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Example 1

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') 

Example 2

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) 

Example 3

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) 

Example 4

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') 

Example 5

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)