本文整理汇总了Python中parsimony.functions.CombinedFunction.add_function方法的典型用法代码示例。如果您正苦于以下问题：Python CombinedFunction.add_function方法的具体用法？Python CombinedFunction.add_function怎么用？Python CombinedFunction.add_function使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类parsimony.functions.CombinedFunction的用法示例。
在下文中一共展示了CombinedFunction.add_function方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_smoothed
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_smoothed(self):
import numpy as np
import parsimony.utils.consts as consts
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions.losses as losses
import parsimony.functions.penalties as penalties
import parsimony.functions.nesterov as nesterov
import parsimony.utils.start_vectors as start_vectors
import parsimony.datasets.simulate.l1_l2_tv as l1_l2_tv
start_vector = start_vectors.RandomStartVector(normalise=True)
np.random.seed(42)
n, p = 75, 100
penalty_start = 0
alpha = 0.9
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
beta = start_vector.get_vector(p)
beta[np.abs(beta) < 0.1] = 0.0
l = 0.618
k = 0.0
g = 0.0
mu_min = 0.001 # consts.TOLERANCE
A = np.eye(p)
A = [A, A, A]
snr = 100.0
X, y, beta_star = l1_l2_tv.load(l, k, g, beta, M, e, A, snr=snr)
function = CombinedFunction()
function.add_function(losses.LinearRegression(X, y,
# penalty_start=penalty_start,
mean=False))
A = nesterov.l1.A_from_variables(p, penalty_start=penalty_start)
function.add_penalty(nesterov.l1.L1(l, A=A, mu=mu_min,
penalty_start=penalty_start))
# function.add_prox(penalties.L1(l, penalty_start=penalty_start))
fista = proximal.FISTA(eps=mu_min, max_iter=20000)
beta = fista.run(function, beta)
assert np.linalg.norm(beta - beta_star) < 5e-2示例2: test_combo_overlapping_nonsmooth
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_combo_overlapping_nonsmooth(self):
import numpy as np
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions as functions
import parsimony.functions.nesterov.gl as gl
import parsimony.datasets.simulate.l1_l2_gl as l1_l2_gl
import parsimony.utils.start_vectors as start_vectors
np.random.seed(42)
# Note that p must be even!
n, p = 25, 30
groups = [range(0, 2 * p / 3), range(p / 3, p)]
weights = [1.5, 0.5]
A = gl.A_from_groups(p, groups=groups, weights=weights)
l = 0.618
k = 1.0 - l
g = 2.718
start_vector = start_vectors.RandomStartVector(normalise=True)
beta = start_vector.get_vector(p)
alpha = 1.0
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
snr = 100.0
X, y, beta_star = l1_l2_gl.load(l, k, g, beta, M, e, A, snr=snr)
eps = 1e-8
max_iter = 10000
beta_start = start_vector.get_vector(p)
mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8]
fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus))
beta_parsimony = beta_start
for mu in mus:
# function = functions.LinearRegressionL1L2GL(X, y, l, k, g,
# A=A, mu=mu,
# penalty_start=0)
function = CombinedFunction()
function.add_function(functions.losses.LinearRegression(X, y,
mean=False))
function.add_penalty(functions.penalties.L2Squared(l=k))
function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu,
penalty_start=0))
function.add_prox(functions.penalties.L1(l=l))
beta_parsimony = fista.run(function, beta_parsimony)
berr = np.linalg.norm(beta_parsimony - beta_star)
# print berr
assert berr < 5e-3
f_parsimony = function.f(beta_parsimony)
f_star = function.f(beta_star)
# print abs(f_parsimony - f_star)
assert abs(f_parsimony - f_star) < 5e-6示例3: test_nonoverlapping_nonsmooth
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_nonoverlapping_nonsmooth(self):
# Spams: http://spams-devel.gforge.inria.fr/doc-python/doc_spams.pdf
import numpy as np
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions as functions
import parsimony.functions.nesterov.gl as gl
import parsimony.datasets.simulate.l1_l2_gl as l1_l2_gl
import parsimony.utils.start_vectors as start_vectors
np.random.seed(42)
