm1(beta_)
return f
def f(self, l):
beta = (abs_beta > l) \
* (self.beta - l * np.sign(self.beta - l))
return maths.norm1(beta) - self.c
def run(self, function, w):
# self.info.clear()
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = []
if self.info_requested(Info.fvalue):
f = []
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
print "len(w):", len(w)
print "max_iter:", self.max_iter
num_iter = [0] * len(w)
for it in xrange(1, self.outer_iter + 1):
all_converged = True
for i in xrange(len(w)):
print "it: %d, i: %d" % (it, i)
if function.has_nesterov_function(i):
print "Block %d has a Nesterov function!" % (i,)
func = mb_losses.MultiblockNesterovFunctionWrapper(
function, w, i)
algorithm = self.conesta
else:
func = mb_losses.MultiblockFunctionWrapper(function, w, i)
algorithm = self.fista
# self.alg_info.clear()
# self.algorithm.set_params(max_iter=self.max_iter - num_iter[i])
# w[i] = self.algorithm.run(func, w_old[i])
if i == 1:
pass
w[i] = algorithm.run(func, w[i])
if algorithm.info_requested(Info.num_iter):
num_iter[i] += algorithm.info_get(Info.num_iter)
if algorithm.info_requested(Info.time):
tval = algorithm.info_get(Info.time)
if algorithm.info_requested(Info.fvalue):
fval = algorithm.info_get(Info.fvalue)
if self.info_requested(Info.time):
t = t + tval
if self.info_requested(Info.fvalue):
f = f + fval
print "l0 :", maths.norm0(w[i]), \
", l1 :", maths.norm1(w[i]), \
", l2²:", maths.norm(w[i]) ** 2.0
print "f:", fval[-1]
for i in xrange(len(w)):
# Take one ISTA step for use in the stopping criterion.
step = function.step(w, i)
w_tilde = function.prox(w[:i] +
[w[i] - step * function.grad(w, i)] +
w[i + 1:], i, step)
# func = mb_losses.MultiblockFunctionWrapper(function, w, i)
# step2 = func.step(w[i])
# w_tilde2 = func.prox(w[i] - step2 * func.grad(w[i]), step2)
#
# print "diff:", maths.norm(w_tilde - w_tilde2)
print "err:", maths.norm(w[i] - w_tilde) * (1.0 / step)
if (1.0 / step) * maths.norm(w[i] - w_tilde) > self.eps:
all_converged = False
break
if all_converged:
print "All converged!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
# # If all blocks have used max_iter iterations, stop.
# if np.all(np.asarray(num_iter) >= self.max_iter):
# break
# it += 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, num_iter)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return w
def run(self, function, w):
if self.info_requested(Info.ok):
self.info_set(Info.ok, False)
if self.info_requested(Info.time):
t = [0.0]
if self.info_requested(Info.fvalue):
f = [function.f(w)]
if self.info_requested(Info.converged):
self.info_set(Info.converged, False)
self.algorithm = FISTA(info=self.fista_info,
eps=self.eps,
max_iter=self.max_iter,
min_iter=self.min_iter)
print "len(w):", len(w)
print "max_iter:", self.algorithm.max_iter
num_iter = [0] * len(w)
# w_old = [0] * len(w)
# it = 0
# while True:
for it in xrange(1, self.outer_iter + 1):
all_converged = True
for i in xrange(len(w)):
print "it: %d, i: %d" % (it, i)
# for j in xrange(len(w)):
# w_old[j] = w[j]
if i == 0:
pass
if i == 1:
pass
if i == 2:
pass
func = mb_losses.MultiblockFunctionWrapper(function, w, i)
# self.fista_info.clear()
# self.algorithm.set_params(max_iter=self.max_iter - num_iter[i])
# w[i] = self.algorithm.run(func, w_old[i])
w[i] = self.algorithm.run(func, w[i])
num_iter[i] += self.algorithm.num_iter
if self.algorithm.info_requested(Info.time):
tval = self.algorithm.info_get(Info.time)
if self.algorithm.info_requested(Info.fvalue):
fval = self.algorithm.info_get(Info.fvalue)
# if Info.converged in self.fista_info:
# if not self.fista_info[Info.converged] \
# or self.fista_info[Info.num_iter] > 1:
# all_converged = False
# if maths.norm(w_old[i] - w[i]) < self.eps:
# converged = True
# break
if self.info_requested(Info.time):
t = t + tval
if self.info_requested(Info.fvalue):
f = f + fval
print "l0 :", maths.norm0(w[i]), \
", l1 :", maths.norm1(w[i]), \
", l2²:", maths.norm(w[i]) ** 2.0
if self.algorithm.info_requested(Info.fvalue):
print "f:", fval[-1]
for i in xrange(len(w)):
# Take one ISTA step for use in the stopping criterion.
step = function.step(w, i)
w_tilde = function.prox(w[:i] +
[w[i] - step * function.grad(w, i)] +
w[i + 1:], i, step)
# func = mb_losses.MultiblockFunctionWrapper(function, w, i)
# step2 = func.step(w[i])
# w_tilde2 = func.prox(w[i] - step2 * func.grad(w[i]), step2)
#
# print "diff:", maths.norm(w_tilde - w_tilde2)
# print "err:", maths.norm(w[i] - w_tilde) * (1.0 / step)
if maths.norm(w[i] - w_tilde) > step * self.eps:
all_converged = False
break
if all_converged:
print "All converged!"
if self.info_requested(Info.converged):
self.info_set(Info.converged, True)
break
# # If all blocks have used max_iter iterations, stop.
# if np.all(np.asarray(num_iter) >= self.max_iter):
# break
# it += 1
if self.info_requested(Info.num_iter):
self.info_set(Info.num_iter, num_iter)
if self.info_requested(Info.time):
self.info_set(Info.time, t)
if self.info_requested(Info.fvalue):
self.info_set(Info.fvalue, f)
if self.info_requested(Info.ok):
self.info_set(Info.ok, True)
return w