def main(argv):
input_directory = UNTESTED_DIR
training_file = DEFAULT_TRAINING_FILE
InputMethod = "FFT"
try:
opts, args = getopt.getopt(argv, "hmfi:", ["input="])
except:
print "Error gettiong options"
Usage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
Usage()
sys.exit()
elif opt in ('-m'):
InputMethod = "MEL"
elif opt in ('-f'):
InputMethod = "FFT"
elif opt in ('-i', '--input'):
input_directory = arg
with open('objs.pickle') as f:
clf = pickle.load(f)
if InputMethod == "FFT":
print "Using FFT Training"
X, file_names = blind_read_fft(input_directory)
file_extension = 'fft.npy'
elif InputMethod == "MEL":
print "Using MFCC Training"
X, file_names = MakeMFCC.blind_read_ceps(input_directory)
file_extension = 'ceps.npy'
else:
print "No valid input method"
sys.exit()
y_pred = clf.predict(X)
print y_pred
pass_dir = "/home/colin/Documents/Octave/CatMeow/TestRun/WithCat/"
fail_dir = "/home/colin/Documents/Octave/CatMeow/TestRun/NoCat/"
# TODO: Verify output directories exist
# TODO: Have these be inputs
for fn, y in zip(file_names, y_pred):
wav_file_name = ntpath.basename(fn.replace(file_extension, 'wav'))
print "wav_file_name = " + wav_file_name
original_wav_file = os.path.join(UNTESTED_DIR, wav_file_name)
print "original_wav_file = " + original_wav_file
if y == 0:
out_fn = os.path.join(pass_dir, wav_file_name)
else:
out_fn = os.path.join(fail_dir, wav_file_name)
print "Copying file " + original_wav_file + " to " + out_fn
shutil.copy2(original_wav_file, out_fn)
def main(argv):
input_directory = UNTESTED_DIR
training_file = DEFAULT_TRAINING_FILE
InputMethod = "FFT"
try:
opts, args = getopt.getopt(argv, "hmfi:", ["input="])
except:
print "Error gettiong options"
Usage()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
Usage()
sys.exit()
elif opt in ('-m'):
InputMethod="MEL"
elif opt in ('-f'):
InputMethod="FFT"
elif opt in ('-i', '--input'):
input_directory = arg
with open('objs.pickle') as f:
clf = pickle.load(f)
if InputMethod == "FFT":
print "Using FFT Training"
X, file_names = blind_read_fft(input_directory)
file_extension = 'fft.npy'
elif InputMethod == "MEL":
print "Using MFCC Training"
X, file_names = MakeMFCC.blind_read_ceps(input_directory)
file_extension = 'ceps.npy'
else:
print "No valid input method"
sys.exit()
y_pred = clf.predict(X)
print y_pred
pass_dir = "/home/colin/Documents/Octave/CatMeow/TestRun/WithCat/"
fail_dir = "/home/colin/Documents/Octave/CatMeow/TestRun/NoCat/"
# TODO: Verify output directories exist
# TODO: Have these be inputs
for fn, y in zip(file_names, y_pred):
wav_file_name = ntpath.basename(fn.replace(file_extension, 'wav'))
print "wav_file_name = " + wav_file_name
original_wav_file = os.path.join(UNTESTED_DIR, wav_file_name)
print "original_wav_file = " + original_wav_file
if y == 0:
out_fn = os.path.join(pass_dir, wav_file_name)
else:
out_fn = os.path.join(fail_dir, wav_file_name)
print "Copying file " + original_wav_file + " to " + out_fn
shutil.copy2(original_wav_file, out_fn)
# from .user import HiddenMarkovModel as hmm
import AlphaBeta
import Posteriors as post
import HiddenMarkovModel as hmm
import EMTraining as emt
import MakeMFCC
import sounddevice as sd
import scipy.io.wavfile as wv
fs = 16000
sd.default.samplerate = fs
sd.default.channels = 1
window = 25
hamming_window = MakeMFCC.make_window(25, fs)
utterances = 20
odessa_hmm = hmm.HiddenMarkovModel("Odessa", 10)
lights_on_hmm = hmm.HiddenMarkovModel("Lights On", 20)
lights_off_hmm = hmm.HiddenMarkovModel("Lights off", 20)
play_music_hmm = hmm.HiddenMarkovModel("Play Music", 15)
stop_music_hmm = hmm.HiddenMarkovModel("Stop Music", 20)
time_hmm = hmm.HiddenMarkovModel("Time", 20)
odessa_mfccs = []
lights_on_mfccs = []
lights_off_mfccs = []
play_music_mfccs = []
stop_music_mfccs = []
time_mfccs = []
for i in range(1, utterances + 1):
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)
