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#!/usr/bin/python2.6

from numpy import *
from optparse import OptionParser
from mlpy import *
  
# Command line parsing
parser = OptionParser()
parser.add_option("-d", "--data", metavar = "FILE", action = "store", type = "string",
                  dest = "data", help = "data - required")
parser.add_option("-t", "--theta", action = "store", type = "float",
                  dest = "theta", help = "theta (default 0.001)", default=0.001)
parser.add_option("-s", "--standardize", action = "store_true", default = False,
                  dest = "stand", help = "standardize data")
parser.add_option("-n", "--normalize", action = "store_true", default = False,
                  dest = "norm", help = "normalize data")
parser.add_option("-m", "--min", action = "store", type = "float",
                  dest = "min", help = "min value (default -5)", default = -5)
parser.add_option("-M", "--max", action = "store", type = "float",
                  dest = "max", help = "max value (default 5)", default = 5)
parser.add_option("-p", "--steps", action = "store", type = "int",
                  dest = "steps", help = "steps (default 11)", default = 11)
parser.add_option("-e", "--scale", action = "store", type = "string",
                  dest = "scale",  help = "scale (lin or log, default log)", default = "log")


(options, args) = parser.parse_args()
if not options.data:
    parser.error("option -d (data) is required")
if not options.scale in ["lin", "log"]:
    parser.error("option -e (scale) should be 'lin' or 'log'")
    

# Sigma values, max loops
if options.scale == 'lin':
    sigma = linspace(options.min, options.max, options.steps)
elif options.scale == 'log':
    sigma = logspace(options.min, options.max, options.steps)
T     = 20

# Data
x, y = data_fromfile(options.data)
if options.stand:
    x = data_standardize(x)
if options.norm:
    x = data_normalize(x)

print "samples:", x.shape[0]
print "features:", x.shape[1]
    
# Try sigmas
print "theta %f" % options.theta
for s in sigma:
    ir = Irelief(T = T, sigma = s, theta = options.theta)
    try:
        ir.weights(x, y)
    except SigmaError, e:
        print "sigma %e: %s" % (s, e)
    else:
        if T == ir.loops:
            print "sigma %e: more than %d loop(s)" % (s, ir.loops)
        else:
            print "sigma %e: %d loop(s)" % (s, ir.loops)