class gameboard:
mat = []
X = 0
Y = 0
color = colors.colors()
blocks = Blocks.Blocks()
def __init__(self, x, y, mode=0):
self.mat = Matrix.matrix(x, y, mode)
self.X = x
self.Y = y
self.mat.VERBOSE = False
def getMatrix(self):
# returns the pixel matrix
return self.mat
def setPixel(self, x, y, color):
# sets the pixel at coordinate (x, y) to color
r = color[0]
g = color[1]
b = color[2]
if r >= 0 and g >= 0 and b >= 0 and r < 256 and g < 256 and b < 256:
self.mat.setPixel(x, y, color)
else:
raise ValueError("value(s) out of range")
def setBlock(self, x, y, block):
if x >= 0 and y >= 0 and x < self.mat.Xmax and y < self.mat.Ymax:
if self.mat.VERBOSE:
print("new " + str(block) + ": (" + str(x) + ", " + str(y) +
")")
if block == 'i_block':
object = self.blocks.i_block(x, y)
for px in range(len(object[0])):
self.setPixel(object[0][px][0], object[0][px][1],
object[1])
elif block == 'l_block':
object = self.blocks.l_block(x, y)
for px in range(len(object[0])):
self.setPixel(object[0][px][0], object[0][px][1],
object[1])
elif block == 'z_block':
object = self.blocks.z_block(x, y)
for px in range(len(object[0])):
self.setPixel(object[0][px][0], object[0][px][1],
object[1])
elif block == 'o_block':
object = self.blocks.o_block(x, y)
for px in range(len(object[0])):
self.setPixel(object[0][px][0], object[0][px][1],
object[1])
elif block == 't_block':
object = self.blocks.t_block(x, y)
for px in range(len(object[0])):
self.setPixel(object[0][px][0], object[0][px][1],
object[1])
else:
raise ValueError("'" + str(block) + "' is no block")
else:
raise ValueError("value(s) out of range")
n_trial, n_channel, n_sample = original_signal.shape[:]
# pre-processing data
# get std and mean
if standard_before:
params = io.loadmat(other_path)
mean = params['mean']
std = params['std']
original_signal = (original_signal-mean)/std
epoch_length = int(Fs * window_time_length / 1000.)
perturb_length = math.floor(Fs * perturb_time / 1000.)
# ============================ build model ==============================
processers = [
Blocks.Xdawn(n_filters=4, with_xdawn_templates=True, apply_filters=True),
Blocks.CovarianceFeature(with_mean_templates=False),
Blocks.TangentSpaceFeature(mean_metric='riemann', name='TangentSpace({})'.format('riemann'))
]
classifier = Blocks.LogisticRegression()
model = Pipeline(processers, classifier)
model.load(load_path)
keras_model = model.get_keras_model(input_shape=(n_channel, epoch_length))
# =========================== target attack ==============================
acc_dict = {}
clean_acc_dict = {}
for target_char in target_char_list:
# get stimuli of the target char