# Note that p must be even!
n, p = 25, 20
groups = [range(0, p / 2), range(p / 2, p)]
# weights = [1.5, 0.5]
A = gl.A_from_groups(p, groups=groups) # , weights=weights)
l = 0.0
k = 0.0
g = 1.0
start_vector = start_vectors.RandomStartVector(normalise=True)
beta = start_vector.get_vector(p)
alpha = 1.0
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
snr = 100.0
X, y, beta_star = l1_l2_gl.load(l, k, g, beta, M, e, A, snr=snr)
eps = 1e-8
max_iter = 8500
beta_start = start_vector.get_vector(p)
mus = [5e-2, 5e-4, 5e-6, 5e-8]
fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus))
beta_parsimony = beta_start
for mu in mus:
# function = functions.LinearRegressionL1L2GL(X, y, l, k, g,
# A=A, mu=mu,
# penalty_start=0)
function = CombinedFunction()
function.add_function(functions.losses.LinearRegression(X, y,
mean=False))
function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu,
penalty_start=0))
beta_parsimony = fista.run(function, beta_parsimony)
try:
import spams
params = {"loss": "square",
"regul": "group-lasso-l2",
"groups": np.array([1] * (p / 2) + [2] * (p / 2),
dtype=np.int32),
"lambda1": g,
"max_it": max_iter,
"tol": eps,
"ista": False,
"numThreads": -1,
}
beta_spams, optim_info = \
spams.fistaFlat(Y=np.asfortranarray(y),
X=np.asfortranarray(X),
W0=np.asfortranarray(beta_start),
return_optim_info=True,
**params)
except ImportError:
beta_spams = np.asarray([[14.01111427],
[35.56508563],
[27.38245962],
[22.39716553],
[5.835744940],
[5.841502910],
[2.172209350],
[32.40227785],
[22.48364756],
[26.48822401],
[0.770391500],
[36.28288883],
[31.14118214],
[7.938279340],
[6.800713150],
[6.862914540],
[11.38161678],
[19.63087584],
[16.15855845],
#.........这里部分代码省略.........
示例4: test_nonoverlapping_smooth
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_nonoverlapping_smooth(self):
# Spams: http://spams-devel.gforge.inria.fr/doc-python/doc_spams.pdf
import numpy as np
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions as functions
import parsimony.functions.nesterov.gl as gl
import parsimony.datasets.simulate.l1_l2_glmu as l1_l2_glmu
import parsimony.utils.start_vectors as start_vectors
np.random.seed(42)
# Note that p must be even!
n, p = 25, 20
groups = [range(0, p / 2), range(p / 2, p)]
# weights = [1.5, 0.5]
A = gl.A_from_groups(p, groups=groups) # , weights=weights)
l = 0.0
k = 0.0
g = 0.9
start_vector = start_vectors.RandomStartVector(normalise=True)
beta = start_vector.get_vector(p)
alpha = 1.0
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
snr = 100.0
mu_min = 5e-8
X, y, beta_star = l1_l2_glmu.load(l, k, g, beta, M, e, A,
mu=mu_min, snr=snr)
eps = 1e-8
max_iter = 18000
beta_start = start_vector.get_vector(p)
mus = [5e-0, 5e-2, 5e-4, 5e-6, 5e-8]
fista = proximal.FISTA(eps=eps, max_iter=max_iter / len(mus))
beta_parsimony = beta_start
for mu in mus:
# function = functions.LinearRegressionL1L2GL(X, y, l, k, g,
# A=A, mu=mu,
# penalty_start=0)
function = CombinedFunction()
function.add_function(functions.losses.LinearRegression(X, y,
mean=False))
function.add_penalty(gl.GroupLassoOverlap(l=g, A=A, mu=mu,
penalty_start=0))
beta_parsimony = fista.run(function, beta_parsimony)
try:
import spams
params = {"loss": "square",
"regul": "group-lasso-l2",
"groups": np.array([1] * (p / 2) + [2] * (p / 2),
dtype=np.int32),
"lambda1": g,
"max_it": max_iter,
"tol": eps,
"ista": False,
"numThreads": -1,
}
beta_spams, optim_info = \
spams.fistaFlat(Y=np.asfortranarray(y),
X=np.asfortranarray(X),
W0=np.asfortranarray(beta_start),
return_optim_info=True,
**params)
# print beta_spams
except ImportError:
beta_spams = np.asarray([[15.56784201],
[39.51679274],
[30.42583205],
[24.8816362],
[6.48671072],
[6.48350546],
[2.41477318],
[36.00285723],
[24.98522184],
[29.43128643],
[0.85520539],
[40.31463542],
[34.60084146],
[8.82322513],
[7.55741642],
[7.62364398],
#.........这里部分代码省略.........
