if (len(sys.argv) != 5):
print "Usage: " + sys.argv[0] + " <inputfile> <tags> <n> <outputfile>"
exit(0)
inputfile = sys.argv[1]
if (sys.argv[2] == "all"):
t = read_tags()
tags = t.all_tags
else:
tags = sys.argv[2].split(",")
print tags
n = int(sys.argv[3])
outputfile = sys.argv[4]
columnNames = load_column_names(inputfile)
tagsIdx = map(lambda x: columnNames.index(x), tags)
print tagsIdx
print "Opening files"
f = open(inputfile, "r")
nOther = 0
nPositive = []
for i in range(len(tags)):
nPositive.append(0)
outf = open(outputfile, "w")
print "Processing"
lines = f.readlines()
nLines = len(lines)-1
def main():
# Create dataset
inputs = ["jrms", "rms", "mean_10", "mean_100", "max_10", "max_100", "stddev_10", "stddev_100"]
# inputs = ["max_100", "rms"]
# inputs = ["max_100", "mean_100"]
# inputs = ["rms"]
mode = "train"
if (len(sys.argv) >= 2):
if (sys.argv[1] == "test"):
mode = "test"
print "Create dataset"
data = load_data("out.txt")
columnNames = load_column_names("out.txt")
ds = create_data_set(data, columnNames, inputs, "jumping")
targets = ds['target']
#print "sum of targest: " + str(targets.sum())
# Create network
if (mode == "train"):
print "Build network"
if (len(sys.argv) >= 3):
nEpochs = int(sys.argv[2])
else:
nEpochs = 1
net = buildNetwork(len(inputs), N_HIDDEN, 1, bias=True, hiddenclass=TanhLayer)
trainer = BackpropTrainer(net, ds, verbose = True)
#net = buildNetwork(len(inputs), N_HIDDEN, 1, bias=True, hiddenclass=TanhLayer)
print "Train"
print "Base error: " + str(trainer.testOnData())
results = testOnClassData(trainer)
print "Percentage error: " + str(relativePercentError( results, targets )) + "%"
print "Training started"
trainer.trainEpochs(nEpochs)
print "Training done: Saving model"
NetworkWriter.writeToFile(net, "model.xml")
else:
net = NetworkReader.readFrom("model.xml")
trainer = BackpropTrainer(net, ds, verbose = True)
#print "Final error: " + str(trainer.testOnData())
results = testOnClassData(trainer)
print "Percentage error (final): " + str(relativePercentError( results, targets )) + "%"
# Plot
# Data
frames = data[:,1]
nplots = 3
subplot(nplots, 1, 1)
dataType = inputs[0]
title("Data (" + dataType + ")")
setp(plot(frames, ds['input'][:,0], color="black", marker=".", linestyle='None'))
xlabel("frame")
ylabel(dataType)
# nplots = len(inputs) + 2
# for i in range(0, len(inputs)):
# subplot(nplots, 1, i+1)
# dataType = inputs[i]
# title("Data (" + dataType + ")")
# setp(plot(frames, ds['input'][:,i], color="black", marker=".", linestyle='None'))
# xlabel("frame")
# ylabel(inputs[i])
# Correct classification (target)
subplot(nplots, 1, nplots-1)
title("Target classification")
targets = targets[:,0]
setp(plot(frames, targets, color="blue"))
# Classification (from NN)
subplot(nplots, 1, nplots)
title("NN classification and errors")
setp(plot(frames, results), color="blue")
errors = [[],[]]
for i in range(len(results)):
if (results[i] != targets[i]):
errors[0].append(frames[i])
errors[1].append(0.5)
setp(plot(errors[0], errors[1]), color="red", marker='.', linestyle='None', alpha=0.5)
savefig("figure.png")
if (len(sys.argv) != 5):
print "Usage: " + sys.argv[0] + " <inputfile> <tags> <n> <outputfile>"
exit(0)
inputfile = sys.argv[1]
if (sys.argv[2] == "all"):
t = read_tags()
tags = t.all_tags
else:
tags = sys.argv[2].split(",")
print tags
n = int(sys.argv[3])
outputfile = sys.argv[4]
columnNames = load_column_names(inputfile)
tagsIdx = map(lambda x: columnNames.index(x), tags)
print tagsIdx
print "Opening files"
f = open(inputfile, "r")
nOther = 0
nPositive = []
for i in range(len(tags)):
nPositive.append(0)
outf = open(outputfile, "w")
print "Processing"
lines = f.readlines()
nLines = len(lines) - 1
