我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用numpy.Array()。
def reconstruct_batch(self, output, batch_id, chosen_labels=None): """ Create the song associated with the network output Args: output (list[np.Array]): The ouput of the network (size batch_size*output_dim) batch_id (int): The batch that we must reconstruct chosen_labels (list[np.Array[batch_size, int]]): the sampled class at each timestep (useful to reconstruct the generated song) Return: Song: The reconstructed song """ raise NotImplementedError('Abstract class')
def reconstruct_batch(self, output, batch_id, chosen_labels=None): """ Create the song associated with the network output Args: output (list[np.Array]): The ouput of the network (size batch_size*output_dim) batch_id (int): The batch id chosen_labels (list[np.Array[batch_size, int]]): the sampled class at each timestep (useful to reconstruct the generated song) Return: Song: The reconstructed song """ assert Relative.HAS_EMPTY == True processed_song = Relative.RelativeSong() processed_song.first_note = music.Note() processed_song.first_note.note = 56 # TODO: Define what should be the first note print('Reconstruct') for i, note in enumerate(output): relative = Relative.RelativeNote() # Here if we did sample the output, we should get which has heen the selected output if not chosen_labels or i == len(chosen_labels): # If chosen_labels, the last generated note has not been sampled chosen_label = int(np.argmax(note[batch_id,:])) # Cast np.int64 to int to avoid compatibility with mido else: chosen_label = int(chosen_labels[i][batch_id]) print(chosen_label, end=' ') # TODO: Add a text output connector if chosen_label == 0: # <next> token relative.pitch_class = None #relative.scale = # Note used #relative.prev_tick = else: relative.pitch_class = chosen_label-1 #relative.scale = #relative.prev_tick = processed_song.notes.append(relative) print() return self.reconstruct_song(processed_song)
def score(self,xnew): """ Generate scores for new x values xNew should be an array-like object where each row represents a test point Return the predicted mean and standard deviation [mu,s] @param{np.Array} xnew. An numpy array where each row corrosponds to an observation @output{Array} mu. A list containing predicted mean values @output{Array} s. A list containing predicted standard deviations """ self._validate_xnew(xnew) #mu,sd = self.gp.predict(xnew,return_std=True) #return {'mu':mu.T.tolist()[0], 'sd':sd.tolist()} #K_trans = self.kernel(X, self.xTrain) #y_mean = K_trans.dot(self.alpha_) # Line 4 (y_mean = f_star) #y_mean = self.y_train_mean + y_mean # undo normal. # Compute variance of predictive distribution #y_var = self.kernel_.diag(X) #y_var -= np.einsum("ki,kj,ij->k", K_trans, K_trans, K_inv) # Check if any of the variances is negative because of # numerical issues. If yes: set the variance to 0. #y_var_negative = y_var < 0 #if np.any(y_var_negative): # warnings.warn("Predicted variances smaller than 0. " # "Setting those variances to 0.") # y_var[y_var_negative] = 0.0 #return y_mean, np.sqrt(y_var)
