def test1():
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
Y = np.random.standard_normal(500)
Y[100:150] += 7
Y[250:300] += 14
sparsity = l1norm(500, l=1.0)
# Create D
D = (np.identity(500) + np.diag([-1] * 499, k=1))[:-1]
D = sparse.csr_matrix(D)
fused = l1norm(D, l=19.5)
p = container(loss, sparsity, fused)
soln1 = blockwise(pen, Y)
solver = FISTA(p.problem())
solver.fit(max_its=800, tol=1e-10)
soln2 = solver.problem.coefs
# plot solution
pylab.figure(num=1)
pylab.clf()
pylab.scatter(np.arange(Y.shape[0]), Y, c="r")
pylab.plot(soln1, c="y", linewidth=6)
pylab.plot(soln2, c="b", linewidth=2)
def test1():
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 quadratic
Y = np.random.standard_normal(500); Y[100:150] += 7; Y[250:300] += 14
sparsity = l1norm(500, lagrange=1.0)
#Create D
D = (np.identity(500) + np.diag([-1]*499,k=1))[:-1]
D = sparse.csr_matrix(D)
fused = l1norm.linear(D, lagrange=19.5)
loss = quadratic.shift(-Y, lagrange=0.5)
p = container(loss, sparsity, fused)
soln1 = blockwise([sparsity, fused], Y)
solver = FISTA(p)
solver.fit(max_its=800,tol=1e-10)
soln2 = solver.composite.coefs
#plot solution
pylab.figure(num=1)
pylab.clf()
pylab.scatter(np.arange(Y.shape[0]), Y, c='r')
pylab.plot(soln1, c='y', linewidth=6)
pylab.plot(soln2, c='b', linewidth=2)
def test1():
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
Y = np.random.standard_normal(500)
Y[100:150] += 7
Y[250:300] += 14
sparsity = l1norm(500, l=1.0)
#Create D
D = (np.identity(500) + np.diag([-1] * 499, k=1))[:-1]
D = sparse.csr_matrix(D)
fused = l1norm(D, l=19.5)
p = container(loss, sparsity, fused)
soln1 = blockwise(pen, Y)
solver = FISTA(p.problem())
solver.fit(max_its=800, tol=1e-10)
soln2 = solver.problem.coefs
#plot solution
pylab.figure(num=1)
pylab.clf()
pylab.scatter(np.arange(Y.shape[0]), Y, c='r')
pylab.plot(soln1, c='y', linewidth=6)
pylab.plot(soln2, c='b', linewidth=2)
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())
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())
from container import *
try:
ifst = open("input.txt").read().split("\n")
except:
exit()
ofst = open("output.txt", "w")
ofst_filtr = open("output1.txt", "w")
print("Start.")
c = container()
InData(c.matrices, ifst)
ofst.write("Filled container.\n")
Sort(c.matrices)
OutData(c.matrices, ofst)
OutDataFiltr(c.matrices, ofst_filtr)
Clear(c.matrices)
ofst.write("Empty container.\n")
OutData(c.matrices, ofst)
print("Stop")
ofst.close()
def banana_verify(source_word, goal_word, container, list_of_moves):
'''(str, str, Container, list) -> bool
REQ: len(source_word) > 0
REQ: len(goal_word) > 0
REQ: list_of_moves must have only 'M','G','P'
REQ: len(list_of_moves) >= 0
>>> banana_verify("BANANA", "AAANNB", stack(),
["P","M","P","M","P","M","G","G","G"])
True
>>> banana_verify("BANANA", "AAANNBATMAN", stack(), ["P", "G"])
False
>>> banana_verify("BANANA","AAABBN", bucket(), ["M", "G"])
False
>>> banana_verify("BANANA","NABANA", queue(),
['P','P','M','M','G','G','M','M'])
True
Returns True iff the word created after applying list_of_moves given
is the same as goal_word. If the word created is not the same as the
goal_word, then return False.
'''
# this str will turn into a word, as moves are applies to it
formed_word = str()
gg = container()
# if an exception error is raised, then return false
try:
# for each word in the list of moves
for moves in list_of_moves:
# if moves are equal to 'P'
if moves == 'P':
# store the first letter from the word, which is to be Put
# into container
stored_letter = source_word[0]
# make the source word equal to everything except the
# first letter
source_word = source_word[1:]
# put the letter into container
gg.put(stored_letter)
# if the move is 'M', then
elif moves == 'M':
# store first letter from source_word
word_to_move = source_word[0]
# Remove the first letter from source_word
source_word = source_word[1:]
# add the letter at the end of formed_word
formed_word += word_to_move
# if move is 'G', which is Get, then get next word from container
elif moves == 'G':
# get word from container and store it
get_word = gg.get()
# add that word to end of formed_word
formed_word += get_word
# if the formed_word is the same as goal_word, then return True
if formed_word == goal_word:
return True
else:
return False
# if an error occurs, make sure code does not crash, instead returns False
except:
return False