def update_model(self, training_data, user_ls): ''' update parameters for users in user_ls ''' filtered_data = {u:training_data[u] for u in user_ls} new_param = process_latencies(to_lat_dict(filtered_data), lambda x: stats.gamma.fit( x, floc=0), lambda: (-1., -1., -1.) ) for u in user_ls: self.params[u] = new_param[u]
def estimate_model(self, training_data, val_data): self.params = process_latencies(to_lat_dict(training_data), lambda x: stats.gamma.fit( x, floc=0), lambda: (-1.,-1.,-1.) )
def estimate_model(self,inner_train,inner_val): self.inn_train_model = process_latencies( to_lat_dict(inner_train),kdensity,lambda: 0) self.inn_val_model = process_latencies(to_lat_dict(inner_val),kdensity,lambda: 0)