content: import os
  import sys
  root_path = os.path.abspath("../")
  if root_path not in sys.path:
      sys.path.append(root_path)
  
  import matplotlib.pyplot as plt
  
  from f_NN.Layers import *
  from f_NN.Optimizers import *
  
  from Util.Bases import ClassifierBase
  from Util.ProgressBar import ProgressBar
  
  
  class NNVerbose:
      NONE = 0
      EPOCH = 1
      METRICS = 2
      METRICS_DETAIL = 3
      DETAIL = 4
      DEBUG = 5
  
  
  class NaiveNN(ClassifierBase):
      NaiveNNTiming = Timing()
  
      def __init__(self, **kwargs):
          super(NaiveNN, self).__init__(**kwargs)
          self._layers, self._weights, self._bias = [], [], []
          self._w_optimizer = self._b_optimizer = None
          self._current_dimension = 0
  
          self._params["lr"] = kwargs.get("lr", 0.001)
          self._params["epoch"] = kwargs.get("epoch", 10)
          self._params["optimizer"] = kwargs.get("optimizer", "Adam")
  
      # Utils
  
      @NaiveNNTiming.timeit(level=4)
      def _add_params(self, shape):
          self._weights.append(np.random.randn(*shape))
          self._bias.append(np.zeros((1, shape[1])))
  
      @NaiveNNTiming.timeit(level=4)
      def _add_layer(self, layer, *args):
          current, nxt = args
          self._add_params((current, nxt))
          self._current_dimension = nxt
          self._layers.append(layer)
  
      @NaiveNNTiming.timeit(level=1)
      def _get_activations(self, x):
          activations = [self._layers[0].activate(x, self._weights[0], self._bias[0])]
          for i, layer in enumerate(self._layers[1:]):
              activations.append(layer.activate(
                  activations[-1], self._weights[i + 1], self._bias[i + 1]))
          return activations
  
      @NaiveNNTiming.timeit(level=1)
      def _get_prediction(self, x):
          return self._get_activations(x)[-1]
  
      # Optimizing Process
  
      @NaiveNNTiming.timeit(level=4)
      def _init_optimizers(self, optimizer, lr, epoch):
          opt_fac = OptFactory()
          self._w_optimizer = opt_fac.get_optimizer_by_name(
              optimizer, self._weights, lr, epoch)
          self._b_optimizer = opt_fac.get_optimizer_by_name(
              optimizer, self._bias, lr, epoch)
  
      @NaiveNNTiming.timeit(level=1)
      def _opt(self, i, _activation, _delta):
          self._weights[i] += self._w_optimizer.run(
              i, _activation.T.dot(_delta)
          )
          self._bias[i] += self._b_optimizer.run(
              i, np.sum(_delta, axis=0, keepdims=True)
          )
  
      # API
  
      @NaiveNNTiming.timeit(level=4, prefix="[API] ")
      def add(self, layer):
          if not self._layers:
              self._layers, self._current_dimension = [layer], layer.shape[1]
              self._add_params(layer.shape)
          else:
              nxt = layer.shape[0]
              layer.shape = (self._current_dimension, nxt)
              self._add_layer(layer, self._current_dimension, nxt)
  
      @NaiveNNTiming.timeit(level=1, prefix="[API] ")
      def fit(self, x, y, lr=None, epoch=None, optimizer=None):
          if lr is None:
              lr = self._params["lr"]
          if epoch is None:
              epoch = self._params["epoch"]
          if optimizer is None:
              optimizer = self._params["optimizer"]
          self._init_optimizers(optimizer, lr, epoch)
          layer_width = len(self._layers)
          for counter in range(epoch):
              self._w_optimizer.update()
              self._b_optimizer.update()
              activations = self._get_activations(x)
              deltas = [self._layers[-1].bp_first(y, activations[-1])]
              for i in range(-1, -len(activations), -1):
                  deltas.append(self._layers[i - 1].bp(
                      activations[i - 1], self._weights[i], deltas[-1]
                  ))
              for i in range(layer_width - 1, 0, -1):
                  self._opt(i, activations[i - 1], deltas[layer_width - i - 1])
              self._opt(0, x, deltas[-1])
  
