def load_data_with_id(params,id):
datasets = tumdata.load_tum_data(params,id)
X_train, Y_train, Overlaps_train = datasets[0]
X_val, Y_val, Overlaps_val = datasets[1]
X_test, Y_test, Overlaps_test = datasets[2]
if(params['shufle_data']==1):
X_train,Y_train=dt_utils.shuffle_in_unison_inplace(X_train,Y_train)
X_val,Y_val=dt_utils.shuffle_in_unison_inplace(X_val,Y_val)
rval = [(X_train, Y_train,Overlaps_train), (X_val, Y_val,Overlaps_val),
(X_test, Y_test,Overlaps_test)]
return rval
def load_data(params):
X_train=[]
X_val=[]
X_test=[]
Y_train=[]
Y_test=[]
Y_val=[]
Overlaps_train=[]
Overlaps_val=[]
Overlaps_test=[]
for id in range(len(params["dataset"])):
if params["dataset"][id] ==-1:
continue
datasets = tumdata.load_tum_data(params,id)
x_train, y_train,overlaps_train = datasets[0]
x_val, y_val,overlaps_val = datasets[1]
x_test, y_test,overlaps_test = datasets[2]
if(len(X_train)==0):
X_train=np.array(x_train)
X_val=np.array(x_val)
X_test=np.array(x_test)
Y_train=np.array(y_train)
Y_test=np.array(y_test)
Y_val=np.array(y_val)
Overlaps_train=np.array(overlaps_train)
Overlaps_val=np.array(overlaps_val)
Overlaps_test=np.array(overlaps_test)
else:
X_train=np.concatenate((X_train,x_train),axis=0)
X_val=np.concatenate((X_val,x_val),axis=0)
X_test=np.concatenate((X_test,x_test),axis=0)
Y_train=np.concatenate((Y_train,y_train),axis=0)
Y_val=np.concatenate((Y_val,y_val),axis=0)
Y_test=np.concatenate((Y_test,y_test),axis=0)
Overlaps_train=np.concatenate((Overlaps_train,overlaps_train),axis=0)
Overlaps_val=np.concatenate((Overlaps_val,overlaps_val),axis=0)
Overlaps_test=np.concatenate((Overlaps_test,overlaps_test),axis=0)
if(params['shufle_data']==1):
X_train,Y_train=dt_utils.shuffle_in_unison_inplace(X_train,Y_train)
X_val,Y_val=dt_utils.shuffle_in_unison_inplace(X_val,Y_val)
rval = [(X_train, Y_train,Overlaps_train), (X_val, Y_val,Overlaps_val),
(X_test, Y_test,Overlaps_test)]
return rval
