def __init__(self, rewards=default_rewards):
#dummy initial values
#fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data", np.zeros([1, 1]),
'uint8')
self.categories = create_shared("categories_data", np.zeros([1, 1]),
'uint8')
self.batch_size = self.attributes.shape[0]
#"end_session_now" action
end_action = T.zeros([self.batch_size, 1], dtype='uint8')
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([
self.attributes,
self.categories,
end_action,
],
axis=1).astype(theano.config.floatX)
#indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1] + self.categories.shape[1])
#last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1] - 1
self.rw = rewards
def __init__(self,batch_size = 10):
n_attrs = 3
n_categories = 2
#fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data",np.zeros([batch_size,n_attrs]),'uint8')
self.categories = create_shared("categories_data",np.zeros([batch_size,n_categories]),'uint8')
self.batch_size = self.attributes.shape[0]
#"end_session_now" action
end_action = T.zeros([self.batch_size,1], dtype='uint8')
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([self.attributes,
self.categories,
end_action,
],axis=1).astype(theano.config.floatX)
#indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1]+self.categories.shape[1]
)
#last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1]-1
#fill in one data sample
self.generate_new_data_batch(batch_size)
def __init__(self,rewards = default_rewards):
#dummy initial values
#fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data",np.zeros([1,1]),'uint8')
self.categories = create_shared("categories_data",np.zeros([1,1]),'uint8')
self.batch_size = self.attributes.shape[0]
#"end_session_now" action
end_action = T.zeros([self.batch_size,1], dtype='uint8')
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([self.attributes,
self.categories,
end_action,
],axis=1).astype(theano.config.floatX)
#indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1]+self.categories.shape[1]
)
#last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1]-1
self.rw = rewards
def __init__(self,rewards = default_rewards,min_occurences = 15,experiment_path=""):
#data params
self.experiment_path=experiment_path
self.min_occurences=min_occurences
#fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data",np.zeros([1,1]),'uint8')
self.categories = create_shared("categories_data",np.zeros([1,1]),'uint8')
self.batch_size = self.attributes.shape[0]
#"end_session_now" action
end_action = T.zeros([self.batch_size,1], dtype='uint8')
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([self.attributes,
self.categories,
end_action,
],axis=1).astype(theano.config.floatX)
#indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1]+self.categories.shape[1]
)
#last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1]-1
self.rw = rewards
#dimensions
data_attrs,data_cats,_ = self.get_dataset()
env_state_shapes = (data_cats.shape[1]+data_attrs.shape[1]+1,)
observation_shapes = (env_state_shapes[0] +2,)
#the rest is default
#init default (some shapes will be overloaded below
BaseEnvironment.__init__(self,
env_state_shapes,
observation_shapes)
def __init__(self,
rewards=default_rewards,
min_occurences=15,
experiment_path=""):
#data params
self.experiment_path = experiment_path
self.min_occurences = min_occurences
#fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data", np.zeros([1, 1]),
'uint8')
self.categories = create_shared("categories_data", np.zeros([1, 1]),
'uint8')
self.batch_size = self.attributes.shape[0]
#"end_session_now" action
end_action = T.zeros([self.batch_size, 1], dtype='uint8')
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([
self.attributes,
self.categories,
end_action,
],
axis=1).astype(theano.config.floatX)
#indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1] + self.categories.shape[1])
#last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1] - 1
self.rw = rewards
#dimensions
data_attrs, data_cats, _ = self.get_dataset()
env_state_shapes = (data_cats.shape[1] + data_attrs.shape[1] + 1, )
observation_shapes = (env_state_shapes[0] + 2, )
#the rest is default
#init default (some shapes will be overloaded below
BaseEnvironment.__init__(self,
env_state_shapes,
observation_shapes,
action_shapes=[tuple()])
def __init__(self):
# fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data",
np.zeros([10, n_attrs]), 'uint8')
self.categories = create_shared("categories_data",
np.zeros([10, n_categories]), 'uint8')
self.batch_size = self.attributes.shape[0]
# "end_session_now" action
end_action = T.zeros([self.batch_size, 1], dtype='uint8')
# concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([
self.attributes,
self.categories,
end_action,
],
axis=1).astype(theano.config.floatX)
# indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1] + self.categories.shape[1])
# last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1] - 1
# generate dummy data sample
self.generate_new_data_batch(10)
#fill in the shapes
#single-element lists for states and observations
observation_shapes = [int((self.joint_data.shape[1] + 2).eval())]
env_state_shapes = [int(self.joint_data.shape[1].eval())]
action_shapes = [
(),
]
#use default dtypes: int32 for actions, floatX for states and observations
BaseEnvironment.__init__(
self,
env_state_shapes,
observation_shapes,
action_shapes,
)
def __init__(self):
# fill shared variables with dummy values
self.attributes = create_shared("X_attrs_data", np.zeros([10, n_attrs]), 'uint8')
self.categories = create_shared("categories_data", np.zeros([10, n_categories]), 'uint8')
self.batch_size = self.attributes.shape[0]
# "end_session_now" action
end_action = T.zeros([self.batch_size, 1], dtype='uint8')
# concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([self.attributes,
self.categories,
end_action,
], axis=1).astype(theano.config.floatX)
# indices
self.category_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1] + self.categories.shape[1]
)
# last action id corresponds to the "end session" action
self.end_action_id = self.joint_data.shape[1] - 1
# generate dummy data sample
self.generate_new_data_batch(10)
#fill in the shapes
#single-element lists for states and observations
observation_shapes=[int((self.joint_data.shape[1] + 2).eval())]
env_state_shapes=[int(self.joint_data.shape[1].eval())]
action_shapes=[(),]
#use default dtypes: int32 for actions, floatX for states and observations
BaseEnvironment.__init__(
self,
env_state_shapes,
observation_shapes,
action_shapes,
)
def __init__(
self,
rewards=default_rewards,
):
#fill shared variables with dummy values
self.attributes = create_shared("patient_attributes", np.zeros([1, 1]),
'float32')
self.disease_stages = create_shared("disease_stage_indicator",
np.zeros([1, 1]), 'uint8')
self.batch_size = self.attributes.shape[0]
#concatenate data and cast it to float to avoid gradient problems
self.joint_data = T.concatenate([
self.attributes,
self.disease_stages,
],
axis=1).astype(theano.config.floatX)
#indices
self.terminal_action_ids = T.arange(
self.attributes.shape[1],
self.attributes.shape[1] + self.disease_stages.shape[1])
self.rw = rewards
# dimensions
data_attrs, data_cats, _ = self.get_dataset()
n_actions = data_cats.shape[1] + data_attrs.shape[1]
env_state_shapes = (n_actions + 1, )
observation_shapes = (data_attrs.shape[1] + 1 + n_actions, )
self.n_actions = n_actions
# the rest is default
# init default (some shapes will be overloaded below
BaseEnvironment.__init__(self, env_state_shapes, observation_shapes)