      @NaiveNNTiming.timeit(level=4, prefix="[API] ")
      def predict(self, x, get_raw_results=False, **kwargs):
          y_pred = self._get_prediction(np.atleast_2d(x))
          if get_raw_results:
              return y_pred
          return np.argmax(y_pred, axis=1)
  
  
  class NN(NaiveNN):
      NNTiming = Timing()
  
      def __init__(self, **kwargs):
          super(NN, self).__init__(**kwargs)
          self._available_metrics = {
              key: value for key, value in zip(["acc", "f1-score"], [NN.acc, NN.f1_score])
          }
          self._metrics, self._metric_names, self._logs = [], [], {}
          self.verbose = None
  
          self._params["batch_size"] = kwargs.get("batch_size", 256)
          self._params["train_rate"] = kwargs.get("train_rate", None)
          self._params["metrics"] = kwargs.get("metrics", None)
          self._params["record_period"] = kwargs.get("record_period", 100)
          self._params["verbose"] = kwargs.get("verbose", 1)
  
      # Utils
  
      @NNTiming.timeit(level=1)
      def _get_prediction(self, x, name=None, batch_size=1e6, verbose=None):
          if verbose is None:
              verbose = self.verbose
          single_batch = batch_size / np.prod(x.shape[1:])  # type: float
          single_batch = int(single_batch)
          if not single_batch:
              single_batch = 1
          if single_batch >= len(x):
              return self._get_activations(x).pop()
          epoch = int(len(x) / single_batch)
          if not len(x) % single_batch:
              epoch += 1
          name = "Prediction" if name is None else "Prediction ({})".format(name)
          sub_bar = ProgressBar(max_value=epoch, name=name, start=False)
          if verbose >= NNVerbose.METRICS:
              sub_bar.start()
          rs, count = [self._get_activations(x[:single_batch]).pop()], single_batch
          if verbose >= NNVerbose.METRICS:
              sub_bar.update()
          while count < len(x):
              count += single_batch
              if count >= len(x):
                  rs.append(self._get_activations(x[count - single_batch:]).pop())
              else:
                  rs.append(self._get_activations(x[count - single_batch:count]).pop())
              if verbose >= NNVerbose.METRICS:
                  sub_bar.update()
          return np.vstack(rs)
  
      @NNTiming.timeit(level=4, prefix="[API] ")
      def _preview(self):
          if not self._layers:
              rs = "None"
          else:
              rs = (
                  "Input  :  {:<10s} - {}\n".format("Dimension", self._layers[0].shape[0]) +
                  "\n".join(
                      ["Layer  :  {:<10s} - {}".format(
                          _layer.name, _layer.shape[1]
                      ) for _layer in self._layers[:-1]]
                  ) + "\nCost   :  {:<10s}".format(self._layers[-1].name)
              )
          print("=" * 30 + "\n" + "Structure\n" + "-" * 30 + "\n" + rs + "\n" + "=" * 30)
          print("Optimizer")
          print("-" * 30)
          print(self._w_optimizer)
          print("=" * 30)
  
      @NNTiming.timeit(level=2)
      def _append_log(self, x, y, y_classes, name):
          y_pred = self._get_prediction(x, name)
          y_pred_classes = np.argmax(y_pred, axis=1)
          for i, metric in enumerate(self._metrics):
              self._logs[name][i].append(metric(y_classes, y_pred_classes))
          self._logs[name][-1].append(self._layers[-1].calculate(y, y_pred) / len(y))
  
      @NNTiming.timeit(level=3)
      def _print_metric_logs(self, data_type):
          print()
          print("=" * 47)
          for i, name in enumerate(self._metric_names):
              print("{:<16s} {:<16s}: {:12.8}".format(
                  data_type, name, self._logs[data_type][i][-1]))
          print("{:<16s} {:<16s}: {:12.8}".format(
              data_type, "loss", self._logs[data_type][-1][-1]))
          print("=" * 47)
  