示例5: test_nonsmooth
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_nonsmooth(self):
import numpy as np
import parsimony.utils.consts as consts
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions.losses as losses
import parsimony.functions.penalties as penalties
import parsimony.functions.nesterov as nesterov
import parsimony.utils.start_vectors as start_vectors
import parsimony.datasets.simulate.l1_l2_tv as l1_l2_tv
start_vector = start_vectors.RandomStartVector(normalise=True)
np.random.seed(42)
n, p = 75, 100
alpha = 0.9
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
beta_start = start_vector.get_vector(p)
beta_start[np.abs(beta_start) < 0.1] = 0.0
l = 0.618
k = 0.0
g = 0.0
A = np.eye(p)
A = [A, A, A]
snr = 100.0
X, y, beta_star = l1_l2_tv.load(l, k, g, beta_start, M, e, A, snr=snr)
beta = beta_start
for mu in [5e-2, 5e-3, 5e-4, 5e-5]:
function = CombinedFunction()
function.add_function(losses.LinearRegression(X, y, mean=False))
A = nesterov.l1.linear_operator_from_variables(p, penalty_start=0)
function.add_penalty(nesterov.l1.L1(l, A=A, mu=mu,
penalty_start=0))
fista = proximal.FISTA(eps=consts.TOLERANCE, max_iter=910)
beta = fista.run(function, beta)
berr = np.linalg.norm(beta - beta_star)
# print "berr:", berr
assert berr < 5e-2
# Test proximal operator
beta = beta_start
function = CombinedFunction()
function.add_function(losses.LinearRegression(X, y, mean=False))
A = nesterov.l1.linear_operator_from_variables(p, penalty_start=0)
# function.add_penalty(nesterov.l1.L1(l, A=A, mu=mu_min,
# penalty_start=penalty_start))
function.add_prox(nesterov.l1.L1(l, A=A, mu=5e-5,
penalty_start=0))
fista = proximal.FISTA(eps=consts.TOLERANCE, max_iter=800)
beta = fista.run(function, beta)
berr = np.linalg.norm(beta - beta_star)
# print "berr:", berr
assert berr < 5e-0示例6: test_smoothed_l1tv
# 需要导入模块: from parsimony.functions import CombinedFunction [as 别名]
# 或者: from parsimony.functions.CombinedFunction import add_function [as 别名]
def test_smoothed_l1tv(self):
import numpy as np
from parsimony.functions import CombinedFunction
import parsimony.algorithms.proximal as proximal
import parsimony.functions as functions
import parsimony.functions.penalties as penalties
import parsimony.functions.nesterov.tv as tv
import parsimony.functions.nesterov.l1tv as l1tv
import parsimony.datasets.simulate.l1_l2_tvmu as l1_l2_tvmu
import parsimony.utils.start_vectors as start_vectors
import parsimony.datasets.simulate as simulate
np.random.seed(42)
px = 10
py = 1
pz = 1
shape = (pz, py, px)
n, p = 5, np.prod(shape)
l = 0.618
k = 0.01
g = 1.1
start_vector = start_vectors.RandomStartVector(normalise=True)
beta = start_vector.get_vector(p)
alpha = 1.0
Sigma = alpha * np.eye(p, p) \
+ (1.0 - alpha) * np.random.randn(p, p)
mean = np.zeros(p)
M = np.random.multivariate_normal(mean, Sigma, n)
e = np.random.randn(n, 1)
snr = 100.0
mu = 5e-3
A = tv.linear_operator_from_shape(shape)
# X, y, beta_star = l1_l2_tvmu.load(l=l, k=k, g=g, beta=beta, M=M, e=e,
# A=A, mu=mu, snr=snr)
funs = [simulate.grad.L1(l),
simulate.grad.L2Squared(k),
simulate.grad.TotalVariation(g, A)]
lr = simulate.LinearRegressionData(funs, M, e, snr=snr,
intercept=False)
X, y, beta_star = lr.load(beta)
eps = 1e-8
max_iter = 810
alg = proximal.FISTA(eps=eps, max_iter=max_iter)
function = CombinedFunction()
function.add_function(functions.losses.LinearRegression(X, y,
mean=False))
function.add_penalty(penalties.L2Squared(l=k))
A = l1tv.linear_operator_from_shape(shape, p)
function.add_prox(l1tv.L1TV(l, g, A=A, mu=mu, penalty_start=0))
# A = tv.linear_operator_from_shape(shape)
# function.add_penalty(tv.TotalVariation(l=g, A=A, mu=mu,
# penalty_start=0))
# function.add_prox(penalties.L1(l=l))
beta_start = start_vector.get_vector(p)
beta = alg.run(function, beta_start)
berr = np.linalg.norm(beta - beta_star)
# print "berr:", berr
assert berr < 5e-1
f_parsimony = function.f(beta)
f_star = function.f(beta_star)
ferr = abs(f_parsimony - f_star)
# print "ferr:", ferr
assert ferr < 5e-3