def test2():
import numpy as np
import pylab
from scipy import sparse
from regreg.algorithms import FISTA
from regreg.atoms import l1norm
from regreg.container import container
from regreg.smooth import signal_approximator, smooth_function
n1, n2 = l1norm(1), l1norm(1)
Y = np.array([30.])
l = signal_approximator(Y)
p = container(l, n1, n2)
blockwise(s, Y, p.problem())
def test2():
import numpy as np
import pylab
from scipy import sparse
from regreg.algorithms import FISTA
from regreg.atoms import l1norm
from regreg.container import container
from regreg.smooth import signal_approximator, smooth_function
n1, n2 = l1norm(1), l1norm(1)
Y = np.array([30.0])
l = signal_approximator(Y)
p = container(l, n1, n2)
blockwise(s, Y, p.problem())
import numpy as np import pylab from scipy import sparse from regreg.algorithms import FISTA from regreg.atoms import nonnegative from regreg.container import container from regreg.smooth import signal_approximator, smooth_function n = 100 Y = np.random.standard_normal(n) Y[:-30] += np.arange(n - 30) * 0.2 D = (np.identity(n) - np.diag(np.ones(n - 1), -1))[1:] isotonic = nonnegative.linear(sparse.csr_matrix(D)) loss = signal_approximator(Y) p = container(loss, isotonic) solver = FISTA(p.problem(initial=np.zeros(n))) solver.debug = True vals = solver.fit(max_its=25000, tol=1e-08, backtrack=True) soln = solver.problem.coefs X = np.arange(n) pylab.clf() pylab.scatter(X, Y) pylab.step(X, soln, 'r--')
import numpy as np
import pylab
from scipy import sparse
from regreg.algorithms import FISTA
from regreg.atoms import nonnegative
from regreg.container import container
from regreg.smooth import signal_approximator, smooth_function
n = 100
Y = np.random.standard_normal(n)
Y[:-30] += np.arange(n-30) * 0.2
D = (np.identity(n) - np.diag(np.ones(n-1),-1))[1:]
isotonic = nonnegative.linear(sparse.csr_matrix(D))
loss = signal_approximator(Y)
p = container(loss, isotonic)
solver=FISTA(p.problem(initial=np.zeros(n)))
solver.debug=True
vals = solver.fit(max_its=25000, tol=1e-08, backtrack=True)
soln = solver.problem.coefs
X = np.arange(n)
pylab.clf()
pylab.scatter(X, Y)
pylab.step(X, soln, 'r--')
