def imtv():
parser = argparse.ArgumentParser(
description=
'Runs the graph-fused lasso (GFL) solver on an image. Note: this is currently pretty slow for even medium-sized color images.'
)
parser.add_argument('imagefile',
help='The file containing the image to denoise.')
parser.add_argument('output', help='The file to output the results.')
parser.add_argument(
'--verbose',
type=int,
default=1,
help=
'The level of print statements. 0=none, 1=moderate, 2=all. Default=0.')
parser.add_argument('--time',
action='store_true',
help='Print the timing stats for the algorithm.')
parser.set_defaults()
args = parser.parse_args()
########### Load data from file
if args.verbose:
print 'Loading image'
from scipy.misc import imsave, imread
from utils import hypercube_edges
y = imread(args.imagefile).astype(float)
y_mean = y.mean()
y -= y_mean
edges = hypercube_edges(y.shape)
if args.verbose:
print 'Solving the GFL for {0} variables with {1} edges'.format(
len(y.flatten()), len(edges))
########### Run the C solver
t0 = time.clock()
beta = solve_gfl(y.flatten(),
edges,
verbose=args.verbose,
converge=1e-3,
maxsteps=3000)
t1 = time.clock()
########### Print the timing stats
if args.time:
print 'Solved the GFL in {0}s and {1} total steps of ADMM.'.format(
t1 - t0,
np.array(solver.steps).sum())
########### Save the results to file
if args.verbose:
print 'Saving results to {0}'.format(args.output)
imsave(args.output, beta.reshape(y.shape) + y_mean)
def imtv():
parser = argparse.ArgumentParser(description='Runs the graph-fused lasso (GFL) solver on an image. Note: this is currently pretty slow for even medium-sized color images.')
parser.add_argument('imagefile', help='The file containing the image to denoise.')
parser.add_argument('output', help='The file to output the results.')
parser.add_argument('--verbose', type=int, default=1, help='The level of print statements. 0=none, 1=moderate, 2=all. Default=0.')
parser.add_argument('--time', action='store_true', help='Print the timing stats for the algorithm.')
parser.set_defaults()
args = parser.parse_args()
########### Load data from file
if args.verbose:
print 'Loading image'
from scipy.misc import imsave, imread
from utils import hypercube_edges
y = imread(args.imagefile).astype(float)
y_mean = y.mean()
y -= y_mean
edges = hypercube_edges(y.shape)
if args.verbose:
print 'Solving the GFL for {0} variables with {1} edges'.format(len(y.flatten()), len(edges))
########### Run the C solver
t0 = time.clock()
beta = solve_gfl(y.flatten(), edges, verbose=args.verbose, converge=1e-3, maxsteps=3000)
t1 = time.clock()
########### Print the timing stats
if args.time:
print 'Solved the GFL in {0}s and {1} total steps of ADMM.'.format(t1 - t0, np.array(solver.steps).sum())
########### Save the results to file
if args.verbose:
print 'Saving results to {0}'.format(args.output)
imsave(args.output, beta.reshape(y.shape) + y_mean)
def main():
parser = argparse.ArgumentParser(description='Runs the graph-fused lasso (GFL) solver on a given dataset with a given edge set. The GFL problem is defined as finding Beta such that it minimizes the equation f(Y, Beta) + lambda * g(E, Beta) where f is a smooth, convex loss function, typically 1/2 sum_i (y_i - Beta_i)^2, and g is sum of first differences on edges, sum_(s,t) |Beta_s - Beta_t| for each edge (s,t) in E.')
parser.add_argument('data', help='The CSV file containing the vector of data points.')
parser.add_argument('edges', help='The CSV file containing the edges connecting the variables, with one edge per line.')
parser.add_argument('--output', '--o', help='The file to output the results.')
parser.add_argument('--verbose', type=int, default=1, help='The level of print statements. 0=none, 1=moderate, 2=all. Default=0.')
parser.add_argument('--time', action='store_true', help='Print the timing stats for the algorithm.')
parser.set_defaults()
args = parser.parse_args()
########### Load data from file
if args.verbose:
print 'Loading data'
y = np.loadtxt(args.data, delimiter=',')
edges = np.loadtxt(args.edges, delimiter=',', dtype=int)
if args.verbose:
print 'Solving the GFL for {0} variables with {1} edges'.format(len(y), len(edges))
########### Run the C solver
t0 = time.clock()
beta = solve_gfl(y, edges, verbose=args.verbose)
t1 = time.clock()
########### Print the timing stats
if args.time:
print 'Solved the GFL in {0}s and {1} total steps of ADMM.'.format(t1 - t0, np.array(solver.steps).sum())
########### Save the results to file
if args.output:
if args.verbose:
print 'Saving results to {0}'.format(args.output)
np.savetxt(args.output, beta, delimiter=',')
else:
print 'Results:'
print beta
def main():
parser = argparse.ArgumentParser(
description=
'Runs the graph-fused lasso (GFL) solver on a given dataset with a given edge set. The GFL problem is defined as finding Beta such that it minimizes the equation f(Y, Beta) + lambda * g(E, Beta) where f is a smooth, convex loss function, typically 1/2 sum_i (y_i - Beta_i)^2, and g is sum of first differences on edges, sum_(s,t) |Beta_s - Beta_t| for each edge (s,t) in E.'
)
parser.add_argument(
'data', help='The CSV file containing the vector of data points.')
parser.add_argument(
'edges',
help=
'The CSV file containing the edges connecting the variables, with one edge per line.'
)
parser.add_argument('--output',
'--o',
help='The file to output the results.')
parser.add_argument(
'--verbose',
type=int,
default=1,
help=
'The level of print statements. 0=none, 1=moderate, 2=all. Default=0.')
parser.add_argument('--time',
action='store_true',
help='Print the timing stats for the algorithm.')
parser.set_defaults()
args = parser.parse_args()
########### Load data from file
if args.verbose:
print 'Loading data'
y = np.loadtxt(args.data, delimiter=',')
edges = np.loadtxt(args.edges, delimiter=',', dtype=int)
if args.verbose:
print 'Solving the GFL for {0} variables with {1} edges'.format(
len(y), len(edges))
########### Run the C solver
t0 = time.clock()
beta = solve_gfl(y, edges, verbose=args.verbose)
t1 = time.clock()
########### Print the timing stats
if args.time:
print 'Solved the GFL in {0}s and {1} total steps of ADMM.'.format(
t1 - t0,
np.array(solver.steps).sum())
########### Save the results to file
if args.output:
if args.verbose:
print 'Saving results to {0}'.format(args.output)
np.savetxt(args.output, beta, delimiter=',')
else:
print 'Results:'
print beta
