def loadSingle( self, filename, inverse = False ):
key = filename + ": inverse=%i" % inverse
if not key in self.images:
data = Data()
image = pygame.image.load( filename ).convert_alpha()
if inverse:
image = pygame.transform.flip( image, True, False )
data.image = image
data.rect = image.get_rect()
data.tightImage = image.subsurface( image.get_bounding_rect() )
data.tightRect = data.tightImage.get_rect()
self.images[key] = data
return key
def scaled( self, key, width, height ):
scaledKey = key + ", width=%i, height=%i" % ( width, height )
try:
return self.images[scaledKey]
except KeyError:
data = Data()
image = pygame.transform.smoothscale( self.images[key].tightImage, (width, height ) )
data.image = image
data.rect = image.get_rect()
data.tightImage = image.subsurface( image.get_bounding_rect() )
data.tightRect = data.tightImage.get_rect()
self.images[scaledKey] = data
return data
def block( self, width, height, color ):
key = "block: width=%i, height=%i" % ( width, height )
key += " (r:%i, g:%i, b:%i, a:%i)" % color
if not key in self.images:
data = Data()
image = pygame.Surface( ( width, height ) ).convert_alpha()
image.fill( color )
data.image = image
data.rect = image.get_rect()
data.tightImage = image.subsurface( image.get_bounding_rect() )
data.tightRect = data.tightImage.get_rect()
self.images[key] = data
return key
def loadMulti( self, filename, count, inverse = False ):
keys = [
filename + ": count=%i, inverse=%i, frame=%i" % ( count, inverse, i)
for i in xrange( count )
]
if not keys[0] in self.images:
full = pygame.image.load( filename ).convert_alpha()
width = full.get_width() / count
height = full.get_height()
indices = range( count )
for i in indices:
data = Data()
image = full.subsurface( ( i * width, 0, width, height ) )
if inverse:
image = pygame.transform.flip( image, True, False )
data.image = image
data.rect = image.get_rect()
data.tightImage = image.subsurface( image.get_bounding_rect() )
data.tightRect = data.tightImage.get_rect()
self.images[keys[i]] = data
return keys
from utility import Data, evaluate
import svm_models
import time
if __name__ == "__main__":
start_time = time.time()
data = Data()
data.lda_transform()
acc = []
for X_train, X_test, y_train, y_test in data.load():
svm = svm_models.SklearnSVC(X_train, y_train)
acc.append(svm.score(X_test, y_test))
end_time = time.time()
evaluate(acc)
print('total time: %.4fs' % (end_time - start_time))
from utility import Data, evaluate
import svm_models
import time
if __name__ == "__main__":
start_time = time.time()
data = Data()
data.pca_transform(0.9)
acc = []
for X_train, X_test, y_train, y_test in data.load():
svm = svm_models.SklearnLinearSVC(X_train, y_train)
acc.append(svm.score(X_test, y_test))
end_time = time.time()
evaluate(acc)
print('total time: %.4fs' % (end_time-start_time))
from utility import Data, evaluate, convert_to_ovr
from svm_models import LinearSVM
import time
if __name__ == "__main__":
start_time = time.time()
data = Data()
acc, correct = [], []
for X_train, X_test, y_train, y_test in data.load():
correct_sum, acc_sum = 0, 0
for z_train, z_test in convert_to_ovr(y_train, y_test):
clf = LinearSVM()
clf.fit(X_train, z_train)
correct_cnt, single_acc = clf.score(X_test, z_test)
correct_sum += correct_cnt
acc_sum += single_acc
acc.append(acc_sum / 10)
correct.append(0.1 * correct_sum / len(y_test))
end_time = time.time()
print('Overall Accuracy:')
evaluate(correct)
print('Average Accuracy:')
evaluate(acc)
print('total time: %.4fs' % (end_time - start_time))