def init_W(self, mode='normal', decomposition='ova'):
self.W = {}
if (self.status != 'load_train_data') and (self.status != 'train'):
print("Please load train data first.")
return self.W
self.status = 'init'
self.data_num = len(self.train_Y)
self.data_demension = len(self.train_X[0])
self.decomposition = decomposition
if self.decomposition == 'ovo':
self.class_list = list(
itertools.combinations(np.unique(self.train_Y), 2))
elif self.decomposition == 'ova':
self.class_list = np.unique(self.train_Y)
for class_item in self.class_list:
self.W[class_item] = np.zeros(self.data_demension)
if mode == 'linear_regression_accelerator':
accelerator = linear_regression.Accelerator()
for class_item in self.class_list:
if self.decomposition == 'ovo':
modify_X, modify_Y = utility.DatasetLoader.modify_XY(
self.train_X, self.train_Y, class_item)
self.temp_train_X = self.train_X
self.temp_train_Y = self.train_Y
self.train_X = modify_X
self.train_Y = modify_Y
self.temp_data_num = self.data_num
self.data_num = len(self.train_Y)
self.temp_W = self.W
self.W = self.temp_W[class_item]
self.temp_W[class_item] = accelerator.init_W(self)
self.train_X = self.temp_train_X
self.train_Y = self.temp_train_Y
self.temp_train_X = []
self.temp_train_Y = []
self.data_num = self.temp_data_num
self.temp_data_num = 0
self.W = self.temp_W
self.temp_W = {}
elif self.decomposition == 'ova':
modify_Y = utility.DatasetLoader.modify_Y(
self.train_Y, class_item)
self.temp_train_Y = self.train_Y
self.train_Y = modify_Y
self.temp_W = self.W
self.W = self.temp_W[class_item]
self.temp_W[class_item] = accelerator.init_W(self)
self.train_Y = self.temp_train_Y
self.temp_train_Y = []
self.W = self.temp_W
self.temp_W = {}
return self.W
def init_W(self, mode='normal'):
'''
Init the W
Simple way is init W all zeros
'''
if (self.status != 'load_train_data') and (self.status != 'train'):
print("Please load train data first.")
return self.W
self.status = 'init'
self.data_num = len(self.train_Y)
self.data_demension = len(self.train_X[0])
self.W = np.zeros(self.data_demension)
if mode == 'linear_regression_accelerator':
accelerator = linear_regression.Accelerator()
self.W = accelerator.init_W(self)
return self.W