def doCompile():
args = Options.args
if len(args) == 1:
fileList = glob.glob(args[0])
else:
fileList = args
if fileList and (Options.options.closure or Options.options.forceCompile):
#import pdb; pdb.set_trace()
fileList = CompOrder.getCompileCandidates(fileList)
if Options.options.whatif:
for file in fileList:
print "m3 %s" % file
elif fileList:
for file in fileList:
import M3TypeLib
M3TypeLib.flush()
import UnitCache
UnitCache.flush()
# TBD : This is wasteful but it allows us to carry out destructive operations on types (I think)
import Scaling
Scaling.flush()
compile(fileName=file, mainProg=True)
if Message.errors:
sys.exit("Errors during Compilation")
Message.reset()
else:
compile(fileName=args[0], mainProg=True)
if Message.errors:
sys.exit("Errors during Compilation")
def image(self):
if self.val == Uninitialised:
return "???"
if self.tipe.scaling:
return Scaling.image(self.tipe.scaling, self.val)
else:
return "%s" % self.val
def find_action_images(action): path = 'original_images/' retVal = [] retString = [] # copy images from original directory for file_name in os.listdir(path): if is_action_image(file_name, action): im = Image.open(path + file_name) im = Scaling.scale(im, 40, 45) retVal.append(im) retString.append(file_name) return retVal, retString
def Run():
print 'running the inserter'
[width, height] = Template.getTemplateData()
for sprite in custom_utils.get_working_sprites():
sprite.image = finalImage = Scaling.scale(sprite.image, width, height)
sprite.image = finalImage = Bitify.bitify(sprite.image)
SaveToPlayerFile.saveSprite(sprite)
custom_utils.save_working_sprite(sprite)
print 'save to player file'
SaveToPlayerFile.savePlayerFile()
def Run(): print 'running the inserter' [width, height] = Template.getTemplateData() for sprite in custom_utils.get_working_sprites(): sprite.image = finalImage = Scaling.scale(sprite.image, width, height) sprite.image = finalImage = Bitify.bitify(sprite.image) SaveToPlayerFile.saveSprite(sprite) custom_utils.save_working_sprite(sprite) print 'save to player file' SaveToPlayerFile.savePlayerFile()
dataset['ca'] = dataset['ca'].astype(float)
dataset['thal'] = dataset['thal'].astype(float)
dataset['num'] = dataset['num'].apply(func)
target=dataset['num']
del dataset['num']
'''-------------------------Feature Selection-----------------------------'''
# Correlation Matriz
#FeatureSelection.CorrelationMatrizWithHeatMap(dataset, target)
n_features = 9
dataset_select_Uni = FeatureSelection.Univariate_Selection(dataset, target, n_features)
dataset_select = FeatureSelection.Feature_Importance(dataset, target, n_features)
'''-------------------------Divide Dataset into Train and Test-----------------------------'''
data_train, data_test, target_train, target_test = train_test_split(dataset_select, target, test_size=0.2,
random_state=0)
data_train = data_train.reset_index(drop=True)
target_train = target_train.reset_index(drop=True)
'''-------------------------Scaling-----------------------------'''
colunms = list(data_train)
# data_train_scaled, data_test_scaled = Scaling.Scaling_StandardScaler(data_train, data_test, colunms)
data_train_scaled, data_test_scaled = Scaling.Scaling_MinMaxScaler(data_train, data_test, colunms)
#data_train_scaled, data_test_scaled = Scaling.Scaling_RobustScaler(data_train, data_test, colunms)
Scaling.ScalingComparationScaling(data_train,data_train_scaled)
print("intercept:", reg.intercept_)
print("coefficient for previous box office:", reg.coef_[0])
print("-------------------------------------------------------")
plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']
plt.rcParams['axes.unicode_minus'] = False
plt.plot(x, y, 'o', markersize=15, alpha=0.3)
plt.plot(x, reg.intercept_ + reg.coef_ * x, linewidth=5)
plt.xlabel('前場票房')
plt.ylabel('當場票房')
plt.savefig('前場票房的預測')
plt.show()
#-----------------------------------------------------------------------
#feature scaling for x,y
x = Scaling.scale_x(x)
# x = x/1000
y = Scaling.scale_y(y)
# x = pd.DataFrame(preprocessing.scale(x))
# y = pd.DataFrame(preprocessing.scale(y))
print(x)
print(y)
input("continue")
#設定train,test data
split_date = datetime.datetime.strptime("2019-03-23", "%Y-%m-%d")
split_index = analysis_data[analysis_data.DATE == split_date].index.tolist()[0]
print("split_index:", split_index, "\n")
# x_train = x[0:split_index]
# x_test = x[split_index:]
import Quantization
import Scaling
import os
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
mkdir('./Quantization')
mkdir('./Scale')
Quantization.quantize('10.png', 128, './Quantization/10-level-128.png')
Quantization.quantize('10.png', 32, './Quantization/10-level-32.png')
Quantization.quantize('10.png', 8, './Quantization/10-level-8.png')
Quantization.quantize('10.png', 4, './Quantization/10-level-4.png')
Quantization.quantize('10.png', 2, './Quantization/10-level-2.png')
Scaling.scale('10.png', (192, 128), './Scale/down-scale(192x128).png')
Scaling.scale('10.png', (96, 64), './Scale/down-scale(96x64).png')
Scaling.scale('10.png', (48, 32), './Scale/down-scale(48x32).png')
Scaling.scale('10.png', (24, 16), './Scale/down-scale(24x16).png')
Scaling.scale('10.png', (12, 8), './Scale/down-scale(12x8).png')
Scaling.scale('10.png', (300, 200), './Scale/down-scale(300x200).png')
Scaling.scale('10.png', (450, 300), './Scale/up-scale(450x300).png')
Scaling.scale('10.png', (500, 200), './Scale/scale(500x200).png')
