def main(argv):
output_file = 'objs.pickle'
InputMethod = "FFT"
if argv < 3:
Usage()
sys.exit(2)
try:
opts, args = getopt.getopt(argv, "hmfp:n:o:", ["positive=","negative=","output="])
except:
Usage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
Usage()
sys.exit()
elif opt in ('-p', '--positive'):
positive_training_directory=arg
elif opt in ('-n', '--negative'):
negative_training_directory=arg
elif opt in ('-o', '--output'):
output_file=arg
elif opt in ('-m'):
InputMethod="MEL"
elif opt in ('-f'):
InputMethod="FFT"
print "Using positive training directory ", positive_training_directory
print "Using negative training directory ", negative_training_directory
# Read the FFT data
meow_list = [positive_training_directory, negative_training_directory]
if InputMethod == "FFT":
print "Using FFT Training"
X, y = read_training_ffts(8000, meow_list)
elif InputMethod == "MEL":
print "Using MFCC Training"
X, y = MakeMFCC.read_ceps(meow_list)
else:
print "No valid input method"
sys.exit()
# Split the data into test and train sets
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=0)
# Perform the regression
clf = LogisticRegression()
clf.fit(X_train, y_train)
# Test for an actual prediction
y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)
name = "TestName"
title = "TestTitle"
#plot_confusion_matrix(cm, meow_list, name, title)
print "Confusion matrix of the data"
print cm
score = clf.score(X_test, y_test)
print "Score"
print score
# If desired, this can be used to test alternate cross validation sets
scores = cross_validation.cross_val_score(clf, X, y, cv=5)
print scores
print "Writing output to ", output_file
with open(output_file, 'w') as f:
pickle.dump(clf, f)
