def main():
if raw_input("did you check the folder name to extract wavelets from, and create particular folder to save the classifier? : (q for quit)\n") == 'q': return
folder = '246'
confusion = {'DI':[0,0,0,0,0,0,0,0], 'NE':[0,0,0,0,0,0,0,0], 'SU':[0,0,0,0,0,0,0,0], 'AN':[0,0,0,0,0,0,0,0], 'FE':[0,0,0,0,0,0,0,0], 'SA':[0,0,0,0,0,0,0,0], 'HA':[0,0,0,0,0,0,0,0], 'CO':[0,0,0,0,0,0,0,0]}
#classes, waves = gabor.main()
mypath = "./wavelets(roi-"+folder+"-new)/"
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath,f))]
onlyfiles.sort(key = lambda string : string.split(".")[1])
waves = [] # np.empty(len(onlyfiles), dtype=object)
classes = []
for n in xrange(len(onlyfiles)):
path = mypath + onlyfiles[n]
temp = np.load(path)
waves.append(temp['arr_0'][0])
# loaded wave(lets) have a dimension of 1 x 157squared.
#show_pic(waves[n].reshape((157,157)))
#print waves[n], type(waves[n])
classes.append(onlyfiles[n].split(".")[1][:2])
# classes are extracted right off the file names
# X = preprocessing.scale(waves, axis=0, with_mean=True, with_std=True
print "loaded wavelets"
X = preprocessing.normalize(waves, norm="l1", axis=0, copy=True) # norm="l2", axis=1, copy=True default values
# axis = 1, normalize each sample independently. = 0, normalize indivisual features
# return
print "normalizing wavelets - done"
y = [d[i] for i in classes]
#print y
#print waves
train_index, test_index = shuffle(classes)
X_train,y_train,X_test,y_test = [],[],[],[]
for i in train_index:
X_train.append(X[i])
y_train.append(y[i])
for i in test_index:
X_test.append(X[i])
y_test.append(y[i])
print "train and test datasets created"
#return X_train, y_train, X_test, y_test
classifier = OneVsRestClassifier(svm.SVC(kernel="rbf", gamma=0.7, C=1.0))
classifier.fit(X_train, y_train)
joblib.dump(classifier, 'clf'+folder+'/clf.pkl')
print "Expected values of test data"
print y_test
count = 0
j=0
print "Obtained"
for i in X_test:
val = classifier.predict(i.reshape(1,-1))[0]
if (val == y_test[j]):
count = count +1
confusion[d_inv[y_test[j]]][val-1] += 1
else:
confusion[d_inv[y_test[j]]][val-1] += 1
j = j+1
print val,",",
print
print " DI','NE','SU','AN','FE','SA','HA','C0'"
for each in confusion.iteritems():
print each
print "Accuracy"
print float(count)/len(y_test)
import detect
from stratify import shuffle
# from nolearn.lasagne import TrainSplit
from sklearn.externals import joblib
sys.setrecursionlimit(5000)
images, classes, filenames = detect.main()
d = {'DI': 0, 'CO':0, 'NE': 1, 'SU': 2, 'AN': 3, 'FE': 4, 'SA': 5, 'HA': 6}
X = images
y = [d[i] for i in classes]
# Splitting dateset into train and test using stratified sampling
train_index, test_index = shuffle(classes)
X_train, X_test, y_train, y_test = [], [], [], []
for i in train_index:
X_train.append(X[i])
y_train.append(y[i])
for i in test_index:
X_test.append(X[i])
y_test.append(y[i])
# making data theano compatible
X_train = np.array(X_train)
X_test = np.array(X_test)
y_train = np.array(y_train)
y_test = np.array(y_test)
X_train = X_train.reshape((-1, 1, 157, 157))
def main():
if raw_input(
"did you check the folder name to extract wavelets from, and create particular folder to save the classifier? : (q for quit)\n"
) == 'q':
return
folder = '246'
confusion = {
'DI': [0, 0, 0, 0, 0, 0, 0, 0],
'NE': [0, 0, 0, 0, 0, 0, 0, 0],
'SU': [0, 0, 0, 0, 0, 0, 0, 0],
'AN': [0, 0, 0, 0, 0, 0, 0, 0],
'FE': [0, 0, 0, 0, 0, 0, 0, 0],
'SA': [0, 0, 0, 0, 0, 0, 0, 0],
'HA': [0, 0, 0, 0, 0, 0, 0, 0],
'CO': [0, 0, 0, 0, 0, 0, 0, 0]
}
#classes, waves = gabor.main()
mypath = "./wavelets(roi-" + folder + "-new)/"
onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
onlyfiles.sort(key=lambda string: string.split(".")[1])
waves = [] # np.empty(len(onlyfiles), dtype=object)
classes = []
for n in xrange(len(onlyfiles)):
path = mypath + onlyfiles[n]
temp = np.load(path)
waves.append(temp['arr_0'][0])
# loaded wave(lets) have a dimension of 1 x 157squared.
#show_pic(waves[n].reshape((157,157)))
#print waves[n], type(waves[n])
classes.append(onlyfiles[n].split(".")[1][:2])
# classes are extracted right off the file names
# X = preprocessing.scale(waves, axis=0, with_mean=True, with_std=True
print "loaded wavelets"
X = preprocessing.normalize(
waves, norm="l1", axis=0,
copy=True) # norm="l2", axis=1, copy=True default values
# axis = 1, normalize each sample independently. = 0, normalize indivisual features
# return
print "normalizing wavelets - done"
y = [d[i] for i in classes]
#print y
#print waves
train_index, test_index = shuffle(classes)
X_train, y_train, X_test, y_test = [], [], [], []
for i in train_index:
X_train.append(X[i])
y_train.append(y[i])
for i in test_index:
X_test.append(X[i])
y_test.append(y[i])
print "train and test datasets created"
#return X_train, y_train, X_test, y_test
classifier = OneVsRestClassifier(svm.SVC(kernel="rbf", gamma=0.7, C=1.0))
classifier.fit(X_train, y_train)
joblib.dump(classifier, 'clf' + folder + '/clf.pkl')
print "Expected values of test data"
print y_test
count = 0
j = 0
print "Obtained"
for i in X_test:
val = classifier.predict(i.reshape(1, -1))[0]
if (val == y_test[j]):
count = count + 1
confusion[d_inv[y_test[j]]][val - 1] += 1
else:
confusion[d_inv[y_test[j]]][val - 1] += 1
j = j + 1
print val, ",",
print
print " DI','NE','SU','AN','FE','SA','HA','C0'"
for each in confusion.iteritems():
print each
print "Accuracy"
print float(count) / len(y_test)