      @NNTiming.timeit(level=1, prefix="[API] ")
      def fit(self, x, y, lr=None, epoch=None, batch_size=None, train_rate=None,
              optimizer=None, metrics=None, record_period=None, verbose=None):
          if lr is None:
              lr = self._params["lr"]
          if epoch is None:
              epoch = self._params["epoch"]
          if optimizer is None:
              optimizer = self._params["optimizer"]
          if batch_size is None:
              batch_size = self._params["batch_size"]
          if train_rate is None:
              train_rate = self._params["train_rate"]
          if metrics is None:
              metrics = self._params["metrics"]
          if record_period is None:
              record_period = self._params["record_period"]
          if verbose is None:
              verbose = self._params["verbose"]
          self.verbose = verbose
          self._init_optimizers(optimizer, lr, epoch)
          layer_width = len(self._layers)
          self._preview()
  
          if train_rate is not None:
              train_rate = float(train_rate)
              train_len = int(len(x) * train_rate)
              shuffle_suffix = np.random.permutation(len(x))
              x, y = x[shuffle_suffix], y[shuffle_suffix]
              x_train, y_train = x[:train_len], y[:train_len]
              x_test, y_test = x[train_len:], y[train_len:]
          else:
              x_train = x_test = x
              y_train = y_test = y
          y_train_classes = np.argmax(y_train, axis=1)
          y_test_classes = np.argmax(y_test, axis=1)
  
          train_len = len(x_train)
          batch_size = min(batch_size, train_len)
          do_random_batch = train_len > batch_size
          train_repeat = 1 if not do_random_batch else int(train_len / batch_size) + 1
  
          if metrics is None:
              metrics = []
          self._metrics = self.get_metrics(metrics)
          self._metric_names = [_m.__name__ for _m in metrics]
          self._logs = {
              name: [[] for _ in range(len(metrics) + 1)] for name in ("train", "test")
          }
  
          bar = ProgressBar(max_value=max(1, epoch // record_period), name="Epoch", start=False)
          if self.verbose >= NNVerbose.EPOCH:
              bar.start()
  
          sub_bar = ProgressBar(max_value=train_repeat * record_period - 1, name="Iteration", start=False)
          for counter in range(epoch):
              if self.verbose >= NNVerbose.EPOCH and counter % record_period == 0:
                  sub_bar.start()
              for _ in range(train_repeat):
                  if do_random_batch:
                      batch = np.random.choice(train_len, batch_size)
                      x_batch, y_batch = x_train[batch], y_train[batch]
                  else:
                      x_batch, y_batch = x_train, y_train
                  self._w_optimizer.update()
                  self._b_optimizer.update()
                  activations = self._get_activations(x_batch)
                  deltas = [self._layers[-1].bp_first(y_batch, activations[-1])]
                  for i in range(-1, -len(activations), -1):
                      deltas.append(
                          self._layers[i - 1].bp(activations[i - 1], self._weights[i], deltas[-1])
                      )
                  for i in range(layer_width - 1, 0, -1):
                      self._opt(i, activations[i - 1], deltas[layer_width - i - 1])
                  self._opt(0, x_batch, deltas[-1])
                  if self.verbose >= NNVerbose.EPOCH:
                      if sub_bar.update() and self.verbose >= NNVerbose.METRICS_DETAIL:
                          self._append_log(x_train, y_train, y_train_classes, "train")
                          self._append_log(x_test, y_test, y_test_classes, "test")
                          self._print_metric_logs("train")
                          self._print_metric_logs("test")
              if self.verbose >= NNVerbose.EPOCH:
                  sub_bar.update()
              if (counter + 1) % record_period == 0:
                  self._append_log(x_train, y_train, y_train_classes, "train")
                  self._append_log(x_test, y_test, y_test_classes, "test")
                  if self.verbose >= NNVerbose.METRICS:
                      self._print_metric_logs("train")
                      self._print_metric_logs("test")
                  if self.verbose >= NNVerbose.EPOCH:
                      bar.update(counter // record_period + 1)
                      sub_bar = ProgressBar(max_value=train_repeat * record_period - 1, name="Iteration", start=False)
  
      def draw_logs(self):
          metrics_log, loss_log = {}, {}
          for key, value in sorted(self._logs.items()):
              metrics_log[key], loss_log[key] = value[:-1], value[-1]
          for i, name in enumerate(sorted(self._metric_names)):
              plt.figure()
              plt.title("Metric Type: {}".format(name))
              for key, log in sorted(metrics_log.items()):
                  xs = np.arange(len(log[i])) + 1
                  plt.plot(xs, log[i], label="Data Type: {}".format(key))
              plt.legend(loc=4)
              plt.show()
              plt.close()
          plt.figure()
          plt.title("Cost")
          for key, loss in sorted(loss_log.items()):
              xs = np.arange(len(loss)) + 1
              plt.plot(xs, loss, label="Data Type: {}".format(key))
          plt.legend()
          plt.show()
  